diff --git a/.Rprofile b/.Rprofile new file mode 100644 index 0000000..81b960f --- /dev/null +++ b/.Rprofile @@ -0,0 +1 @@ +source("renv/activate.R") diff --git a/.gitignore b/.gitignore index ad930a7..a1e4413 100644 --- a/.gitignore +++ b/.gitignore @@ -2,15 +2,17 @@ .snakemake ._dag.png .DS_Store -<<<<<<< HEAD **/__pycache__/ *.py[cod] -======= */__pycache__/ ->>>>>>> 1384bcec7fd21b92b2dd6e3203d5fc38ff522141 renv/cellar/ renv/library/ renv/local/ renv/lock/ renv/python/ renv/staging/ +<<<<<<< HEAD +**/*.h5ad +*data/ +*results/ + diff --git a/README.md b/README.md index f3bf174..20aa78b 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ This repository contains a [`snakemake`](https://snakemake.readthedocs.io/en/sta ## Overview: -Below is a DAG showing the rule dependencies in the `snakemake` pipeline. View the [`Snakefile`](https://github.com/settylab/geneTF/blob/main/workflow/Snakefile) to see details on each rule. +Below is a DAG showing the rule dependencies in the `snakemake` pipeline. View the [`Snakefile`](https://github.com/settylab/atac-metacell-utilites/blob/main/workflow/Snakefile) to see details on each rule. ![DAG of workflow](./dag.png) @@ -244,9 +244,9 @@ For example, the following will run/re-run the `chromvar` rule and any of the al ``` snakemake --cores 1 chromvar --allowed_rules peak_tf compute_ins_chip prep_chromvar ``` -When running this pipeline on an HPC system which uses `lmod` to load software, do not load snakemake as a module - this can cause conflicts where the Python version used by `snakemake` is the one provided by the module, not the `gene-TF` conda environment. +When running this pipeline on an HPC system which uses `lmod` to load software, do not load snakemake as a module - this can cause conflicts where the Python version used by `snakemake` is the one provided by the module, not the `atac-metacell-utils` conda environment. -To run using the snakemake version installed with `gene-TF`, make sure the `gene-TF` environment is active, then run the pipeline as follows: +To run using the snakemake version installed with `atac-metacell-utils`, make sure the `atac-metacell-utils` environment is active, then run the pipeline as follows: ``` python -m snakemake --cores 1 name-of-rule diff --git a/config/full-test-config.yaml b/config/full-test-config.yaml new file mode 100644 index 0000000..e0b0bd5 --- /dev/null +++ b/config/full-test-config.yaml @@ -0,0 +1,49 @@ +scripts: "workflow/scripts/" +output: "full-test-results/" +logs: "full-test-logs/" +interactive_clean: True + +renv_loc: "/home/cjordan2/R/x86_64-pc-linux-gnu-library/4.1.0" # + +anndata: + atac: "/fh/fast/setty_m/user/cjordan2/repositories/density-analysis/data/bm_atac_meta_ad.h5ad" #"/fh/fast/setty_m/user/cjordan2/repositories/geneTF/data/test_atac_meta_ad.h5ad" #< + rna: "/fh/fast/setty_m/user/cjordan2/repositories/density-analysis/data/bm_rna_meta_ad.h5ad" #tcell_dep_rna_w_SEACells_meta.h5ad" #"/fh/fast/setty_m/user/cjordan2/repositories/geneTF/data/test_rna_meta_ad.h5ad" # + sc_atac: "/fh/fast/setty_m/user/cjordan2/repositories/density-analysis/data/snapshot_ads/bm_atac_ad.h5ad" #"/fh/fast/setty_m/user/cjordan2/repositories/geneTF/data/test_atac_ad.h5ad" + sc_rna: "/fh/fast/setty_m/user/cjordan2/repositories/density-analysis/data/snapshot_ads/bm_rna_ad.h5ad" #"/fh/fast/setty_m/user/cjordan2/repositories/geneTF/data/test_rna_ad.h5ad" + # documenting what label was used to create the SEACells AnnData - also used for differential accessibility + SEACell_label: "SEACell" +peaks: + width: 150 + genome: "hg38" + meme_file: "data/cis-bp-tf-information.meme" + +ins_chip: + # set verbose to "--verbose" for True + verbose: "" + min_chip_score: 0.15 + min_peak_hits: 30 + +gene_peak_corr_config: + # set test_set to "--test_set" for True + n_jobs: 1 + test_set: "" + n_genes: 20 + +gene_peak_corr_cutoffs: + min_corr: 0.0 + max_pval: 0.1 + min_peaks: 2 + +diff_acc: + to_compare: "EryPre1,proB/Mono,proB/EryPre1,HSC/Mono,HSC" + group_variable: "celltype_combined" + +peak_selection: + target: ["Bcells","proB"] + start: "HSC" + reference: + Ery: "EryPre1" + Mono: "Mono" + min_logFC: -0.25 + max_logFC: 0.25 + diff --git a/full-test-logs/fimo.out b/full-test-logs/fimo.out new file mode 100644 index 0000000..655c240 --- /dev/null +++ b/full-test-logs/fimo.out @@ -0,0 +1,28463 @@ +Using motif +M02753_2.00 of width 12. +Using motif -M02753_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873818 +# Estimated pi_0=0.878537 +Using motif +M02754_2.00 of width 11. +Using motif -M02754_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8794 +# Estimated pi_0=0.886131 +Using motif +M02755_2.00 of width 13. +Using motif -M02755_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874128 +# Estimated pi_0=0.877778 +Using motif +M04046_2.00 of width 11. +Using motif -M04046_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8652 +# Estimated pi_0=0.875758 +Using motif +M04047_2.00 of width 11. +Using motif -M04047_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884952 +# Estimated pi_0=0.899424 +Using motif +M08701_2.00 of width 10. +Using motif -M08701_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8766 +# Estimated pi_0=0.891293 +Using motif +M00111_2.00 of width 10. +Using motif -M00111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8826 +# Estimated pi_0=0.894262 +Using motif +M02756_2.00 of width 12. +Using motif -M02756_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872 +# Estimated pi_0=0.880678 +Using motif +M02757_2.00 of width 11. +Using motif -M02757_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.854286 +# Estimated pi_0=0.857686 +Using motif +M02758_2.00 of width 13. +Using motif -M02758_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8406 +# Estimated pi_0=0.852397 +Using motif +M02759_2.00 of width 12. +Using motif -M02759_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8676 +# Estimated pi_0=0.87248 +Using motif +M02760_2.00 of width 13. +Using motif -M02760_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8662 +# Estimated pi_0=0.877302 +Using motif +M02761_2.00 of width 11. +Using motif -M02761_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8748 +# Estimated pi_0=0.887805 +Using motif +M04048_2.00 of width 11. +Using motif -M04048_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.909155 +Using motif +M04049_2.00 of width 11. +Using motif -M04049_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.887723 +# Estimated pi_0=0.896444 +Using motif +M07783_2.00 of width 15. +Using motif -M07783_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859412 +# Estimated pi_0=0.865091 +Using motif +M08702_2.00 of width 14. +Using motif -M08702_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892432 +# Estimated pi_0=0.900584 +Using motif +M09451_2.00 of width 12. +Using motif -M09451_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880183 +# Estimated pi_0=0.881416 +Using motif +M09751_2.00 of width 9. +Using motif -M09751_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.853774 +# Estimated pi_0=0.854095 +Using motif +M04050_2.00 of width 11. +Using motif -M04050_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8562 +# Estimated pi_0=0.868095 +Using motif +M04051_2.00 of width 12. +Using motif -M04051_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857451 +# Estimated pi_0=0.864 +Using motif +M04052_2.00 of width 11. +Using motif -M04052_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904603 +# Estimated pi_0=0.908296 +Using motif +M04053_2.00 of width 12. +Using motif -M04053_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86098 +# Estimated pi_0=0.863894 +Using motif +M02762_2.00 of width 12. +Using motif -M02762_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8498 +# Estimated pi_0=0.867857 +Using motif +M02763_2.00 of width 11. +Using motif -M02763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8548 +# Estimated pi_0=0.863636 +Using motif +M02764_2.00 of width 13. +Using motif -M02764_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859216 +# Estimated pi_0=0.877842 +Using motif +M02765_2.00 of width 11. +Using motif -M02765_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8702 +# Estimated pi_0=0.870741 +Using motif +M02766_2.00 of width 12. +Using motif -M02766_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8642 +# Estimated pi_0=0.875075 +Using motif +M02767_2.00 of width 13. +Using motif -M02767_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8598 +# Estimated pi_0=0.867797 +Using motif +M04054_2.00 of width 11. +Using motif -M04054_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.850392 +# Estimated pi_0=0.858261 +Using motif +M04055_2.00 of width 11. +Using motif -M04055_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9016 +# Estimated pi_0=0.90375 +Using motif +M07784_2.00 of width 15. +Using motif -M07784_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873465 +# Estimated pi_0=0.884308 +Using motif +M08703_2.00 of width 15. +Using motif -M08703_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927083 +# Estimated pi_0=0.935948 +Using motif +M09755_2.00 of width 9. +Using motif -M09755_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.863962 +# Estimated pi_0=0.871667 +Using motif +M08707_2.00 of width 13. +Using motif -M08707_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M09767_2.00 of width 14. +Using motif -M09767_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00031 +# Estimated pi_0=1 +Using motif +M01659_2.00 of width 11. +Using motif -M01659_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M00116_2.00 of width 11. +Using motif -M00116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00115_2.00 of width 9. +Using motif -M00115_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M02771_2.00 of width 13. +Using motif -M02771_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9926 +# Estimated pi_0=1 +Using motif +M04056_2.00 of width 12. +Using motif -M04056_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990196 +# Estimated pi_0=1 +Using motif +M04057_2.00 of width 12. +Using motif -M04057_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97505 +# Estimated pi_0=1 +Using motif +M02772_2.00 of width 10. +Using motif -M02772_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990551 +# Estimated pi_0=1 +Using motif +M08754_2.00 of width 15. +Using motif -M08754_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.851698 +# Estimated pi_0=0.8605 +Using motif +M02773_2.00 of width 10. +Using motif -M02773_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9354 +# Estimated pi_0=0.958102 +Using motif +M04058_2.00 of width 11. +Using motif -M04058_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8954 +# Estimated pi_0=0.906316 +Using motif +M04059_2.00 of width 11. +Using motif -M04059_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962115 +# Estimated pi_0=0.976761 +Using motif +M08709_2.00 of width 10. +Using motif -M08709_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947308 +# Estimated pi_0=0.951532 +Using motif +M04060_2.00 of width 10. +Using motif -M04060_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877426 +# Estimated pi_0=0.886111 +Using motif +M04061_2.00 of width 10. +Using motif -M04061_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8986 +# Estimated pi_0=0.909916 +Using motif +M02774_2.00 of width 10. +Using motif -M02774_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964714 +# Estimated pi_0=0.966853 +Using motif +M02775_2.00 of width 10. +Using motif -M02775_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936481 +# Estimated pi_0=0.956988 +Using motif +M08710_2.00 of width 11. +Using motif -M08710_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901923 +# Estimated pi_0=0.909643 +Using motif +M09794_2.00 of width 15. +Using motif -M09794_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909216 +# Estimated pi_0=0.915725 +Using motif +M09795_2.00 of width 16. +Using motif -M09795_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949074 +# Estimated pi_0=0.963333 +Using motif +M04062_2.00 of width 10. +Using motif -M04062_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885 +# Estimated pi_0=0.890784 +Using motif +M04063_2.00 of width 10. +Using motif -M04063_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8894 +# Estimated pi_0=0.900577 +Using motif +M04064_2.00 of width 10. +Using motif -M04064_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934 +# Estimated pi_0=0.941488 +Using motif +M04065_2.00 of width 10. +Using motif -M04065_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96928 +# Estimated pi_0=0.975302 +Using motif +M08048_2.00 of width 10. +Using motif -M08048_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936381 +# Estimated pi_0=0.945133 +Using motif +M08711_2.00 of width 11. +Using motif -M08711_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932833 +# Estimated pi_0=0.944024 +Using motif +M09797_2.00 of width 11. +Using motif -M09797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971068 +# Estimated pi_0=0.989006 +Using motif +M09798_2.00 of width 11. +Using motif -M09798_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945391 +# Estimated pi_0=0.947009 +Using motif +M02776_2.00 of width 10. +Using motif -M02776_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98655 +# Estimated pi_0=0.99375 +Using motif +M02777_2.00 of width 10. +Using motif -M02777_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994225 +# Estimated pi_0=0.996985 +Using motif +M04066_2.00 of width 10. +Using motif -M04066_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995657 +# Estimated pi_0=0.999799 +Using motif +M04067_2.00 of width 10. +Using motif -M04067_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978596 +# Estimated pi_0=0.983094 +Using motif +M04068_2.00 of width 10. +Using motif -M04068_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9858 +# Estimated pi_0=0.999196 +Using motif +M04069_2.00 of width 10. +Using motif -M04069_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982841 +# Estimated pi_0=0.986923 +Using motif +M04070_2.00 of width 10. +Using motif -M04070_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9926 +# Estimated pi_0=0.999095 +Using motif +M04071_2.00 of width 10. +Using motif -M04071_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955522 +# Estimated pi_0=0.96 +Using motif +M04072_2.00 of width 10. +Using motif -M04072_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9882 +# Estimated pi_0=0.997259 +Using motif +M04073_2.00 of width 10. +Using motif -M04073_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977229 +# Estimated pi_0=0.979773 +Using motif +M08712_2.00 of width 10. +Using motif -M08712_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94297 +# Estimated pi_0=0.948679 +Using motif +M09453_2.00 of width 10. +Using motif -M09453_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968673 +# Estimated pi_0=0.975169 +Using motif +M09802_2.00 of width 18. +Using motif -M09802_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9322 +# Estimated pi_0=0.945342 +Using motif +M09803_2.00 of width 10. +Using motif -M09803_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942857 +# Estimated pi_0=0.950922 +Using motif +M09806_2.00 of width 10. +Using motif -M09806_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932456 +# Estimated pi_0=0.947383 +Using motif +M08049_2.00 of width 10. +Using motif -M08049_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8908 +# Estimated pi_0=0.898252 +Using motif +M08713_2.00 of width 8. +Using motif -M08713_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944423 +# Estimated pi_0=0.950185 +Using motif +M09454_2.00 of width 8. +Using motif -M09454_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935534 +# Estimated pi_0=0.935534 +Using motif +M04074_2.00 of width 10. +Using motif -M04074_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8792 +# Estimated pi_0=0.88198 +Using motif +M04075_2.00 of width 10. +Using motif -M04075_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8832 +# Estimated pi_0=0.888704 +Using motif +M08714_2.00 of width 14. +Using motif -M08714_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929875 +# Estimated pi_0=0.936207 +Using motif +M04076_2.00 of width 11. +Using motif -M04076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935446 +# Estimated pi_0=0.94354 +Using motif +M04077_2.00 of width 11. +Using motif -M04077_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987328 +# Estimated pi_0=1 +Using motif +M07785_2.00 of width 15. +Using motif -M07785_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889505 +# Estimated pi_0=0.902975 +Using motif +M07786_2.00 of width 16. +Using motif -M07786_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8968 +# Estimated pi_0=0.907193 +Using motif +M07787_2.00 of width 11. +Using motif -M07787_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9578 +# Estimated pi_0=0.977823 +Using motif +M07788_2.00 of width 13. +Using motif -M07788_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921731 +# Estimated pi_0=0.929677 +Using motif +M07789_2.00 of width 13. +Using motif -M07789_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9028 +# Estimated pi_0=0.9028 +Using motif +M08050_2.00 of width 16. +Using motif -M08050_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9152 +# Estimated pi_0=0.920769 +Using motif +M08715_2.00 of width 19. +Using motif -M08715_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.845 +# Estimated pi_0=0.851028 +Using motif +M02778_2.00 of width 10. +Using motif -M02778_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950891 +# Estimated pi_0=0.962714 +Using motif +M04078_2.00 of width 11. +Using motif -M04078_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9202 +# Estimated pi_0=0.926422 +Using motif +M04079_2.00 of width 11. +Using motif -M04079_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9754 +# Estimated pi_0=0.999196 +Using motif +M08716_2.00 of width 9. +Using motif -M08716_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914059 +# Estimated pi_0=0.916471 +Using motif +M09816_2.00 of width 18. +Using motif -M09816_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8794 +# Estimated pi_0=0.88099 +Using motif +M02779_2.00 of width 10. +Using motif -M02779_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9646 +# Estimated pi_0=0.982459 +Using motif +M04080_2.00 of width 8. +Using motif -M04080_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953654 +# Estimated pi_0=0.962237 +Using motif +M04081_2.00 of width 8. +Using motif -M04081_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943495 +# Estimated pi_0=0.951818 +Using motif +M04082_2.00 of width 11. +Using motif -M04082_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9194 +# Estimated pi_0=0.930841 +Using motif +M04083_2.00 of width 11. +Using motif -M04083_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974609 +# Estimated pi_0=0.987619 +Using motif +M02780_2.00 of width 10. +Using motif -M02780_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994033 +# Estimated pi_0=0.998788 +Using motif +M04084_2.00 of width 12. +Using motif -M04084_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9318 +# Estimated pi_0=0.941167 +Using motif +M04085_2.00 of width 12. +Using motif -M04085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947129 +# Estimated pi_0=0.960979 +Using motif +M04086_2.00 of width 12. +Using motif -M04086_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966154 +# Estimated pi_0=0.977401 +Using motif +M04087_2.00 of width 12. +Using motif -M04087_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976716 +# Estimated pi_0=0.987527 +Using motif +M05831_2.00 of width 11. +Using motif -M05831_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995155 +# Estimated pi_0=0.998392 +Using motif +M08717_2.00 of width 7. +Using motif -M08717_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939583 +# Estimated pi_0=0.948077 +Using motif +M02727_2.00 of width 10. +Using motif -M02727_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974958 +# Estimated pi_0=0.987531 +Using motif +M02781_2.00 of width 10. +Using motif -M02781_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966435 +# Estimated pi_0=0.981558 +Using motif +M04088_2.00 of width 11. +Using motif -M04088_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9436 +# Estimated pi_0=0.944912 +Using motif +M04089_2.00 of width 11. +Using motif -M04089_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97703 +# Estimated pi_0=0.993125 +Using motif +M08718_2.00 of width 9. +Using motif -M08718_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976348 +# Estimated pi_0=0.986014 +Using motif +M08769_2.00 of width 17. +Using motif -M08769_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968861 +# Estimated pi_0=0.977017 +Using motif +M04090_2.00 of width 10. +Using motif -M04090_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8988 +# Estimated pi_0=0.902772 +Using motif +M04091_2.00 of width 10. +Using motif -M04091_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935248 +# Estimated pi_0=0.95635 +Using motif +M08719_2.00 of width 9. +Using motif -M08719_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937 +# Estimated pi_0=0.946957 +Using motif +M09455_2.00 of width 10. +Using motif -M09455_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9432 +# Estimated pi_0=0.952909 +Using motif +M04092_2.00 of width 12. +Using motif -M04092_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941746 +# Estimated pi_0=0.951801 +Using motif +M04093_2.00 of width 12. +Using motif -M04093_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9488 +# Estimated pi_0=0.962832 +Using motif +M04094_2.00 of width 12. +Using motif -M04094_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9628 +# Estimated pi_0=0.976121 +Using motif +M04095_2.00 of width 12. +Using motif -M04095_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9798 +# Estimated pi_0=1 +Using motif +M07790_2.00 of width 11. +Using motif -M07790_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942545 +# Estimated pi_0=0.947737 +Using motif +M08051_2.00 of width 13. +Using motif -M08051_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.853 +# Estimated pi_0=0.854118 +Using motif +M08181_2.00 of width 15. +Using motif -M08181_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955962 +# Estimated pi_0=0.966154 +Using motif +M08182_2.00 of width 8. +Using motif -M08182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8456 +# Estimated pi_0=0.845941 +Using motif +M08720_2.00 of width 15. +Using motif -M08720_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.851683 +# Estimated pi_0=0.862759 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925941 +# Estimated pi_0=0.943308 +Using motif +M04096_2.00 of width 10. +Using motif -M04096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925149 +# Estimated pi_0=0.939339 +Using motif +M04097_2.00 of width 10. +Using motif -M04097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04098_2.00 of width 12. +Using motif -M04098_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9564 +# Estimated pi_0=0.966923 +Using motif +M04099_2.00 of width 12. +Using motif -M04099_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972336 +# Estimated pi_0=0.981667 +Using motif +M04100_2.00 of width 12. +Using motif -M04100_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960943 +# Estimated pi_0=0.968645 +Using motif +M04101_2.00 of width 12. +Using motif -M04101_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986916 +# Estimated pi_0=0.992577 +Using motif +M04102_2.00 of width 12. +Using motif -M04102_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977064 +# Estimated pi_0=0.98321 +Using motif +M04103_2.00 of width 12. +Using motif -M04103_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979091 +# Estimated pi_0=0.984111 +Using motif +M04104_2.00 of width 12. +Using motif -M04104_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97463 +# Estimated pi_0=0.98213 +Using motif +M04105_2.00 of width 12. +Using motif -M04105_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982727 +# Estimated pi_0=0.990526 +Using motif +M08721_2.00 of width 13. +Using motif -M08721_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906078 +# Estimated pi_0=0.915862 +Using motif +M08052_2.00 of width 13. +Using motif -M08052_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982293 +# Estimated pi_0=0.989149 +Using motif +M08722_2.00 of width 17. +Using motif -M08722_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9742 +# Estimated pi_0=0.998041 +Using motif +M02782_2.00 of width 10. +Using motif -M02782_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9476 +# Estimated pi_0=0.950986 +Using motif +M02783_2.00 of width 10. +Using motif -M02783_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942 +# Estimated pi_0=0.953582 +Using motif +M04106_2.00 of width 10. +Using motif -M04106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930826 +# Estimated pi_0=0.944296 +Using motif +M04107_2.00 of width 10. +Using motif -M04107_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952353 +# Estimated pi_0=0.961014 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M02784_2.00 of width 10. +Using motif -M02784_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9956 +# Estimated pi_0=1 +Using motif +M04108_2.00 of width 10. +Using motif -M04108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988571 +# Estimated pi_0=1 +Using motif +M04109_2.00 of width 10. +Using motif -M04109_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04110_2.00 of width 10. +Using motif -M04110_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9438 +# Estimated pi_0=0.949515 +Using motif +M04111_2.00 of width 10. +Using motif -M04111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958416 +# Estimated pi_0=0.959804 +Using motif +M00938_2.00 of width 8. +Using motif -M00938_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942941 +# Estimated pi_0=0.956452 +Using motif +M01497_2.00 of width 9. +Using motif -M01497_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983238 +# Estimated pi_0=0.999596 +Using motif +M01498_2.00 of width 8. +Using motif -M01498_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954038 +# Estimated pi_0=0.962937 +Using motif +M02785_2.00 of width 17. +Using motif -M02785_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9334 +# Estimated pi_0=0.939658 +Using motif +M02786_2.00 of width 10. +Using motif -M02786_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9466 +# Estimated pi_0=0.953857 +Using motif +M04112_2.00 of width 19. +Using motif -M04112_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8914 +# Estimated pi_0=0.892079 +Using motif +M04113_2.00 of width 19. +Using motif -M04113_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9294 +# Estimated pi_0=0.937615 +Using motif +M04114_2.00 of width 10. +Using motif -M04114_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929505 +# Estimated pi_0=0.931961 +Using motif +M04115_2.00 of width 10. +Using motif -M04115_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962301 +# Estimated pi_0=0.969767 +Using motif +M05832_2.00 of width 6. +Using motif -M05832_2.00 of width 6. +Computing q-values. +Using motif +M07791_2.00 of width 13. +Using motif -M07791_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9186 +# Estimated pi_0=0.929298 +Using motif +M07792_2.00 of width 11. +Using motif -M07792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9316 +# Estimated pi_0=0.936757 +Using motif +M07793_2.00 of width 11. +Using motif -M07793_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931 +# Estimated pi_0=0.939344 +Using motif +M07794_2.00 of width 13. +Using motif -M07794_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8934 +# Estimated pi_0=0.910182 +Using motif +M07795_2.00 of width 10. +Using motif -M07795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928317 +# Estimated pi_0=0.934953 +Using motif +M07796_2.00 of width 10. +Using motif -M07796_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9212 +# Estimated pi_0=0.925946 +Using motif +M07797_2.00 of width 11. +Using motif -M07797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931881 +# Estimated pi_0=0.951915 +Using motif +M08723_2.00 of width 10. +Using motif -M08723_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921 +# Estimated pi_0=0.925152 +Using motif +M09835_2.00 of width 14. +Using motif -M09835_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962772 +# Estimated pi_0=0.978462 +Using motif +M04116_2.00 of width 12. +Using motif -M04116_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9402 +# Estimated pi_0=0.948 +Using motif +M04117_2.00 of width 12. +Using motif -M04117_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9028 +# Estimated pi_0=0.906602 +Using motif +M04118_2.00 of width 12. +Using motif -M04118_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9478 +# Estimated pi_0=0.958485 +Using motif +M04119_2.00 of width 12. +Using motif -M04119_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903564 +# Estimated pi_0=0.910088 +Using motif +M04120_2.00 of width 12. +Using motif -M04120_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971918 +# Estimated pi_0=0.978764 +Using motif +M04121_2.00 of width 12. +Using motif -M04121_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9336 +# Estimated pi_0=0.947302 +Using motif +M04122_2.00 of width 12. +Using motif -M04122_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960962 +# Estimated pi_0=0.972387 +Using motif +M04123_2.00 of width 12. +Using motif -M04123_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931961 +# Estimated pi_0=0.939254 +Using motif +M08724_2.00 of width 15. +Using motif -M08724_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90878 +# Estimated pi_0=0.914724 +Using motif +M09838_2.00 of width 12. +Using motif -M09838_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950909 +# Estimated pi_0=0.96 +Using motif +M09840_2.00 of width 10. +Using motif -M09840_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952 +# Estimated pi_0=0.968235 +Using motif +M02787_2.00 of width 10. +Using motif -M02787_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9522 +# Estimated pi_0=0.971064 +Using motif +M04124_2.00 of width 10. +Using motif -M04124_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940396 +# Estimated pi_0=0.95626 +Using motif +M04125_2.00 of width 10. +Using motif -M04125_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976198 +# Estimated pi_0=0.979521 +Using motif +M08725_2.00 of width 11. +Using motif -M08725_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9474 +# Estimated pi_0=0.962979 +Using motif +M02788_2.00 of width 10. +Using motif -M02788_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946535 +# Estimated pi_0=0.96031 +Using motif +M04126_2.00 of width 10. +Using motif -M04126_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9222 +# Estimated pi_0=0.929126 +Using motif +M04127_2.00 of width 10. +Using motif -M04127_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931373 +# Estimated pi_0=0.947705 +Using motif +M07798_2.00 of width 10. +Using motif -M07798_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936634 +# Estimated pi_0=0.943303 +Using motif +M08726_2.00 of width 10. +Using motif -M08726_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952 +# Estimated pi_0=0.965493 +Using motif +M09456_2.00 of width 10. +Using motif -M09456_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971833 +# Estimated pi_0=0.98648 +Using motif +M04128_2.00 of width 10. +Using motif -M04128_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914 +# Estimated pi_0=0.923333 +Using motif +M08053_2.00 of width 12. +Using motif -M08053_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874851 +# Estimated pi_0=0.881887 +Using motif +M08727_2.00 of width 12. +Using motif -M08727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915 +# Estimated pi_0=0.915 +Using motif +M09846_2.00 of width 12. +Using motif -M09846_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920594 +# Estimated pi_0=0.923495 +Using motif +M02789_2.00 of width 10. +Using motif -M02789_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962941 +# Estimated pi_0=0.972296 +Using motif +M04129_2.00 of width 10. +Using motif -M04129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940377 +# Estimated pi_0=0.946496 +Using motif +M04130_2.00 of width 10. +Using motif -M04130_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9784 +# Estimated pi_0=1 +Using motif +M08728_2.00 of width 14. +Using motif -M08728_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9314 +# Estimated pi_0=0.943556 +Using motif +M02790_2.00 of width 10. +Using motif -M02790_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8782 +# Estimated pi_0=0.888288 +Using motif +M02791_2.00 of width 10. +Using motif -M02791_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897228 +# Estimated pi_0=0.897228 +Using motif +M04131_2.00 of width 10. +Using motif -M04131_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8778 +# Estimated pi_0=0.881188 +Using motif +M04132_2.00 of width 10. +Using motif -M04132_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896238 +# Estimated pi_0=0.906476 +Using motif +M04133_2.00 of width 10. +Using motif -M04133_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8932 +# Estimated pi_0=0.898095 +Using motif +M04134_2.00 of width 10. +Using motif -M04134_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.881386 +# Estimated pi_0=0.893458 +Using motif +M07799_2.00 of width 10. +Using motif -M07799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909 +# Estimated pi_0=0.917248 +Using motif +M07800_2.00 of width 11. +Using motif -M07800_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90297 +# Estimated pi_0=0.904466 +Using motif +M07801_2.00 of width 11. +Using motif -M07801_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9262 +# Estimated pi_0=0.932571 +Using motif +M07802_2.00 of width 11. +Using motif -M07802_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9142 +# Estimated pi_0=0.925641 +Using motif +M07803_2.00 of width 8. +Using motif -M07803_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94188 +# Estimated pi_0=0.946723 +Using motif +M07804_2.00 of width 11. +Using motif -M07804_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9194 +# Estimated pi_0=0.9194 +Using motif +M08054_2.00 of width 12. +Using motif -M08054_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8756 +# Estimated pi_0=0.896098 +Using motif +M08729_2.00 of width 11. +Using motif -M08729_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916 +# Estimated pi_0=0.928667 +Using motif +M09457_2.00 of width 8. +Using motif -M09457_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9064 +# Estimated pi_0=0.914231 +Using motif +M04135_2.00 of width 10. +Using motif -M04135_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927287 +# Estimated pi_0=0.934384 +Using motif +M04136_2.00 of width 10. +Using motif -M04136_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944493 +# Estimated pi_0=0.949041 +Using motif +M08055_2.00 of width 13. +Using motif -M08055_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902835 +# Estimated pi_0=0.909697 +Using motif +M08730_2.00 of width 14. +Using motif -M08730_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913821 +# Estimated pi_0=0.917483 +Using motif +M04137_2.00 of width 8. +Using motif -M04137_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937739 +# Estimated pi_0=0.953497 +Using motif +M04138_2.00 of width 8. +Using motif -M04138_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952653 +# Estimated pi_0=0.9548 +Using motif +M07805_2.00 of width 11. +Using motif -M07805_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908644 +# Estimated pi_0=0.915143 +Using motif +M07806_2.00 of width 8. +Using motif -M07806_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933529 +# Estimated pi_0=0.946857 +Using motif +M08731_2.00 of width 10. +Using motif -M08731_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913793 +# Estimated pi_0=0.91904 +Using motif +M09458_2.00 of width 10. +Using motif -M09458_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936296 +# Estimated pi_0=0.941491 +Using motif +M08732_2.00 of width 9. +Using motif -M08732_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950392 +# Estimated pi_0=0.964167 +Using motif +M09459_2.00 of width 8. +Using motif -M09459_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9764 +# Estimated pi_0=0.99121 +Using motif +M09863_2.00 of width 16. +Using motif -M09863_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9474 +# Estimated pi_0=0.97021 +Using motif +M09864_2.00 of width 20. +Using motif -M09864_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9272 +# Estimated pi_0=0.9272 +Using motif +M04139_2.00 of width 10. +Using motif -M04139_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9286 +# Estimated pi_0=0.938727 +Using motif +M04140_2.00 of width 14. +Using motif -M04140_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9296 +# Estimated pi_0=0.93664 +Using motif +M04141_2.00 of width 14. +Using motif -M04141_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9382 +# Estimated pi_0=0.956863 +Using motif +M04142_2.00 of width 14. +Using motif -M04142_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949109 +# Estimated pi_0=0.963205 +Using motif +M04143_2.00 of width 14. +Using motif -M04143_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96549 +# Estimated pi_0=0.97963 +Using motif +M04144_2.00 of width 12. +Using motif -M04144_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9792 +# Estimated pi_0=0.993439 +Using motif +M04145_2.00 of width 12. +Using motif -M04145_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998872 +# Estimated pi_0=0.999497 +Using motif +M02792_2.00 of width 10. +Using motif -M02792_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967586 +# Estimated pi_0=0.986509 +Using motif +M04146_2.00 of width 11. +Using motif -M04146_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921121 +# Estimated pi_0=0.92752 +Using motif +M04147_2.00 of width 11. +Using motif -M04147_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982857 +# Estimated pi_0=0.999598 +Using motif +M07807_2.00 of width 15. +Using motif -M07807_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8798 +# Estimated pi_0=0.88 +Using motif +M07808_2.00 of width 15. +Using motif -M07808_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8758 +# Estimated pi_0=0.88887 +Using motif +M07809_2.00 of width 15. +Using motif -M07809_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900594 +# Estimated pi_0=0.911273 +Using motif +M07810_2.00 of width 11. +Using motif -M07810_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936 +# Estimated pi_0=0.946116 +Using motif +M07811_2.00 of width 14. +Using motif -M07811_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904 +# Estimated pi_0=0.904 +Using motif +M08056_2.00 of width 11. +Using motif -M08056_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9674 +# Estimated pi_0=0.977986 +Using motif +M08733_2.00 of width 12. +Using motif -M08733_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9148 +# Estimated pi_0=0.9148 +Using motif +M09460_2.00 of width 10. +Using motif -M09460_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947833 +# Estimated pi_0=0.957226 +Using motif +M09867_2.00 of width 14. +Using motif -M09867_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961121 +# Estimated pi_0=0.973893 +Using motif +M09868_2.00 of width 14. +Using motif -M09868_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.951688 +Using motif +M09869_2.00 of width 8. +Using motif -M09869_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945161 +# Estimated pi_0=0.955172 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970957 +# Estimated pi_0=0.97747 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04149_2.00 of width 19. +Using motif -M04149_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M08057_2.00 of width 13. +Using motif -M08057_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955868 +# Estimated pi_0=0.967657 +Using motif +M08735_2.00 of width 10. +Using motif -M08735_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962876 +# Estimated pi_0=0.965917 +Using motif +M01722_2.00 of width 9. +Using motif -M01722_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905 +# Estimated pi_0=0.921739 +Using motif +M04150_2.00 of width 10. +Using motif -M04150_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950159 +# Estimated pi_0=0.962179 +Using motif +M04151_2.00 of width 10. +Using motif -M04151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95792 +# Estimated pi_0=0.961098 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9274 +# Estimated pi_0=0.937547 +Using motif +M02793_2.00 of width 10. +Using motif -M02793_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879 +# Estimated pi_0=0.879 +Using motif +M04152_2.00 of width 10. +Using motif -M04152_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872 +# Estimated pi_0=0.879608 +Using motif +M04153_2.00 of width 10. +Using motif -M04153_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8906 +# Estimated pi_0=0.899245 +Using motif +M04154_2.00 of width 10. +Using motif -M04154_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89 +# Estimated pi_0=0.893922 +Using motif +M04155_2.00 of width 10. +Using motif -M04155_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8704 +# Estimated pi_0=0.880762 +Using motif +M02794_2.00 of width 10. +Using motif -M02794_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924118 +# Estimated pi_0=0.937711 +Using motif +M04156_2.00 of width 12. +Using motif -M04156_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958039 +# Estimated pi_0=0.97175 +Using motif +M04157_2.00 of width 12. +Using motif -M04157_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970784 +# Estimated pi_0=0.981371 +Using motif +M08736_2.00 of width 18. +Using motif -M08736_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902481 +# Estimated pi_0=0.91359 +Using motif +M04158_2.00 of width 10. +Using motif -M04158_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941188 +# Estimated pi_0=0.945109 +Using motif +M04159_2.00 of width 10. +Using motif -M04159_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9472 +# Estimated pi_0=0.958148 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9564 +# Estimated pi_0=0.970435 +Using motif +M04160_2.00 of width 10. +Using motif -M04160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979099 +# Estimated pi_0=0.98366 +Using motif +M04161_2.00 of width 10. +Using motif -M04161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9774 +# Estimated pi_0=1 +Using motif +M08737_2.00 of width 9. +Using motif -M08737_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94093 +# Estimated pi_0=0.95 +Using motif +M02796_2.00 of width 10. +Using motif -M02796_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914717 +# Estimated pi_0=0.929367 +Using motif +M02797_2.00 of width 10. +Using motif -M02797_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9184 +# Estimated pi_0=0.923459 +Using motif +M04162_2.00 of width 18. +Using motif -M04162_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908983 +# Estimated pi_0=0.915441 +Using motif +M04163_2.00 of width 18. +Using motif -M04163_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93072 +# Estimated pi_0=0.940645 +Using motif +M04164_2.00 of width 18. +Using motif -M04164_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89505 +# Estimated pi_0=0.911618 +Using motif +M04165_2.00 of width 18. +Using motif -M04165_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922951 +# Estimated pi_0=0.931489 +Using motif +M09875_2.00 of width 22. +Using motif -M09875_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909703 +# Estimated pi_0=0.921898 +Using motif +M09876_2.00 of width 22. +Using motif -M09876_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8962 +# Estimated pi_0=0.902063 +Using motif +M04166_2.00 of width 10. +Using motif -M04166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974133 +# Estimated pi_0=0.983135 +Using motif +M04167_2.00 of width 10. +Using motif -M04167_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9578 +# Estimated pi_0=0.972923 +Using motif +M04168_2.00 of width 10. +Using motif -M04168_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96396 +# Estimated pi_0=0.976235 +Using motif +M04169_2.00 of width 10. +Using motif -M04169_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975514 +# Estimated pi_0=0.982319 +Using motif +M08738_2.00 of width 9. +Using motif -M08738_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950874 +# Estimated pi_0=0.952583 +Using motif +M04170_2.00 of width 10. +Using motif -M04170_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949615 +# Estimated pi_0=0.96125 +Using motif +M01743_2.00 of width 8. +Using motif -M01743_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94099 +# Estimated pi_0=0.961224 +Using motif +M02798_2.00 of width 10. +Using motif -M02798_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M04171_2.00 of width 10. +Using motif -M04171_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04172_2.00 of width 10. +Using motif -M04172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991135 +# Estimated pi_0=0.997475 +Using motif +M04173_2.00 of width 10. +Using motif -M04173_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99964 +# Estimated pi_0=1 +Using motif +M04174_2.00 of width 10. +Using motif -M04174_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980762 +# Estimated pi_0=0.992581 +Using motif +M04175_2.00 of width 10. +Using motif -M04175_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04176_2.00 of width 10. +Using motif -M04176_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M04177_2.00 of width 18. +Using motif -M04177_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899 +# Estimated pi_0=0.909048 +Using motif +M04178_2.00 of width 18. +Using motif -M04178_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912844 +# Estimated pi_0=0.923788 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985333 +# Estimated pi_0=1 +Using motif +M02800_2.00 of width 10. +Using motif -M02800_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04179_2.00 of width 10. +Using motif -M04179_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9946 +# Estimated pi_0=1 +Using motif +M04180_2.00 of width 10. +Using motif -M04180_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988889 +# Estimated pi_0=0.999899 +Using motif +M04181_2.00 of width 10. +Using motif -M04181_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893 +# Estimated pi_0=0.898235 +Using motif +M04182_2.00 of width 10. +Using motif -M04182_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8886 +# Estimated pi_0=0.889412 +Using motif +M02801_2.00 of width 10. +Using motif -M02801_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977703 +# Estimated pi_0=0.984505 +Using motif +M04183_2.00 of width 12. +Using motif -M04183_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963462 +# Estimated pi_0=0.971026 +Using motif +M04184_2.00 of width 12. +Using motif -M04184_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926602 +# Estimated pi_0=0.932807 +Using motif +M04185_2.00 of width 12. +Using motif -M04185_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937857 +# Estimated pi_0=0.952455 +Using motif +M04186_2.00 of width 12. +Using motif -M04186_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999594 +# Estimated pi_0=1 +Using motif +M04187_2.00 of width 12. +Using motif -M04187_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918218 +# Estimated pi_0=0.932676 +Using motif +M04188_2.00 of width 12. +Using motif -M04188_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9388 +# Estimated pi_0=0.944444 +Using motif +M04189_2.00 of width 12. +Using motif -M04189_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973 +# Estimated pi_0=0.982034 +Using motif +M04190_2.00 of width 12. +Using motif -M04190_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9806 +# Estimated pi_0=1 +Using motif +M02802_2.00 of width 12. +Using motif -M02802_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8936 +# Estimated pi_0=0.907154 +Using motif +M04191_2.00 of width 10. +Using motif -M04191_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87098 +# Estimated pi_0=0.878679 +Using motif +M04192_2.00 of width 10. +Using motif -M04192_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9138 +# Estimated pi_0=0.9138 +Using motif +M04193_2.00 of width 10. +Using motif -M04193_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971485 +# Estimated pi_0=0.986258 +Using motif +M04194_2.00 of width 10. +Using motif -M04194_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974951 +# Estimated pi_0=1 +Using motif +M02803_2.00 of width 10. +Using motif -M02803_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04195_2.00 of width 10. +Using motif -M04195_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967073 +# Estimated pi_0=0.970795 +Using motif +M04196_2.00 of width 10. +Using motif -M04196_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947379 +# Estimated pi_0=0.95563 +Using motif +M04197_2.00 of width 10. +Using motif -M04197_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964065 +# Estimated pi_0=0.973068 +Using motif +M04198_2.00 of width 10. +Using motif -M04198_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9832 +# Estimated pi_0=0.997157 +Using motif +M08739_2.00 of width 11. +Using motif -M08739_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927097 +# Estimated pi_0=0.938902 +Using motif +M02804_2.00 of width 12. +Using motif -M02804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00081 +# Estimated pi_0=1 +Using motif +M04199_2.00 of width 10. +Using motif -M04199_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997692 +# Estimated pi_0=1 +Using motif +M04200_2.00 of width 10. +Using motif -M04200_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04201_2.00 of width 10. +Using motif -M04201_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9658 +# Estimated pi_0=0.988228 +Using motif +M04202_2.00 of width 10. +Using motif -M04202_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M04203_2.00 of width 10. +Using motif -M04203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992294 +# Estimated pi_0=1 +Using motif +M04204_2.00 of width 10. +Using motif -M04204_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997949 +# Estimated pi_0=1 +Using motif +M02805_2.00 of width 10. +Using motif -M02805_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954862 +# Estimated pi_0=0.965065 +Using motif +M04205_2.00 of width 9. +Using motif -M04205_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947402 +# Estimated pi_0=0.953117 +Using motif +M04206_2.00 of width 9. +Using motif -M04206_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958806 +# Estimated pi_0=0.965294 +Using motif +M04207_2.00 of width 10. +Using motif -M04207_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905794 +# Estimated pi_0=0.916571 +Using motif +M04208_2.00 of width 10. +Using motif -M04208_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941343 +# Estimated pi_0=0.955698 +Using motif +M04209_2.00 of width 10. +Using motif -M04209_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908627 +# Estimated pi_0=0.919478 +Using motif +M04210_2.00 of width 10. +Using motif -M04210_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.932318 +Using motif +M04211_2.00 of width 10. +Using motif -M04211_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930073 +# Estimated pi_0=0.935782 +Using motif +M02806_2.00 of width 10. +Using motif -M02806_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971887 +# Estimated pi_0=0.984103 +Using motif +M08058_2.00 of width 18. +Using motif -M08058_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982256 +# Estimated pi_0=0.997474 +Using motif +M08740_2.00 of width 10. +Using motif -M08740_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941495 +# Estimated pi_0=0.951339 +Using motif +M04212_2.00 of width 12. +Using motif -M04212_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04213_2.00 of width 12. +Using motif -M04213_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M01726_2.00 of width 10. +Using motif -M01726_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8998 +# Estimated pi_0=0.913455 +Using motif +M02728_2.00 of width 7. +Using motif -M02728_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939604 +# Estimated pi_0=0.954479 +Using motif +M02807_2.00 of width 10. +Using motif -M02807_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925667 +# Estimated pi_0=0.935105 +Using motif +M02808_2.00 of width 10. +Using motif -M02808_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945286 +# Estimated pi_0=0.948228 +Using motif +M04214_2.00 of width 8. +Using motif -M04214_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934455 +# Estimated pi_0=0.954825 +Using motif +M04215_2.00 of width 8. +Using motif -M04215_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95771 +# Estimated pi_0=0.969945 +Using motif +M09461_2.00 of width 12. +Using motif -M09461_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991834 +# Estimated pi_0=0.99533 +Using motif +M02809_2.00 of width 12. +Using motif -M02809_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9366 +# Estimated pi_0=0.948921 +Using motif +M02810_2.00 of width 12. +Using motif -M02810_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.888 +# Estimated pi_0=0.899833 +Using motif +M04216_2.00 of width 10. +Using motif -M04216_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865 +# Estimated pi_0=0.874808 +Using motif +M04217_2.00 of width 10. +Using motif -M04217_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8884 +# Estimated pi_0=0.893396 +Using motif +M04218_2.00 of width 8. +Using motif -M04218_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9466 +# Estimated pi_0=0.9466 +Using motif +M02811_2.00 of width 10. +Using motif -M02811_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985347 +# Estimated pi_0=0.999296 +Using motif +M03591_2.00 of width 12. +Using motif -M03591_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962 +# Estimated pi_0=0.971471 +Using motif +M04219_2.00 of width 10. +Using motif -M04219_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946 +# Estimated pi_0=0.952358 +Using motif +M04220_2.00 of width 10. +Using motif -M04220_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93098 +# Estimated pi_0=0.936415 +Using motif +M04221_2.00 of width 10. +Using motif -M04221_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9442 +# Estimated pi_0=0.954959 +Using motif +M04222_2.00 of width 10. +Using motif -M04222_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951456 +# Estimated pi_0=0.965082 +Using motif +M08059_2.00 of width 10. +Using motif -M08059_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983564 +# Estimated pi_0=0.993895 +Using motif +M08741_2.00 of width 13. +Using motif -M08741_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907723 +# Estimated pi_0=0.919687 +Using motif +M09462_2.00 of width 12. +Using motif -M09462_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9124 +# Estimated pi_0=0.927538 +Using motif +M02812_2.00 of width 10. +Using motif -M02812_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M02813_2.00 of width 10. +Using motif -M02813_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04223_2.00 of width 10. +Using motif -M04223_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998012 +# Estimated pi_0=1 +Using motif +M04224_2.00 of width 10. +Using motif -M04224_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04225_2.00 of width 10. +Using motif -M04225_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989888 +# Estimated pi_0=0.994388 +Using motif +M04226_2.00 of width 10. +Using motif -M04226_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08742_2.00 of width 18. +Using motif -M08742_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952987 +# Estimated pi_0=0.955636 +Using motif +M01718_2.00 of width 10. +Using motif -M01718_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908824 +# Estimated pi_0=0.921985 +Using motif +M02821_2.00 of width 8. +Using motif -M02821_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978 +# Estimated pi_0=0.992757 +Using motif +M02822_2.00 of width 18. +Using motif -M02822_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994423 +# Estimated pi_0=0.998883 +Using motif +M02823_2.00 of width 18. +Using motif -M02823_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980588 +# Estimated pi_0=0.99131 +Using motif +M01131_2.00 of width 8. +Using motif -M01131_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975313 +# Estimated pi_0=0.998769 +Using motif +M05834_2.00 of width 10. +Using motif -M05834_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05835_2.00 of width 8. +Using motif -M05835_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M07812_2.00 of width 14. +Using motif -M07812_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958462 +# Estimated pi_0=0.977551 +Using motif +M08064_2.00 of width 11. +Using motif -M08064_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990222 +# Estimated pi_0=0.995155 +Using motif +M08781_2.00 of width 12. +Using motif -M08781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99899 +# Estimated pi_0=1 +Using motif +M04008_2.00 of width 9. +Using motif -M04008_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M08782_2.00 of width 7. +Using motif -M08782_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M09926_2.00 of width 13. +Using motif -M09926_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00102 +# Estimated pi_0=1 +Using motif +M02729_2.00 of width 10. +Using motif -M02729_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M02824_2.00 of width 10. +Using motif -M02824_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04009_2.00 of width 12. +Using motif -M04009_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982453 +# Estimated pi_0=0.995684 +Using motif +M04227_2.00 of width 12. +Using motif -M04227_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04228_2.00 of width 12. +Using motif -M04228_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M08783_2.00 of width 12. +Using motif -M08783_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04229_2.00 of width 12. +Using motif -M04229_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940396 +# Estimated pi_0=0.940396 +Using motif +M04230_2.00 of width 12. +Using motif -M04230_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958 +# Estimated pi_0=0.966055 +Using motif +M08784_2.00 of width 11. +Using motif -M08784_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9528 +# Estimated pi_0=0.968254 +Using motif +M02730_2.00 of width 11. +Using motif -M02730_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953077 +# Estimated pi_0=0.954151 +Using motif +M02825_2.00 of width 12. +Using motif -M02825_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9394 +# Estimated pi_0=0.948522 +Using motif +M02826_2.00 of width 14. +Using motif -M02826_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97549 +# Estimated pi_0=0.991389 +Using motif +M04010_2.00 of width 12. +Using motif -M04010_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956262 +# Estimated pi_0=0.970476 +Using motif +M04011_2.00 of width 12. +Using motif -M04011_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9278 +# Estimated pi_0=0.934074 +Using motif +M04012_2.00 of width 13. +Using motif -M04012_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9646 +# Estimated pi_0=0.981418 +Using motif +M04231_2.00 of width 12. +Using motif -M04231_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9242 +# Estimated pi_0=0.925149 +Using motif +M04232_2.00 of width 12. +Using motif -M04232_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9356 +# Estimated pi_0=0.945841 +Using motif +M04233_2.00 of width 12. +Using motif -M04233_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9268 +# Estimated pi_0=0.934107 +Using motif +M04234_2.00 of width 12. +Using motif -M04234_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.93875 +Using motif +M09932_2.00 of width 24. +Using motif -M09932_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M09933_2.00 of width 17. +Using motif -M09933_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02827_2.00 of width 12. +Using motif -M02827_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02828_2.00 of width 12. +Using motif -M02828_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04013_2.00 of width 10. +Using motif -M04013_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04235_2.00 of width 12. +Using motif -M04235_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04236_2.00 of width 12. +Using motif -M04236_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04237_2.00 of width 12. +Using motif -M04237_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04238_2.00 of width 12. +Using motif -M04238_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M08785_2.00 of width 11. +Using motif -M08785_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M02829_2.00 of width 14. +Using motif -M02829_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967843 +# Estimated pi_0=0.975929 +Using motif +M02830_2.00 of width 14. +Using motif -M02830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962524 +# Estimated pi_0=0.974963 +Using motif +M04014_2.00 of width 12. +Using motif -M04014_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912157 +# Estimated pi_0=0.921197 +Using motif +M04015_2.00 of width 12. +Using motif -M04015_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9534 +# Estimated pi_0=0.960862 +Using motif +M04239_2.00 of width 12. +Using motif -M04239_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9384 +# Estimated pi_0=0.949748 +Using motif +M04240_2.00 of width 12. +Using motif -M04240_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9236 +# Estimated pi_0=0.931619 +Using motif +M04241_2.00 of width 12. +Using motif -M04241_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974182 +# Estimated pi_0=1 +Using motif +M04242_2.00 of width 12. +Using motif -M04242_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8948 +# Estimated pi_0=0.902476 +Using motif +M04243_2.00 of width 12. +Using motif -M04243_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.97977 +Using motif +M04244_2.00 of width 12. +Using motif -M04244_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997398 +# Estimated pi_0=0.999799 +Using motif +M02831_2.00 of width 12. +Using motif -M02831_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04016_2.00 of width 10. +Using motif -M04016_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04245_2.00 of width 12. +Using motif -M04245_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982913 +# Estimated pi_0=1 +Using motif +M04246_2.00 of width 12. +Using motif -M04246_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9714 +# Estimated pi_0=1 +Using motif +M08786_2.00 of width 13. +Using motif -M08786_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M09937_2.00 of width 10. +Using motif -M09937_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03649_2.00 of width 10. +Using motif -M03649_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04247_2.00 of width 19. +Using motif -M04247_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04248_2.00 of width 20. +Using motif -M04248_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04249_2.00 of width 19. +Using motif -M04249_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M08065_2.00 of width 14. +Using motif -M08065_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994839 +# Estimated pi_0=0.997487 +Using motif +M08787_2.00 of width 11. +Using motif -M08787_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976556 +# Estimated pi_0=0.988324 +Using motif +M09484_2.00 of width 10. +Using motif -M09484_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988075 +# Estimated pi_0=0.993684 +Using motif +M09938_2.00 of width 11. +Using motif -M09938_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9506 +# Estimated pi_0=0.960893 +Using motif +M04017_2.00 of width 10. +Using motif -M04017_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9876 +# Estimated pi_0=1 +Using motif +M04250_2.00 of width 12. +Using motif -M04250_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.959231 +Using motif +M04251_2.00 of width 12. +Using motif -M04251_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04252_2.00 of width 12. +Using motif -M04252_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939802 +# Estimated pi_0=0.947477 +Using motif +M04253_2.00 of width 12. +Using motif -M04253_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9838 +# Estimated pi_0=1 +Using motif +M08788_2.00 of width 11. +Using motif -M08788_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996883 +# Estimated pi_0=1 +Using motif +M09941_2.00 of width 8. +Using motif -M09941_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09942_2.00 of width 12. +Using motif -M09942_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934757 +# Estimated pi_0=0.938909 +Using motif +M08789_2.00 of width 14. +Using motif -M08789_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998478 +# Estimated pi_0=0.999397 +Using motif +M04018_2.00 of width 12. +Using motif -M04018_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933 +# Estimated pi_0=0.944262 +Using motif +M04019_2.00 of width 11. +Using motif -M04019_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9588 +# Estimated pi_0=0.967273 +Using motif +M04020_2.00 of width 14. +Using motif -M04020_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954455 +# Estimated pi_0=0.964667 +Using motif +M04021_2.00 of width 11. +Using motif -M04021_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9778 +# Estimated pi_0=1 +Using motif +M04254_2.00 of width 14. +Using motif -M04254_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94198 +# Estimated pi_0=0.945128 +Using motif +M04255_2.00 of width 12. +Using motif -M04255_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961481 +# Estimated pi_0=0.964182 +Using motif +M04256_2.00 of width 14. +Using motif -M04256_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9356 +# Estimated pi_0=0.947227 +Using motif +M04257_2.00 of width 12. +Using motif -M04257_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981667 +# Estimated pi_0=0.996284 +Using motif +M08790_2.00 of width 12. +Using motif -M08790_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8972 +# Estimated pi_0=0.9055 +Using motif +M09947_2.00 of width 8. +Using motif -M09947_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949307 +# Estimated pi_0=0.963582 +Using motif +M04022_2.00 of width 12. +Using motif -M04022_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963689 +# Estimated pi_0=0.973445 +Using motif +M04023_2.00 of width 10. +Using motif -M04023_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948713 +# Estimated pi_0=0.964538 +Using motif +M04258_2.00 of width 12. +Using motif -M04258_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9602 +# Estimated pi_0=0.977121 +Using motif +M04259_2.00 of width 9. +Using motif -M04259_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9554 +# Estimated pi_0=0.959412 +Using motif +M04260_2.00 of width 12. +Using motif -M04260_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95703 +# Estimated pi_0=0.968527 +Using motif +M04261_2.00 of width 11. +Using motif -M04261_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9758 +# Estimated pi_0=1 +Using motif +M04262_2.00 of width 12. +Using motif -M04262_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978416 +# Estimated pi_0=0.999598 +Using motif +M04263_2.00 of width 9. +Using motif -M04263_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99956 +# Estimated pi_0=1 +Using motif +M04264_2.00 of width 12. +Using motif -M04264_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98099 +# Estimated pi_0=1 +Using motif +M04265_2.00 of width 12. +Using motif -M04265_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9938 +# Estimated pi_0=0.999397 +Using motif +M08066_2.00 of width 12. +Using motif -M08066_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958286 +# Estimated pi_0=0.96087 +Using motif +M08791_2.00 of width 11. +Using motif -M08791_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998125 +# Estimated pi_0=0.999799 +Using motif +M09948_2.00 of width 8. +Using motif -M09948_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9592 +# Estimated pi_0=0.96 +Using motif +M09949_2.00 of width 12. +Using motif -M09949_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9312 +# Estimated pi_0=0.933267 +Using motif +M09952_2.00 of width 12. +Using motif -M09952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964464 +# Estimated pi_0=0.975862 +Using motif +M09953_2.00 of width 12. +Using motif -M09953_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9452 +# Estimated pi_0=0.952593 +Using motif +M09954_2.00 of width 15. +Using motif -M09954_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9042 +# Estimated pi_0=0.90902 +Using motif +M09955_2.00 of width 15. +Using motif -M09955_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955088 +# Estimated pi_0=0.966375 +Using motif +M08792_2.00 of width 11. +Using motif -M08792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982162 +# Estimated pi_0=0.984768 +Using motif +M09485_2.00 of width 10. +Using motif -M09485_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999694 +# Estimated pi_0=1 +Using motif +M02832_2.00 of width 11. +Using motif -M02832_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998756 +# Estimated pi_0=0.999196 +Using motif +M04024_2.00 of width 9. +Using motif -M04024_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99898 +# Estimated pi_0=0.999899 +Using motif +M04266_2.00 of width 11. +Using motif -M04266_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9836 +# Estimated pi_0=0.999192 +Using motif +M04267_2.00 of width 11. +Using motif -M04267_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M07813_2.00 of width 17. +Using motif -M07813_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995307 +# Estimated pi_0=0.999397 +Using motif +M08793_2.00 of width 13. +Using motif -M08793_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948679 +# Estimated pi_0=0.955079 +Using motif +M09486_2.00 of width 12. +Using motif -M09486_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988095 +# Estimated pi_0=0.993886 +Using motif +M09959_2.00 of width 11. +Using motif -M09959_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990248 +# Estimated pi_0=0.994842 +Using motif +M02833_2.00 of width 14. +Using motif -M02833_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999608 +# Estimated pi_0=1 +Using motif +M04025_2.00 of width 12. +Using motif -M04025_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975556 +# Estimated pi_0=0.988143 +Using motif +M04026_2.00 of width 11. +Using motif -M04026_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9482 +# Estimated pi_0=0.959568 +Using motif +M04268_2.00 of width 12. +Using motif -M04268_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969505 +# Estimated pi_0=0.981053 +Using motif +M04269_2.00 of width 12. +Using motif -M04269_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04270_2.00 of width 12. +Using motif -M04270_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9902 +# Estimated pi_0=0.998256 +Using motif +M04271_2.00 of width 12. +Using motif -M04271_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9812 +# Estimated pi_0=1 +Using motif +M08794_2.00 of width 17. +Using motif -M08794_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997143 +# Estimated pi_0=0.999497 +Using motif +M04272_2.00 of width 12. +Using motif -M04272_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9632 +# Estimated pi_0=0.9632 +Using motif +M04273_2.00 of width 12. +Using motif -M04273_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08795_2.00 of width 9. +Using motif -M08795_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991195 +# Estimated pi_0=0.996768 +Using motif +M02834_2.00 of width 13. +Using motif -M02834_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M04027_2.00 of width 14. +Using motif -M04027_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94381 +# Estimated pi_0=0.94434 +Using motif +M04274_2.00 of width 14. +Using motif -M04274_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97084 +# Estimated pi_0=0.982642 +Using motif +M04275_2.00 of width 14. +Using motif -M04275_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997576 +# Estimated pi_0=0.998492 +Using motif +M04276_2.00 of width 14. +Using motif -M04276_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967921 +# Estimated pi_0=0.982353 +Using motif +M08796_2.00 of width 12. +Using motif -M08796_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999286 +# Estimated pi_0=1 +Using motif +M02835_2.00 of width 11. +Using motif -M02835_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994717 +# Estimated pi_0=0.998889 +Using motif +M04277_2.00 of width 13. +Using motif -M04277_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995725 +# Estimated pi_0=0.999397 +Using motif +M04278_2.00 of width 13. +Using motif -M04278_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982963 +# Estimated pi_0=0.999598 +Using motif +M04279_2.00 of width 13. +Using motif -M04279_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04280_2.00 of width 13. +Using motif -M04280_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M04281_2.00 of width 11. +Using motif -M04281_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9506 +# Estimated pi_0=0.956792 +Using motif +M04282_2.00 of width 12. +Using motif -M04282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97549 +# Estimated pi_0=1 +Using motif +M04283_2.00 of width 11. +Using motif -M04283_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9942 +# Estimated pi_0=1 +Using motif +M04284_2.00 of width 11. +Using motif -M04284_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9824 +# Estimated pi_0=1 +Using motif +M05836_2.00 of width 10. +Using motif -M05836_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05837_2.00 of width 11. +Using motif -M05837_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M07814_2.00 of width 19. +Using motif -M07814_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M07815_2.00 of width 11. +Using motif -M07815_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974925 +# Estimated pi_0=0.986629 +Using motif +M07816_2.00 of width 15. +Using motif -M07816_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992523 +# Estimated pi_0=0.999196 +Using motif +M07817_2.00 of width 13. +Using motif -M07817_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97408 +# Estimated pi_0=0.984229 +Using motif +M07818_2.00 of width 15. +Using motif -M07818_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992867 +# Estimated pi_0=0.99899 +Using motif +M07819_2.00 of width 14. +Using motif -M07819_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9814 +# Estimated pi_0=0.99641 +Using motif +M07820_2.00 of width 15. +Using motif -M07820_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963571 +# Estimated pi_0=0.975252 +Using motif +M08067_2.00 of width 11. +Using motif -M08067_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984444 +# Estimated pi_0=0.993333 +Using motif +M08068_2.00 of width 15. +Using motif -M08068_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M08797_2.00 of width 11. +Using motif -M08797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98902 +# Estimated pi_0=0.99665 +Using motif +M09487_2.00 of width 12. +Using motif -M09487_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989333 +# Estimated pi_0=0.999095 +Using motif +M01813_2.00 of width 8. +Using motif -M01813_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M02836_2.00 of width 9. +Using motif -M02836_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M02837_2.00 of width 12. +Using motif -M02837_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994955 +# Estimated pi_0=1 +Using motif +M02838_2.00 of width 9. +Using motif -M02838_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M02839_2.00 of width 12. +Using motif -M02839_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999469 +# Estimated pi_0=1 +Using motif +M04285_2.00 of width 11. +Using motif -M04285_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990935 +# Estimated pi_0=0.999899 +Using motif +M04286_2.00 of width 12. +Using motif -M04286_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04287_2.00 of width 11. +Using motif -M04287_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04288_2.00 of width 12. +Using motif -M04288_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M04289_2.00 of width 11. +Using motif -M04289_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952475 +# Estimated pi_0=0.962857 +Using motif +M04290_2.00 of width 12. +Using motif -M04290_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9698 +# Estimated pi_0=0.972456 +Using motif +M04291_2.00 of width 11. +Using motif -M04291_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9725 +# Estimated pi_0=0.992069 +Using motif +M04292_2.00 of width 12. +Using motif -M04292_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992353 +# Estimated pi_0=0.999899 +Using motif +M04293_2.00 of width 12. +Using motif -M04293_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9252 +# Estimated pi_0=0.935167 +Using motif +M04294_2.00 of width 12. +Using motif -M04294_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91703 +# Estimated pi_0=0.928103 +Using motif +M04295_2.00 of width 12. +Using motif -M04295_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909505 +# Estimated pi_0=0.916415 +Using motif +M04296_2.00 of width 12. +Using motif -M04296_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9678 +# Estimated pi_0=0.985068 +Using motif +M04297_2.00 of width 12. +Using motif -M04297_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924 +# Estimated pi_0=0.924 +Using motif +M04298_2.00 of width 12. +Using motif -M04298_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948381 +# Estimated pi_0=0.969576 +Using motif +M04299_2.00 of width 12. +Using motif -M04299_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941584 +# Estimated pi_0=0.957321 +Using motif +M04300_2.00 of width 12. +Using motif -M04300_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977059 +# Estimated pi_0=1 +Using motif +M04301_2.00 of width 12. +Using motif -M04301_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04302_2.00 of width 12. +Using motif -M04302_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M02840_2.00 of width 10. +Using motif -M02840_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M02841_2.00 of width 10. +Using motif -M02841_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M04028_2.00 of width 12. +Using motif -M04028_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04303_2.00 of width 12. +Using motif -M04303_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04304_2.00 of width 12. +Using motif -M04304_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04305_2.00 of width 12. +Using motif -M04305_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994118 +# Estimated pi_0=1 +Using motif +M04306_2.00 of width 12. +Using motif -M04306_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994194 +# Estimated pi_0=1 +Using motif +M08798_2.00 of width 12. +Using motif -M08798_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.999598 +Using motif +M04307_2.00 of width 10. +Using motif -M04307_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864 +# Estimated pi_0=0.870962 +Using motif +M04308_2.00 of width 10. +Using motif -M04308_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9202 +# Estimated pi_0=0.925965 +Using motif +M07821_2.00 of width 15. +Using motif -M07821_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M08799_2.00 of width 18. +Using motif -M08799_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999695 +# Estimated pi_0=0.999799 +Using motif +M08184_2.00 of width 8. +Using motif -M08184_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984356 +# Estimated pi_0=0.988939 +Using motif +M08185_2.00 of width 10. +Using motif -M08185_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985763 +# Estimated pi_0=0.991959 +Using motif +M08186_2.00 of width 9. +Using motif -M08186_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998051 +# Estimated pi_0=0.998693 +Using motif +M08800_2.00 of width 13. +Using motif -M08800_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8834 +# Estimated pi_0=0.892941 +Using motif +M09488_2.00 of width 15. +Using motif -M09488_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995385 +# Estimated pi_0=0.998291 +Using motif +M09975_2.00 of width 15. +Using motif -M09975_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993069 +# Estimated pi_0=0.999899 +Using motif +M02842_2.00 of width 14. +Using motif -M02842_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951048 +# Estimated pi_0=0.962969 +Using motif +M02843_2.00 of width 12. +Using motif -M02843_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9192 +# Estimated pi_0=0.921584 +Using motif +M02844_2.00 of width 12. +Using motif -M02844_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939216 +# Estimated pi_0=0.950355 +Using motif +M02845_2.00 of width 14. +Using motif -M02845_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955094 +# Estimated pi_0=0.9625 +Using motif +M02846_2.00 of width 12. +Using motif -M02846_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9392 +# Estimated pi_0=0.945088 +Using motif +M02847_2.00 of width 14. +Using motif -M02847_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958 +# Estimated pi_0=0.968194 +Using motif +M04029_2.00 of width 13. +Using motif -M04029_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951485 +# Estimated pi_0=0.953178 +Using motif +M04030_2.00 of width 13. +Using motif -M04030_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921 +# Estimated pi_0=0.926731 +Using motif +M04031_2.00 of width 12. +Using motif -M04031_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944466 +# Estimated pi_0=0.965333 +Using motif +M04309_2.00 of width 14. +Using motif -M04309_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924 +# Estimated pi_0=0.924 +Using motif +M04310_2.00 of width 12. +Using motif -M04310_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919231 +# Estimated pi_0=0.930476 +Using motif +M04311_2.00 of width 12. +Using motif -M04311_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9218 +# Estimated pi_0=0.9218 +Using motif +M04312_2.00 of width 14. +Using motif -M04312_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908911 +# Estimated pi_0=0.916792 +Using motif +M04313_2.00 of width 12. +Using motif -M04313_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920392 +# Estimated pi_0=0.936535 +Using motif +M04314_2.00 of width 12. +Using motif -M04314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9454 +# Estimated pi_0=0.954495 +Using motif +M04315_2.00 of width 12. +Using motif -M04315_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907 +# Estimated pi_0=0.913208 +Using motif +M04316_2.00 of width 12. +Using motif -M04316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879406 +# Estimated pi_0=0.881698 +Using motif +M04317_2.00 of width 13. +Using motif -M04317_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92463 +# Estimated pi_0=0.928438 +Using motif +M04318_2.00 of width 12. +Using motif -M04318_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942593 +# Estimated pi_0=0.95374 +Using motif +M04319_2.00 of width 12. +Using motif -M04319_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9486 +# Estimated pi_0=0.962958 +Using motif +M04320_2.00 of width 13. +Using motif -M04320_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931154 +# Estimated pi_0=0.94065 +Using motif +M04032_2.00 of width 10. +Using motif -M04032_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995248 +# Estimated pi_0=0.999899 +Using motif +M04033_2.00 of width 9. +Using motif -M04033_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M04321_2.00 of width 12. +Using motif -M04321_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990099 +# Estimated pi_0=1 +Using motif +M04322_2.00 of width 12. +Using motif -M04322_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04323_2.00 of width 12. +Using motif -M04323_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9852 +# Estimated pi_0=0.997968 +Using motif +M04324_2.00 of width 12. +Using motif -M04324_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M07822_2.00 of width 11. +Using motif -M07822_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9276 +# Estimated pi_0=0.941695 +Using motif +M07823_2.00 of width 11. +Using motif -M07823_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931683 +# Estimated pi_0=0.946462 +Using motif +M07824_2.00 of width 11. +Using motif -M07824_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9058 +# Estimated pi_0=0.9058 +Using motif +M07825_2.00 of width 10. +Using motif -M07825_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950891 +# Estimated pi_0=0.967068 +Using motif +M08187_2.00 of width 8. +Using motif -M08187_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875 +# Estimated pi_0=0.876822 +Using motif +M08188_2.00 of width 10. +Using motif -M08188_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998667 +# Estimated pi_0=0.999899 +Using motif +M08801_2.00 of width 11. +Using motif -M08801_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9172 +# Estimated pi_0=0.92434 +Using motif +M09489_2.00 of width 12. +Using motif -M09489_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996559 +# Estimated pi_0=0.998995 +Using motif +M02658_2.00 of width 11. +Using motif -M02658_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02848_2.00 of width 12. +Using motif -M02848_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04034_2.00 of width 10. +Using motif -M04034_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M09982_2.00 of width 12. +Using motif -M09982_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M02849_2.00 of width 12. +Using motif -M02849_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M02850_2.00 of width 12. +Using motif -M02850_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04325_2.00 of width 12. +Using motif -M04325_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04326_2.00 of width 12. +Using motif -M04326_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M09986_2.00 of width 10. +Using motif -M09986_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04327_2.00 of width 12. +Using motif -M04327_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=1 +Using motif +M04328_2.00 of width 12. +Using motif -M04328_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979608 +# Estimated pi_0=0.999296 +Using motif +M07826_2.00 of width 13. +Using motif -M07826_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9942 +# Estimated pi_0=0.998586 +Using motif +M07827_2.00 of width 11. +Using motif -M07827_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996804 +# Estimated pi_0=0.997688 +Using motif +M07828_2.00 of width 15. +Using motif -M07828_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967925 +# Estimated pi_0=0.984944 +Using motif +M08069_2.00 of width 11. +Using motif -M08069_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997938 +# Estimated pi_0=0.998593 +Using motif +M08802_2.00 of width 9. +Using motif -M08802_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989286 +# Estimated pi_0=0.992784 +Using motif +M04329_2.00 of width 12. +Using motif -M04329_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966538 +# Estimated pi_0=0.973821 +Using motif +M04330_2.00 of width 11. +Using motif -M04330_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976238 +# Estimated pi_0=0.982314 +Using motif +M05838_2.00 of width 8. +Using motif -M05838_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99868 +# Estimated pi_0=0.999899 +Using motif +M05839_2.00 of width 10. +Using motif -M05839_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00172 +# Estimated pi_0=1 +Using motif +M07829_2.00 of width 15. +Using motif -M07829_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974 +# Estimated pi_0=0.983234 +Using motif +M08070_2.00 of width 11. +Using motif -M08070_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997423 +# Estimated pi_0=0.99809 +Using motif +M08803_2.00 of width 11. +Using motif -M08803_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991299 +# Estimated pi_0=0.996224 +Using motif +M02851_2.00 of width 10. +Using motif -M02851_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M02852_2.00 of width 10. +Using motif -M02852_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04331_2.00 of width 12. +Using motif -M04331_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04332_2.00 of width 12. +Using motif -M04332_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05840_2.00 of width 11. +Using motif -M05840_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M07830_2.00 of width 14. +Using motif -M07830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998687 +# Estimated pi_0=0.998894 +Using motif +M07831_2.00 of width 11. +Using motif -M07831_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999293 +# Estimated pi_0=0.999296 +Using motif +M07832_2.00 of width 14. +Using motif -M07832_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M08804_2.00 of width 12. +Using motif -M08804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999495 +# Estimated pi_0=0.999899 +Using motif +M09993_2.00 of width 14. +Using motif -M09993_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.997789 +Using motif +M09994_2.00 of width 14. +Using motif -M09994_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M04035_2.00 of width 10. +Using motif -M04035_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M04036_2.00 of width 12. +Using motif -M04036_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95068 +# Estimated pi_0=0.967642 +Using motif +M04333_2.00 of width 11. +Using motif -M04333_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M04334_2.00 of width 12. +Using motif -M04334_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968911 +# Estimated pi_0=0.97 +Using motif +M04335_2.00 of width 9. +Using motif -M04335_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990196 +# Estimated pi_0=1 +Using motif +M04336_2.00 of width 11. +Using motif -M04336_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990385 +# Estimated pi_0=1 +Using motif +M04337_2.00 of width 12. +Using motif -M04337_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9822 +# Estimated pi_0=1 +Using motif +M04338_2.00 of width 9. +Using motif -M04338_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990099 +# Estimated pi_0=1 +Using motif +M07833_2.00 of width 11. +Using motif -M07833_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989565 +# Estimated pi_0=0.993128 +Using motif +M08071_2.00 of width 11. +Using motif -M08071_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990735 +# Estimated pi_0=0.998889 +Using motif +M08805_2.00 of width 12. +Using motif -M08805_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990984 +# Estimated pi_0=0.998247 +Using motif +M09490_2.00 of width 12. +Using motif -M09490_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997128 +# Estimated pi_0=0.998995 +Using motif +M04037_2.00 of width 9. +Using motif -M04037_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985094 +# Estimated pi_0=0.998173 +Using motif +M04038_2.00 of width 10. +Using motif -M04038_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967961 +# Estimated pi_0=0.997419 +Using motif +M04339_2.00 of width 12. +Using motif -M04339_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04340_2.00 of width 11. +Using motif -M04340_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M04341_2.00 of width 11. +Using motif -M04341_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04342_2.00 of width 11. +Using motif -M04342_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9976 +# Estimated pi_0=0.999698 +Using motif +M05841_2.00 of width 11. +Using motif -M05841_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05842_2.00 of width 17. +Using motif -M05842_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M07834_2.00 of width 20. +Using motif -M07834_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9662 +# Estimated pi_0=0.983187 +Using motif +M07835_2.00 of width 11. +Using motif -M07835_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M07836_2.00 of width 15. +Using motif -M07836_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985636 +# Estimated pi_0=0.998154 +Using motif +M07837_2.00 of width 15. +Using motif -M07837_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999077 +# Estimated pi_0=1 +Using motif +M07838_2.00 of width 11. +Using motif -M07838_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997263 +# Estimated pi_0=0.997576 +Using motif +M08072_2.00 of width 13. +Using motif -M08072_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998763 +# Estimated pi_0=0.999196 +Using motif +M08073_2.00 of width 14. +Using motif -M08073_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99434 +# Estimated pi_0=0.998995 +Using motif +M08806_2.00 of width 11. +Using motif -M08806_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M04343_2.00 of width 9. +Using motif -M04343_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974563 +# Estimated pi_0=0.977225 +Using motif +M04344_2.00 of width 9. +Using motif -M04344_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04345_2.00 of width 14. +Using motif -M04345_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992169 +# Estimated pi_0=0.998081 +Using motif +M04346_2.00 of width 15. +Using motif -M04346_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991152 +# Estimated pi_0=0.995736 +Using motif +M04347_2.00 of width 16. +Using motif -M04347_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988545 +# Estimated pi_0=0.99809 +Using motif +M04348_2.00 of width 14. +Using motif -M04348_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970377 +# Estimated pi_0=0.977959 +Using motif +M04349_2.00 of width 15. +Using motif -M04349_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967899 +# Estimated pi_0=0.971049 +Using motif +M04350_2.00 of width 16. +Using motif -M04350_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977798 +# Estimated pi_0=0.996465 +Using motif +M08807_2.00 of width 19. +Using motif -M08807_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987135 +# Estimated pi_0=0.990103 +Using motif +M01812_2.00 of width 9. +Using motif -M01812_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919802 +# Estimated pi_0=0.930088 +Using motif +M04351_2.00 of width 13. +Using motif -M04351_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998883 +# Estimated pi_0=0.999196 +Using motif +M04352_2.00 of width 15. +Using motif -M04352_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978776 +# Estimated pi_0=0.987742 +Using motif +M04353_2.00 of width 16. +Using motif -M04353_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964486 +# Estimated pi_0=0.971842 +Using motif +M04354_2.00 of width 15. +Using motif -M04354_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972913 +# Estimated pi_0=0.98011 +Using motif +M04355_2.00 of width 16. +Using motif -M04355_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947478 +# Estimated pi_0=0.957554 +Using motif +M04356_2.00 of width 13. +Using motif -M04356_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M02853_2.00 of width 15. +Using motif -M02853_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993596 +# Estimated pi_0=0.996181 +Using motif +M04357_2.00 of width 15. +Using motif -M04357_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969394 +# Estimated pi_0=0.9775 +Using motif +M04358_2.00 of width 16. +Using motif -M04358_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973394 +# Estimated pi_0=0.985896 +Using motif +M04359_2.00 of width 15. +Using motif -M04359_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979298 +# Estimated pi_0=0.990682 +Using motif +M04360_2.00 of width 16. +Using motif -M04360_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9528 +# Estimated pi_0=0.964878 +Using motif +M04361_2.00 of width 15. +Using motif -M04361_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976934 +# Estimated pi_0=0.987283 +Using motif +M04362_2.00 of width 16. +Using motif -M04362_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962885 +# Estimated pi_0=0.967077 +Using motif +M04363_2.00 of width 15. +Using motif -M04363_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979802 +# Estimated pi_0=0.987609 +Using motif +M04364_2.00 of width 16. +Using motif -M04364_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958 +# Estimated pi_0=0.970645 +Using motif +M07839_2.00 of width 15. +Using motif -M07839_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986057 +# Estimated pi_0=0.994948 +Using motif +M08074_2.00 of width 21. +Using motif -M08074_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997374 +# Estimated pi_0=0.997889 +Using motif +M08808_2.00 of width 18. +Using motif -M08808_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964841 +# Estimated pi_0=0.966296 +Using motif +M02854_2.00 of width 21. +Using motif -M02854_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998446 +# Estimated pi_0=0.998995 +Using motif +M04039_2.00 of width 17. +Using motif -M04039_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978462 +# Estimated pi_0=0.991505 +Using motif +M04365_2.00 of width 13. +Using motif -M04365_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M04366_2.00 of width 15. +Using motif -M04366_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974783 +# Estimated pi_0=0.978129 +Using motif +M04367_2.00 of width 16. +Using motif -M04367_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968214 +# Estimated pi_0=0.977935 +Using motif +M04368_2.00 of width 13. +Using motif -M04368_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M04369_2.00 of width 15. +Using motif -M04369_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974634 +# Estimated pi_0=0.984 +Using motif +M04370_2.00 of width 16. +Using motif -M04370_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973524 +# Estimated pi_0=0.989689 +Using motif +M04371_2.00 of width 10. +Using motif -M04371_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998782 +# Estimated pi_0=0.998794 +Using motif +M04372_2.00 of width 13. +Using motif -M04372_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965946 +# Estimated pi_0=0.970395 +Using motif +M04373_2.00 of width 10. +Using motif -M04373_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999162 +# Estimated pi_0=1 +Using motif +M04374_2.00 of width 13. +Using motif -M04374_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973375 +# Estimated pi_0=0.975802 +Using motif +M08809_2.00 of width 18. +Using motif -M08809_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991523 +# Estimated pi_0=0.995258 +Using motif +M02855_2.00 of width 12. +Using motif -M02855_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999565 +# Estimated pi_0=1 +Using motif +M02856_2.00 of width 21. +Using motif -M02856_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994586 +# Estimated pi_0=0.999192 +Using motif +M02857_2.00 of width 12. +Using motif -M02857_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998283 +# Estimated pi_0=0.999296 +Using motif +M02858_2.00 of width 15. +Using motif -M02858_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987484 +# Estimated pi_0=0.997387 +Using motif +M07840_2.00 of width 13. +Using motif -M07840_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973188 +# Estimated pi_0=0.988705 +Using motif +M07841_2.00 of width 15. +Using motif -M07841_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995487 +# Estimated pi_0=0.999196 +Using motif +M08075_2.00 of width 19. +Using motif -M08075_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995556 +# Estimated pi_0=0.998593 +Using motif +M08810_2.00 of width 18. +Using motif -M08810_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995916 +# Estimated pi_0=0.998291 +Using motif +M04040_2.00 of width 18. +Using motif -M04040_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990395 +# Estimated pi_0=0.993439 +Using motif +M08811_2.00 of width 11. +Using motif -M08811_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98152 +# Estimated pi_0=0.990052 +Using motif +M04041_2.00 of width 12. +Using motif -M04041_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936514 +# Estimated pi_0=0.953611 +Using motif +M04042_2.00 of width 12. +Using motif -M04042_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9112 +# Estimated pi_0=0.911683 +Using motif +M04043_2.00 of width 11. +Using motif -M04043_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9608 +# Estimated pi_0=0.97952 +Using motif +M04044_2.00 of width 10. +Using motif -M04044_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97549 +# Estimated pi_0=1 +Using motif +M04375_2.00 of width 12. +Using motif -M04375_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941923 +# Estimated pi_0=0.955878 +Using motif +M04376_2.00 of width 12. +Using motif -M04376_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9382 +# Estimated pi_0=0.949421 +Using motif +M04377_2.00 of width 12. +Using motif -M04377_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917 +# Estimated pi_0=0.917 +Using motif +M04378_2.00 of width 12. +Using motif -M04378_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9188 +# Estimated pi_0=0.922593 +Using motif +M04379_2.00 of width 12. +Using motif -M04379_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945545 +# Estimated pi_0=0.957519 +Using motif +M02859_2.00 of width 10. +Using motif -M02859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M04380_2.00 of width 12. +Using motif -M04380_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04381_2.00 of width 12. +Using motif -M04381_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M08812_2.00 of width 11. +Using motif -M08812_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9885 +# Estimated pi_0=0.992953 +Using motif +M04045_2.00 of width 12. +Using motif -M04045_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99814 +# Estimated pi_0=1 +Using motif +M08076_2.00 of width 11. +Using motif -M08076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M08813_2.00 of width 12. +Using motif -M08813_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.998894 +Using motif +M10023_2.00 of width 13. +Using motif -M10023_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998469 +# Estimated pi_0=1 +Using motif +M10024_2.00 of width 14. +Using motif -M10024_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M10028_2.00 of width 18. +Using motif -M10028_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987143 +# Estimated pi_0=1 +Using motif +M10029_2.00 of width 14. +Using motif -M10029_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04382_2.00 of width 12. +Using motif -M04382_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04383_2.00 of width 12. +Using motif -M04383_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989 +# Estimated pi_0=1 +Using motif +M01849_2.00 of width 9. +Using motif -M01849_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M07842_2.00 of width 8. +Using motif -M07842_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949744 +# Estimated pi_0=0.960779 +Using motif +M08080_2.00 of width 11. +Using motif -M08080_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9022 +# Estimated pi_0=0.92094 +Using motif +M08190_2.00 of width 10. +Using motif -M08190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963366 +# Estimated pi_0=0.970256 +Using motif +M08848_2.00 of width 10. +Using motif -M08848_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966709 +# Estimated pi_0=0.972527 +Using motif +M10089_2.00 of width 13. +Using motif -M10089_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980617 +# Estimated pi_0=0.985638 +Using motif +M10090_2.00 of width 12. +Using motif -M10090_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984396 +# Estimated pi_0=0.989319 +Using motif +M10091_2.00 of width 12. +Using motif -M10091_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918 +# Estimated pi_0=0.932066 +Using motif +M10092_2.00 of width 9. +Using motif -M10092_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978113 +# Estimated pi_0=0.993412 +Using motif +M10093_2.00 of width 11. +Using motif -M10093_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984971 +# Estimated pi_0=0.991383 +Using motif +M01165_2.00 of width 10. +Using motif -M01165_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M04384_2.00 of width 20. +Using motif -M04384_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92902 +# Estimated pi_0=0.9292 +Using motif +M04385_2.00 of width 20. +Using motif -M04385_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9656 +# Estimated pi_0=0.976522 +Using motif +M08271_2.00 of width 12. +Using motif -M08271_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957624 +# Estimated pi_0=0.96637 +Using motif +M07562_2.00 of width 9. +Using motif -M07562_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991789 +# Estimated pi_0=0.995657 +Using motif +M08850_2.00 of width 10. +Using motif -M08850_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8896 +# Estimated pi_0=0.894766 +Using motif +M04386_2.00 of width 10. +Using motif -M04386_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931327 +# Estimated pi_0=0.945032 +Using motif +M04387_2.00 of width 10. +Using motif -M04387_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921094 +# Estimated pi_0=0.934872 +Using motif +M07563_2.00 of width 9. +Using motif -M07563_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917343 +# Estimated pi_0=0.921457 +Using motif +M07843_2.00 of width 21. +Using motif -M07843_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869851 +# Estimated pi_0=0.880606 +Using motif +M07844_2.00 of width 15. +Using motif -M07844_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895046 +# Estimated pi_0=0.897386 +Using motif +M08082_2.00 of width 21. +Using motif -M08082_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901863 +# Estimated pi_0=0.906036 +Using motif +M08272_2.00 of width 21. +Using motif -M08272_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8602 +# Estimated pi_0=0.878406 +Using motif +M08851_2.00 of width 20. +Using motif -M08851_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865985 +# Estimated pi_0=0.869281 +Using motif +M00217_2.00 of width 11. +Using motif -M00217_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M00218_2.00 of width 10. +Using motif -M00218_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981695 +# Estimated pi_0=0.986096 +Using motif +M00219_2.00 of width 10. +Using motif -M00219_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978471 +# Estimated pi_0=0.982905 +Using motif +M08232_2.00 of width 6. +Using motif -M08232_2.00 of width 6. +Computing q-values. +Using motif +M08273_2.00 of width 12. +Using motif -M08273_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8986 +# Estimated pi_0=0.905862 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998113 +# Estimated pi_0=1 +Using motif +M04389_2.00 of width 11. +Using motif -M04389_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985 +# Estimated pi_0=1 +Using motif +M04390_2.00 of width 19. +Using motif -M04390_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04391_2.00 of width 19. +Using motif -M04391_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M07564_2.00 of width 28. +Using motif -M07564_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902243 +# Estimated pi_0=0.907304 +Using motif +M08274_2.00 of width 27. +Using motif -M08274_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924737 +# Estimated pi_0=0.936076 +Using motif +M08852_2.00 of width 24. +Using motif -M08852_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994317 +# Estimated pi_0=0.996263 +Using motif +M00220_2.00 of width 8. +Using motif -M00220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967018 +# Estimated pi_0=0.975 +Using motif +M00221_2.00 of width 7. +Using motif -M00221_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996458 +# Estimated pi_0=0.997085 +Using motif +M00222_2.00 of width 10. +Using motif -M00222_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995464 +# Estimated pi_0=0.996566 +Using motif +M02873_2.00 of width 9. +Using motif -M02873_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971538 +# Estimated pi_0=0.980552 +Using motif +M04392_2.00 of width 10. +Using motif -M04392_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952167 +# Estimated pi_0=0.960779 +Using motif +M04393_2.00 of width 10. +Using motif -M04393_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945893 +# Estimated pi_0=0.957467 +Using motif +M04394_2.00 of width 10. +Using motif -M04394_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965946 +# Estimated pi_0=0.978486 +Using motif +M04395_2.00 of width 10. +Using motif -M04395_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952621 +# Estimated pi_0=0.964943 +Using motif +M08853_2.00 of width 10. +Using motif -M08853_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9425 +# Estimated pi_0=0.952544 +Using motif +M08275_2.00 of width 15. +Using motif -M08275_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.810385 +# Estimated pi_0=0.81215 +Using motif +M08233_2.00 of width 11. +Using motif -M08233_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890609 +# Estimated pi_0=0.896066 +Using motif +M10107_2.00 of width 9. +Using motif -M10107_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9004 +# Estimated pi_0=0.913088 +Using motif +M02731_2.00 of width 12. +Using motif -M02731_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998071 +# Estimated pi_0=0.999296 +Using motif +M02874_2.00 of width 15. +Using motif -M02874_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98427 +# Estimated pi_0=0.988854 +Using motif +M04396_2.00 of width 12. +Using motif -M04396_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96717 +# Estimated pi_0=0.971751 +Using motif +M04397_2.00 of width 12. +Using motif -M04397_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976735 +# Estimated pi_0=0.981398 +Using motif +M07845_2.00 of width 15. +Using motif -M07845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982599 +# Estimated pi_0=0.986875 +Using motif +M08083_2.00 of width 10. +Using motif -M08083_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995579 +# Estimated pi_0=0.997374 +Using motif +M08191_2.00 of width 8. +Using motif -M08191_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986272 +# Estimated pi_0=0.993403 +Using motif +M08192_2.00 of width 9. +Using motif -M08192_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M08276_2.00 of width 15. +Using motif -M08276_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957654 +# Estimated pi_0=0.963687 +Using motif +M08854_2.00 of width 14. +Using motif -M08854_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964624 +# Estimated pi_0=0.970164 +Using motif +M09500_2.00 of width 12. +Using motif -M09500_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984831 +# Estimated pi_0=0.988969 +Using motif +M08277_2.00 of width 21. +Using motif -M08277_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 1.1e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968925 +# Estimated pi_0=0.973542 +Using motif +M08855_2.00 of width 13. +Using motif -M08855_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980829 +# Estimated pi_0=0.983878 +Using motif +M04398_2.00 of width 21. +Using motif -M04398_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950282 +# Estimated pi_0=0.959255 +Using motif +M04399_2.00 of width 21. +Using motif -M04399_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996375 +# Estimated pi_0=0.998794 +Using motif +M08278_2.00 of width 21. +Using motif -M08278_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88396 +# Estimated pi_0=0.895574 +Using motif +M08856_2.00 of width 22. +Using motif -M08856_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929615 +# Estimated pi_0=0.937703 +Using motif +M04400_2.00 of width 11. +Using motif -M04400_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894 +# Estimated pi_0=0.902185 +Using motif +M04401_2.00 of width 11. +Using motif -M04401_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8912 +# Estimated pi_0=0.897196 +Using motif +M08857_2.00 of width 19. +Using motif -M08857_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892774 +# Estimated pi_0=0.896265 +Using motif +M08100_2.00 of width 14. +Using motif -M08100_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.862476 +# Estimated pi_0=0.867222 +Using motif +M02641_2.00 of width 11. +Using motif -M02641_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9266 +# Estimated pi_0=0.942313 +Using motif +M02732_2.00 of width 10. +Using motif -M02732_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93549 +# Estimated pi_0=0.942207 +Using motif +M02875_2.00 of width 14. +Using motif -M02875_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917833 +# Estimated pi_0=0.927586 +Using motif +M02876_2.00 of width 12. +Using motif -M02876_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9146 +# Estimated pi_0=0.921441 +Using motif +M04402_2.00 of width 15. +Using motif -M04402_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906 +# Estimated pi_0=0.914872 +Using motif +M04403_2.00 of width 15. +Using motif -M04403_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919412 +# Estimated pi_0=0.941449 +Using motif +M08279_2.00 of width 9. +Using motif -M08279_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928732 +# Estimated pi_0=0.931647 +Using motif +M07565_2.00 of width 18. +Using motif -M07565_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9124 +# Estimated pi_0=0.923433 +Using motif +M07566_2.00 of width 15. +Using motif -M07566_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95808 +# Estimated pi_0=0.967602 +Using motif +M08280_2.00 of width 15. +Using motif -M08280_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987324 +# Estimated pi_0=0.99267 +Using motif +M08858_2.00 of width 23. +Using motif -M08858_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909496 +# Estimated pi_0=0.914228 +Using motif +M07567_2.00 of width 12. +Using motif -M07567_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968257 +# Estimated pi_0=0.985902 +Using motif +M08234_2.00 of width 18. +Using motif -M08234_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9482 +# Estimated pi_0=0.951698 +Using motif +M08281_2.00 of width 12. +Using motif -M08281_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915385 +# Estimated pi_0=0.916975 +Using motif +M08859_2.00 of width 20. +Using motif -M08859_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975858 +# Estimated pi_0=0.983053 +Using motif +M07568_2.00 of width 30. +Using motif -M07568_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897565 +# Estimated pi_0=0.897586 +Using motif +M08235_2.00 of width 15. +Using motif -M08235_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981043 +# Estimated pi_0=0.991751 +Using motif +M08282_2.00 of width 12. +Using motif -M08282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976436 +# Estimated pi_0=0.992147 +Using motif +M08860_2.00 of width 24. +Using motif -M08860_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983718 +# Estimated pi_0=0.991713 +Using motif +M07846_2.00 of width 14. +Using motif -M07846_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942642 +# Estimated pi_0=0.956815 +Using motif +M07847_2.00 of width 15. +Using motif -M07847_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941008 +# Estimated pi_0=0.947943 +Using motif +M07848_2.00 of width 16. +Using motif -M07848_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934059 +# Estimated pi_0=0.949928 +Using motif +M07849_2.00 of width 15. +Using motif -M07849_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92495 +# Estimated pi_0=0.937293 +Using motif +M07850_2.00 of width 15. +Using motif -M07850_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934815 +# Estimated pi_0=0.946835 +Using motif +M07851_2.00 of width 16. +Using motif -M07851_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9088 +# Estimated pi_0=0.923101 +Using motif +M07852_2.00 of width 21. +Using motif -M07852_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9254 +# Estimated pi_0=0.937185 +Using motif +M07853_2.00 of width 15. +Using motif -M07853_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921346 +# Estimated pi_0=0.930299 +Using motif +M07854_2.00 of width 15. +Using motif -M07854_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943208 +# Estimated pi_0=0.957073 +Using motif +M08084_2.00 of width 21. +Using motif -M08084_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928571 +# Estimated pi_0=0.937447 +Using motif +M08861_2.00 of width 22. +Using motif -M08861_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903273 +# Estimated pi_0=0.907438 +Using motif +M09501_2.00 of width 20. +Using motif -M09501_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933559 +# Estimated pi_0=0.938356 +Using motif +M10117_2.00 of width 21. +Using motif -M10117_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9172 +# Estimated pi_0=0.930219 +Using motif +M08283_2.00 of width 12. +Using motif -M08283_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997868 +# Estimated pi_0=0.998593 +Using motif +M08862_2.00 of width 16. +Using motif -M08862_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998985 +# Estimated pi_0=0.999497 +Using motif +M10125_2.00 of width 16. +Using motif -M10125_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M10126_2.00 of width 11. +Using motif -M10126_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M10127_2.00 of width 11. +Using motif -M10127_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999391 +# Estimated pi_0=1 +Using motif +M10128_2.00 of width 15. +Using motif -M10128_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999091 +# Estimated pi_0=0.999497 +Using motif +M10129_2.00 of width 11. +Using motif -M10129_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999796 +# Estimated pi_0=1 +Using motif +M10130_2.00 of width 9. +Using motif -M10130_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998985 +# Estimated pi_0=1 +Using motif +M08284_2.00 of width 15. +Using motif -M08284_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856275 +# Estimated pi_0=0.861356 +Using motif +M04404_2.00 of width 17. +Using motif -M04404_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9202 +# Estimated pi_0=0.93254 +Using motif +M04405_2.00 of width 17. +Using motif -M04405_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9128 +# Estimated pi_0=0.914175 +Using motif +M07569_2.00 of width 9. +Using motif -M07569_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920513 +# Estimated pi_0=0.932609 +Using motif +M07570_2.00 of width 18. +Using motif -M07570_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977094 +# Estimated pi_0=0.985549 +Using motif +M07571_2.00 of width 27. +Using motif -M07571_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980824 +# Estimated pi_0=0.983568 +Using motif +M08285_2.00 of width 9. +Using motif -M08285_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975796 +# Estimated pi_0=0.981497 +Using motif +M02663_2.00 of width 6. +Using motif -M02663_2.00 of width 6. +Computing q-values. +Using motif +M02664_2.00 of width 10. +Using motif -M02664_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959661 +# Estimated pi_0=0.969942 +Using motif +M08236_2.00 of width 7. +Using motif -M08236_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M08286_2.00 of width 15. +Using motif -M08286_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9628 +# Estimated pi_0=0.981104 +Using motif +M08863_2.00 of width 13. +Using motif -M08863_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947862 +# Estimated pi_0=0.958613 +Using motif +M10133_2.00 of width 8. +Using motif -M10133_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930992 +# Estimated pi_0=0.939518 +Using motif +M10134_2.00 of width 13. +Using motif -M10134_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954126 +# Estimated pi_0=0.960989 +Using motif +M08287_2.00 of width 8. +Using motif -M08287_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926928 +# Estimated pi_0=0.936506 +Using motif +M08864_2.00 of width 22. +Using motif -M08864_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.5e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.2e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866357 +# Estimated pi_0=0.871975 +Using motif +M10137_2.00 of width 13. +Using motif -M10137_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89669 +# Estimated pi_0=0.903896 +Using motif +M02877_2.00 of width 11. +Using motif -M02877_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9758 +# Estimated pi_0=0.996907 +Using motif +M04406_2.00 of width 23. +Using motif -M04406_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996277 +# Estimated pi_0=0.999698 +Using motif +M04407_2.00 of width 14. +Using motif -M04407_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995138 +# Estimated pi_0=1 +Using motif +M04408_2.00 of width 23. +Using motif -M04408_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00077 +# Estimated pi_0=1 +Using motif +M04409_2.00 of width 14. +Using motif -M04409_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987451 +# Estimated pi_0=1 +Using motif +M05845_2.00 of width 13. +Using motif -M05845_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992475 +# Estimated pi_0=1 +Using motif +M07855_2.00 of width 15. +Using motif -M07855_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9296 +# Estimated pi_0=0.944928 +Using motif +M07856_2.00 of width 11. +Using motif -M07856_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939608 +# Estimated pi_0=0.942407 +Using motif +M07857_2.00 of width 15. +Using motif -M07857_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9044 +# Estimated pi_0=0.921967 +Using motif +M07858_2.00 of width 15. +Using motif -M07858_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915294 +# Estimated pi_0=0.92354 +Using motif +M07859_2.00 of width 15. +Using motif -M07859_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9218 +# Estimated pi_0=0.933125 +Using motif +M07860_2.00 of width 15. +Using motif -M07860_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935728 +# Estimated pi_0=0.951942 +Using motif +M07861_2.00 of width 13. +Using motif -M07861_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926931 +# Estimated pi_0=0.931552 +Using motif +M08085_2.00 of width 12. +Using motif -M08085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970615 +# Estimated pi_0=0.979333 +Using motif +M08237_2.00 of width 15. +Using motif -M08237_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9036 +# Estimated pi_0=0.9036 +Using motif +M08288_2.00 of width 12. +Using motif -M08288_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9114 +# Estimated pi_0=0.918413 +Using motif +M08865_2.00 of width 12. +Using motif -M08865_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945818 +# Estimated pi_0=0.945818 +Using motif +M10138_2.00 of width 17. +Using motif -M10138_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995419 +# Estimated pi_0=0.998579 +Using motif +M10139_2.00 of width 20. +Using motif -M10139_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921373 +# Estimated pi_0=0.939398 +Using motif +M08866_2.00 of width 10. +Using motif -M08866_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924058 +# Estimated pi_0=0.931111 +Using motif +M08867_2.00 of width 9. +Using motif -M08867_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928 +# Estimated pi_0=0.937589 +Using motif +M04410_2.00 of width 10. +Using motif -M04410_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930394 +# Estimated pi_0=0.936265 +Using motif +M04411_2.00 of width 10. +Using motif -M04411_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93265 +# Estimated pi_0=0.941259 +Using motif +M08086_2.00 of width 10. +Using motif -M08086_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934621 +# Estimated pi_0=0.938588 +Using motif +M08868_2.00 of width 14. +Using motif -M08868_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920952 +# Estimated pi_0=0.924167 +Using motif +M09502_2.00 of width 10. +Using motif -M09502_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923077 +# Estimated pi_0=0.935 +Using motif +M10151_2.00 of width 14. +Using motif -M10151_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946721 +# Estimated pi_0=0.953191 +Using motif +M02878_2.00 of width 17. +Using motif -M02878_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9346 +# Estimated pi_0=0.955867 +Using motif +M05846_2.00 of width 17. +Using motif -M05846_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883148 +# Estimated pi_0=0.897415 +Using motif +M07550_2.00 of width 15. +Using motif -M07550_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922333 +# Estimated pi_0=0.93 +Using motif +M07551_2.00 of width 11. +Using motif -M07551_2.00 of width 11. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.9e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994184 +# Estimated pi_0=0.995377 +Using motif +M07552_2.00 of width 19. +Using motif -M07552_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986452 +# Estimated pi_0=0.991282 +Using motif +M07862_2.00 of width 21. +Using motif -M07862_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868411 +# Estimated pi_0=0.878779 +Using motif +M07863_2.00 of width 16. +Using motif -M07863_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8822 +# Estimated pi_0=0.894225 +Using motif +M07864_2.00 of width 15. +Using motif -M07864_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901835 +# Estimated pi_0=0.919231 +Using motif +M07865_2.00 of width 16. +Using motif -M07865_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889744 +# Estimated pi_0=0.900423 +Using motif +M07866_2.00 of width 15. +Using motif -M07866_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9125 +# Estimated pi_0=0.922302 +Using motif +M07867_2.00 of width 13. +Using motif -M07867_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947246 +# Estimated pi_0=0.950909 +Using motif +M07868_2.00 of width 15. +Using motif -M07868_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91375 +# Estimated pi_0=0.927712 +Using motif +M07869_2.00 of width 15. +Using motif -M07869_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885484 +# Estimated pi_0=0.890853 +Using motif +M07870_2.00 of width 15. +Using motif -M07870_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886602 +# Estimated pi_0=0.894032 +Using motif +M07871_2.00 of width 15. +Using motif -M07871_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87505 +# Estimated pi_0=0.884167 +Using motif +M07872_2.00 of width 15. +Using motif -M07872_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889739 +# Estimated pi_0=0.895188 +Using motif +M07873_2.00 of width 21. +Using motif -M07873_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87122 +# Estimated pi_0=0.871736 +Using motif +M07874_2.00 of width 15. +Using motif -M07874_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886111 +# Estimated pi_0=0.893387 +Using motif +M07875_2.00 of width 15. +Using motif -M07875_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86396 +# Estimated pi_0=0.87252 +Using motif +M07876_2.00 of width 15. +Using motif -M07876_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884483 +# Estimated pi_0=0.895252 +Using motif +M07877_2.00 of width 15. +Using motif -M07877_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884231 +# Estimated pi_0=0.898779 +Using motif +M07878_2.00 of width 15. +Using motif -M07878_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88 +# Estimated pi_0=0.889635 +Using motif +M07879_2.00 of width 15. +Using motif -M07879_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877 +# Estimated pi_0=0.886364 +Using motif +M07880_2.00 of width 15. +Using motif -M07880_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891927 +# Estimated pi_0=0.891927 +Using motif +M07881_2.00 of width 15. +Using motif -M07881_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8976 +# Estimated pi_0=0.909173 +Using motif +M07882_2.00 of width 12. +Using motif -M07882_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926261 +# Estimated pi_0=0.941519 +Using motif +M07883_2.00 of width 17. +Using motif -M07883_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8696 +# Estimated pi_0=0.876036 +Using motif +M07884_2.00 of width 15. +Using motif -M07884_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8968 +# Estimated pi_0=0.901077 +Using motif +M07885_2.00 of width 15. +Using motif -M07885_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88855 +# Estimated pi_0=0.889474 +Using motif +M07886_2.00 of width 15. +Using motif -M07886_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886786 +# Estimated pi_0=0.893582 +Using motif +M07887_2.00 of width 18. +Using motif -M07887_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879815 +# Estimated pi_0=0.892374 +Using motif +M07888_2.00 of width 15. +Using motif -M07888_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920993 +# Estimated pi_0=0.927771 +Using motif +M07889_2.00 of width 14. +Using motif -M07889_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915752 +# Estimated pi_0=0.923514 +Using motif +M07890_2.00 of width 13. +Using motif -M07890_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9114 +# Estimated pi_0=0.931733 +Using motif +M07891_2.00 of width 18. +Using motif -M07891_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891429 +# Estimated pi_0=0.892598 +Using motif +M07892_2.00 of width 15. +Using motif -M07892_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912593 +# Estimated pi_0=0.924414 +Using motif +M07893_2.00 of width 15. +Using motif -M07893_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903462 +# Estimated pi_0=0.914453 +Using motif +M07894_2.00 of width 17. +Using motif -M07894_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.862308 +# Estimated pi_0=0.873043 +Using motif +M07895_2.00 of width 18. +Using motif -M07895_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898532 +# Estimated pi_0=0.912162 +Using motif +M07896_2.00 of width 15. +Using motif -M07896_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904103 +# Estimated pi_0=0.915676 +Using motif +M07897_2.00 of width 15. +Using motif -M07897_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932063 +# Estimated pi_0=0.938462 +Using motif +M07898_2.00 of width 14. +Using motif -M07898_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9138 +# Estimated pi_0=0.92518 +Using motif +M07899_2.00 of width 20. +Using motif -M07899_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897344 +# Estimated pi_0=0.899091 +Using motif +M07900_2.00 of width 17. +Using motif -M07900_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867885 +# Estimated pi_0=0.869667 +Using motif +M07901_2.00 of width 11. +Using motif -M07901_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921 +# Estimated pi_0=0.93493 +Using motif +M07902_2.00 of width 15. +Using motif -M07902_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9176 +# Estimated pi_0=0.929929 +Using motif +M07903_2.00 of width 13. +Using motif -M07903_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919423 +# Estimated pi_0=0.922636 +Using motif +M07904_2.00 of width 15. +Using motif -M07904_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903036 +# Estimated pi_0=0.907077 +Using motif +M07905_2.00 of width 12. +Using motif -M07905_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93812 +# Estimated pi_0=0.950414 +Using motif +M07906_2.00 of width 18. +Using motif -M07906_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8972 +# Estimated pi_0=0.903248 +Using motif +M07907_2.00 of width 18. +Using motif -M07907_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906981 +# Estimated pi_0=0.919857 +Using motif +M07908_2.00 of width 21. +Using motif -M07908_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.858545 +# Estimated pi_0=0.871206 +Using motif +M07909_2.00 of width 15. +Using motif -M07909_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955852 +# Estimated pi_0=0.960121 +Using motif +M07910_2.00 of width 20. +Using motif -M07910_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856577 +# Estimated pi_0=0.864317 +Using motif +M07911_2.00 of width 18. +Using motif -M07911_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86973 +# Estimated pi_0=0.881185 +Using motif +M07912_2.00 of width 15. +Using motif -M07912_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898218 +# Estimated pi_0=0.916993 +Using motif +M07913_2.00 of width 15. +Using motif -M07913_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889928 +# Estimated pi_0=0.893919 +Using motif +M07914_2.00 of width 15. +Using motif -M07914_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8964 +# Estimated pi_0=0.908243 +Using motif +M07915_2.00 of width 21. +Using motif -M07915_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93283 +# Estimated pi_0=0.944103 +Using motif +M07916_2.00 of width 17. +Using motif -M07916_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910769 +# Estimated pi_0=0.926197 +Using motif +M07917_2.00 of width 15. +Using motif -M07917_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90972 +# Estimated pi_0=0.923404 +Using motif +M07918_2.00 of width 14. +Using motif -M07918_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8844 +# Estimated pi_0=0.892444 +Using motif +M07919_2.00 of width 15. +Using motif -M07919_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892885 +# Estimated pi_0=0.907114 +Using motif +M07920_2.00 of width 18. +Using motif -M07920_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898812 +# Estimated pi_0=0.903158 +Using motif +M07921_2.00 of width 15. +Using motif -M07921_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901622 +# Estimated pi_0=0.917343 +Using motif +M07922_2.00 of width 13. +Using motif -M07922_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911681 +# Estimated pi_0=0.924865 +Using motif +M07923_2.00 of width 15. +Using motif -M07923_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893333 +# Estimated pi_0=0.90803 +Using motif +M07924_2.00 of width 15. +Using motif -M07924_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902286 +# Estimated pi_0=0.912057 +Using motif +M07925_2.00 of width 15. +Using motif -M07925_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8896 +# Estimated pi_0=0.897258 +Using motif +M08087_2.00 of width 19. +Using motif -M08087_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9158 +# Estimated pi_0=0.924681 +Using motif +M08238_2.00 of width 15. +Using motif -M08238_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8566 +# Estimated pi_0=0.864463 +Using motif +M08289_2.00 of width 15. +Using motif -M08289_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.850185 +# Estimated pi_0=0.866716 +Using motif +M08869_2.00 of width 19. +Using motif -M08869_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88932 +# Estimated pi_0=0.904898 +Using motif +M09503_2.00 of width 20. +Using motif -M09503_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8994 +# Estimated pi_0=0.904444 +Using motif +M09504_2.00 of width 20. +Using motif -M09504_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983108 +# Estimated pi_0=0.990778 +Using motif +M04412_2.00 of width 15. +Using motif -M04412_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99 +# Estimated pi_0=1 +Using motif +M04413_2.00 of width 15. +Using motif -M04413_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967379 +# Estimated pi_0=0.985946 +Using motif +M04414_2.00 of width 16. +Using motif -M04414_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932871 +# Estimated pi_0=0.94 +Using motif +M04415_2.00 of width 16. +Using motif -M04415_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935577 +# Estimated pi_0=0.939823 +Using motif +M08290_2.00 of width 9. +Using motif -M08290_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904056 +# Estimated pi_0=0.911195 +Using motif +M08870_2.00 of width 22. +Using motif -M08870_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.6e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867682 +# Estimated pi_0=0.875491 +Using motif +M09505_2.00 of width 8. +Using motif -M09505_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872598 +# Estimated pi_0=0.879441 +Using motif +M07572_2.00 of width 21. +Using motif -M07572_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947434 +# Estimated pi_0=0.952129 +Using motif +M08291_2.00 of width 21. +Using motif -M08291_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97441 +# Estimated pi_0=0.980535 +Using motif +M08292_2.00 of width 21. +Using motif -M08292_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996224 +# Estimated pi_0=0.997889 +Using motif +M00223_2.00 of width 9. +Using motif -M00223_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935798 +# Estimated pi_0=0.940364 +Using motif +M00224_2.00 of width 8. +Using motif -M00224_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891667 +# Estimated pi_0=0.897778 +Using motif +M00225_2.00 of width 12. +Using motif -M00225_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00226_2.00 of width 8. +Using motif -M00226_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.887379 +# Estimated pi_0=0.88785 +Using motif +M08293_2.00 of width 15. +Using motif -M08293_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902462 +# Estimated pi_0=0.90962 +Using motif +M08871_2.00 of width 14. +Using motif -M08871_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886415 +# Estimated pi_0=0.897517 +Using motif +M08239_2.00 of width 20. +Using motif -M08239_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88495 +# Estimated pi_0=0.890092 +Using motif +M08294_2.00 of width 12. +Using motif -M08294_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.928852 +Using motif +M07573_2.00 of width 18. +Using motif -M07573_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969323 +# Estimated pi_0=0.974251 +Using motif +M08295_2.00 of width 15. +Using motif -M08295_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986928 +# Estimated pi_0=0.992746 +Using motif +M08872_2.00 of width 20. +Using motif -M08872_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994421 +# Estimated pi_0=0.996465 +Using motif +M02879_2.00 of width 17. +Using motif -M02879_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928364 +# Estimated pi_0=0.935616 +Using motif +M08296_2.00 of width 15. +Using motif -M08296_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909016 +# Estimated pi_0=0.918089 +Using motif +M08873_2.00 of width 20. +Using motif -M08873_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890135 +# Estimated pi_0=0.892129 +Using motif +M05847_2.00 of width 13. +Using motif -M05847_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9778 +# Estimated pi_0=0.994643 +Using motif +M08874_2.00 of width 19. +Using motif -M08874_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9498 +# Estimated pi_0=0.964444 +Using motif +M02642_2.00 of width 11. +Using motif -M02642_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935263 +# Estimated pi_0=0.943077 +Using motif +M04416_2.00 of width 15. +Using motif -M04416_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898879 +# Estimated pi_0=0.914161 +Using motif +M04417_2.00 of width 15. +Using motif -M04417_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911456 +# Estimated pi_0=0.916557 +Using motif +M08875_2.00 of width 11. +Using motif -M08875_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.959456 +Using motif +M02880_2.00 of width 14. +Using motif -M02880_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9218 +# Estimated pi_0=0.929618 +Using motif +M04418_2.00 of width 11. +Using motif -M04418_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908807 +# Estimated pi_0=0.923478 +Using motif +M04419_2.00 of width 11. +Using motif -M04419_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891 +# Estimated pi_0=0.906462 +Using motif +M04420_2.00 of width 12. +Using motif -M04420_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875446 +# Estimated pi_0=0.894032 +Using motif +M08876_2.00 of width 19. +Using motif -M08876_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.5e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868966 +# Estimated pi_0=0.879456 +Using motif +M02881_2.00 of width 13. +Using motif -M02881_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955126 +# Estimated pi_0=0.973665 +Using motif +M04421_2.00 of width 20. +Using motif -M04421_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939286 +# Estimated pi_0=0.948056 +Using motif +M04422_2.00 of width 20. +Using motif -M04422_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917 +# Estimated pi_0=0.930957 +Using motif +M04423_2.00 of width 20. +Using motif -M04423_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9264 +# Estimated pi_0=0.932523 +Using motif +M02643_2.00 of width 11. +Using motif -M02643_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918 +# Estimated pi_0=0.931486 +Using motif +M00227_2.00 of width 10. +Using motif -M00227_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M00228_2.00 of width 10. +Using motif -M00228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04424_2.00 of width 16. +Using motif -M04424_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999695 +# Estimated pi_0=1 +Using motif +M04425_2.00 of width 16. +Using motif -M04425_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04426_2.00 of width 16. +Using motif -M04426_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04427_2.00 of width 16. +Using motif -M04427_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M08877_2.00 of width 13. +Using motif -M08877_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989146 +# Estimated pi_0=0.997056 +Using motif +M08878_2.00 of width 19. +Using motif -M08878_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906627 +# Estimated pi_0=0.9096 +Using motif +M04428_2.00 of width 18. +Using motif -M04428_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963841 +# Estimated pi_0=0.965486 +Using motif +M04429_2.00 of width 18. +Using motif -M04429_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957857 +# Estimated pi_0=0.965244 +Using motif +M07574_2.00 of width 9. +Using motif -M07574_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949242 +# Estimated pi_0=0.957647 +Using motif +M08240_2.00 of width 14. +Using motif -M08240_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947704 +# Estimated pi_0=0.949296 +Using motif +M08297_2.00 of width 15. +Using motif -M08297_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954184 +# Estimated pi_0=0.961098 +Using motif +M08298_2.00 of width 11. +Using motif -M08298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905512 +# Estimated pi_0=0.914172 +Using motif +M00130_2.00 of width 10. +Using motif -M00130_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912414 +# Estimated pi_0=0.922909 +Using motif +M07575_2.00 of width 18. +Using motif -M07575_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926154 +# Estimated pi_0=0.934247 +Using motif +M04430_2.00 of width 11. +Using motif -M04430_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914902 +# Estimated pi_0=0.929041 +Using motif +M04431_2.00 of width 12. +Using motif -M04431_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8706 +# Estimated pi_0=0.879083 +Using motif +M04432_2.00 of width 11. +Using motif -M04432_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917143 +# Estimated pi_0=0.932239 +Using motif +M08299_2.00 of width 15. +Using motif -M08299_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.843551 +# Estimated pi_0=0.85854 +Using motif +M08879_2.00 of width 11. +Using motif -M08879_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903964 +# Estimated pi_0=0.913165 +Using motif +M10186_2.00 of width 7. +Using motif -M10186_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973125 +# Estimated pi_0=0.98337 +Using motif +M08088_2.00 of width 13. +Using motif -M08088_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8822 +# Estimated pi_0=0.886667 +Using motif +M08880_2.00 of width 15. +Using motif -M08880_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870962 +# Estimated pi_0=0.884769 +Using motif +M04433_2.00 of width 11. +Using motif -M04433_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04434_2.00 of width 11. +Using motif -M04434_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9718 +# Estimated pi_0=0.98688 +Using motif +M02882_2.00 of width 17. +Using motif -M02882_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04435_2.00 of width 14. +Using motif -M04435_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04436_2.00 of width 14. +Using motif -M04436_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00983_2.00 of width 10. +Using motif -M00983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942913 +# Estimated pi_0=0.953226 +Using motif +M00984_2.00 of width 8. +Using motif -M00984_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9276 +# Estimated pi_0=0.937196 +Using motif +M00985_2.00 of width 10. +Using motif -M00985_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8568 +# Estimated pi_0=0.863883 +Using motif +M07926_2.00 of width 15. +Using motif -M07926_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965783 +# Estimated pi_0=0.970333 +Using motif +M08881_2.00 of width 17. +Using motif -M08881_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954337 +# Estimated pi_0=0.959892 +Using motif +M02883_2.00 of width 14. +Using motif -M02883_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912427 +# Estimated pi_0=0.9275 +Using motif +M02884_2.00 of width 14. +Using motif -M02884_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.887843 +# Estimated pi_0=0.889811 +Using motif +M04437_2.00 of width 13. +Using motif -M04437_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906 +# Estimated pi_0=0.916774 +Using motif +M04438_2.00 of width 13. +Using motif -M04438_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915906 +# Estimated pi_0=0.924667 +Using motif +M07927_2.00 of width 20. +Using motif -M07927_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8572 +# Estimated pi_0=0.871094 +Using motif +M07928_2.00 of width 15. +Using motif -M07928_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870182 +# Estimated pi_0=0.878487 +Using motif +M07929_2.00 of width 20. +Using motif -M07929_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.861157 +# Estimated pi_0=0.869037 +Using motif +M08882_2.00 of width 17. +Using motif -M08882_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914054 +# Estimated pi_0=0.920599 +Using motif +M09506_2.00 of width 10. +Using motif -M09506_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941475 +# Estimated pi_0=0.95103 +Using motif +M10188_2.00 of width 12. +Using motif -M10188_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.926609 +Using motif +M07576_2.00 of width 15. +Using motif -M07576_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968774 +# Estimated pi_0=0.973837 +Using motif +M08300_2.00 of width 13. +Using motif -M08300_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974463 +# Estimated pi_0=0.981183 +Using motif +M08883_2.00 of width 20. +Using motif -M08883_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979878 +# Estimated pi_0=0.988796 +Using motif +M07577_2.00 of width 9. +Using motif -M07577_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M08301_2.00 of width 9. +Using motif -M08301_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940667 +# Estimated pi_0=0.947875 +Using motif +M07578_2.00 of width 9. +Using motif -M07578_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947778 +# Estimated pi_0=0.956463 +Using motif +M00229_2.00 of width 9. +Using motif -M00229_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928571 +# Estimated pi_0=0.940556 +Using motif +M00230_2.00 of width 10. +Using motif -M00230_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932258 +# Estimated pi_0=0.935114 +Using motif +M00231_2.00 of width 8. +Using motif -M00231_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922264 +# Estimated pi_0=0.929116 +Using motif +M00232_2.00 of width 10. +Using motif -M00232_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952769 +# Estimated pi_0=0.963536 +Using motif +M00233_2.00 of width 10. +Using motif -M00233_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918182 +# Estimated pi_0=0.931667 +Using motif +M02885_2.00 of width 11. +Using motif -M02885_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907451 +# Estimated pi_0=0.918824 +Using motif +M02886_2.00 of width 15. +Using motif -M02886_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927961 +# Estimated pi_0=0.938868 +Using motif +M04439_2.00 of width 13. +Using motif -M04439_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915902 +# Estimated pi_0=0.922446 +Using motif +M04440_2.00 of width 13. +Using motif -M04440_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932 +# Estimated pi_0=0.94 +Using motif +M08302_2.00 of width 9. +Using motif -M08302_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905546 +# Estimated pi_0=0.914658 +Using motif +M08884_2.00 of width 18. +Using motif -M08884_2.00 of width 18. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916923 +# Estimated pi_0=0.91907 +Using motif +M10197_2.00 of width 12. +Using motif -M10197_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941441 +# Estimated pi_0=0.957647 +Using motif +M07930_2.00 of width 15. +Using motif -M07930_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.839038 +# Estimated pi_0=0.842075 +Using motif +M08089_2.00 of width 14. +Using motif -M08089_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8514 +# Estimated pi_0=0.863538 +Using motif +M08885_2.00 of width 17. +Using motif -M08885_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867 +# Estimated pi_0=0.881022 +Using motif +M09507_2.00 of width 20. +Using motif -M09507_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869244 +# Estimated pi_0=0.872615 +Using motif +M04441_2.00 of width 10. +Using motif -M04441_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956863 +# Estimated pi_0=0.960503 +Using motif +M04442_2.00 of width 10. +Using motif -M04442_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93514 +# Estimated pi_0=0.957412 +Using motif +M04443_2.00 of width 10. +Using motif -M04443_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956056 +# Estimated pi_0=0.961899 +Using motif +M04444_2.00 of width 10. +Using motif -M04444_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947723 +# Estimated pi_0=0.961084 +Using motif +M08303_2.00 of width 15. +Using motif -M08303_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916634 +# Estimated pi_0=0.925378 +Using motif +M08886_2.00 of width 8. +Using motif -M08886_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941905 +# Estimated pi_0=0.947516 +Using motif +M07579_2.00 of width 11. +Using motif -M07579_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966909 +# Estimated pi_0=0.972609 +Using motif +M08241_2.00 of width 15. +Using motif -M08241_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M08304_2.00 of width 9. +Using motif -M08304_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990166 +# Estimated pi_0=0.99134 +Using motif +M02665_2.00 of width 20. +Using motif -M02665_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924818 +# Estimated pi_0=0.935515 +Using motif +M10203_2.00 of width 14. +Using motif -M10203_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948608 +# Estimated pi_0=0.951977 +Using motif +M07580_2.00 of width 30. +Using motif -M07580_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996216 +# Estimated pi_0=0.999095 +Using motif +M00234_2.00 of width 10. +Using motif -M00234_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9902 +# Estimated pi_0=1 +Using motif +M00235_2.00 of width 10. +Using motif -M00235_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=1 +Using motif +M04445_2.00 of width 13. +Using motif -M04445_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9688 +# Estimated pi_0=0.985 +Using motif +M04446_2.00 of width 13. +Using motif -M04446_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9788 +# Estimated pi_0=1 +Using motif +M04447_2.00 of width 13. +Using motif -M04447_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975248 +# Estimated pi_0=0.988264 +Using motif +M04448_2.00 of width 13. +Using motif -M04448_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M08305_2.00 of width 15. +Using motif -M08305_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935495 +# Estimated pi_0=0.948029 +Using motif +M08887_2.00 of width 24. +Using motif -M08887_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933611 +# Estimated pi_0=0.943067 +Using motif +M01171_2.00 of width 10. +Using motif -M01171_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.854857 +# Estimated pi_0=0.864286 +Using motif +M02887_2.00 of width 14. +Using motif -M02887_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8896 +# Estimated pi_0=0.904242 +Using motif +M04449_2.00 of width 14. +Using motif -M04449_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895577 +# Estimated pi_0=0.905273 +Using motif +M04450_2.00 of width 14. +Using motif -M04450_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929016 +# Estimated pi_0=0.941944 +Using motif +M04451_2.00 of width 22. +Using motif -M04451_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993472 +# Estimated pi_0=0.996667 +Using motif +M04452_2.00 of width 22. +Using motif -M04452_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996364 +# Estimated pi_0=0.99799 +Using motif +M08090_2.00 of width 12. +Using motif -M08090_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992615 +# Estimated pi_0=0.993939 +Using motif +M08306_2.00 of width 9. +Using motif -M08306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983118 +# Estimated pi_0=0.988513 +Using motif +M08888_2.00 of width 12. +Using motif -M08888_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992994 +# Estimated pi_0=0.997462 +Using motif +M00775_2.00 of width 10. +Using motif -M00775_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907255 +# Estimated pi_0=0.912308 +Using motif +M07581_2.00 of width 21. +Using motif -M07581_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903881 +# Estimated pi_0=0.911636 +Using motif +M00986_2.00 of width 9. +Using motif -M00986_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9444 +# Estimated pi_0=0.959712 +Using motif +M00987_2.00 of width 10. +Using motif -M00987_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9402 +# Estimated pi_0=0.95181 +Using motif +M00988_2.00 of width 9. +Using motif -M00988_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9396 +# Estimated pi_0=0.952364 +Using motif +M04453_2.00 of width 19. +Using motif -M04453_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995 +# Estimated pi_0=1 +Using motif +M04454_2.00 of width 19. +Using motif -M04454_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985484 +# Estimated pi_0=1 +Using motif +M04455_2.00 of width 11. +Using motif -M04455_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883 +# Estimated pi_0=0.903673 +Using motif +M04456_2.00 of width 11. +Using motif -M04456_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.852475 +# Estimated pi_0=0.859259 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970115 +# Estimated pi_0=0.974149 +Using motif +M08889_2.00 of width 12. +Using motif -M08889_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979538 +# Estimated pi_0=0.984896 +Using motif +M02888_2.00 of width 11. +Using motif -M02888_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913139 +# Estimated pi_0=0.915924 +Using motif +M04457_2.00 of width 10. +Using motif -M04457_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913548 +# Estimated pi_0=0.913906 +Using motif +M04458_2.00 of width 10. +Using motif -M04458_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915478 +# Estimated pi_0=0.925691 +Using motif +M07582_2.00 of width 6. +Using motif -M07582_2.00 of width 6. +Computing q-values. +Using motif +M07583_2.00 of width 15. +Using motif -M07583_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M07584_2.00 of width 24. +Using motif -M07584_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999594 +# Estimated pi_0=1 +Using motif +M08242_2.00 of width 9. +Using motif -M08242_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M08308_2.00 of width 12. +Using motif -M08308_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987836 +# Estimated pi_0=0.991852 +Using motif +M08890_2.00 of width 20. +Using motif -M08890_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998964 +# Estimated pi_0=1 +Using motif +M07585_2.00 of width 12. +Using motif -M07585_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962817 +# Estimated pi_0=0.969071 +Using motif +M08309_2.00 of width 27. +Using motif -M08309_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944526 +# Estimated pi_0=0.949294 +Using motif +M08891_2.00 of width 20. +Using motif -M08891_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951888 +# Estimated pi_0=0.952982 +Using motif +M08310_2.00 of width 21. +Using motif -M08310_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.851791 +# Estimated pi_0=0.858289 +Using motif +M08892_2.00 of width 22. +Using motif -M08892_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.858684 +# Estimated pi_0=0.864472 +Using motif +M07586_2.00 of width 24. +Using motif -M07586_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.837 +# Estimated pi_0=0.844237 +Using motif +M07587_2.00 of width 24. +Using motif -M07587_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8394 +# Estimated pi_0=0.845303 +Using motif +M04459_2.00 of width 18. +Using motif -M04459_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9246 +# Estimated pi_0=0.940435 +Using motif +M04460_2.00 of width 18. +Using motif -M04460_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970896 +# Estimated pi_0=0.980326 +Using motif +M07588_2.00 of width 15. +Using motif -M07588_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909068 +# Estimated pi_0=0.912 +Using motif +M02889_2.00 of width 16. +Using motif -M02889_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891776 +# Estimated pi_0=0.893504 +Using motif +M02890_2.00 of width 16. +Using motif -M02890_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91901 +# Estimated pi_0=0.930909 +Using motif +M04461_2.00 of width 13. +Using motif -M04461_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8824 +# Estimated pi_0=0.8824 +Using motif +M04462_2.00 of width 13. +Using motif -M04462_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907869 +# Estimated pi_0=0.915286 +Using motif +M10216_2.00 of width 12. +Using motif -M10216_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93648 +# Estimated pi_0=0.942133 +Using motif +M01172_2.00 of width 10. +Using motif -M01172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900863 +# Estimated pi_0=0.905379 +Using motif +M08893_2.00 of width 22. +Using motif -M08893_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.9e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87281 +# Estimated pi_0=0.879048 +Using motif +M04463_2.00 of width 11. +Using motif -M04463_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914261 +# Estimated pi_0=0.924605 +Using motif +M04464_2.00 of width 11. +Using motif -M04464_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903762 +# Estimated pi_0=0.918496 +Using motif +M08091_2.00 of width 11. +Using motif -M08091_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9366 +# Estimated pi_0=0.943419 +Using motif +M08243_2.00 of width 10. +Using motif -M08243_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916727 +# Estimated pi_0=0.924658 +Using motif +M08894_2.00 of width 10. +Using motif -M08894_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914259 +# Estimated pi_0=0.922424 +Using motif +M07589_2.00 of width 12. +Using motif -M07589_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909649 +# Estimated pi_0=0.917226 +Using motif +M08244_2.00 of width 7. +Using motif -M08244_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985444 +# Estimated pi_0=0.988556 +Using motif +M08311_2.00 of width 18. +Using motif -M08311_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914783 +# Estimated pi_0=0.91803 +Using motif +M04465_2.00 of width 20. +Using motif -M04465_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997857 +# Estimated pi_0=0.998894 +Using motif +M04466_2.00 of width 20. +Using motif -M04466_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99644 +# Estimated pi_0=0.998586 +Using motif +M08986_2.00 of width 15. +Using motif -M08986_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926053 +# Estimated pi_0=0.927308 +Using motif +M02891_2.00 of width 10. +Using motif -M02891_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900315 +# Estimated pi_0=0.908903 +Using motif +M02892_2.00 of width 10. +Using motif -M02892_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892237 +# Estimated pi_0=0.896522 +Using motif +M04467_2.00 of width 13. +Using motif -M04467_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89039 +# Estimated pi_0=0.896959 +Using motif +M04468_2.00 of width 13. +Using motif -M04468_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891007 +# Estimated pi_0=0.899036 +Using motif +M04469_2.00 of width 13. +Using motif -M04469_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896769 +# Estimated pi_0=0.904596 +Using motif +M04470_2.00 of width 13. +Using motif -M04470_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870256 +# Estimated pi_0=0.872029 +Using motif +M04471_2.00 of width 16. +Using motif -M04471_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889916 +# Estimated pi_0=0.899441 +Using motif +M04472_2.00 of width 16. +Using motif -M04472_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917115 +# Estimated pi_0=0.927919 +Using motif +M07590_2.00 of width 33. +Using motif -M07590_2.00 of width 33. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.9e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97 +# Estimated pi_0=0.973641 +Using motif +M07591_2.00 of width 12. +Using motif -M07591_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991295 +# Estimated pi_0=0.992143 +Using motif +M08312_2.00 of width 18. +Using motif -M08312_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985111 +# Estimated pi_0=0.990515 +Using motif +M08895_2.00 of width 19. +Using motif -M08895_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991579 +# Estimated pi_0=0.993333 +Using motif +M05848_2.00 of width 17. +Using motif -M05848_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957922 +# Estimated pi_0=0.965549 +Using motif +M07592_2.00 of width 15. +Using motif -M07592_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924962 +# Estimated pi_0=0.932814 +Using motif +M04473_2.00 of width 20. +Using motif -M04473_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95872 +# Estimated pi_0=0.967792 +Using motif +M04474_2.00 of width 15. +Using motif -M04474_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967563 +# Estimated pi_0=0.973413 +Using motif +M04475_2.00 of width 20. +Using motif -M04475_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95292 +# Estimated pi_0=0.955338 +Using motif +M04476_2.00 of width 15. +Using motif -M04476_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987159 +# Estimated pi_0=0.993814 +Using motif +M07593_2.00 of width 21. +Using motif -M07593_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967 +# Estimated pi_0=0.975029 +Using motif +M04477_2.00 of width 10. +Using motif -M04477_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985578 +# Estimated pi_0=0.990543 +Using motif +M04478_2.00 of width 10. +Using motif -M04478_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977231 +# Estimated pi_0=0.9875 +Using motif +M04479_2.00 of width 18. +Using motif -M04479_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989466 +# Estimated pi_0=0.994974 +Using motif +M04480_2.00 of width 18. +Using motif -M04480_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971862 +# Estimated pi_0=0.974465 +Using motif +M07594_2.00 of width 29. +Using motif -M07594_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917969 +# Estimated pi_0=0.928696 +Using motif +M07595_2.00 of width 15. +Using motif -M07595_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897681 +# Estimated pi_0=0.911462 +Using motif +M07596_2.00 of width 24. +Using motif -M07596_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9592 +# Estimated pi_0=0.970769 +Using motif +M07597_2.00 of width 12. +Using motif -M07597_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.5e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982513 +# Estimated pi_0=0.987347 +Using motif +M08245_2.00 of width 15. +Using motif -M08245_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967077 +# Estimated pi_0=0.974863 +Using motif +M08313_2.00 of width 33. +Using motif -M08313_2.00 of width 33. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925714 +# Estimated pi_0=0.931081 +Using motif +M08896_2.00 of width 24. +Using motif -M08896_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939463 +# Estimated pi_0=0.947636 +Using motif +M09508_2.00 of width 8. +Using motif -M09508_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8862 +# Estimated pi_0=0.9 +Using motif +M08897_2.00 of width 14. +Using motif -M08897_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988784 +# Estimated pi_0=0.995417 +Using motif +M09509_2.00 of width 12. +Using motif -M09509_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986825 +# Estimated pi_0=0.999381 +Using motif +M07598_2.00 of width 21. +Using motif -M07598_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992486 +# Estimated pi_0=0.995773 +Using motif +M07599_2.00 of width 20. +Using motif -M07599_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9378 +# Estimated pi_0=0.949831 +Using motif +M08314_2.00 of width 24. +Using motif -M08314_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08898_2.00 of width 22. +Using motif -M08898_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986842 +# Estimated pi_0=0.997245 +Using motif +M04481_2.00 of width 12. +Using motif -M04481_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9084 +# Estimated pi_0=0.92605 +Using motif +M04482_2.00 of width 12. +Using motif -M04482_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874851 +# Estimated pi_0=0.879029 +Using motif +M07600_2.00 of width 8. +Using motif -M07600_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980805 +# Estimated pi_0=0.981635 +Using motif +M08315_2.00 of width 15. +Using motif -M08315_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977483 +# Estimated pi_0=0.981758 +Using motif +M08899_2.00 of width 20. +Using motif -M08899_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96563 +# Estimated pi_0=0.971377 +Using motif +M07601_2.00 of width 21. +Using motif -M07601_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857358 +# Estimated pi_0=0.866015 +Using motif +M04483_2.00 of width 17. +Using motif -M04483_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989231 +# Estimated pi_0=0.995625 +Using motif +M04484_2.00 of width 17. +Using motif -M04484_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968462 +# Estimated pi_0=0.97645 +Using motif +M02893_2.00 of width 14. +Using motif -M02893_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8736 +# Estimated pi_0=0.883492 +Using motif +M04485_2.00 of width 15. +Using motif -M04485_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8944 +# Estimated pi_0=0.903846 +Using motif +M04486_2.00 of width 15. +Using motif -M04486_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922923 +# Estimated pi_0=0.9316 +Using motif +M04487_2.00 of width 15. +Using motif -M04487_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904118 +# Estimated pi_0=0.916061 +Using motif +M04488_2.00 of width 15. +Using motif -M04488_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942772 +# Estimated pi_0=0.951 +Using motif +M08900_2.00 of width 9. +Using motif -M08900_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889109 +# Estimated pi_0=0.894923 +Using motif +M10226_2.00 of width 9. +Using motif -M10226_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8796 +# Estimated pi_0=0.883486 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969829 +# Estimated pi_0=0.975269 +Using motif +M07602_2.00 of width 15. +Using motif -M07602_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962963 +# Estimated pi_0=0.971828 +Using motif +M08316_2.00 of width 12. +Using motif -M08316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966757 +# Estimated pi_0=0.968621 +Using motif +M08901_2.00 of width 12. +Using motif -M08901_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968725 +# Estimated pi_0=0.978506 +Using motif +M04489_2.00 of width 22. +Using motif -M04489_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.91 +Using motif +M04490_2.00 of width 22. +Using motif -M04490_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8996 +# Estimated pi_0=0.90473 +Using motif +M08246_2.00 of width 10. +Using motif -M08246_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919161 +# Estimated pi_0=0.923165 +Using motif +M08317_2.00 of width 15. +Using motif -M08317_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886457 +# Estimated pi_0=0.891053 +Using motif +M02894_2.00 of width 15. +Using motif -M02894_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880885 +# Estimated pi_0=0.889481 +Using motif +M04491_2.00 of width 16. +Using motif -M04491_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896923 +# Estimated pi_0=0.916622 +Using motif +M04492_2.00 of width 16. +Using motif -M04492_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903036 +# Estimated pi_0=0.91007 +Using motif +M04493_2.00 of width 15. +Using motif -M04493_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884078 +# Estimated pi_0=0.906259 +Using motif +M04494_2.00 of width 15. +Using motif -M04494_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924844 +# Estimated pi_0=0.934966 +Using motif +M08902_2.00 of width 15. +Using motif -M08902_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906167 +# Estimated pi_0=0.919747 +Using motif +M10229_2.00 of width 9. +Using motif -M10229_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914857 +# Estimated pi_0=0.922239 +Using motif +M07603_2.00 of width 18. +Using motif -M07603_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950074 +# Estimated pi_0=0.959091 +Using motif +M04495_2.00 of width 25. +Using motif -M04495_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04496_2.00 of width 26. +Using motif -M04496_2.00 of width 26. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M07604_2.00 of width 9. +Using motif -M07604_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927 +# Estimated pi_0=0.93664 +Using motif +M07605_2.00 of width 30. +Using motif -M07605_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.1e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98178 +# Estimated pi_0=0.984365 +Using motif +M07606_2.00 of width 12. +Using motif -M07606_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9464 +# Estimated pi_0=0.958483 +Using motif +M02895_2.00 of width 12. +Using motif -M02895_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940971 +# Estimated pi_0=0.949859 +Using motif +M04497_2.00 of width 10. +Using motif -M04497_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89534 +# Estimated pi_0=0.9 +Using motif +M04498_2.00 of width 10. +Using motif -M04498_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924299 +# Estimated pi_0=0.933007 +Using motif +M08318_2.00 of width 20. +Using motif -M08318_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906296 +# Estimated pi_0=0.91303 +Using motif +M08903_2.00 of width 20. +Using motif -M08903_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98381 +# Estimated pi_0=0.988265 +Using motif +M07607_2.00 of width 9. +Using motif -M07607_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9212 +# Estimated pi_0=0.930462 +Using motif +M07608_2.00 of width 21. +Using motif -M07608_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986272 +# Estimated pi_0=0.994564 +Using motif +M08247_2.00 of width 15. +Using motif -M08247_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=0.999497 +Using motif +M08319_2.00 of width 24. +Using motif -M08319_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976087 +# Estimated pi_0=0.982826 +Using motif +M08904_2.00 of width 22. +Using motif -M08904_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988108 +# Estimated pi_0=0.993229 +Using motif +M08320_2.00 of width 9. +Using motif -M08320_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946 +# Estimated pi_0=0.95814 +Using motif +M08248_2.00 of width 24. +Using motif -M08248_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M02896_2.00 of width 17. +Using motif -M02896_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04499_2.00 of width 16. +Using motif -M04499_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M04500_2.00 of width 16. +Using motif -M04500_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04501_2.00 of width 16. +Using motif -M04501_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04502_2.00 of width 16. +Using motif -M04502_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M02897_2.00 of width 12. +Using motif -M02897_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970226 +# Estimated pi_0=0.97848 +Using motif +M04503_2.00 of width 14. +Using motif -M04503_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977365 +# Estimated pi_0=0.98186 +Using motif +M04504_2.00 of width 14. +Using motif -M04504_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958621 +# Estimated pi_0=0.96439 +Using motif +M04505_2.00 of width 14. +Using motif -M04505_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974756 +# Estimated pi_0=0.980112 +Using motif +M04506_2.00 of width 14. +Using motif -M04506_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976164 +# Estimated pi_0=0.987253 +Using motif +M00236_2.00 of width 9. +Using motif -M00236_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M00237_2.00 of width 8. +Using motif -M00237_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M00238_2.00 of width 9. +Using motif -M00238_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04507_2.00 of width 21. +Using motif -M04507_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992789 +# Estimated pi_0=0.997766 +Using motif +M04508_2.00 of width 21. +Using motif -M04508_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991186 +# Estimated pi_0=0.995204 +Using motif +M08905_2.00 of width 10. +Using motif -M08905_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=0.999698 +Using motif +M10235_2.00 of width 24. +Using motif -M10235_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999592 +# Estimated pi_0=0.999698 +Using motif +M04509_2.00 of width 12. +Using motif -M04509_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911973 +# Estimated pi_0=0.919302 +Using motif +M04510_2.00 of width 12. +Using motif -M04510_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918824 +# Estimated pi_0=0.932317 +Using motif +M08321_2.00 of width 12. +Using motif -M08321_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.5e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889936 +# Estimated pi_0=0.892298 +Using motif +M08906_2.00 of width 15. +Using motif -M08906_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901324 +# Estimated pi_0=0.908916 +Using motif +M07609_2.00 of width 30. +Using motif -M07609_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951858 +# Estimated pi_0=0.961985 +Using motif +M08322_2.00 of width 15. +Using motif -M08322_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971266 +# Estimated pi_0=0.975529 +Using motif +M04511_2.00 of width 12. +Using motif -M04511_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893171 +# Estimated pi_0=0.901606 +Using motif +M04512_2.00 of width 12. +Using motif -M04512_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906765 +# Estimated pi_0=0.911586 +Using motif +M04513_2.00 of width 12. +Using motif -M04513_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928837 +# Estimated pi_0=0.937059 +Using motif +M04514_2.00 of width 12. +Using motif -M04514_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93245 +# Estimated pi_0=0.941071 +Using motif +M08323_2.00 of width 15. +Using motif -M08323_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895758 +# Estimated pi_0=0.899871 +Using motif +M08907_2.00 of width 19. +Using motif -M08907_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886351 +# Estimated pi_0=0.890419 +Using motif +M00142_2.00 of width 8. +Using motif -M00142_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968039 +# Estimated pi_0=0.97947 +Using motif +M07610_2.00 of width 24. +Using motif -M07610_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86252 +# Estimated pi_0=0.867651 +Using motif +M04515_2.00 of width 21. +Using motif -M04515_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994128 +# Estimated pi_0=1 +Using motif +M04516_2.00 of width 21. +Using motif -M04516_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966422 +# Estimated pi_0=0.970448 +Using motif +M07611_2.00 of width 18. +Using motif -M07611_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856909 +# Estimated pi_0=0.866015 +Using motif +M02898_2.00 of width 12. +Using motif -M02898_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929635 +# Estimated pi_0=0.929938 +Using motif +M04517_2.00 of width 12. +Using motif -M04517_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913023 +# Estimated pi_0=0.923333 +Using motif +M04518_2.00 of width 12. +Using motif -M04518_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91505 +# Estimated pi_0=0.923385 +Using motif +M04519_2.00 of width 12. +Using motif -M04519_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8298 +# Estimated pi_0=0.83211 +Using motif +M04520_2.00 of width 12. +Using motif -M04520_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8362 +# Estimated pi_0=0.8375 +Using motif +M04521_2.00 of width 10. +Using motif -M04521_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967759 +# Estimated pi_0=0.976517 +Using motif +M04522_2.00 of width 10. +Using motif -M04522_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982123 +# Estimated pi_0=0.987021 +Using motif +M08324_2.00 of width 11. +Using motif -M08324_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925152 +# Estimated pi_0=0.930336 +Using motif +M08908_2.00 of width 16. +Using motif -M08908_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925692 +# Estimated pi_0=0.934304 +Using motif +M08325_2.00 of width 9. +Using motif -M08325_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984718 +# Estimated pi_0=0.984718 +Using motif +M00239_2.00 of width 11. +Using motif -M00239_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00240_2.00 of width 11. +Using motif -M00240_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04523_2.00 of width 21. +Using motif -M04523_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991707 +# Estimated pi_0=0.994667 +Using motif +M04524_2.00 of width 21. +Using motif -M04524_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997544 +# Estimated pi_0=0.999497 +Using motif +M08909_2.00 of width 10. +Using motif -M08909_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991447 +# Estimated pi_0=0.995838 +Using motif +M07612_2.00 of width 8. +Using motif -M07612_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903077 +# Estimated pi_0=0.917397 +Using motif +M02899_2.00 of width 16. +Using motif -M02899_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991304 +# Estimated pi_0=0.998894 +Using motif +M07931_2.00 of width 15. +Using motif -M07931_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98126 +# Estimated pi_0=0.992865 +Using motif +M08910_2.00 of width 22. +Using motif -M08910_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9478 +# Estimated pi_0=0.956032 +Using motif +M09510_2.00 of width 15. +Using motif -M09510_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947379 +# Estimated pi_0=0.957255 +Using motif +M10247_2.00 of width 22. +Using motif -M10247_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969298 +# Estimated pi_0=0.983258 +Using motif +M10248_2.00 of width 21. +Using motif -M10248_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938957 +# Estimated pi_0=0.94439 +Using motif +M07613_2.00 of width 15. +Using motif -M07613_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99619 +# Estimated pi_0=0.998392 +Using motif +M04525_2.00 of width 17. +Using motif -M04525_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933056 +# Estimated pi_0=0.939758 +Using motif +M04526_2.00 of width 17. +Using motif -M04526_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926308 +# Estimated pi_0=0.933614 +Using motif +M07932_2.00 of width 15. +Using motif -M07932_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8916 +# Estimated pi_0=0.903906 +Using motif +M08092_2.00 of width 15. +Using motif -M08092_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894211 +# Estimated pi_0=0.898922 +Using motif +M08326_2.00 of width 9. +Using motif -M08326_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901678 +# Estimated pi_0=0.905556 +Using motif +M08911_2.00 of width 22. +Using motif -M08911_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.801138 +# Estimated pi_0=0.811258 +Using motif +M04527_2.00 of width 19. +Using motif -M04527_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873504 +# Estimated pi_0=0.877937 +Using motif +M04528_2.00 of width 19. +Using motif -M04528_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866723 +# Estimated pi_0=0.877042 +Using motif +M07614_2.00 of width 21. +Using motif -M07614_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.3e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.803684 +# Estimated pi_0=0.811496 +Using motif +M07615_2.00 of width 9. +Using motif -M07615_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905138 +# Estimated pi_0=0.909211 +Using motif +M07616_2.00 of width 9. +Using motif -M07616_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886942 +# Estimated pi_0=0.887642 +Using motif +M08327_2.00 of width 9. +Using motif -M08327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914 +# Estimated pi_0=0.932276 +Using motif +M07617_2.00 of width 15. +Using motif -M07617_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955909 +# Estimated pi_0=0.965663 +Using motif +M08249_2.00 of width 14. +Using motif -M08249_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986202 +# Estimated pi_0=0.990313 +Using motif +M08328_2.00 of width 12. +Using motif -M08328_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984 +# Estimated pi_0=0.988449 +Using motif +M08912_2.00 of width 20. +Using motif -M08912_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959028 +# Estimated pi_0=0.966424 +Using motif +M07618_2.00 of width 12. +Using motif -M07618_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930222 +# Estimated pi_0=0.942156 +Using motif +M08329_2.00 of width 15. +Using motif -M08329_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994643 +# Estimated pi_0=0.999899 +Using motif +M08913_2.00 of width 24. +Using motif -M08913_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M07619_2.00 of width 24. +Using motif -M07619_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.821176 +# Estimated pi_0=0.829353 +Using motif +M04529_2.00 of width 16. +Using motif -M04529_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885455 +# Estimated pi_0=0.89195 +Using motif +M04530_2.00 of width 16. +Using motif -M04530_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884828 +# Estimated pi_0=0.887613 +Using motif +M07620_2.00 of width 27. +Using motif -M07620_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99547 +# Estimated pi_0=0.999598 +Using motif +M02900_2.00 of width 19. +Using motif -M02900_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04531_2.00 of width 14. +Using motif -M04531_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967103 +# Estimated pi_0=0.980144 +Using motif +M04532_2.00 of width 14. +Using motif -M04532_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966909 +# Estimated pi_0=0.985033 +Using motif +M07621_2.00 of width 15. +Using motif -M07621_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.833019 +# Estimated pi_0=0.836752 +Using motif +M08330_2.00 of width 21. +Using motif -M08330_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87767 +# Estimated pi_0=0.884112 +Using motif +M02901_2.00 of width 17. +Using motif -M02901_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938087 +# Estimated pi_0=0.944088 +Using motif +M07622_2.00 of width 15. +Using motif -M07622_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M08331_2.00 of width 15. +Using motif -M08331_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M08914_2.00 of width 24. +Using motif -M08914_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993886 +# Estimated pi_0=0.997172 +Using motif +M04533_2.00 of width 20. +Using motif -M04533_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9554 +# Estimated pi_0=0.969461 +Using motif +M04534_2.00 of width 20. +Using motif -M04534_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961132 +# Estimated pi_0=0.970886 +Using motif +M08332_2.00 of width 24. +Using motif -M08332_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990778 +# Estimated pi_0=0.999293 +Using motif +M02902_2.00 of width 9. +Using motif -M02902_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9368 +# Estimated pi_0=0.946423 +Using motif +M04535_2.00 of width 15. +Using motif -M04535_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990588 +# Estimated pi_0=1 +Using motif +M04536_2.00 of width 15. +Using motif -M04536_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9756 +# Estimated pi_0=1 +Using motif +M02903_2.00 of width 18. +Using motif -M02903_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956415 +# Estimated pi_0=0.967568 +Using motif +M04537_2.00 of width 13. +Using motif -M04537_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907576 +# Estimated pi_0=0.911127 +Using motif +M04538_2.00 of width 13. +Using motif -M04538_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899038 +# Estimated pi_0=0.903438 +Using motif +M04539_2.00 of width 13. +Using motif -M04539_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915636 +# Estimated pi_0=0.923046 +Using motif +M04540_2.00 of width 13. +Using motif -M04540_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921709 +# Estimated pi_0=0.930519 +Using motif +M07623_2.00 of width 21. +Using motif -M07623_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981468 +# Estimated pi_0=0.986778 +Using motif +M08333_2.00 of width 15. +Using motif -M08333_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956159 +# Estimated pi_0=0.966742 +Using motif +M08915_2.00 of width 20. +Using motif -M08915_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95651 +# Estimated pi_0=0.957829 +Using motif +M08250_2.00 of width 21. +Using motif -M08250_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08334_2.00 of width 30. +Using motif -M08334_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991728 +# Estimated pi_0=0.994472 +Using motif +M07624_2.00 of width 15. +Using motif -M07624_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946972 +# Estimated pi_0=0.956 +Using motif +M08335_2.00 of width 12. +Using motif -M08335_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957822 +# Estimated pi_0=0.971948 +Using motif +M07625_2.00 of width 30. +Using motif -M07625_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93118 +# Estimated pi_0=0.936 +Using motif +M02904_2.00 of width 17. +Using motif -M02904_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976143 +# Estimated pi_0=0.979763 +Using motif +M04541_2.00 of width 14. +Using motif -M04541_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953585 +# Estimated pi_0=0.961075 +Using motif +M04542_2.00 of width 14. +Using motif -M04542_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939459 +# Estimated pi_0=0.94529 +Using motif +M05849_2.00 of width 11. +Using motif -M05849_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917813 +# Estimated pi_0=0.924533 +Using motif +M07626_2.00 of width 6. +Using motif -M07626_2.00 of width 6. +Computing q-values. +Using motif +M08251_2.00 of width 15. +Using motif -M08251_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926729 +# Estimated pi_0=0.936149 +Using motif +M08336_2.00 of width 18. +Using motif -M08336_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962883 +# Estimated pi_0=0.972552 +Using motif +M04543_2.00 of width 11. +Using motif -M04543_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95219 +# Estimated pi_0=0.963053 +Using motif +M04544_2.00 of width 11. +Using motif -M04544_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928 +# Estimated pi_0=0.941429 +Using motif +M05850_2.00 of width 16. +Using motif -M05850_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9644 +# Estimated pi_0=0.979 +Using motif +M08252_2.00 of width 15. +Using motif -M08252_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918596 +# Estimated pi_0=0.925758 +Using motif +M08337_2.00 of width 15. +Using motif -M08337_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923168 +# Estimated pi_0=0.933986 +Using motif +M08253_2.00 of width 13. +Using motif -M08253_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8538 +# Estimated pi_0=0.868067 +Using motif +M08338_2.00 of width 13. +Using motif -M08338_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872427 +# Estimated pi_0=0.87719 +Using motif +M07627_2.00 of width 14. +Using motif -M07627_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94197 +# Estimated pi_0=0.947534 +Using motif +M07628_2.00 of width 15. +Using motif -M07628_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985444 +# Estimated pi_0=0.988511 +Using motif +M02905_2.00 of width 12. +Using motif -M02905_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920183 +# Estimated pi_0=0.922222 +Using motif +M02906_2.00 of width 14. +Using motif -M02906_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9154 +# Estimated pi_0=0.928288 +Using motif +M04545_2.00 of width 12. +Using motif -M04545_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9236 +# Estimated pi_0=0.937121 +Using motif +M04546_2.00 of width 12. +Using motif -M04546_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900396 +# Estimated pi_0=0.906038 +Using motif +M08339_2.00 of width 15. +Using motif -M08339_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889901 +# Estimated pi_0=0.900168 +Using motif +M04547_2.00 of width 12. +Using motif -M04547_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M04548_2.00 of width 12. +Using motif -M04548_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998316 +# Estimated pi_0=0.999799 +Using motif +M08340_2.00 of width 21. +Using motif -M08340_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8078 +# Estimated pi_0=0.814909 +Using motif +M07629_2.00 of width 17. +Using motif -M07629_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996718 +# Estimated pi_0=0.998995 +Using motif +M07630_2.00 of width 7. +Using motif -M07630_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984138 +# Estimated pi_0=0.986321 +Using motif +M07631_2.00 of width 21. +Using motif -M07631_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04549_2.00 of width 16. +Using motif -M04549_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986825 +# Estimated pi_0=0.998469 +Using motif +M04550_2.00 of width 16. +Using motif -M04550_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986303 +# Estimated pi_0=0.99373 +Using motif +M07632_2.00 of width 15. +Using motif -M07632_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994486 +# Estimated pi_0=0.999598 +Using motif +M07933_2.00 of width 17. +Using motif -M07933_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986235 +# Estimated pi_0=0.990785 +Using motif +M07934_2.00 of width 15. +Using motif -M07934_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981125 +# Estimated pi_0=0.989688 +Using motif +M07935_2.00 of width 15. +Using motif -M07935_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997938 +# Estimated pi_0=0.998894 +Using motif +M07936_2.00 of width 15. +Using motif -M07936_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993189 +# Estimated pi_0=0.998586 +Using motif +M08916_2.00 of width 20. +Using motif -M08916_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989935 +# Estimated pi_0=0.996615 +Using motif +M07633_2.00 of width 21. +Using motif -M07633_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981326 +# Estimated pi_0=0.985397 +Using motif +M07634_2.00 of width 11. +Using motif -M07634_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M07635_2.00 of width 21. +Using motif -M07635_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996 +# Estimated pi_0=0.998191 +Using motif +M04551_2.00 of width 15. +Using motif -M04551_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919692 +# Estimated pi_0=0.92961 +Using motif +M04552_2.00 of width 12. +Using motif -M04552_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947517 +# Estimated pi_0=0.951977 +Using motif +M04553_2.00 of width 15. +Using motif -M04553_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931125 +# Estimated pi_0=0.931677 +Using motif +M08341_2.00 of width 21. +Using motif -M08341_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913269 +# Estimated pi_0=0.924237 +Using motif +M08917_2.00 of width 20. +Using motif -M08917_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917248 +# Estimated pi_0=0.927034 +Using motif +M00241_2.00 of width 8. +Using motif -M00241_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917778 +# Estimated pi_0=0.931687 +Using motif +M00242_2.00 of width 8. +Using motif -M00242_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892358 +# Estimated pi_0=0.901944 +Using motif +M04554_2.00 of width 22. +Using motif -M04554_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87438 +# Estimated pi_0=0.87536 +Using motif +M04555_2.00 of width 22. +Using motif -M04555_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903407 +# Estimated pi_0=0.908645 +Using motif +M02907_2.00 of width 12. +Using motif -M02907_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.84099 +# Estimated pi_0=0.853455 +Using motif +M02908_2.00 of width 20. +Using motif -M02908_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.82099 +# Estimated pi_0=0.828224 +Using motif +M02909_2.00 of width 19. +Using motif -M02909_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8138 +# Estimated pi_0=0.821982 +Using motif +M08918_2.00 of width 16. +Using motif -M08918_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911 +# Estimated pi_0=0.911 +Using motif +M08093_2.00 of width 13. +Using motif -M08093_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M08342_2.00 of width 15. +Using motif -M08342_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898014 +# Estimated pi_0=0.906144 +Using motif +M04556_2.00 of width 14. +Using motif -M04556_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04557_2.00 of width 14. +Using motif -M04557_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M08919_2.00 of width 9. +Using motif -M08919_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02910_2.00 of width 11. +Using motif -M02910_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912206 +# Estimated pi_0=0.92686 +Using motif +M04558_2.00 of width 13. +Using motif -M04558_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914074 +# Estimated pi_0=0.920146 +Using motif +M04559_2.00 of width 13. +Using motif -M04559_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90708 +# Estimated pi_0=0.912562 +Using motif +M08920_2.00 of width 20. +Using motif -M08920_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.3e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.822667 +# Estimated pi_0=0.829155 +Using motif +M07636_2.00 of width 15. +Using motif -M07636_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985238 +# Estimated pi_0=0.992984 +Using motif +M08254_2.00 of width 15. +Using motif -M08254_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928952 +# Estimated pi_0=0.935588 +Using motif +M07637_2.00 of width 21. +Using motif -M07637_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975028 +# Estimated pi_0=0.977377 +Using motif +M08343_2.00 of width 21. +Using motif -M08343_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986304 +# Estimated pi_0=0.990154 +Using motif +M08921_2.00 of width 20. +Using motif -M08921_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983699 +# Estimated pi_0=0.986702 +Using motif +M07638_2.00 of width 15. +Using motif -M07638_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930256 +# Estimated pi_0=0.935827 +Using motif +M04560_2.00 of width 15. +Using motif -M04560_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926408 +# Estimated pi_0=0.941667 +Using motif +M04561_2.00 of width 10. +Using motif -M04561_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872 +# Estimated pi_0=0.885357 +Using motif +M04562_2.00 of width 10. +Using motif -M04562_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890693 +# Estimated pi_0=0.907846 +Using motif +M04563_2.00 of width 15. +Using motif -M04563_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912 +# Estimated pi_0=0.91931 +Using motif +M08344_2.00 of width 21. +Using motif -M08344_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9094 +# Estimated pi_0=0.91625 +Using motif +M08922_2.00 of width 11. +Using motif -M08922_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940163 +# Estimated pi_0=0.952625 +Using motif +M08445_2.00 of width 12. +Using motif -M08445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954847 +# Estimated pi_0=0.960227 +Using motif +M08923_2.00 of width 12. +Using motif -M08923_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932951 +# Estimated pi_0=0.942706 +Using motif +M07639_2.00 of width 21. +Using motif -M07639_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8856 +# Estimated pi_0=0.889333 +Using motif +M07640_2.00 of width 24. +Using motif -M07640_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938407 +# Estimated pi_0=0.951241 +Using motif +M02911_2.00 of width 16. +Using motif -M02911_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9246 +# Estimated pi_0=0.929381 +Using motif +M04564_2.00 of width 15. +Using motif -M04564_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904762 +# Estimated pi_0=0.909559 +Using motif +M04565_2.00 of width 15. +Using motif -M04565_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905128 +# Estimated pi_0=0.913636 +Using motif +M08345_2.00 of width 14. +Using motif -M08345_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880194 +# Estimated pi_0=0.893538 +Using motif +M07641_2.00 of width 15. +Using motif -M07641_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960278 +# Estimated pi_0=0.964321 +Using motif +M02912_2.00 of width 15. +Using motif -M02912_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890693 +# Estimated pi_0=0.90058 +Using motif +M04566_2.00 of width 15. +Using motif -M04566_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8978 +# Estimated pi_0=0.911942 +Using motif +M04567_2.00 of width 15. +Using motif -M04567_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928547 +# Estimated pi_0=0.935524 +Using motif +M07642_2.00 of width 15. +Using motif -M07642_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.835098 +# Estimated pi_0=0.846202 +Using motif +M08255_2.00 of width 10. +Using motif -M08255_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933 +# Estimated pi_0=0.951579 +Using motif +M07643_2.00 of width 29. +Using motif -M07643_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999289 +# Estimated pi_0=1 +Using motif +M07644_2.00 of width 7. +Using motif -M07644_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972418 +# Estimated pi_0=0.986878 +Using motif +M07645_2.00 of width 6. +Using motif -M07645_2.00 of width 6. +Computing q-values. +Using motif +M07646_2.00 of width 9. +Using motif -M07646_2.00 of width 9. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 4.6e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997085 +# Estimated pi_0=0.997085 +Using motif +M05851_2.00 of width 15. +Using motif -M05851_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992455 +# Estimated pi_0=0.995313 +Using motif +M07647_2.00 of width 9. +Using motif -M07647_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950467 +# Estimated pi_0=0.951481 +Using motif +M04568_2.00 of width 10. +Using motif -M04568_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935327 +# Estimated pi_0=0.945068 +Using motif +M04569_2.00 of width 10. +Using motif -M04569_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932381 +# Estimated pi_0=0.940741 +Using motif +M08924_2.00 of width 9. +Using motif -M08924_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935283 +# Estimated pi_0=0.9475 +Using motif +M04570_2.00 of width 20. +Using motif -M04570_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08094_2.00 of width 15. +Using motif -M08094_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880392 +# Estimated pi_0=0.883301 +Using motif +M08925_2.00 of width 15. +Using motif -M08925_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8828 +# Estimated pi_0=0.888224 +Using motif +M09511_2.00 of width 15. +Using motif -M09511_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8748 +# Estimated pi_0=0.884112 +Using motif +M07648_2.00 of width 29. +Using motif -M07648_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998163 +# Estimated pi_0=0.999698 +Using motif +M07649_2.00 of width 23. +Using motif -M07649_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.849703 +# Estimated pi_0=0.858548 +Using motif +M02666_2.00 of width 6. +Using motif -M02666_2.00 of width 6. +Computing q-values. +Using motif +M07650_2.00 of width 28. +Using motif -M07650_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8452 +# Estimated pi_0=0.856381 +Using motif +M04571_2.00 of width 15. +Using motif -M04571_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8826 +# Estimated pi_0=0.889043 +Using motif +M04572_2.00 of width 14. +Using motif -M04572_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865243 +# Estimated pi_0=0.8684 +Using motif +M04573_2.00 of width 15. +Using motif -M04573_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8916 +# Estimated pi_0=0.897818 +Using motif +M04574_2.00 of width 14. +Using motif -M04574_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872745 +# Estimated pi_0=0.88708 +Using motif +M07651_2.00 of width 29. +Using motif -M07651_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8444 +# Estimated pi_0=0.859339 +Using motif +M08256_2.00 of width 7. +Using motif -M08256_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980927 +# Estimated pi_0=0.989841 +Using motif +M08346_2.00 of width 27. +Using motif -M08346_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920154 +# Estimated pi_0=0.925563 +Using motif +M07652_2.00 of width 12. +Using motif -M07652_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8368 +# Estimated pi_0=0.847213 +Using motif +M08347_2.00 of width 12. +Using motif -M08347_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8634 +# Estimated pi_0=0.87168 +Using motif +M07653_2.00 of width 12. +Using motif -M07653_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M07654_2.00 of width 14. +Using motif -M07654_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925045 +# Estimated pi_0=0.929919 +Using motif +M02913_2.00 of width 17. +Using motif -M02913_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997333 +# Estimated pi_0=1 +Using motif +M04575_2.00 of width 19. +Using motif -M04575_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990155 +# Estimated pi_0=0.993503 +Using motif +M04576_2.00 of width 19. +Using motif -M04576_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953137 +# Estimated pi_0=0.959091 +Using motif +M04577_2.00 of width 19. +Using motif -M04577_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936923 +# Estimated pi_0=0.94 +Using motif +M04578_2.00 of width 19. +Using motif -M04578_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983402 +# Estimated pi_0=0.984821 +Using motif +M07655_2.00 of width 11. +Using motif -M07655_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906733 +# Estimated pi_0=0.908544 +Using motif +M02914_2.00 of width 12. +Using motif -M02914_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8886 +# Estimated pi_0=0.90193 +Using motif +M04579_2.00 of width 11. +Using motif -M04579_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910196 +# Estimated pi_0=0.921345 +Using motif +M04580_2.00 of width 11. +Using motif -M04580_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9144 +# Estimated pi_0=0.916762 +Using motif +M07937_2.00 of width 15. +Using motif -M07937_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.85215 +# Estimated pi_0=0.860313 +Using motif +M08095_2.00 of width 13. +Using motif -M08095_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8688 +# Estimated pi_0=0.879672 +Using motif +M08926_2.00 of width 9. +Using motif -M08926_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900331 +# Estimated pi_0=0.913431 +Using motif +M04581_2.00 of width 15. +Using motif -M04581_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961373 +# Estimated pi_0=0.979456 +Using motif +M04582_2.00 of width 15. +Using motif -M04582_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982097 +# Estimated pi_0=0.994891 +Using motif +M08348_2.00 of width 12. +Using motif -M08348_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960385 +# Estimated pi_0=0.969726 +Using motif +M08927_2.00 of width 12. +Using motif -M08927_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9582 +# Estimated pi_0=0.966974 +Using motif +M02915_2.00 of width 15. +Using motif -M02915_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894476 +# Estimated pi_0=0.909683 +Using motif +M04583_2.00 of width 13. +Using motif -M04583_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915644 +# Estimated pi_0=0.93269 +Using motif +M04584_2.00 of width 13. +Using motif -M04584_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904909 +# Estimated pi_0=0.911311 +Using motif +M08349_2.00 of width 9. +Using motif -M08349_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908788 +# Estimated pi_0=0.920927 +Using motif +M10279_2.00 of width 12. +Using motif -M10279_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91604 +# Estimated pi_0=0.928112 +Using motif +M02916_2.00 of width 13. +Using motif -M02916_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990758 +# Estimated pi_0=0.994124 +Using motif +M02917_2.00 of width 13. +Using motif -M02917_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981795 +# Estimated pi_0=0.991354 +Using motif +M04585_2.00 of width 13. +Using motif -M04585_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983571 +# Estimated pi_0=0.990157 +Using motif +M04586_2.00 of width 13. +Using motif -M04586_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973023 +# Estimated pi_0=0.976848 +Using motif +M08257_2.00 of width 12. +Using motif -M08257_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932941 +# Estimated pi_0=0.943125 +Using motif +M08350_2.00 of width 9. +Using motif -M08350_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930093 +# Estimated pi_0=0.930093 +Using motif +M08928_2.00 of width 11. +Using motif -M08928_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941864 +# Estimated pi_0=0.956025 +Using motif +M10282_2.00 of width 12. +Using motif -M10282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.986444 +Using motif +M08351_2.00 of width 9. +Using motif -M08351_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9194 +# Estimated pi_0=0.928615 +Using motif +M07656_2.00 of width 15. +Using motif -M07656_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M02918_2.00 of width 10. +Using motif -M02918_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956436 +# Estimated pi_0=0.972739 +Using motif +M04587_2.00 of width 11. +Using motif -M04587_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966535 +# Estimated pi_0=0.975385 +Using motif +M04588_2.00 of width 11. +Using motif -M04588_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973391 +# Estimated pi_0=0.981486 +Using motif +M08352_2.00 of width 9. +Using motif -M08352_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966154 +# Estimated pi_0=0.985629 +Using motif +M04589_2.00 of width 15. +Using motif -M04589_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988846 +# Estimated pi_0=0.997857 +Using motif +M04590_2.00 of width 15. +Using motif -M04590_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977 +# Estimated pi_0=0.994759 +Using motif +M04591_2.00 of width 15. +Using motif -M04591_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985743 +# Estimated pi_0=1 +Using motif +M04592_2.00 of width 15. +Using motif -M04592_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956238 +# Estimated pi_0=0.969404 +Using motif +M07657_2.00 of width 21. +Using motif -M07657_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907871 +# Estimated pi_0=0.911519 +Using motif +M08353_2.00 of width 18. +Using motif -M08353_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927391 +# Estimated pi_0=0.931728 +Using motif +M08929_2.00 of width 21. +Using motif -M08929_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932078 +# Estimated pi_0=0.93976 +Using motif +M07658_2.00 of width 15. +Using motif -M07658_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8008 +# Estimated pi_0=0.81456 +Using motif +M02919_2.00 of width 15. +Using motif -M02919_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989333 +# Estimated pi_0=0.996768 +Using motif +M04593_2.00 of width 17. +Using motif -M04593_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965686 +# Estimated pi_0=0.974765 +Using motif +M04594_2.00 of width 17. +Using motif -M04594_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978912 +# Estimated pi_0=0.98234 +Using motif +M05852_2.00 of width 13. +Using motif -M05852_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961368 +# Estimated pi_0=0.973072 +Using motif +M08354_2.00 of width 15. +Using motif -M08354_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900952 +# Estimated pi_0=0.90816 +Using motif +M08258_2.00 of width 15. +Using motif -M08258_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98638 +# Estimated pi_0=0.993927 +Using motif +M08355_2.00 of width 18. +Using motif -M08355_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988221 +# Estimated pi_0=0.991263 +Using motif +M07659_2.00 of width 21. +Using motif -M07659_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970597 +# Estimated pi_0=0.975529 +Using motif +M07660_2.00 of width 30. +Using motif -M07660_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.788039 +# Estimated pi_0=0.802273 +Using motif +M05853_2.00 of width 18. +Using motif -M05853_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974891 +# Estimated pi_0=0.981648 +Using motif +M08356_2.00 of width 21. +Using motif -M08356_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99567 +# Estimated pi_0=0.997387 +Using motif +M08930_2.00 of width 24. +Using motif -M08930_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979012 +# Estimated pi_0=0.981466 +Using motif +M07661_2.00 of width 15. +Using motif -M07661_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925043 +# Estimated pi_0=0.938113 +Using motif +M08259_2.00 of width 17. +Using motif -M08259_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932816 +# Estimated pi_0=0.948333 +Using motif +M08357_2.00 of width 21. +Using motif -M08357_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868598 +# Estimated pi_0=0.872252 +Using motif +M08931_2.00 of width 20. +Using motif -M08931_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921154 +# Estimated pi_0=0.935385 +Using motif +M08260_2.00 of width 21. +Using motif -M08260_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.3e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873544 +# Estimated pi_0=0.874321 +Using motif +M08358_2.00 of width 12. +Using motif -M08358_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906099 +# Estimated pi_0=0.910357 +Using motif +M08932_2.00 of width 22. +Using motif -M08932_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.1e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.876051 +# Estimated pi_0=0.878742 +Using motif +M04595_2.00 of width 12. +Using motif -M04595_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978641 +# Estimated pi_0=1 +Using motif +M04596_2.00 of width 12. +Using motif -M04596_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988824 +# Estimated pi_0=1 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955644 +# Estimated pi_0=0.971139 +Using motif +M04598_2.00 of width 15. +Using motif -M04598_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968431 +# Estimated pi_0=0.976258 +Using motif +M07662_2.00 of width 21. +Using motif -M07662_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88688 +# Estimated pi_0=0.895032 +Using motif +M06465_2.00 of width 14. +Using motif -M06465_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889126 +# Estimated pi_0=0.902238 +Using motif +M04599_2.00 of width 12. +Using motif -M04599_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04600_2.00 of width 12. +Using motif -M04600_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M08359_2.00 of width 21. +Using motif -M08359_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892885 +# Estimated pi_0=0.90626 +Using motif +M08933_2.00 of width 20. +Using motif -M08933_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962807 +# Estimated pi_0=0.968272 +Using motif +M07663_2.00 of width 15. +Using motif -M07663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925913 +# Estimated pi_0=0.935526 +Using motif +M07664_2.00 of width 15. +Using motif -M07664_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994256 +# Estimated pi_0=0.996162 +Using motif +M07665_2.00 of width 12. +Using motif -M07665_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9448 +# Estimated pi_0=0.956306 +Using motif +M08360_2.00 of width 21. +Using motif -M08360_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95392 +# Estimated pi_0=0.963353 +Using motif +M08934_2.00 of width 20. +Using motif -M08934_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965493 +# Estimated pi_0=0.968362 +Using motif +M08261_2.00 of width 15. +Using motif -M08261_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929624 +# Estimated pi_0=0.939125 +Using motif +M08361_2.00 of width 18. +Using motif -M08361_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87969 +# Estimated pi_0=0.87969 +Using motif +M08935_2.00 of width 16. +Using motif -M08935_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917219 +# Estimated pi_0=0.921765 +Using motif +M07666_2.00 of width 18. +Using motif -M07666_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908119 +# Estimated pi_0=0.920952 +Using motif +M07667_2.00 of width 21. +Using motif -M07667_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900175 +# Estimated pi_0=0.907702 +Using motif +M08362_2.00 of width 24. +Using motif -M08362_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.882308 +# Estimated pi_0=0.893208 +Using motif +M08936_2.00 of width 20. +Using motif -M08936_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88717 +# Estimated pi_0=0.907532 +Using motif +M07668_2.00 of width 18. +Using motif -M07668_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898358 +# Estimated pi_0=0.90585 +Using motif +M07669_2.00 of width 9. +Using motif -M07669_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934797 +# Estimated pi_0=0.940645 +Using motif +M04601_2.00 of width 15. +Using motif -M04601_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996404 +# Estimated pi_0=0.999598 +Using motif +M04602_2.00 of width 15. +Using motif -M04602_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998101 +# Estimated pi_0=0.999095 +Using motif +M05854_2.00 of width 15. +Using motif -M05854_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983315 +# Estimated pi_0=0.987644 +Using motif +M05855_2.00 of width 15. +Using motif -M05855_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999388 +# Estimated pi_0=1 +Using motif +M07670_2.00 of width 28. +Using motif -M07670_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998071 +# Estimated pi_0=0.999095 +Using motif +M07671_2.00 of width 39. +Using motif -M07671_2.00 of width 39. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856529 +# Estimated pi_0=0.868722 +Using motif +M02920_2.00 of width 12. +Using motif -M02920_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909346 +# Estimated pi_0=0.916333 +Using motif +M00243_2.00 of width 7. +Using motif -M00243_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8758 +# Estimated pi_0=0.885042 +Using motif +M00244_2.00 of width 12. +Using motif -M00244_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00245_2.00 of width 7. +Using motif -M00245_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8682 +# Estimated pi_0=0.885167 +Using motif +M00246_2.00 of width 7. +Using motif -M00246_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886139 +# Estimated pi_0=0.891111 +Using motif +M00247_2.00 of width 11. +Using motif -M00247_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M00248_2.00 of width 13. +Using motif -M00248_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00249_2.00 of width 11. +Using motif -M00249_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891327 +# Estimated pi_0=0.901538 +Using motif +M08937_2.00 of width 20. +Using motif -M08937_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.5e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868252 +# Estimated pi_0=0.874847 +Using motif +M07672_2.00 of width 12. +Using motif -M07672_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995684 +# Estimated pi_0=0.998384 +Using motif +M07673_2.00 of width 18. +Using motif -M07673_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923762 +# Estimated pi_0=0.934516 +Using motif +M07674_2.00 of width 29. +Using motif -M07674_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991299 +# Estimated pi_0=0.997588 +Using motif +M07675_2.00 of width 21. +Using motif -M07675_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00081 +# Estimated pi_0=1 +Using motif +M04603_2.00 of width 17. +Using motif -M04603_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9116 +# Estimated pi_0=0.923697 +Using motif +M04604_2.00 of width 17. +Using motif -M04604_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8416 +# Estimated pi_0=0.842157 +Using motif +M02921_2.00 of width 11. +Using motif -M02921_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939528 +# Estimated pi_0=0.947051 +Using motif +M04605_2.00 of width 15. +Using motif -M04605_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926667 +# Estimated pi_0=0.933947 +Using motif +M04606_2.00 of width 15. +Using motif -M04606_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948661 +# Estimated pi_0=0.956914 +Using motif +M08096_2.00 of width 11. +Using motif -M08096_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908921 +# Estimated pi_0=0.917037 +Using motif +M08363_2.00 of width 15. +Using motif -M08363_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890667 +# Estimated pi_0=0.898734 +Using motif +M08938_2.00 of width 22. +Using motif -M08938_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.2e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.819375 +# Estimated pi_0=0.82869 +Using motif +M10294_2.00 of width 10. +Using motif -M10294_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.928105 +Using motif +M10300_2.00 of width 13. +Using motif -M10300_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880971 +# Estimated pi_0=0.894094 +Using motif +M04607_2.00 of width 10. +Using motif -M04607_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958069 +# Estimated pi_0=0.967871 +Using motif +M04608_2.00 of width 10. +Using motif -M04608_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944189 +# Estimated pi_0=0.950633 +Using motif +M00152_2.00 of width 9. +Using motif -M00152_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968522 +# Estimated pi_0=0.973626 +Using motif +M08939_2.00 of width 8. +Using motif -M08939_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972814 +# Estimated pi_0=0.976522 +Using motif +M10301_2.00 of width 13. +Using motif -M10301_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99899 +# Estimated pi_0=1 +Using motif +M10302_2.00 of width 12. +Using motif -M10302_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983548 +# Estimated pi_0=0.993011 +Using motif +M10303_2.00 of width 13. +Using motif -M10303_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M10306_2.00 of width 9. +Using motif -M10306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963358 +# Estimated pi_0=0.969036 +Using motif +M07676_2.00 of width 21. +Using motif -M07676_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.8e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.828077 +# Estimated pi_0=0.836794 +Using motif +M07677_2.00 of width 21. +Using motif -M07677_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.832252 +# Estimated pi_0=0.848682 +Using motif +M07678_2.00 of width 27. +Using motif -M07678_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 9e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8444 +# Estimated pi_0=0.854079 +Using motif +M07679_2.00 of width 9. +Using motif -M07679_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992545 +# Estimated pi_0=1 +Using motif +M08364_2.00 of width 15. +Using motif -M08364_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90717 +# Estimated pi_0=0.923733 +Using motif +M08940_2.00 of width 13. +Using motif -M08940_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944925 +# Estimated pi_0=0.951138 +Using motif +M10307_2.00 of width 13. +Using motif -M10307_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943846 +# Estimated pi_0=0.953333 +Using motif +M07680_2.00 of width 9. +Using motif -M07680_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949615 +# Estimated pi_0=0.961067 +Using motif +M07681_2.00 of width 30. +Using motif -M07681_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896863 +# Estimated pi_0=0.910407 +Using motif +M07682_2.00 of width 30. +Using motif -M07682_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.850392 +# Estimated pi_0=0.860161 +Using motif +M07683_2.00 of width 12. +Using motif -M07683_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968662 +# Estimated pi_0=0.978478 +Using motif +M04609_2.00 of width 15. +Using motif -M04609_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8568 +# Estimated pi_0=0.860943 +Using motif +M04610_2.00 of width 15. +Using motif -M04610_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860594 +# Estimated pi_0=0.863048 +Using motif +M07684_2.00 of width 15. +Using motif -M07684_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914638 +# Estimated pi_0=0.922857 +Using motif +M04611_2.00 of width 11. +Using motif -M04611_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9976 +# Estimated pi_0=1 +Using motif +M04612_2.00 of width 11. +Using motif -M04612_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08365_2.00 of width 15. +Using motif -M08365_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869903 +# Estimated pi_0=0.880972 +Using motif +M07685_2.00 of width 12. +Using motif -M07685_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938305 +# Estimated pi_0=0.94882 +Using motif +M07686_2.00 of width 18. +Using motif -M07686_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981222 +# Estimated pi_0=0.983548 +Using motif +M04613_2.00 of width 19. +Using motif -M04613_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04614_2.00 of width 19. +Using motif -M04614_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M08366_2.00 of width 15. +Using motif -M08366_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961529 +# Estimated pi_0=0.96578 +Using motif +M08941_2.00 of width 24. +Using motif -M08941_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996791 +# Estimated pi_0=0.998593 +Using motif +M07687_2.00 of width 27. +Using motif -M07687_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944118 +# Estimated pi_0=0.959487 +Using motif +M07688_2.00 of width 12. +Using motif -M07688_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924771 +# Estimated pi_0=0.937481 +Using motif +M07689_2.00 of width 27. +Using motif -M07689_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.844673 +# Estimated pi_0=0.854305 +Using motif +M07690_2.00 of width 15. +Using motif -M07690_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900792 +# Estimated pi_0=0.908288 +Using motif +M08262_2.00 of width 7. +Using motif -M08262_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964902 +# Estimated pi_0=0.96537 +Using motif +M08367_2.00 of width 27. +Using motif -M08367_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8838 +# Estimated pi_0=0.886863 +Using motif +M04615_2.00 of width 23. +Using motif -M04615_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96495 +# Estimated pi_0=0.977099 +Using motif +M04616_2.00 of width 23. +Using motif -M04616_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971456 +# Estimated pi_0=1 +Using motif +M07691_2.00 of width 21. +Using motif -M07691_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.7e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.837119 +# Estimated pi_0=0.840781 +Using motif +M02922_2.00 of width 14. +Using motif -M02922_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962745 +# Estimated pi_0=0.979653 +Using motif +M04617_2.00 of width 17. +Using motif -M04617_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9538 +# Estimated pi_0=0.961651 +Using motif +M04618_2.00 of width 17. +Using motif -M04618_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9532 +# Estimated pi_0=0.964928 +Using motif +M08942_2.00 of width 17. +Using motif -M08942_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895094 +# Estimated pi_0=0.901345 +Using motif +M08368_2.00 of width 18. +Using motif -M08368_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921678 +# Estimated pi_0=0.930184 +Using motif +M08943_2.00 of width 21. +Using motif -M08943_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91952 +# Estimated pi_0=0.930602 +Using motif +M07692_2.00 of width 30. +Using motif -M07692_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991099 +# Estimated pi_0=0.993299 +Using motif +M03682_2.00 of width 14. +Using motif -M03682_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9012 +# Estimated pi_0=0.92082 +Using motif +M07693_2.00 of width 21. +Using motif -M07693_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931901 +# Estimated pi_0=0.9385 +Using motif +M07694_2.00 of width 21. +Using motif -M07694_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907626 +# Estimated pi_0=0.912949 +Using motif +M07695_2.00 of width 6. +Using motif -M07695_2.00 of width 6. +Computing q-values. +Using motif +M08263_2.00 of width 22. +Using motif -M08263_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910693 +# Estimated pi_0=0.924462 +Using motif +M08369_2.00 of width 9. +Using motif -M08369_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980143 +# Estimated pi_0=0.985503 +Using motif +M02923_2.00 of width 14. +Using motif -M02923_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952353 +# Estimated pi_0=0.969167 +Using motif +M07696_2.00 of width 9. +Using motif -M07696_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92125 +# Estimated pi_0=0.925113 +Using motif +M04619_2.00 of width 10. +Using motif -M04619_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9118 +# Estimated pi_0=0.926667 +Using motif +M04620_2.00 of width 10. +Using motif -M04620_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96283 +# Estimated pi_0=0.981436 +Using motif +M08264_2.00 of width 15. +Using motif -M08264_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00041 +# Estimated pi_0=1 +Using motif +M08370_2.00 of width 9. +Using motif -M08370_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911228 +# Estimated pi_0=0.921439 +Using motif +M08944_2.00 of width 20. +Using motif -M08944_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M07697_2.00 of width 18. +Using motif -M07697_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.998492 +Using motif +M07698_2.00 of width 12. +Using motif -M07698_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919815 +# Estimated pi_0=0.930677 +Using motif +M07699_2.00 of width 7. +Using motif -M07699_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M01173_2.00 of width 11. +Using motif -M01173_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989704 +# Estimated pi_0=0.998894 +Using motif +M05856_2.00 of width 17. +Using motif -M05856_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99021 +# Estimated pi_0=0.998782 +Using motif +M07700_2.00 of width 18. +Using motif -M07700_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985586 +# Estimated pi_0=0.999188 +Using motif +M08371_2.00 of width 15. +Using motif -M08371_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963394 +# Estimated pi_0=0.970279 +Using motif +M04621_2.00 of width 14. +Using motif -M04621_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982264 +# Estimated pi_0=0.989326 +Using motif +M04622_2.00 of width 14. +Using motif -M04622_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983636 +# Estimated pi_0=0.997436 +Using motif +M08372_2.00 of width 24. +Using motif -M08372_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940328 +# Estimated pi_0=0.947826 +Using motif +M08945_2.00 of width 24. +Using motif -M08945_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986667 +# Estimated pi_0=0.990421 +Using motif +M08373_2.00 of width 18. +Using motif -M08373_2.00 of width 18. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922821 +# Estimated pi_0=0.927861 +Using motif +M07701_2.00 of width 18. +Using motif -M07701_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859123 +# Estimated pi_0=0.86063 +Using motif +M07702_2.00 of width 12. +Using motif -M07702_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955974 +# Estimated pi_0=0.961326 +Using motif +M05857_2.00 of width 19. +Using motif -M05857_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.968992 +Using motif +M07703_2.00 of width 7. +Using motif -M07703_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.848515 +# Estimated pi_0=0.848515 +Using motif +M07704_2.00 of width 21. +Using motif -M07704_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938222 +# Estimated pi_0=0.949176 +Using motif +M07705_2.00 of width 27. +Using motif -M07705_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984713 +# Estimated pi_0=0.992063 +Using motif +M07706_2.00 of width 30. +Using motif -M07706_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993696 +# Estimated pi_0=0.996061 +Using motif +M07707_2.00 of width 6. +Using motif -M07707_2.00 of width 6. +Computing q-values. +Using motif +M07708_2.00 of width 27. +Using motif -M07708_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08265_2.00 of width 21. +Using motif -M08265_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08374_2.00 of width 18. +Using motif -M08374_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M08946_2.00 of width 24. +Using motif -M08946_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M07709_2.00 of width 9. +Using motif -M07709_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962981 +# Estimated pi_0=0.967333 +Using motif +M08375_2.00 of width 27. +Using motif -M08375_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952061 +# Estimated pi_0=0.967816 +Using motif +M08947_2.00 of width 20. +Using motif -M08947_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M07710_2.00 of width 21. +Using motif -M07710_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997538 +# Estimated pi_0=0.999598 +Using motif +M07711_2.00 of width 15. +Using motif -M07711_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8976 +# Estimated pi_0=0.907241 +Using motif +M07712_2.00 of width 30. +Using motif -M07712_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996244 +# Estimated pi_0=0.997071 +Using motif +M08376_2.00 of width 15. +Using motif -M08376_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975301 +# Estimated pi_0=0.975301 +Using motif +M08948_2.00 of width 17. +Using motif -M08948_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978276 +# Estimated pi_0=0.982211 +Using motif +M02924_2.00 of width 18. +Using motif -M02924_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M04623_2.00 of width 19. +Using motif -M04623_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996508 +# Estimated pi_0=1 +Using motif +M04624_2.00 of width 19. +Using motif -M04624_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995269 +# Estimated pi_0=0.998392 +Using motif +M04625_2.00 of width 19. +Using motif -M04625_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988037 +# Estimated pi_0=0.998041 +Using motif +M04626_2.00 of width 19. +Using motif -M04626_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991017 +# Estimated pi_0=0.997846 +Using motif +M08377_2.00 of width 24. +Using motif -M08377_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M08378_2.00 of width 33. +Using motif -M08378_2.00 of width 33. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9775 +# Estimated pi_0=0.979686 +Using motif +M08949_2.00 of width 20. +Using motif -M08949_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986595 +# Estimated pi_0=0.989846 +Using motif +M07713_2.00 of width 9. +Using motif -M07713_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993369 +# Estimated pi_0=0.998191 +Using motif +M07714_2.00 of width 21. +Using motif -M07714_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913846 +# Estimated pi_0=0.924925 +Using motif +M07715_2.00 of width 15. +Using motif -M07715_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8556 +# Estimated pi_0=0.864779 +Using motif +M07716_2.00 of width 14. +Using motif -M07716_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920133 +# Estimated pi_0=0.932849 +Using motif +M07717_2.00 of width 12. +Using motif -M07717_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87463 +# Estimated pi_0=0.887164 +Using motif +M05858_2.00 of width 11. +Using motif -M05858_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911068 +# Estimated pi_0=0.920645 +Using motif +M07718_2.00 of width 21. +Using motif -M07718_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.843333 +# Estimated pi_0=0.847541 +Using motif +M07719_2.00 of width 9. +Using motif -M07719_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962667 +# Estimated pi_0=0.972961 +Using motif +M08379_2.00 of width 13. +Using motif -M08379_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957607 +# Estimated pi_0=0.972118 +Using motif +M08950_2.00 of width 12. +Using motif -M08950_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959487 +# Estimated pi_0=0.968095 +Using motif +M07720_2.00 of width 29. +Using motif -M07720_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956689 +# Estimated pi_0=0.962061 +Using motif +M00250_2.00 of width 10. +Using motif -M00250_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00251_2.00 of width 9. +Using motif -M00251_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00252_2.00 of width 10. +Using motif -M00252_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M08380_2.00 of width 21. +Using motif -M08380_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976739 +# Estimated pi_0=0.980314 +Using motif +M07721_2.00 of width 7. +Using motif -M07721_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920882 +# Estimated pi_0=0.924189 +Using motif +M07722_2.00 of width 27. +Using motif -M07722_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89313 +# Estimated pi_0=0.901678 +Using motif +M08266_2.00 of width 20. +Using motif -M08266_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996891 +# Estimated pi_0=0.999296 +Using motif +M08381_2.00 of width 21. +Using motif -M08381_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972025 +# Estimated pi_0=0.977514 +Using motif +M07723_2.00 of width 15. +Using motif -M07723_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902521 +# Estimated pi_0=0.905103 +Using motif +M07724_2.00 of width 7. +Using motif -M07724_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976209 +# Estimated pi_0=0.98254 +Using motif +M07725_2.00 of width 12. +Using motif -M07725_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M07726_2.00 of width 15. +Using motif -M07726_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04627_2.00 of width 13. +Using motif -M04627_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86835 +# Estimated pi_0=0.877355 +Using motif +M04628_2.00 of width 13. +Using motif -M04628_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898693 +# Estimated pi_0=0.90038 +Using motif +M07727_2.00 of width 12. +Using motif -M07727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.843119 +# Estimated pi_0=0.852101 +Using motif +M07728_2.00 of width 18. +Using motif -M07728_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967184 +# Estimated pi_0=0.986748 +Using motif +M07729_2.00 of width 24. +Using motif -M07729_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8886 +# Estimated pi_0=0.901528 +Using motif +M07730_2.00 of width 24. +Using motif -M07730_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952844 +# Estimated pi_0=0.965399 +Using motif +M07731_2.00 of width 30. +Using motif -M07731_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857451 +# Estimated pi_0=0.864561 +Using motif +M07732_2.00 of width 8. +Using motif -M07732_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926724 +# Estimated pi_0=0.927863 +Using motif +M08382_2.00 of width 21. +Using motif -M08382_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9825 +# Estimated pi_0=0.987795 +Using motif +M07733_2.00 of width 15. +Using motif -M07733_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938089 +# Estimated pi_0=0.942727 +Using motif +M07734_2.00 of width 21. +Using motif -M07734_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.807 +# Estimated pi_0=0.814955 +Using motif +M08267_2.00 of width 14. +Using motif -M08267_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990938 +# Estimated pi_0=0.994949 +Using motif +M08383_2.00 of width 24. +Using motif -M08383_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921625 +# Estimated pi_0=0.925989 +Using motif +M08384_2.00 of width 24. +Using motif -M08384_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8548 +# Estimated pi_0=0.861833 +Using motif +M08951_2.00 of width 20. +Using motif -M08951_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912353 +# Estimated pi_0=0.930884 +Using motif +M08952_2.00 of width 22. +Using motif -M08952_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915873 +# Estimated pi_0=0.917351 +Using motif +M07735_2.00 of width 9. +Using motif -M07735_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963333 +# Estimated pi_0=0.977814 +Using motif +M08268_2.00 of width 17. +Using motif -M08268_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900385 +# Estimated pi_0=0.908857 +Using motif +M08385_2.00 of width 12. +Using motif -M08385_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95685 +# Estimated pi_0=0.971294 +Using motif +M07736_2.00 of width 21. +Using motif -M07736_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925733 +# Estimated pi_0=0.930833 +Using motif +M07737_2.00 of width 27. +Using motif -M07737_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941468 +# Estimated pi_0=0.951892 +Using motif +M08386_2.00 of width 18. +Using motif -M08386_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965191 +# Estimated pi_0=0.972 +Using motif +M08953_2.00 of width 19. +Using motif -M08953_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921887 +# Estimated pi_0=0.927407 +Using motif +M04629_2.00 of width 16. +Using motif -M04629_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866733 +# Estimated pi_0=0.873269 +Using motif +M04630_2.00 of width 16. +Using motif -M04630_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9038 +# Estimated pi_0=0.910667 +Using motif +M04631_2.00 of width 16. +Using motif -M04631_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8978 +# Estimated pi_0=0.912281 +Using motif +M04632_2.00 of width 16. +Using motif -M04632_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899245 +# Estimated pi_0=0.904034 +Using motif +M08387_2.00 of width 9. +Using motif -M08387_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8134 +# Estimated pi_0=0.8134 +Using motif +M08954_2.00 of width 9. +Using motif -M08954_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8866 +# Estimated pi_0=0.902759 +Using motif +M07738_2.00 of width 6. +Using motif -M07738_2.00 of width 6. +Computing q-values. +Using motif +M07739_2.00 of width 21. +Using motif -M07739_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M08388_2.00 of width 11. +Using motif -M08388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928841 +# Estimated pi_0=0.939766 +Using motif +M08955_2.00 of width 22. +Using motif -M08955_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866719 +# Estimated pi_0=0.873935 +Using motif +M07740_2.00 of width 28. +Using motif -M07740_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993789 +# Estimated pi_0=0.998291 +Using motif +M07741_2.00 of width 15. +Using motif -M07741_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979589 +# Estimated pi_0=0.985297 +Using motif +M07742_2.00 of width 29. +Using motif -M07742_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982921 +# Estimated pi_0=0.985946 +Using motif +M07743_2.00 of width 7. +Using motif -M07743_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976952 +# Estimated pi_0=0.988555 +Using motif +M07744_2.00 of width 11. +Using motif -M07744_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9374 +# Estimated pi_0=0.950738 +Using motif +M07745_2.00 of width 12. +Using motif -M07745_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954013 +# Estimated pi_0=0.958683 +Using motif +M07746_2.00 of width 24. +Using motif -M07746_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893663 +# Estimated pi_0=0.908 +Using motif +M07747_2.00 of width 21. +Using motif -M07747_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984333 +# Estimated pi_0=0.989529 +Using motif +M07748_2.00 of width 18. +Using motif -M07748_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8556 +# Estimated pi_0=0.868657 +Using motif +M07749_2.00 of width 18. +Using motif -M07749_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M07750_2.00 of width 12. +Using motif -M07750_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945385 +# Estimated pi_0=0.952727 +Using motif +M07751_2.00 of width 15. +Using motif -M07751_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890376 +# Estimated pi_0=0.897183 +Using motif +M02938_2.00 of width 13. +Using motif -M02938_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M07752_2.00 of width 15. +Using motif -M07752_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04633_2.00 of width 14. +Using motif -M04633_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885825 +# Estimated pi_0=0.886481 +Using motif +M04634_2.00 of width 14. +Using motif -M04634_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8948 +# Estimated pi_0=0.905303 +Using motif +M04635_2.00 of width 14. +Using motif -M04635_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8914 +# Estimated pi_0=0.901062 +Using motif +M04636_2.00 of width 14. +Using motif -M04636_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9224 +# Estimated pi_0=0.935044 +Using motif +M08269_2.00 of width 17. +Using motif -M08269_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996866 +# Estimated pi_0=1 +Using motif +M08389_2.00 of width 9. +Using motif -M08389_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977117 +# Estimated pi_0=0.986901 +Using motif +M07753_2.00 of width 18. +Using motif -M07753_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921186 +# Estimated pi_0=0.927536 +Using motif +M08390_2.00 of width 15. +Using motif -M08390_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9732 +# Estimated pi_0=0.987802 +Using motif +M07754_2.00 of width 12. +Using motif -M07754_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912121 +# Estimated pi_0=0.921419 +Using motif +M07755_2.00 of width 15. +Using motif -M07755_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951181 +# Estimated pi_0=0.958057 +Using motif +M07756_2.00 of width 6. +Using motif -M07756_2.00 of width 6. +Computing q-values. +Using motif +M08391_2.00 of width 11. +Using motif -M08391_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950291 +# Estimated pi_0=0.963372 +Using motif +M08956_2.00 of width 12. +Using motif -M08956_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934035 +# Estimated pi_0=0.947515 +Using motif +M07757_2.00 of width 8. +Using motif -M07757_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859817 +# Estimated pi_0=0.859817 +Using motif +M07758_2.00 of width 7. +Using motif -M07758_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903333 +# Estimated pi_0=0.915484 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9888 +# Estimated pi_0=1 +Using motif +M04637_2.00 of width 18. +Using motif -M04637_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932789 +# Estimated pi_0=0.937966 +Using motif +M04638_2.00 of width 18. +Using motif -M04638_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95531 +# Estimated pi_0=0.962581 +Using motif +M04639_2.00 of width 19. +Using motif -M04639_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952126 +# Estimated pi_0=0.959218 +Using motif +M04640_2.00 of width 23. +Using motif -M04640_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04641_2.00 of width 19. +Using motif -M04641_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954028 +# Estimated pi_0=0.964828 +Using motif +M04642_2.00 of width 23. +Using motif -M04642_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M07759_2.00 of width 18. +Using motif -M07759_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.808214 +# Estimated pi_0=0.82016 +Using motif +M07760_2.00 of width 12. +Using motif -M07760_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945926 +# Estimated pi_0=0.952857 +Using motif +M08392_2.00 of width 15. +Using motif -M08392_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948689 +# Estimated pi_0=0.961529 +Using motif +M08957_2.00 of width 22. +Using motif -M08957_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948214 +# Estimated pi_0=0.954932 +Using motif +M07761_2.00 of width 15. +Using motif -M07761_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981905 +# Estimated pi_0=0.988526 +Using motif +M07762_2.00 of width 21. +Using motif -M07762_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8572 +# Estimated pi_0=0.862162 +Using motif +M07763_2.00 of width 11. +Using motif -M07763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M02925_2.00 of width 13. +Using motif -M02925_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982764 +# Estimated pi_0=0.992609 +Using motif +M04643_2.00 of width 11. +Using motif -M04643_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994463 +# Estimated pi_0=0.999095 +Using motif +M04644_2.00 of width 11. +Using motif -M04644_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975224 +# Estimated pi_0=0.98 +Using motif +M04645_2.00 of width 12. +Using motif -M04645_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911351 +# Estimated pi_0=0.913121 +Using motif +M04646_2.00 of width 12. +Using motif -M04646_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925873 +# Estimated pi_0=0.93375 +Using motif +M07764_2.00 of width 27. +Using motif -M07764_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889517 +# Estimated pi_0=0.891733 +Using motif +M07765_2.00 of width 18. +Using motif -M07765_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975652 +# Estimated pi_0=0.978105 +Using motif +M02926_2.00 of width 11. +Using motif -M02926_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968302 +# Estimated pi_0=0.984932 +Using motif +M02927_2.00 of width 11. +Using motif -M02927_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967921 +# Estimated pi_0=0.976709 +Using motif +M02928_2.00 of width 12. +Using motif -M02928_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.965833 +Using motif +M04647_2.00 of width 14. +Using motif -M04647_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965926 +# Estimated pi_0=0.974016 +Using motif +M04648_2.00 of width 14. +Using motif -M04648_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98678 +# Estimated pi_0=1 +Using motif +M07766_2.00 of width 39. +Using motif -M07766_2.00 of width 39. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996869 +# Estimated pi_0=0.996985 +Using motif +M07767_2.00 of width 12. +Using motif -M07767_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989197 +# Estimated pi_0=0.992188 +Using motif +M04649_2.00 of width 19. +Using motif -M04649_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964065 +# Estimated pi_0=0.977605 +Using motif +M04650_2.00 of width 19. +Using motif -M04650_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963333 +# Estimated pi_0=0.974788 +Using motif +M08270_2.00 of width 16. +Using motif -M08270_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M08393_2.00 of width 15. +Using motif -M08393_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978367 +# Estimated pi_0=0.991875 +Using motif +M08958_2.00 of width 18. +Using motif -M08958_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909412 +# Estimated pi_0=0.915143 +Using motif +M04651_2.00 of width 17. +Using motif -M04651_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04652_2.00 of width 17. +Using motif -M04652_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M07768_2.00 of width 30. +Using motif -M07768_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9358 +# Estimated pi_0=0.944 +Using motif +M07769_2.00 of width 21. +Using motif -M07769_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961074 +# Estimated pi_0=0.968639 +Using motif +M07770_2.00 of width 9. +Using motif -M07770_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M08394_2.00 of width 21. +Using motif -M08394_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938 +# Estimated pi_0=0.953803 +Using motif +M07771_2.00 of width 12. +Using motif -M07771_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879434 +# Estimated pi_0=0.885273 +Using motif +M04653_2.00 of width 22. +Using motif -M04653_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9582 +# Estimated pi_0=0.976506 +Using motif +M04654_2.00 of width 22. +Using motif -M04654_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9578 +# Estimated pi_0=0.97071 +Using motif +M07772_2.00 of width 18. +Using motif -M07772_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943148 +# Estimated pi_0=0.953056 +Using motif +M08395_2.00 of width 15. +Using motif -M08395_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920296 +# Estimated pi_0=0.92775 +Using motif +M08959_2.00 of width 24. +Using motif -M08959_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M07773_2.00 of width 30. +Using motif -M07773_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.7e-06 have been dropped to reclaim memory. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867703 +# Estimated pi_0=0.870839 +Using motif +M07774_2.00 of width 9. +Using motif -M07774_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941594 +# Estimated pi_0=0.947386 +Using motif +M07775_2.00 of width 13. +Using motif -M07775_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879223 +# Estimated pi_0=0.886727 +Using motif +M07776_2.00 of width 28. +Using motif -M07776_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988808 +# Estimated pi_0=0.991414 +Using motif +M08396_2.00 of width 21. +Using motif -M08396_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986703 +# Estimated pi_0=0.987204 +Using motif +M08960_2.00 of width 18. +Using motif -M08960_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990157 +# Estimated pi_0=0.99202 +Using motif +M02929_2.00 of width 15. +Using motif -M02929_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983506 +# Estimated pi_0=0.99 +Using motif +M04655_2.00 of width 11. +Using motif -M04655_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978849 +# Estimated pi_0=0.991623 +Using motif +M04656_2.00 of width 11. +Using motif -M04656_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.985635 +Using motif +M08397_2.00 of width 12. +Using motif -M08397_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978217 +# Estimated pi_0=0.988962 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948932 +# Estimated pi_0=0.96446 +Using motif +M02930_2.00 of width 14. +Using motif -M02930_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927719 +# Estimated pi_0=0.935075 +Using motif +M04657_2.00 of width 13. +Using motif -M04657_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917237 +# Estimated pi_0=0.926347 +Using motif +M04658_2.00 of width 13. +Using motif -M04658_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909231 +# Estimated pi_0=0.92 +Using motif +M08398_2.00 of width 14. +Using motif -M08398_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905075 +# Estimated pi_0=0.910811 +Using motif +M07777_2.00 of width 21. +Using motif -M07777_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M07778_2.00 of width 36. +Using motif -M07778_2.00 of width 36. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986982 +# Estimated pi_0=0.995077 +Using motif +M08399_2.00 of width 21. +Using motif -M08399_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987251 +# Estimated pi_0=0.995025 +Using motif +M07779_2.00 of width 30. +Using motif -M07779_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874915 +# Estimated pi_0=0.883399 +Using motif +M08400_2.00 of width 12. +Using motif -M08400_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M07780_2.00 of width 9. +Using motif -M07780_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867379 +# Estimated pi_0=0.888571 +Using motif +M07781_2.00 of width 12. +Using motif -M07781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961176 +# Estimated pi_0=0.96521 +Using motif +M08401_2.00 of width 18. +Using motif -M08401_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971048 +# Estimated pi_0=0.986571 +Using motif +M08961_2.00 of width 22. +Using motif -M08961_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998788 +# Estimated pi_0=0.998894 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08104_2.00 of width 11. +Using motif -M08104_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9772 +# Estimated pi_0=0.989186 +Using motif +M09018_2.00 of width 14. +Using motif -M09018_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988 +# Estimated pi_0=1 +Using motif +M10405_2.00 of width 14. +Using motif -M10405_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M04659_2.00 of width 12. +Using motif -M04659_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947578 +# Estimated pi_0=0.94982 +Using motif +M04660_2.00 of width 12. +Using motif -M04660_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971097 +# Estimated pi_0=0.973373 +Using motif +M02939_2.00 of width 15. +Using motif -M02939_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951068 +# Estimated pi_0=0.956975 +Using motif +M01205_2.00 of width 9. +Using motif -M01205_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M01203_2.00 of width 9. +Using motif -M01203_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M01204_2.00 of width 10. +Using motif -M01204_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.991381 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9852 +# Estimated pi_0=0.992513 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989466 +# Estimated pi_0=0.998163 +Using motif +M04662_2.00 of width 9. +Using motif -M04662_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995979 +# Estimated pi_0=0.997677 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925941 +# Estimated pi_0=0.944228 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9152 +# Estimated pi_0=0.921481 +Using motif +M04664_2.00 of width 15. +Using motif -M04664_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902376 +# Estimated pi_0=0.913451 +Using motif +M09519_2.00 of width 10. +Using motif -M09519_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973372 +# Estimated pi_0=0.976292 +Using motif +M00735_2.00 of width 10. +Using motif -M00735_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971356 +# Estimated pi_0=0.982283 +Using motif +M00736_2.00 of width 10. +Using motif -M00736_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974245 +# Estimated pi_0=0.981436 +Using motif +M00737_2.00 of width 10. +Using motif -M00737_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979574 +# Estimated pi_0=0.983298 +Using motif +M00738_2.00 of width 8. +Using motif -M00738_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975294 +# Estimated pi_0=0.981158 +Using motif +M00739_2.00 of width 9. +Using motif -M00739_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972258 +# Estimated pi_0=0.984944 +Using motif +M00740_2.00 of width 10. +Using motif -M00740_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979172 +# Estimated pi_0=0.984127 +Using motif +M00741_2.00 of width 10. +Using motif -M00741_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969474 +# Estimated pi_0=0.974066 +Using motif +M00742_2.00 of width 9. +Using motif -M00742_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969172 +# Estimated pi_0=0.972948 +Using motif +M08105_2.00 of width 10. +Using motif -M08105_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982537 +# Estimated pi_0=0.985792 +Using motif +M09020_2.00 of width 11. +Using motif -M09020_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935965 +# Estimated pi_0=0.944336 +Using motif +M01499_2.00 of width 9. +Using motif -M01499_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M01500_2.00 of width 9. +Using motif -M01500_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M02940_2.00 of width 18. +Using motif -M02940_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M02941_2.00 of width 10. +Using motif -M02941_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04665_2.00 of width 12. +Using motif -M04665_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04666_2.00 of width 12. +Using motif -M04666_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M02942_2.00 of width 14. +Using motif -M02942_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M03714_2.00 of width 14. +Using motif -M03714_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M04667_2.00 of width 10. +Using motif -M04667_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M04668_2.00 of width 10. +Using motif -M04668_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04669_2.00 of width 12. +Using motif -M04669_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9852 +# Estimated pi_0=1 +Using motif +M04670_2.00 of width 11. +Using motif -M04670_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04671_2.00 of width 12. +Using motif -M04671_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04672_2.00 of width 11. +Using motif -M04672_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M02943_2.00 of width 14. +Using motif -M02943_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M02944_2.00 of width 14. +Using motif -M02944_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03715_2.00 of width 11. +Using motif -M03715_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M04673_2.00 of width 10. +Using motif -M04673_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00324 +# Estimated pi_0=1 +Using motif +M04674_2.00 of width 10. +Using motif -M04674_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04675_2.00 of width 12. +Using motif -M04675_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9778 +# Estimated pi_0=1 +Using motif +M04676_2.00 of width 12. +Using motif -M04676_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M09022_2.00 of width 11. +Using motif -M09022_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M02945_2.00 of width 14. +Using motif -M02945_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04677_2.00 of width 10. +Using motif -M04677_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M04678_2.00 of width 10. +Using motif -M04678_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M01501_2.00 of width 9. +Using motif -M01501_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M01502_2.00 of width 9. +Using motif -M01502_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M02946_2.00 of width 17. +Using motif -M02946_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M02947_2.00 of width 18. +Using motif -M02947_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M02948_2.00 of width 10. +Using motif -M02948_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M04679_2.00 of width 10. +Using motif -M04679_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04680_2.00 of width 10. +Using motif -M04680_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M09023_2.00 of width 14. +Using motif -M09023_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975644 +# Estimated pi_0=1 +Using motif +M10422_2.00 of width 12. +Using motif -M10422_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10423_2.00 of width 15. +Using motif -M10423_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M10424_2.00 of width 10. +Using motif -M10424_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9494 +# Estimated pi_0=0.951176 +Using motif +M10425_2.00 of width 15. +Using motif -M10425_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944118 +# Estimated pi_0=0.949143 +Using motif +M10426_2.00 of width 10. +Using motif -M10426_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953 +# Estimated pi_0=0.953 +Using motif +M01911_2.00 of width 9. +Using motif -M01911_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9424 +# Estimated pi_0=0.946043 +Using motif +M01916_2.00 of width 8. +Using motif -M01916_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9175 +# Estimated pi_0=0.926324 +Using motif +M01912_2.00 of width 10. +Using motif -M01912_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8278 +# Estimated pi_0=0.8278 +Using motif +M01913_2.00 of width 10. +Using motif -M01913_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.805 +# Estimated pi_0=0.808039 +Using motif +M01914_2.00 of width 9. +Using motif -M01914_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.881368 +# Estimated pi_0=0.886929 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997403 +# Estimated pi_0=1 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975733 +# Estimated pi_0=0.980774 +Using motif +M01915_2.00 of width 10. +Using motif -M01915_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898641 +# Estimated pi_0=0.90566 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985385 +# Estimated pi_0=0.985823 +Using motif +M01927_2.00 of width 11. +Using motif -M01927_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04681_2.00 of width 13. +Using motif -M04681_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04682_2.00 of width 13. +Using motif -M04682_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M01928_2.00 of width 10. +Using motif -M01928_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M01929_2.00 of width 8. +Using motif -M01929_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04683_2.00 of width 12. +Using motif -M04683_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04684_2.00 of width 12. +Using motif -M04684_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04685_2.00 of width 10. +Using motif -M04685_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04686_2.00 of width 12. +Using motif -M04686_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04687_2.00 of width 12. +Using motif -M04687_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04688_2.00 of width 10. +Using motif -M04688_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04689_2.00 of width 12. +Using motif -M04689_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04690_2.00 of width 10. +Using motif -M04690_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04691_2.00 of width 12. +Using motif -M04691_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M01930_2.00 of width 10. +Using motif -M01930_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04692_2.00 of width 12. +Using motif -M04692_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04693_2.00 of width 12. +Using motif -M04693_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09028_2.00 of width 16. +Using motif -M09028_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M01931_2.00 of width 10. +Using motif -M01931_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04694_2.00 of width 12. +Using motif -M04694_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04695_2.00 of width 12. +Using motif -M04695_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M01961_2.00 of width 10. +Using motif -M01961_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873571 +# Estimated pi_0=0.877851 +Using motif +M02949_2.00 of width 18. +Using motif -M02949_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M02950_2.00 of width 18. +Using motif -M02950_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M02951_2.00 of width 16. +Using motif -M02951_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04696_2.00 of width 11. +Using motif -M04696_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8412 +# Estimated pi_0=0.855726 +Using motif +M04697_2.00 of width 16. +Using motif -M04697_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920198 +# Estimated pi_0=0.933271 +Using motif +M04698_2.00 of width 11. +Using motif -M04698_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.81098 +# Estimated pi_0=0.820179 +Using motif +M04699_2.00 of width 16. +Using motif -M04699_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M09029_2.00 of width 10. +Using motif -M09029_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8816 +# Estimated pi_0=0.897241 +Using motif +M02952_2.00 of width 12. +Using motif -M02952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919612 +# Estimated pi_0=0.922115 +Using motif +M02953_2.00 of width 12. +Using motif -M02953_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9196 +# Estimated pi_0=0.935574 +Using motif +M02954_2.00 of width 14. +Using motif -M02954_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9696 +# Estimated pi_0=0.975514 +Using motif +M02955_2.00 of width 14. +Using motif -M02955_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04700_2.00 of width 16. +Using motif -M04700_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974019 +# Estimated pi_0=0.986 +Using motif +M07938_2.00 of width 15. +Using motif -M07938_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.79604 +# Estimated pi_0=0.804561 +Using motif +M09030_2.00 of width 14. +Using motif -M09030_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867719 +# Estimated pi_0=0.87069 +Using motif +M09521_2.00 of width 10. +Using motif -M09521_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8858 +# Estimated pi_0=0.89792 +Using motif +M10444_2.00 of width 15. +Using motif -M10444_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9976 +# Estimated pi_0=1 +Using motif +M10445_2.00 of width 12. +Using motif -M10445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864356 +# Estimated pi_0=0.87 +Using motif +M02956_2.00 of width 18. +Using motif -M02956_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M02957_2.00 of width 18. +Using motif -M02957_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02958_2.00 of width 18. +Using motif -M02958_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04701_2.00 of width 16. +Using motif -M04701_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04702_2.00 of width 16. +Using motif -M04702_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M09031_2.00 of width 11. +Using motif -M09031_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924306 +# Estimated pi_0=0.928533 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8906 +# Estimated pi_0=0.899 +Using motif +M02959_2.00 of width 12. +Using motif -M02959_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968269 +# Estimated pi_0=0.986923 +Using motif +M04703_2.00 of width 14. +Using motif -M04703_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9276 +# Estimated pi_0=0.938983 +Using motif +M04704_2.00 of width 14. +Using motif -M04704_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989703 +# Estimated pi_0=0.999278 +Using motif +M09032_2.00 of width 10. +Using motif -M09032_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8938 +# Estimated pi_0=0.897925 +Using motif +M02960_2.00 of width 14. +Using motif -M02960_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04705_2.00 of width 14. +Using motif -M04705_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04706_2.00 of width 14. +Using motif -M04706_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09033_2.00 of width 13. +Using motif -M09033_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897241 +# Estimated pi_0=0.901944 +Using motif +M09522_2.00 of width 12. +Using motif -M09522_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8794 +# Estimated pi_0=0.893628 +Using motif +M07939_2.00 of width 11. +Using motif -M07939_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919344 +# Estimated pi_0=0.930676 +Using motif +M07940_2.00 of width 11. +Using motif -M07940_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91578 +# Estimated pi_0=0.926081 +Using motif +M07941_2.00 of width 11. +Using motif -M07941_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914118 +# Estimated pi_0=0.92719 +Using motif +M08107_2.00 of width 11. +Using motif -M08107_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92126 +# Estimated pi_0=0.931779 +Using motif +M09034_2.00 of width 13. +Using motif -M09034_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900177 +# Estimated pi_0=0.90702 +Using motif +M09523_2.00 of width 10. +Using motif -M09523_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8898 +# Estimated pi_0=0.8898 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8424 +# Estimated pi_0=0.84396 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891238 +# Estimated pi_0=0.898361 +Using motif +M09035_2.00 of width 14. +Using motif -M09035_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8975 +# Estimated pi_0=0.905616 +Using motif +M02961_2.00 of width 12. +Using motif -M02961_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995862 +# Estimated pi_0=1 +Using motif +M02962_2.00 of width 12. +Using motif -M02962_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04707_2.00 of width 14. +Using motif -M04707_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950196 +# Estimated pi_0=0.960721 +Using motif +M07942_2.00 of width 15. +Using motif -M07942_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.849159 +# Estimated pi_0=0.858291 +Using motif +M07943_2.00 of width 15. +Using motif -M07943_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8466 +# Estimated pi_0=0.854513 +Using motif +M08109_2.00 of width 11. +Using motif -M08109_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865577 +# Estimated pi_0=0.87377 +Using motif +M09036_2.00 of width 13. +Using motif -M09036_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869268 +# Estimated pi_0=0.874729 +Using motif +M09524_2.00 of width 10. +Using motif -M09524_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8486 +# Estimated pi_0=0.856923 +Using motif +M02963_2.00 of width 14. +Using motif -M02963_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948039 +# Estimated pi_0=0.958797 +Using motif +M09044_2.00 of width 15. +Using motif -M09044_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943619 +# Estimated pi_0=0.958194 +Using motif +M02964_2.00 of width 10. +Using motif -M02964_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974959 +# Estimated pi_0=0.986 +Using motif +M04708_2.00 of width 11. +Using motif -M04708_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9266 +# Estimated pi_0=0.947194 +Using motif +M04709_2.00 of width 11. +Using motif -M04709_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936604 +# Estimated pi_0=0.941846 +Using motif +M04710_2.00 of width 11. +Using motif -M04710_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971683 +# Estimated pi_0=0.984286 +Using motif +M04711_2.00 of width 11. +Using motif -M04711_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9464 +# Estimated pi_0=0.965652 +Using motif +M09046_2.00 of width 11. +Using motif -M09046_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940169 +# Estimated pi_0=0.949296 +Using motif +M09528_2.00 of width 10. +Using motif -M09528_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956381 +# Estimated pi_0=0.96454 +Using motif +M04712_2.00 of width 16. +Using motif -M04712_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9452 +# Estimated pi_0=0.953214 +Using motif +M04713_2.00 of width 13. +Using motif -M04713_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980545 +# Estimated pi_0=1 +Using motif +M04714_2.00 of width 16. +Using motif -M04714_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970784 +# Estimated pi_0=0.986441 +Using motif +M04715_2.00 of width 13. +Using motif -M04715_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998291 +# Estimated pi_0=0.998291 +Using motif +M02965_2.00 of width 14. +Using motif -M02965_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=0.995228 +Using motif +M07944_2.00 of width 19. +Using motif -M07944_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968675 +# Estimated pi_0=0.974759 +Using motif +M07945_2.00 of width 19. +Using motif -M07945_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971235 +# Estimated pi_0=0.975455 +Using motif +M07946_2.00 of width 15. +Using motif -M07946_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957193 +# Estimated pi_0=0.962147 +Using motif +M08026_2.00 of width 15. +Using motif -M08026_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963976 +# Estimated pi_0=0.966961 +Using motif +M09047_2.00 of width 17. +Using motif -M09047_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960234 +# Estimated pi_0=0.964972 +Using motif +M02966_2.00 of width 12. +Using motif -M02966_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9586 +# Estimated pi_0=0.967914 +Using motif +M04716_2.00 of width 12. +Using motif -M04716_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961982 +# Estimated pi_0=0.97859 +Using motif +M04717_2.00 of width 12. +Using motif -M04717_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962202 +# Estimated pi_0=0.970169 +Using motif +M04718_2.00 of width 12. +Using motif -M04718_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993889 +# Estimated pi_0=0.996181 +Using motif +M04719_2.00 of width 12. +Using motif -M04719_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969703 +# Estimated pi_0=0.97933 +Using motif +M02967_2.00 of width 11. +Using motif -M02967_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982807 +# Estimated pi_0=0.997172 +Using motif +M04720_2.00 of width 11. +Using motif -M04720_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952871 +# Estimated pi_0=0.968696 +Using motif +M04721_2.00 of width 11. +Using motif -M04721_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982282 +# Estimated pi_0=0.989158 +Using motif +M09048_2.00 of width 16. +Using motif -M09048_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939098 +# Estimated pi_0=0.947516 +Using motif +M02968_2.00 of width 10. +Using motif -M02968_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970495 +# Estimated pi_0=0.981818 +Using motif +M04722_2.00 of width 12. +Using motif -M04722_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957778 +# Estimated pi_0=0.967417 +Using motif +M04723_2.00 of width 12. +Using motif -M04723_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95536 +# Estimated pi_0=0.963472 +Using motif +M04724_2.00 of width 12. +Using motif -M04724_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983647 +# Estimated pi_0=0.986776 +Using motif +M04725_2.00 of width 12. +Using motif -M04725_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969286 +# Estimated pi_0=0.976023 +Using motif +M04726_2.00 of width 12. +Using motif -M04726_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973922 +# Estimated pi_0=0.984586 +Using motif +M04727_2.00 of width 12. +Using motif -M04727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.988757 +Using motif +M04728_2.00 of width 12. +Using motif -M04728_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990168 +# Estimated pi_0=0.992959 +Using motif +M04729_2.00 of width 12. +Using motif -M04729_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982987 +# Estimated pi_0=0.991934 +Using motif +M09049_2.00 of width 13. +Using motif -M09049_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93232 +# Estimated pi_0=0.942778 +Using motif +M02969_2.00 of width 10. +Using motif -M02969_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954257 +# Estimated pi_0=0.967083 +Using motif +M04730_2.00 of width 11. +Using motif -M04730_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9306 +# Estimated pi_0=0.94082 +Using motif +M04731_2.00 of width 12. +Using motif -M04731_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9596 +# Estimated pi_0=0.968393 +Using motif +M04732_2.00 of width 11. +Using motif -M04732_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9384 +# Estimated pi_0=0.953099 +Using motif +M04733_2.00 of width 10. +Using motif -M04733_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9446 +# Estimated pi_0=0.955161 +Using motif +M04734_2.00 of width 12. +Using motif -M04734_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04735_2.00 of width 11. +Using motif -M04735_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9538 +# Estimated pi_0=0.969548 +Using motif +M02970_2.00 of width 10. +Using motif -M02970_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96875 +# Estimated pi_0=0.987027 +Using motif +M04736_2.00 of width 16. +Using motif -M04736_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96495 +# Estimated pi_0=0.988592 +Using motif +M04737_2.00 of width 21. +Using motif -M04737_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967921 +# Estimated pi_0=0.96902 +Using motif +M04738_2.00 of width 16. +Using motif -M04738_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978835 +# Estimated pi_0=1 +Using motif +M04739_2.00 of width 21. +Using motif -M04739_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991469 +# Estimated pi_0=0.99602 +Using motif +M02971_2.00 of width 12. +Using motif -M02971_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972593 +# Estimated pi_0=0.985442 +Using motif +M02972_2.00 of width 12. +Using motif -M02972_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980472 +# Estimated pi_0=1 +Using motif +M04740_2.00 of width 12. +Using motif -M04740_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944356 +# Estimated pi_0=0.944356 +Using motif +M04741_2.00 of width 12. +Using motif -M04741_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959545 +# Estimated pi_0=0.972258 +Using motif +M04742_2.00 of width 12. +Using motif -M04742_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981274 +# Estimated pi_0=0.990316 +Using motif +M04743_2.00 of width 12. +Using motif -M04743_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963158 +# Estimated pi_0=0.969486 +Using motif +M04744_2.00 of width 12. +Using motif -M04744_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966 +# Estimated pi_0=0.974065 +Using motif +M04745_2.00 of width 12. +Using motif -M04745_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970485 +# Estimated pi_0=0.984615 +Using motif +M04746_2.00 of width 12. +Using motif -M04746_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956832 +# Estimated pi_0=0.976988 +Using motif +M04747_2.00 of width 12. +Using motif -M04747_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976308 +# Estimated pi_0=0.985189 +Using motif +M07947_2.00 of width 15. +Using motif -M07947_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885769 +# Estimated pi_0=0.8975 +Using motif +M07948_2.00 of width 14. +Using motif -M07948_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902 +# Estimated pi_0=0.912562 +Using motif +M07949_2.00 of width 11. +Using motif -M07949_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9138 +# Estimated pi_0=0.91875 +Using motif +M08198_2.00 of width 10. +Using motif -M08198_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971273 +# Estimated pi_0=0.988095 +Using motif +M09050_2.00 of width 14. +Using motif -M09050_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94396 +# Estimated pi_0=0.960585 +Using motif +M09529_2.00 of width 10. +Using motif -M09529_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923366 +# Estimated pi_0=0.932542 +Using motif +M02973_2.00 of width 11. +Using motif -M02973_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9874 +# Estimated pi_0=1 +Using motif +M02974_2.00 of width 15. +Using motif -M02974_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9616 +# Estimated pi_0=0.97375 +Using motif +M02975_2.00 of width 16. +Using motif -M02975_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997216 +# Estimated pi_0=0.999497 +Using motif +M02976_2.00 of width 11. +Using motif -M02976_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9704 +# Estimated pi_0=0.9704 +Using motif +M02977_2.00 of width 15. +Using motif -M02977_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973 +# Estimated pi_0=0.984875 +Using motif +M02978_2.00 of width 16. +Using motif -M02978_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996869 +# Estimated pi_0=0.999296 +Using motif +M04748_2.00 of width 12. +Using motif -M04748_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9494 +# Estimated pi_0=0.95563 +Using motif +M04749_2.00 of width 12. +Using motif -M04749_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971262 +# Estimated pi_0=0.99658 +Using motif +M04750_2.00 of width 12. +Using motif -M04750_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9598 +# Estimated pi_0=0.963774 +Using motif +M04751_2.00 of width 12. +Using motif -M04751_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986 +# Estimated pi_0=1 +Using motif +M09530_2.00 of width 10. +Using motif -M09530_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980784 +# Estimated pi_0=0.986879 +Using motif +M02979_2.00 of width 10. +Using motif -M02979_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968205 +# Estimated pi_0=0.978013 +Using motif +M02980_2.00 of width 10. +Using motif -M02980_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974655 +# Estimated pi_0=0.992376 +Using motif +M02981_2.00 of width 17. +Using motif -M02981_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902574 +# Estimated pi_0=0.912605 +Using motif +M02982_2.00 of width 10. +Using motif -M02982_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9322 +# Estimated pi_0=0.94019 +Using motif +M04752_2.00 of width 11. +Using motif -M04752_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9398 +# Estimated pi_0=0.94729 +Using motif +M04753_2.00 of width 11. +Using motif -M04753_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9362 +# Estimated pi_0=0.943304 +Using motif +M04754_2.00 of width 11. +Using motif -M04754_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950891 +# Estimated pi_0=0.963597 +Using motif +M04755_2.00 of width 11. +Using motif -M04755_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9578 +# Estimated pi_0=0.967338 +Using motif +M09051_2.00 of width 11. +Using motif -M09051_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.965031 +Using motif +M09531_2.00 of width 10. +Using motif -M09531_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94 +# Estimated pi_0=0.956296 +Using motif +M10488_2.00 of width 14. +Using motif -M10488_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.961203 +Using motif +M02645_2.00 of width 9. +Using motif -M02645_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971287 +# Estimated pi_0=0.988088 +Using motif +M02983_2.00 of width 10. +Using motif -M02983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964356 +# Estimated pi_0=0.982545 +Using motif +M02984_2.00 of width 18. +Using motif -M02984_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943762 +# Estimated pi_0=0.954876 +Using motif +M02985_2.00 of width 10. +Using motif -M02985_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.967899 +Using motif +M02986_2.00 of width 18. +Using motif -M02986_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926733 +# Estimated pi_0=0.944412 +Using motif +M07950_2.00 of width 21. +Using motif -M07950_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930485 +# Estimated pi_0=0.940444 +Using motif +M07951_2.00 of width 21. +Using motif -M07951_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924038 +# Estimated pi_0=0.939836 +Using motif +M09052_2.00 of width 13. +Using motif -M09052_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949913 +# Estimated pi_0=0.959518 +Using motif +M09532_2.00 of width 10. +Using motif -M09532_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977538 +# Estimated pi_0=0.977727 +Using motif +M10493_2.00 of width 10. +Using motif -M10493_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9714 +# Estimated pi_0=0.979506 +Using motif +M10494_2.00 of width 13. +Using motif -M10494_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998291 +# Estimated pi_0=0.998291 +Using motif +M02733_2.00 of width 10. +Using motif -M02733_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982128 +# Estimated pi_0=0.989841 +Using motif +M02987_2.00 of width 12. +Using motif -M02987_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978761 +# Estimated pi_0=0.986548 +Using motif +M09053_2.00 of width 15. +Using motif -M09053_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973237 +# Estimated pi_0=0.978994 +Using motif +M09533_2.00 of width 10. +Using motif -M09533_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977616 +# Estimated pi_0=0.985989 +Using motif +M02988_2.00 of width 11. +Using motif -M02988_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978321 +# Estimated pi_0=0.99037 +Using motif +M02989_2.00 of width 11. +Using motif -M02989_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983688 +# Estimated pi_0=0.993474 +Using motif +M04756_2.00 of width 12. +Using motif -M04756_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957931 +# Estimated pi_0=0.966531 +Using motif +M04757_2.00 of width 12. +Using motif -M04757_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982316 +# Estimated pi_0=0.984479 +Using motif +M04758_2.00 of width 12. +Using motif -M04758_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9664 +# Estimated pi_0=0.972993 +Using motif +M04759_2.00 of width 12. +Using motif -M04759_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97131 +# Estimated pi_0=0.978011 +Using motif +M09054_2.00 of width 15. +Using motif -M09054_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953768 +# Estimated pi_0=0.96023 +Using motif +M09534_2.00 of width 10. +Using motif -M09534_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98128 +# Estimated pi_0=0.991263 +Using motif +M02990_2.00 of width 15. +Using motif -M02990_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9152 +# Estimated pi_0=0.922393 +Using motif +M02991_2.00 of width 10. +Using motif -M02991_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956952 +# Estimated pi_0=0.971235 +Using motif +M02992_2.00 of width 10. +Using motif -M02992_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9438 +# Estimated pi_0=0.953966 +Using motif +M02993_2.00 of width 14. +Using motif -M02993_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9646 +# Estimated pi_0=0.971376 +Using motif +M02994_2.00 of width 10. +Using motif -M02994_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959688 +# Estimated pi_0=0.971293 +Using motif +M02995_2.00 of width 14. +Using motif -M02995_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9518 +# Estimated pi_0=0.958286 +Using motif +M04760_2.00 of width 11. +Using motif -M04760_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9578 +# Estimated pi_0=0.972857 +Using motif +M04761_2.00 of width 16. +Using motif -M04761_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9484 +# Estimated pi_0=0.960165 +Using motif +M04762_2.00 of width 16. +Using motif -M04762_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9636 +# Estimated pi_0=0.97027 +Using motif +M04763_2.00 of width 11. +Using motif -M04763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974245 +# Estimated pi_0=0.985257 +Using motif +M04764_2.00 of width 11. +Using motif -M04764_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965769 +# Estimated pi_0=0.975467 +Using motif +M04765_2.00 of width 16. +Using motif -M04765_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955446 +# Estimated pi_0=0.971024 +Using motif +M04766_2.00 of width 16. +Using motif -M04766_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9512 +# Estimated pi_0=0.967368 +Using motif +M04767_2.00 of width 11. +Using motif -M04767_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95534 +# Estimated pi_0=0.967482 +Using motif +M05864_2.00 of width 10. +Using motif -M05864_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979712 +# Estimated pi_0=0.999296 +Using motif +M08111_2.00 of width 18. +Using motif -M08111_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940606 +# Estimated pi_0=0.948111 +Using motif +M09055_2.00 of width 18. +Using motif -M09055_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906849 +# Estimated pi_0=0.910617 +Using motif +M02996_2.00 of width 10. +Using motif -M02996_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948966 +# Estimated pi_0=0.95625 +Using motif +M04768_2.00 of width 11. +Using motif -M04768_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944356 +# Estimated pi_0=0.960671 +Using motif +M04769_2.00 of width 11. +Using motif -M04769_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954426 +# Estimated pi_0=0.968696 +Using motif +M05865_2.00 of width 11. +Using motif -M05865_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965347 +# Estimated pi_0=0.973543 +Using motif +M07952_2.00 of width 11. +Using motif -M07952_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912692 +# Estimated pi_0=0.915596 +Using motif +M07953_2.00 of width 11. +Using motif -M07953_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920962 +# Estimated pi_0=0.92928 +Using motif +M07954_2.00 of width 14. +Using motif -M07954_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894958 +# Estimated pi_0=0.90082 +Using motif +M07955_2.00 of width 15. +Using motif -M07955_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.876667 +# Estimated pi_0=0.881008 +Using motif +M07956_2.00 of width 13. +Using motif -M07956_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901 +# Estimated pi_0=0.917612 +Using motif +M09056_2.00 of width 14. +Using motif -M09056_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918879 +# Estimated pi_0=0.923871 +Using motif +M09535_2.00 of width 10. +Using motif -M09535_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948119 +# Estimated pi_0=0.950492 +Using motif +M02734_2.00 of width 10. +Using motif -M02734_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9625 +# Estimated pi_0=0.975935 +Using motif +M02735_2.00 of width 9. +Using motif -M02735_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971376 +# Estimated pi_0=0.976258 +Using motif +M02997_2.00 of width 10. +Using motif -M02997_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971009 +# Estimated pi_0=0.977063 +Using motif +M02998_2.00 of width 14. +Using motif -M02998_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965091 +# Estimated pi_0=0.984204 +Using motif +M02999_2.00 of width 10. +Using motif -M02999_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9832 +# Estimated pi_0=0.998376 +Using motif +M03000_2.00 of width 14. +Using motif -M03000_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955098 +# Estimated pi_0=0.96624 +Using motif +M04770_2.00 of width 11. +Using motif -M04770_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959029 +# Estimated pi_0=0.979 +Using motif +M04771_2.00 of width 16. +Using motif -M04771_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966733 +# Estimated pi_0=0.982192 +Using motif +M04772_2.00 of width 11. +Using motif -M04772_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983273 +# Estimated pi_0=0.988984 +Using motif +M04773_2.00 of width 16. +Using motif -M04773_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9686 +# Estimated pi_0=0.991056 +Using motif +M04774_2.00 of width 11. +Using motif -M04774_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962836 +# Estimated pi_0=0.977765 +Using motif +M04775_2.00 of width 16. +Using motif -M04775_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96976 +# Estimated pi_0=0.974932 +Using motif +M04776_2.00 of width 16. +Using motif -M04776_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957228 +# Estimated pi_0=0.959417 +Using motif +M04777_2.00 of width 11. +Using motif -M04777_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967597 +# Estimated pi_0=0.980494 +Using motif +M09057_2.00 of width 13. +Using motif -M09057_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937829 +# Estimated pi_0=0.948276 +Using motif +M09536_2.00 of width 10. +Using motif -M09536_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979048 +# Estimated pi_0=0.985475 +Using motif +M04778_2.00 of width 20. +Using motif -M04778_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945741 +# Estimated pi_0=0.95009 +Using motif +M04779_2.00 of width 20. +Using motif -M04779_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983611 +# Estimated pi_0=0.985056 +Using motif +M09058_2.00 of width 13. +Using motif -M09058_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948831 +# Estimated pi_0=0.952069 +Using motif +M03001_2.00 of width 10. +Using motif -M03001_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966606 +# Estimated pi_0=0.973245 +Using motif +M04780_2.00 of width 11. +Using motif -M04780_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9352 +# Estimated pi_0=0.947869 +Using motif +M04781_2.00 of width 12. +Using motif -M04781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9936 +# Estimated pi_0=1 +Using motif +M04782_2.00 of width 11. +Using motif -M04782_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941 +# Estimated pi_0=0.943429 +Using motif +M07957_2.00 of width 10. +Using motif -M07957_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933654 +# Estimated pi_0=0.947347 +Using motif +M07958_2.00 of width 11. +Using motif -M07958_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925577 +# Estimated pi_0=0.933504 +Using motif +M08112_2.00 of width 11. +Using motif -M08112_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925321 +# Estimated pi_0=0.926964 +Using motif +M09059_2.00 of width 12. +Using motif -M09059_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9338 +# Estimated pi_0=0.946331 +Using motif +M03002_2.00 of width 12. +Using motif -M03002_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986015 +# Estimated pi_0=0.994348 +Using motif +M03003_2.00 of width 13. +Using motif -M03003_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986726 +# Estimated pi_0=0.99533 +Using motif +M04783_2.00 of width 23. +Using motif -M04783_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998894 +# Estimated pi_0=0.998894 +Using motif +M04784_2.00 of width 13. +Using motif -M04784_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948462 +# Estimated pi_0=0.951818 +Using motif +M04785_2.00 of width 13. +Using motif -M04785_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964464 +# Estimated pi_0=0.975436 +Using motif +M04786_2.00 of width 12. +Using motif -M04786_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998586 +# Estimated pi_0=0.998593 +Using motif +M04787_2.00 of width 23. +Using motif -M04787_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996531 +# Estimated pi_0=0.998693 +Using motif +M04788_2.00 of width 23. +Using motif -M04788_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04789_2.00 of width 13. +Using motif -M04789_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935 +# Estimated pi_0=0.94547 +Using motif +M04790_2.00 of width 23. +Using motif -M04790_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997436 +# Estimated pi_0=0.998392 +Using motif +M09060_2.00 of width 14. +Using motif -M09060_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978497 +# Estimated pi_0=0.981675 +Using motif +M03004_2.00 of width 10. +Using motif -M03004_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973725 +# Estimated pi_0=0.982614 +Using motif +M04791_2.00 of width 11. +Using motif -M04791_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959065 +# Estimated pi_0=0.963175 +Using motif +M04792_2.00 of width 11. +Using motif -M04792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953982 +# Estimated pi_0=0.967439 +Using motif +M09061_2.00 of width 10. +Using motif -M09061_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909381 +# Estimated pi_0=0.915455 +Using motif +M03005_2.00 of width 14. +Using motif -M03005_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995464 +# Estimated pi_0=0.99799 +Using motif +M03006_2.00 of width 10. +Using motif -M03006_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9622 +# Estimated pi_0=0.980296 +Using motif +M04793_2.00 of width 11. +Using motif -M04793_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952294 +# Estimated pi_0=0.956336 +Using motif +M04794_2.00 of width 14. +Using motif -M04794_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966614 +# Estimated pi_0=0.976906 +Using motif +M04795_2.00 of width 11. +Using motif -M04795_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965545 +# Estimated pi_0=0.978375 +Using motif +M04796_2.00 of width 11. +Using motif -M04796_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.961406 +Using motif +M04797_2.00 of width 12. +Using motif -M04797_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04798_2.00 of width 14. +Using motif -M04798_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971923 +# Estimated pi_0=0.977853 +Using motif +M04799_2.00 of width 11. +Using motif -M04799_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950588 +# Estimated pi_0=0.95968 +Using motif +M09062_2.00 of width 11. +Using motif -M09062_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9556 +# Estimated pi_0=0.962431 +Using motif +M03007_2.00 of width 10. +Using motif -M03007_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9488 +# Estimated pi_0=0.96112 +Using motif +M04800_2.00 of width 11. +Using motif -M04800_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9152 +# Estimated pi_0=0.928696 +Using motif +M04801_2.00 of width 14. +Using motif -M04801_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941879 +# Estimated pi_0=0.947229 +Using motif +M04802_2.00 of width 11. +Using motif -M04802_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95561 +# Estimated pi_0=0.967034 +Using motif +M09063_2.00 of width 14. +Using motif -M09063_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935918 +# Estimated pi_0=0.939543 +Using motif +M01477_2.00 of width 10. +Using motif -M01477_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987034 +# Estimated pi_0=0.998687 +Using motif +M02678_2.00 of width 7. +Using motif -M02678_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990957 +# Estimated pi_0=0.996985 +Using motif +M03008_2.00 of width 14. +Using motif -M03008_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984624 +# Estimated pi_0=0.984624 +Using motif +M04803_2.00 of width 12. +Using motif -M04803_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986515 +# Estimated pi_0=0.994315 +Using motif +M04804_2.00 of width 12. +Using motif -M04804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989534 +# Estimated pi_0=0.993503 +Using motif +M04805_2.00 of width 13. +Using motif -M04805_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993005 +# Estimated pi_0=0.995102 +Using motif +M04806_2.00 of width 13. +Using motif -M04806_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99033 +# Estimated pi_0=0.992959 +Using motif +M09064_2.00 of width 17. +Using motif -M09064_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966353 +# Estimated pi_0=0.973904 +Using motif +M09537_2.00 of width 12. +Using motif -M09537_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981326 +# Estimated pi_0=0.983915 +Using motif +M03012_2.00 of width 7. +Using motif -M03012_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04807_2.00 of width 17. +Using motif -M04807_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04808_2.00 of width 17. +Using motif -M04808_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M01227_2.00 of width 8. +Using motif -M01227_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990598 +# Estimated pi_0=1 +Using motif +M00253_2.00 of width 11. +Using motif -M00253_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00254_2.00 of width 10. +Using motif -M00254_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998788 +# Estimated pi_0=1 +Using motif +M00255_2.00 of width 12. +Using motif -M00255_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00256_2.00 of width 13. +Using motif -M00256_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00257_2.00 of width 10. +Using motif -M00257_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00258_2.00 of width 11. +Using motif -M00258_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M00259_2.00 of width 8. +Using motif -M00259_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M03013_2.00 of width 13. +Using motif -M03013_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996853 +# Estimated pi_0=0.997789 +Using motif +M03014_2.00 of width 14. +Using motif -M03014_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03015_2.00 of width 11. +Using motif -M03015_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M09083_2.00 of width 15. +Using motif -M09083_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999495 +# Estimated pi_0=0.999899 +Using motif +M10526_2.00 of width 16. +Using motif -M10526_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M03016_2.00 of width 14. +Using motif -M03016_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998492 +# Estimated pi_0=0.998492 +Using motif +M03017_2.00 of width 8. +Using motif -M03017_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998794 +Using motif +M03018_2.00 of width 13. +Using motif -M03018_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M04809_2.00 of width 14. +Using motif -M04809_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04810_2.00 of width 14. +Using motif -M04810_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09084_2.00 of width 10. +Using motif -M09084_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M10527_2.00 of width 18. +Using motif -M10527_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99799 +# Estimated pi_0=0.99799 +Using motif +M10528_2.00 of width 14. +Using motif -M10528_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M10530_2.00 of width 13. +Using motif -M10530_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=1 +Using motif +M09085_2.00 of width 12. +Using motif -M09085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994565 +# Estimated pi_0=0.997259 +Using motif +M00836_2.00 of width 10. +Using motif -M00836_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M00837_2.00 of width 9. +Using motif -M00837_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00082 +# Estimated pi_0=1 +Using motif +M00838_2.00 of width 8. +Using motif -M00838_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M00839_2.00 of width 11. +Using motif -M00839_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M01976_2.00 of width 8. +Using motif -M01976_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00081 +# Estimated pi_0=1 +Using motif +M04811_2.00 of width 30. +Using motif -M04811_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04812_2.00 of width 30. +Using motif -M04812_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M08115_2.00 of width 12. +Using motif -M08115_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996082 +# Estimated pi_0=0.999698 +Using motif +M09086_2.00 of width 9. +Using motif -M09086_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98836 +# Estimated pi_0=0.99066 +Using motif +M09541_2.00 of width 12. +Using motif -M09541_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998557 +# Estimated pi_0=1 +Using motif +M03019_2.00 of width 14. +Using motif -M03019_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03020_2.00 of width 8. +Using motif -M03020_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03021_2.00 of width 11. +Using motif -M03021_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952773 +# Estimated pi_0=0.964575 +Using motif +M04813_2.00 of width 11. +Using motif -M04813_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963942 +# Estimated pi_0=0.970057 +Using motif +M04814_2.00 of width 11. +Using motif -M04814_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94381 +# Estimated pi_0=0.953372 +Using motif +M09087_2.00 of width 10. +Using motif -M09087_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995751 +# Estimated pi_0=0.999196 +Using motif +M10537_2.00 of width 14. +Using motif -M10537_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997755 +# Estimated pi_0=0.999095 +Using motif +M04815_2.00 of width 20. +Using motif -M04815_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04816_2.00 of width 20. +Using motif -M04816_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M07959_2.00 of width 15. +Using motif -M07959_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999695 +# Estimated pi_0=1 +Using motif +M09088_2.00 of width 12. +Using motif -M09088_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998351 +# Estimated pi_0=0.999899 +Using motif +M09542_2.00 of width 12. +Using motif -M09542_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999479 +# Estimated pi_0=1 +Using motif +M10540_2.00 of width 15. +Using motif -M10540_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999293 +# Estimated pi_0=0.999497 +Using motif +M05866_2.00 of width 10. +Using motif -M05866_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08116_2.00 of width 11. +Using motif -M08116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993263 +# Estimated pi_0=0.996382 +Using motif +M09089_2.00 of width 9. +Using motif -M09089_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995104 +# Estimated pi_0=0.998392 +Using motif +M04817_2.00 of width 12. +Using motif -M04817_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04818_2.00 of width 12. +Using motif -M04818_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04819_2.00 of width 16. +Using motif -M04819_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04820_2.00 of width 20. +Using motif -M04820_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04821_2.00 of width 16. +Using motif -M04821_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04822_2.00 of width 20. +Using motif -M04822_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M07960_2.00 of width 15. +Using motif -M07960_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M07961_2.00 of width 11. +Using motif -M07961_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999388 +# Estimated pi_0=1 +Using motif +M07962_2.00 of width 11. +Using motif -M07962_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M08028_2.00 of width 10. +Using motif -M08028_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996364 +# Estimated pi_0=0.997085 +Using motif +M08117_2.00 of width 15. +Using motif -M08117_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999692 +# Estimated pi_0=1 +Using motif +M08199_2.00 of width 13. +Using motif -M08199_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996308 +# Estimated pi_0=0.998081 +Using motif +M09090_2.00 of width 12. +Using motif -M09090_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999286 +# Estimated pi_0=0.999899 +Using motif +M09543_2.00 of width 10. +Using motif -M09543_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M09544_2.00 of width 16. +Using motif -M09544_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999184 +# Estimated pi_0=1 +Using motif +M09545_2.00 of width 10. +Using motif -M09545_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998693 +Using motif +M00793_2.00 of width 9. +Using motif -M00793_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M02680_2.00 of width 14. +Using motif -M02680_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M10548_2.00 of width 16. +Using motif -M10548_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M01011_2.00 of width 10. +Using motif -M01011_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912673 +# Estimated pi_0=0.920187 +Using motif +M08118_2.00 of width 11. +Using motif -M08118_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03022_2.00 of width 8. +Using motif -M03022_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03023_2.00 of width 14. +Using motif -M03023_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03024_2.00 of width 12. +Using motif -M03024_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963866 +# Estimated pi_0=0.971018 +Using motif +M09091_2.00 of width 12. +Using motif -M09091_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968774 +# Estimated pi_0=0.972809 +Using motif +M10549_2.00 of width 10. +Using motif -M10549_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998985 +# Estimated pi_0=0.999196 +Using motif +M10550_2.00 of width 14. +Using motif -M10550_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M08119_2.00 of width 11. +Using motif -M08119_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09092_2.00 of width 9. +Using motif -M09092_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M09546_2.00 of width 12. +Using motif -M09546_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994459 +# Estimated pi_0=1 +Using motif +M04823_2.00 of width 12. +Using motif -M04823_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04824_2.00 of width 12. +Using motif -M04824_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M09093_2.00 of width 12. +Using motif -M09093_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M01977_2.00 of width 10. +Using motif -M01977_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999694 +# Estimated pi_0=1 +Using motif +M03025_2.00 of width 10. +Using motif -M03025_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990727 +# Estimated pi_0=1 +Using motif +M04825_2.00 of width 11. +Using motif -M04825_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M04826_2.00 of width 11. +Using motif -M04826_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M08120_2.00 of width 14. +Using motif -M08120_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09094_2.00 of width 10. +Using motif -M09094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994869 +# Estimated pi_0=0.998392 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03027_2.00 of width 17. +Using motif -M03027_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04827_2.00 of width 13. +Using motif -M04827_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04828_2.00 of width 13. +Using motif -M04828_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04829_2.00 of width 12. +Using motif -M04829_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04830_2.00 of width 12. +Using motif -M04830_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998593 +# Estimated pi_0=0.998593 +Using motif +M09095_2.00 of width 12. +Using motif -M09095_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993723 +# Estimated pi_0=0.998794 +Using motif +M10556_2.00 of width 13. +Using motif -M10556_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997889 +# Estimated pi_0=0.997889 +Using motif +M04831_2.00 of width 16. +Using motif -M04831_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04832_2.00 of width 20. +Using motif -M04832_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04833_2.00 of width 16. +Using motif -M04833_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04834_2.00 of width 20. +Using motif -M04834_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09096_2.00 of width 13. +Using motif -M09096_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994074 +# Estimated pi_0=0.995306 +Using motif +M01007_2.00 of width 9. +Using motif -M01007_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953981 +# Estimated pi_0=0.963077 +Using motif +M04835_2.00 of width 19. +Using motif -M04835_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8488 +# Estimated pi_0=0.8488 +Using motif +M04836_2.00 of width 17. +Using motif -M04836_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976348 +# Estimated pi_0=0.988138 +Using motif +M03028_2.00 of width 14. +Using motif -M03028_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983364 +# Estimated pi_0=0.995876 +Using motif +M03029_2.00 of width 18. +Using motif -M03029_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03030_2.00 of width 11. +Using motif -M03030_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03031_2.00 of width 9. +Using motif -M03031_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04837_2.00 of width 20. +Using motif -M04837_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04838_2.00 of width 20. +Using motif -M04838_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M01978_2.00 of width 8. +Using motif -M01978_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03032_2.00 of width 17. +Using motif -M03032_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997879 +# Estimated pi_0=0.99809 +Using motif +M03033_2.00 of width 12. +Using motif -M03033_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983762 +# Estimated pi_0=0.998298 +Using motif +M04839_2.00 of width 11. +Using motif -M04839_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991921 +# Estimated pi_0=0.999196 +Using motif +M04840_2.00 of width 11. +Using motif -M04840_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999208 +# Estimated pi_0=1 +Using motif +M01008_2.00 of width 8. +Using motif -M01008_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03034_2.00 of width 7. +Using motif -M03034_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03035_2.00 of width 13. +Using motif -M03035_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=0.999497 +Using motif +M10558_2.00 of width 16. +Using motif -M10558_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03036_2.00 of width 14. +Using motif -M03036_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03037_2.00 of width 11. +Using motif -M03037_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998291 +# Estimated pi_0=0.998291 +Using motif +M03038_2.00 of width 12. +Using motif -M03038_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M04841_2.00 of width 12. +Using motif -M04841_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996939 +# Estimated pi_0=0.997879 +Using motif +M04842_2.00 of width 12. +Using motif -M04842_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998866 +# Estimated pi_0=0.999598 +Using motif +M04843_2.00 of width 12. +Using motif -M04843_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99959 +# Estimated pi_0=1 +Using motif +M04844_2.00 of width 12. +Using motif -M04844_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998462 +# Estimated pi_0=0.999598 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04846_2.00 of width 15. +Using motif -M04846_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99798 +# Estimated pi_0=0.99799 +Using motif +M00164_2.00 of width 10. +Using motif -M00164_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999184 +# Estimated pi_0=1 +Using motif +M04847_2.00 of width 11. +Using motif -M04847_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9896 +# Estimated pi_0=0.994973 +Using motif +M04848_2.00 of width 11. +Using motif -M04848_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9846 +# Estimated pi_0=1 +Using motif +M03039_2.00 of width 14. +Using motif -M03039_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03040_2.00 of width 7. +Using motif -M03040_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03041_2.00 of width 12. +Using motif -M03041_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942 +# Estimated pi_0=0.942 +Using motif +M04849_2.00 of width 10. +Using motif -M04849_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981739 +# Estimated pi_0=0.996224 +Using motif +M04850_2.00 of width 10. +Using motif -M04850_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997333 +# Estimated pi_0=1 +Using motif +M09097_2.00 of width 9. +Using motif -M09097_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995408 +# Estimated pi_0=0.99799 +Using motif +M10562_2.00 of width 11. +Using motif -M10562_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996869 +# Estimated pi_0=0.999196 +Using motif +M10563_2.00 of width 14. +Using motif -M10563_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997755 +# Estimated pi_0=0.99799 +Using motif +M03042_2.00 of width 14. +Using motif -M03042_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03043_2.00 of width 7. +Using motif -M03043_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04851_2.00 of width 14. +Using motif -M04851_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04852_2.00 of width 13. +Using motif -M04852_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04853_2.00 of width 14. +Using motif -M04853_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04854_2.00 of width 13. +Using motif -M04854_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M03044_2.00 of width 14. +Using motif -M03044_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03045_2.00 of width 7. +Using motif -M03045_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04855_2.00 of width 12. +Using motif -M04855_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04856_2.00 of width 12. +Using motif -M04856_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M10565_2.00 of width 12. +Using motif -M10565_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M04857_2.00 of width 17. +Using motif -M04857_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999492 +# Estimated pi_0=0.999899 +Using motif +M04858_2.00 of width 17. +Using motif -M04858_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M02736_2.00 of width 12. +Using motif -M02736_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997789 +# Estimated pi_0=0.997789 +Using motif +M02737_2.00 of width 8. +Using motif -M02737_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997374 +# Estimated pi_0=0.997374 +Using motif +M03046_2.00 of width 8. +Using motif -M03046_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994822 +# Estimated pi_0=0.996382 +Using motif +M03047_2.00 of width 14. +Using motif -M03047_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997857 +# Estimated pi_0=0.998291 +Using motif +M03048_2.00 of width 13. +Using motif -M03048_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998384 +# Estimated pi_0=0.999598 +Using motif +M04859_2.00 of width 10. +Using motif -M04859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04860_2.00 of width 10. +Using motif -M04860_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09098_2.00 of width 13. +Using motif -M09098_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99697 +# Estimated pi_0=0.997387 +Using motif +M03049_2.00 of width 14. +Using motif -M03049_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03050_2.00 of width 7. +Using motif -M03050_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998872 +# Estimated pi_0=1 +Using motif +M03051_2.00 of width 14. +Using motif -M03051_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956324 +# Estimated pi_0=0.967931 +Using motif +M01983_2.00 of width 10. +Using motif -M01983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M02681_2.00 of width 8. +Using motif -M02681_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00242 +# Estimated pi_0=1 +Using motif +M02738_2.00 of width 8. +Using motif -M02738_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M02739_2.00 of width 6. +Using motif -M02739_2.00 of width 6. +Computing q-values. +Using motif +M04861_2.00 of width 15. +Using motif -M04861_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04862_2.00 of width 15. +Using motif -M04862_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M07963_2.00 of width 11. +Using motif -M07963_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M07964_2.00 of width 18. +Using motif -M07964_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988684 +# Estimated pi_0=0.994787 +Using motif +M08202_2.00 of width 20. +Using motif -M08202_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9466 +# Estimated pi_0=0.957842 +Using motif +M08203_2.00 of width 10. +Using motif -M08203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M09115_2.00 of width 19. +Using motif -M09115_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994118 +# Estimated pi_0=0.996784 +Using motif +M09549_2.00 of width 10. +Using motif -M09549_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997157 +# Estimated pi_0=0.997273 +Using motif +M10577_2.00 of width 10. +Using motif -M10577_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9494 +# Estimated pi_0=0.956696 +Using motif +M10578_2.00 of width 14. +Using motif -M10578_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M10579_2.00 of width 14. +Using motif -M10579_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9866 +# Estimated pi_0=1 +Using motif +M10580_2.00 of width 13. +Using motif -M10580_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998778 +# Estimated pi_0=1 +Using motif +M10581_2.00 of width 10. +Using motif -M10581_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M10582_2.00 of width 10. +Using motif -M10582_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03063_2.00 of width 8. +Using motif -M03063_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03064_2.00 of width 8. +Using motif -M03064_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04863_2.00 of width 12. +Using motif -M04863_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04864_2.00 of width 11. +Using motif -M04864_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04865_2.00 of width 12. +Using motif -M04865_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04866_2.00 of width 11. +Using motif -M04866_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M07965_2.00 of width 15. +Using motif -M07965_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M09116_2.00 of width 11. +Using motif -M09116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993673 +# Estimated pi_0=0.994242 +Using motif +M10586_2.00 of width 9. +Using motif -M10586_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992617 +# Estimated pi_0=1 +Using motif +M10587_2.00 of width 10. +Using motif -M10587_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M10588_2.00 of width 10. +Using motif -M10588_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03065_2.00 of width 8. +Using motif -M03065_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04867_2.00 of width 10. +Using motif -M04867_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9976 +# Estimated pi_0=1 +Using motif +M04868_2.00 of width 12. +Using motif -M04868_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04869_2.00 of width 10. +Using motif -M04869_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04870_2.00 of width 12. +Using motif -M04870_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04871_2.00 of width 10. +Using motif -M04871_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04872_2.00 of width 12. +Using motif -M04872_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04873_2.00 of width 10. +Using motif -M04873_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04874_2.00 of width 12. +Using motif -M04874_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03066_2.00 of width 8. +Using motif -M03066_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04875_2.00 of width 10. +Using motif -M04875_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04876_2.00 of width 10. +Using motif -M04876_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M09117_2.00 of width 10. +Using motif -M09117_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990794 +# Estimated pi_0=0.995204 +Using motif +M04877_2.00 of width 12. +Using motif -M04877_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04878_2.00 of width 12. +Using motif -M04878_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08124_2.00 of width 13. +Using motif -M08124_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998571 +# Estimated pi_0=0.999497 +Using motif +M09118_2.00 of width 13. +Using motif -M09118_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997513 +# Estimated pi_0=0.999698 +Using motif +M10592_2.00 of width 10. +Using motif -M10592_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04879_2.00 of width 9. +Using motif -M04879_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04880_2.00 of width 9. +Using motif -M04880_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M07966_2.00 of width 11. +Using motif -M07966_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998125 +# Estimated pi_0=1 +Using motif +M07967_2.00 of width 18. +Using motif -M07967_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990963 +# Estimated pi_0=0.995567 +Using motif +M07968_2.00 of width 18. +Using motif -M07968_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993529 +# Estimated pi_0=0.99899 +Using motif +M07969_2.00 of width 10. +Using motif -M07969_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M07970_2.00 of width 10. +Using motif -M07970_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M08125_2.00 of width 11. +Using motif -M08125_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M09119_2.00 of width 19. +Using motif -M09119_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992283 +# Estimated pi_0=0.99551 +Using motif +M09550_2.00 of width 10. +Using motif -M09550_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993867 +# Estimated pi_0=0.999188 +Using motif +M10594_2.00 of width 10. +Using motif -M10594_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936436 +# Estimated pi_0=0.9464 +Using motif +M10595_2.00 of width 10. +Using motif -M10595_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M10596_2.00 of width 10. +Using motif -M10596_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999697 +# Estimated pi_0=1 +Using motif +M03067_2.00 of width 10. +Using motif -M03067_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937009 +# Estimated pi_0=0.948593 +Using motif +M04881_2.00 of width 9. +Using motif -M04881_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9378 +# Estimated pi_0=0.949524 +Using motif +M04882_2.00 of width 9. +Using motif -M04882_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977794 +# Estimated pi_0=0.981932 +Using motif +M02020_2.00 of width 9. +Using motif -M02020_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909515 +# Estimated pi_0=0.918699 +Using motif +M03068_2.00 of width 10. +Using motif -M03068_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96384 +# Estimated pi_0=0.975625 +Using motif +M03069_2.00 of width 16. +Using motif -M03069_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932661 +# Estimated pi_0=0.950365 +Using motif +M03070_2.00 of width 11. +Using motif -M03070_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913725 +# Estimated pi_0=0.926667 +Using motif +M04883_2.00 of width 9. +Using motif -M04883_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930857 +# Estimated pi_0=0.942754 +Using motif +M04884_2.00 of width 9. +Using motif -M04884_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964848 +# Estimated pi_0=0.968857 +Using motif +M05868_2.00 of width 9. +Using motif -M05868_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936952 +# Estimated pi_0=0.949116 +Using motif +M08129_2.00 of width 15. +Using motif -M08129_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M09125_2.00 of width 12. +Using motif -M09125_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97937 +# Estimated pi_0=0.986292 +Using motif +M09558_2.00 of width 20. +Using motif -M09558_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985325 +# Estimated pi_0=0.989198 +Using motif +M04885_2.00 of width 16. +Using motif -M04885_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940952 +# Estimated pi_0=0.946624 +Using motif +M04886_2.00 of width 16. +Using motif -M04886_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946847 +# Estimated pi_0=0.951948 +Using motif +M03071_2.00 of width 17. +Using motif -M03071_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9678 +# Estimated pi_0=0.984663 +Using motif +M03072_2.00 of width 10. +Using motif -M03072_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997009 +# Estimated pi_0=1 +Using motif +M03073_2.00 of width 12. +Using motif -M03073_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04887_2.00 of width 10. +Using motif -M04887_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04888_2.00 of width 10. +Using motif -M04888_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03074_2.00 of width 10. +Using motif -M03074_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03075_2.00 of width 16. +Using motif -M03075_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997544 +# Estimated pi_0=1 +Using motif +M04889_2.00 of width 18. +Using motif -M04889_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971971 +# Estimated pi_0=0.980491 +Using motif +M04890_2.00 of width 18. +Using motif -M04890_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9534 +# Estimated pi_0=0.973497 +Using motif +M04891_2.00 of width 18. +Using motif -M04891_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04892_2.00 of width 18. +Using motif -M04892_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M10610_2.00 of width 11. +Using motif -M10610_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93339 +# Estimated pi_0=0.948289 +Using motif +M04893_2.00 of width 18. +Using motif -M04893_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935462 +# Estimated pi_0=0.945316 +Using motif +M04894_2.00 of width 18. +Using motif -M04894_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959677 +# Estimated pi_0=0.968902 +Using motif +M00260_2.00 of width 11. +Using motif -M00260_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M00261_2.00 of width 9. +Using motif -M00261_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00262_2.00 of width 8. +Using motif -M00262_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M00263_2.00 of width 10. +Using motif -M00263_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00264_2.00 of width 9. +Using motif -M00264_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M00265_2.00 of width 8. +Using motif -M00265_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03076_2.00 of width 13. +Using motif -M03076_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04895_2.00 of width 8. +Using motif -M04895_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04896_2.00 of width 8. +Using motif -M04896_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04897_2.00 of width 11. +Using motif -M04897_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04898_2.00 of width 11. +Using motif -M04898_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04899_2.00 of width 11. +Using motif -M04899_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04900_2.00 of width 11. +Using motif -M04900_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03077_2.00 of width 10. +Using motif -M03077_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04901_2.00 of width 8. +Using motif -M04901_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9614 +# Estimated pi_0=0.970642 +Using motif +M04902_2.00 of width 8. +Using motif -M04902_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04903_2.00 of width 8. +Using motif -M04903_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04904_2.00 of width 8. +Using motif -M04904_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03078_2.00 of width 8. +Using motif -M03078_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04905_2.00 of width 8. +Using motif -M04905_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04906_2.00 of width 8. +Using motif -M04906_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04907_2.00 of width 8. +Using motif -M04907_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04908_2.00 of width 8. +Using motif -M04908_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04909_2.00 of width 8. +Using motif -M04909_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04910_2.00 of width 8. +Using motif -M04910_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04911_2.00 of width 8. +Using motif -M04911_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M09128_2.00 of width 9. +Using motif -M09128_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998871 +# Estimated pi_0=1 +Using motif +M04912_2.00 of width 8. +Using motif -M04912_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04913_2.00 of width 8. +Using motif -M04913_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04914_2.00 of width 8. +Using motif -M04914_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04915_2.00 of width 12. +Using motif -M04915_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04916_2.00 of width 12. +Using motif -M04916_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03079_2.00 of width 13. +Using motif -M03079_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04917_2.00 of width 8. +Using motif -M04917_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04918_2.00 of width 8. +Using motif -M04918_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04919_2.00 of width 8. +Using motif -M04919_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9906 +# Estimated pi_0=1 +Using motif +M03080_2.00 of width 10. +Using motif -M03080_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04920_2.00 of width 8. +Using motif -M04920_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04921_2.00 of width 8. +Using motif -M04921_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M01503_2.00 of width 8. +Using motif -M01503_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M01504_2.00 of width 8. +Using motif -M01504_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03081_2.00 of width 8. +Using motif -M03081_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04922_2.00 of width 8. +Using motif -M04922_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04923_2.00 of width 8. +Using motif -M04923_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04924_2.00 of width 8. +Using motif -M04924_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04925_2.00 of width 8. +Using motif -M04925_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04926_2.00 of width 8. +Using motif -M04926_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04927_2.00 of width 8. +Using motif -M04927_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M09129_2.00 of width 10. +Using motif -M09129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03082_2.00 of width 9. +Using motif -M03082_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03083_2.00 of width 9. +Using motif -M03083_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03084_2.00 of width 8. +Using motif -M03084_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04928_2.00 of width 8. +Using motif -M04928_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00303 +# Estimated pi_0=1 +Using motif +M04929_2.00 of width 8. +Using motif -M04929_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M04930_2.00 of width 8. +Using motif -M04930_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999718 +# Estimated pi_0=1 +Using motif +M04931_2.00 of width 8. +Using motif -M04931_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03085_2.00 of width 12. +Using motif -M03085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03086_2.00 of width 11. +Using motif -M03086_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04932_2.00 of width 10. +Using motif -M04932_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04933_2.00 of width 10. +Using motif -M04933_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04934_2.00 of width 10. +Using motif -M04934_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04935_2.00 of width 10. +Using motif -M04935_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M09130_2.00 of width 12. +Using motif -M09130_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M09131_2.00 of width 11. +Using motif -M09131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04936_2.00 of width 8. +Using motif -M04936_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04937_2.00 of width 8. +Using motif -M04937_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04938_2.00 of width 8. +Using motif -M04938_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04939_2.00 of width 10. +Using motif -M04939_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911485 +# Estimated pi_0=0.924727 +Using motif +M04940_2.00 of width 10. +Using motif -M04940_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9312 +# Estimated pi_0=0.932277 +Using motif +M00266_2.00 of width 9. +Using motif -M00266_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00267_2.00 of width 9. +Using motif -M00267_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00268_2.00 of width 9. +Using motif -M00268_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M00269_2.00 of width 9. +Using motif -M00269_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03087_2.00 of width 8. +Using motif -M03087_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03088_2.00 of width 11. +Using motif -M03088_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989541 +# Estimated pi_0=1 +Using motif +M04941_2.00 of width 8. +Using motif -M04941_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04942_2.00 of width 8. +Using motif -M04942_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04943_2.00 of width 8. +Using motif -M04943_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04944_2.00 of width 8. +Using motif -M04944_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04945_2.00 of width 8. +Using motif -M04945_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04946_2.00 of width 8. +Using motif -M04946_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05869_2.00 of width 10. +Using motif -M05869_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03089_2.00 of width 9. +Using motif -M03089_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997905 +# Estimated pi_0=1 +Using motif +M03090_2.00 of width 8. +Using motif -M03090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999293 +# Estimated pi_0=1 +Using motif +M03091_2.00 of width 9. +Using motif -M03091_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03092_2.00 of width 8. +Using motif -M03092_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04947_2.00 of width 8. +Using motif -M04947_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999116 +# Estimated pi_0=1 +Using motif +M04948_2.00 of width 8. +Using motif -M04948_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M00270_2.00 of width 7. +Using motif -M00270_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00272_2.00 of width 8. +Using motif -M00272_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00273_2.00 of width 11. +Using motif -M00273_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00274_2.00 of width 10. +Using motif -M00274_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00275_2.00 of width 10. +Using motif -M00275_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04949_2.00 of width 8. +Using motif -M04949_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00019 +# Estimated pi_0=1 +Using motif +M04950_2.00 of width 8. +Using motif -M04950_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M09132_2.00 of width 13. +Using motif -M09132_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999492 +# Estimated pi_0=0.999698 +Using motif +M03093_2.00 of width 8. +Using motif -M03093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99602 +# Estimated pi_0=0.999095 +Using motif +M03094_2.00 of width 12. +Using motif -M03094_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962899 +# Estimated pi_0=0.967097 +Using motif +M04951_2.00 of width 12. +Using motif -M04951_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9756 +# Estimated pi_0=0.98593 +Using motif +M04952_2.00 of width 12. +Using motif -M04952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9332 +# Estimated pi_0=0.946496 +Using motif +M00276_2.00 of width 11. +Using motif -M00276_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00277_2.00 of width 10. +Using motif -M00277_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03095_2.00 of width 8. +Using motif -M03095_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04953_2.00 of width 8. +Using motif -M04953_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04954_2.00 of width 8. +Using motif -M04954_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04955_2.00 of width 8. +Using motif -M04955_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04956_2.00 of width 8. +Using motif -M04956_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04957_2.00 of width 8. +Using motif -M04957_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M04958_2.00 of width 8. +Using motif -M04958_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03096_2.00 of width 10. +Using motif -M03096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04959_2.00 of width 8. +Using motif -M04959_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04960_2.00 of width 8. +Using motif -M04960_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04961_2.00 of width 8. +Using motif -M04961_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M09133_2.00 of width 10. +Using motif -M09133_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995165 +# Estimated pi_0=0.998384 +Using motif +M02083_2.00 of width 9. +Using motif -M02083_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03097_2.00 of width 10. +Using motif -M03097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04962_2.00 of width 8. +Using motif -M04962_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04963_2.00 of width 8. +Using motif -M04963_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04964_2.00 of width 8. +Using motif -M04964_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04965_2.00 of width 8. +Using motif -M04965_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04966_2.00 of width 8. +Using motif -M04966_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04967_2.00 of width 8. +Using motif -M04967_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M10651_2.00 of width 9. +Using motif -M10651_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04968_2.00 of width 8. +Using motif -M04968_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04969_2.00 of width 8. +Using motif -M04969_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04970_2.00 of width 8. +Using motif -M04970_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04971_2.00 of width 8. +Using motif -M04971_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04972_2.00 of width 8. +Using motif -M04972_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04973_2.00 of width 8. +Using motif -M04973_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M08457_2.00 of width 8. +Using motif -M08457_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M10652_2.00 of width 30. +Using motif -M10652_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997173 +# Estimated pi_0=0.999598 +Using motif +M04974_2.00 of width 10. +Using motif -M04974_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04975_2.00 of width 10. +Using motif -M04975_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04976_2.00 of width 8. +Using motif -M04976_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03098_2.00 of width 10. +Using motif -M03098_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03099_2.00 of width 11. +Using motif -M03099_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03100_2.00 of width 10. +Using motif -M03100_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03101_2.00 of width 11. +Using motif -M03101_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04977_2.00 of width 11. +Using motif -M04977_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04978_2.00 of width 11. +Using motif -M04978_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04979_2.00 of width 11. +Using motif -M04979_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04980_2.00 of width 11. +Using motif -M04980_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M04981_2.00 of width 11. +Using motif -M04981_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04982_2.00 of width 11. +Using motif -M04982_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04983_2.00 of width 11. +Using motif -M04983_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04984_2.00 of width 11. +Using motif -M04984_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M09134_2.00 of width 11. +Using motif -M09134_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03102_2.00 of width 10. +Using motif -M03102_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04985_2.00 of width 8. +Using motif -M04985_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04986_2.00 of width 8. +Using motif -M04986_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04987_2.00 of width 8. +Using motif -M04987_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M02084_2.00 of width 10. +Using motif -M02084_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09135_2.00 of width 9. +Using motif -M09135_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03103_2.00 of width 10. +Using motif -M03103_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03104_2.00 of width 17. +Using motif -M03104_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03105_2.00 of width 14. +Using motif -M03105_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04988_2.00 of width 8. +Using motif -M04988_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04989_2.00 of width 8. +Using motif -M04989_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04990_2.00 of width 8. +Using motif -M04990_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04991_2.00 of width 8. +Using motif -M04991_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02085_2.00 of width 10. +Using motif -M02085_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03106_2.00 of width 10. +Using motif -M03106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03107_2.00 of width 15. +Using motif -M03107_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M09136_2.00 of width 12. +Using motif -M09136_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M02086_2.00 of width 8. +Using motif -M02086_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03108_2.00 of width 10. +Using motif -M03108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03109_2.00 of width 16. +Using motif -M03109_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03110_2.00 of width 12. +Using motif -M03110_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04992_2.00 of width 8. +Using motif -M04992_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04993_2.00 of width 12. +Using motif -M04993_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04994_2.00 of width 8. +Using motif -M04994_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04995_2.00 of width 8. +Using motif -M04995_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04996_2.00 of width 12. +Using motif -M04996_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05870_2.00 of width 8. +Using motif -M05870_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M09137_2.00 of width 13. +Using motif -M09137_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03111_2.00 of width 9. +Using motif -M03111_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04997_2.00 of width 8. +Using motif -M04997_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04998_2.00 of width 8. +Using motif -M04998_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04999_2.00 of width 8. +Using motif -M04999_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05000_2.00 of width 8. +Using motif -M05000_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05001_2.00 of width 8. +Using motif -M05001_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03112_2.00 of width 8. +Using motif -M03112_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05002_2.00 of width 8. +Using motif -M05002_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05003_2.00 of width 8. +Using motif -M05003_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05004_2.00 of width 8. +Using motif -M05004_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05005_2.00 of width 8. +Using motif -M05005_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05006_2.00 of width 8. +Using motif -M05006_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05007_2.00 of width 8. +Using motif -M05007_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00278_2.00 of width 7. +Using motif -M00278_2.00 of width 7. +Computing q-values. +Using motif +M00279_2.00 of width 11. +Using motif -M00279_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M00280_2.00 of width 13. +Using motif -M00280_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M00281_2.00 of width 10. +Using motif -M00281_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.932477 +Using motif +M00282_2.00 of width 8. +Using motif -M00282_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03113_2.00 of width 11. +Using motif -M03113_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03114_2.00 of width 11. +Using motif -M03114_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05008_2.00 of width 8. +Using motif -M05008_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05009_2.00 of width 8. +Using motif -M05009_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05010_2.00 of width 11. +Using motif -M05010_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05011_2.00 of width 11. +Using motif -M05011_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05012_2.00 of width 10. +Using motif -M05012_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993836 +# Estimated pi_0=0.99797 +Using motif +M05013_2.00 of width 10. +Using motif -M05013_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05014_2.00 of width 8. +Using motif -M05014_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9666 +# Estimated pi_0=0.968119 +Using motif +M09138_2.00 of width 10. +Using motif -M09138_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M00451_2.00 of width 8. +Using motif -M00451_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97 +# Estimated pi_0=1 +Using motif +M05016_2.00 of width 7. +Using motif -M05016_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05017_2.00 of width 8. +Using motif -M05017_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9934 +# Estimated pi_0=1 +Using motif +M09139_2.00 of width 17. +Using motif -M09139_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998894 +Using motif +M00416_2.00 of width 8. +Using motif -M00416_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M03115_2.00 of width 9. +Using motif -M03115_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05018_2.00 of width 11. +Using motif -M05018_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05019_2.00 of width 11. +Using motif -M05019_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M09140_2.00 of width 8. +Using motif -M09140_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02087_2.00 of width 10. +Using motif -M02087_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05020_2.00 of width 8. +Using motif -M05020_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05021_2.00 of width 8. +Using motif -M05021_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M10672_2.00 of width 10. +Using motif -M10672_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987255 +# Estimated pi_0=1 +Using motif +M03116_2.00 of width 15. +Using motif -M03116_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03117_2.00 of width 8. +Using motif -M03117_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05022_2.00 of width 8. +Using motif -M05022_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997308 +# Estimated pi_0=1 +Using motif +M05023_2.00 of width 8. +Using motif -M05023_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00117 +# Estimated pi_0=1 +Using motif +M05024_2.00 of width 10. +Using motif -M05024_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05025_2.00 of width 10. +Using motif -M05025_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03118_2.00 of width 8. +Using motif -M03118_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05026_2.00 of width 8. +Using motif -M05026_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05027_2.00 of width 8. +Using motif -M05027_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05028_2.00 of width 8. +Using motif -M05028_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05029_2.00 of width 8. +Using motif -M05029_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00283_2.00 of width 10. +Using motif -M00283_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00284_2.00 of width 8. +Using motif -M00284_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03119_2.00 of width 8. +Using motif -M03119_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05030_2.00 of width 8. +Using motif -M05030_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05031_2.00 of width 8. +Using motif -M05031_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03120_2.00 of width 8. +Using motif -M03120_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03121_2.00 of width 8. +Using motif -M03121_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03122_2.00 of width 11. +Using motif -M03122_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03123_2.00 of width 12. +Using motif -M03123_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979649 +# Estimated pi_0=0.989362 +Using motif +M05032_2.00 of width 12. +Using motif -M05032_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965545 +# Estimated pi_0=0.984737 +Using motif +M05033_2.00 of width 12. +Using motif -M05033_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9616 +# Estimated pi_0=0.973906 +Using motif +M05034_2.00 of width 8. +Using motif -M05034_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983788 +# Estimated pi_0=0.994922 +Using motif +M05035_2.00 of width 8. +Using motif -M05035_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993263 +# Estimated pi_0=0.998995 +Using motif +M00285_2.00 of width 12. +Using motif -M00285_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00286_2.00 of width 7. +Using motif -M00286_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00287_2.00 of width 10. +Using motif -M00287_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03124_2.00 of width 8. +Using motif -M03124_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05036_2.00 of width 8. +Using motif -M05036_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05037_2.00 of width 8. +Using motif -M05037_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05038_2.00 of width 8. +Using motif -M05038_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05039_2.00 of width 9. +Using motif -M05039_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05040_2.00 of width 8. +Using motif -M05040_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05041_2.00 of width 9. +Using motif -M05041_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M10676_2.00 of width 14. +Using motif -M10676_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05042_2.00 of width 10. +Using motif -M05042_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.990053 +Using motif +M05043_2.00 of width 10. +Using motif -M05043_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985694 +# Estimated pi_0=0.99224 +Using motif +M05044_2.00 of width 9. +Using motif -M05044_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9068 +# Estimated pi_0=0.914476 +Using motif +M05045_2.00 of width 10. +Using motif -M05045_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980667 +# Estimated pi_0=0.998687 +Using motif +M05046_2.00 of width 10. +Using motif -M05046_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971163 +# Estimated pi_0=0.985161 +Using motif +M05047_2.00 of width 9. +Using motif -M05047_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983564 +# Estimated pi_0=1 +Using motif +M05048_2.00 of width 8. +Using motif -M05048_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05049_2.00 of width 8. +Using motif -M05049_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05050_2.00 of width 8. +Using motif -M05050_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M09141_2.00 of width 11. +Using motif -M09141_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03125_2.00 of width 10. +Using motif -M03125_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05051_2.00 of width 8. +Using motif -M05051_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05052_2.00 of width 8. +Using motif -M05052_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05053_2.00 of width 8. +Using motif -M05053_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05054_2.00 of width 8. +Using motif -M05054_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05055_2.00 of width 8. +Using motif -M05055_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05056_2.00 of width 8. +Using motif -M05056_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M03126_2.00 of width 10. +Using motif -M03126_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05057_2.00 of width 8. +Using motif -M05057_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9948 +# Estimated pi_0=1 +Using motif +M05058_2.00 of width 8. +Using motif -M05058_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9934 +# Estimated pi_0=1 +Using motif +M05059_2.00 of width 8. +Using motif -M05059_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9898 +# Estimated pi_0=1 +Using motif +M00288_2.00 of width 8. +Using motif -M00288_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00289_2.00 of width 8. +Using motif -M00289_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00290_2.00 of width 8. +Using motif -M00290_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03127_2.00 of width 18. +Using motif -M03127_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03128_2.00 of width 8. +Using motif -M03128_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05060_2.00 of width 8. +Using motif -M05060_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05061_2.00 of width 8. +Using motif -M05061_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05062_2.00 of width 8. +Using motif -M05062_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05063_2.00 of width 8. +Using motif -M05063_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05064_2.00 of width 8. +Using motif -M05064_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05065_2.00 of width 8. +Using motif -M05065_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9892 +# Estimated pi_0=1 +Using motif +M05066_2.00 of width 8. +Using motif -M05066_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05067_2.00 of width 8. +Using motif -M05067_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05068_2.00 of width 8. +Using motif -M05068_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05069_2.00 of width 8. +Using motif -M05069_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05070_2.00 of width 8. +Using motif -M05070_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05071_2.00 of width 8. +Using motif -M05071_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00402 +# Estimated pi_0=1 +Using motif +M03129_2.00 of width 10. +Using motif -M03129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M03130_2.00 of width 11. +Using motif -M03130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05072_2.00 of width 11. +Using motif -M05072_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05073_2.00 of width 11. +Using motif -M05073_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05074_2.00 of width 11. +Using motif -M05074_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03131_2.00 of width 11. +Using motif -M03131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03132_2.00 of width 11. +Using motif -M03132_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03133_2.00 of width 11. +Using motif -M03133_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03134_2.00 of width 11. +Using motif -M03134_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05075_2.00 of width 11. +Using motif -M05075_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05076_2.00 of width 11. +Using motif -M05076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05077_2.00 of width 11. +Using motif -M05077_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05078_2.00 of width 11. +Using motif -M05078_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05079_2.00 of width 11. +Using motif -M05079_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05080_2.00 of width 11. +Using motif -M05080_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05081_2.00 of width 11. +Using motif -M05081_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05082_2.00 of width 11. +Using motif -M05082_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03135_2.00 of width 9. +Using motif -M03135_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998894 +# Estimated pi_0=0.998894 +Using motif +M03136_2.00 of width 11. +Using motif -M03136_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05083_2.00 of width 11. +Using motif -M05083_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05084_2.00 of width 11. +Using motif -M05084_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05085_2.00 of width 11. +Using motif -M05085_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05086_2.00 of width 11. +Using motif -M05086_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00291_2.00 of width 9. +Using motif -M00291_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00292_2.00 of width 9. +Using motif -M00292_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03137_2.00 of width 10. +Using motif -M03137_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03138_2.00 of width 10. +Using motif -M03138_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05087_2.00 of width 8. +Using motif -M05087_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05088_2.00 of width 8. +Using motif -M05088_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05089_2.00 of width 8. +Using motif -M05089_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05090_2.00 of width 8. +Using motif -M05090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05091_2.00 of width 8. +Using motif -M05091_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05092_2.00 of width 8. +Using motif -M05092_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05093_2.00 of width 8. +Using motif -M05093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00385_2.00 of width 8. +Using motif -M00385_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99027 +# Estimated pi_0=0.993909 +Using motif +M10681_2.00 of width 10. +Using motif -M10681_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05094_2.00 of width 10. +Using motif -M05094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996 +# Estimated pi_0=1 +Using motif +M05095_2.00 of width 10. +Using motif -M05095_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9818 +# Estimated pi_0=1 +Using motif +M05096_2.00 of width 10. +Using motif -M05096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9484 +# Estimated pi_0=0.952427 +Using motif +M05097_2.00 of width 10. +Using motif -M05097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9556 +# Estimated pi_0=0.967339 +Using motif +M08130_2.00 of width 11. +Using motif -M08130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M09142_2.00 of width 14. +Using motif -M09142_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.998794 +Using motif +M05098_2.00 of width 8. +Using motif -M05098_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05099_2.00 of width 8. +Using motif -M05099_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05100_2.00 of width 8. +Using motif -M05100_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05101_2.00 of width 8. +Using motif -M05101_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05102_2.00 of width 8. +Using motif -M05102_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05103_2.00 of width 8. +Using motif -M05103_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05104_2.00 of width 10. +Using motif -M05104_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05105_2.00 of width 10. +Using motif -M05105_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05106_2.00 of width 10. +Using motif -M05106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05107_2.00 of width 10. +Using motif -M05107_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05108_2.00 of width 10. +Using motif -M05108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05109_2.00 of width 10. +Using motif -M05109_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05110_2.00 of width 10. +Using motif -M05110_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05111_2.00 of width 10. +Using motif -M05111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05112_2.00 of width 10. +Using motif -M05112_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M05113_2.00 of width 10. +Using motif -M05113_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05114_2.00 of width 10. +Using motif -M05114_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05115_2.00 of width 10. +Using motif -M05115_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05116_2.00 of width 11. +Using motif -M05116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05117_2.00 of width 11. +Using motif -M05117_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M05118_2.00 of width 11. +Using motif -M05118_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05119_2.00 of width 11. +Using motif -M05119_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03139_2.00 of width 10. +Using motif -M03139_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03140_2.00 of width 10. +Using motif -M03140_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05120_2.00 of width 16. +Using motif -M05120_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05121_2.00 of width 16. +Using motif -M05121_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M00293_2.00 of width 10. +Using motif -M00293_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00294_2.00 of width 8. +Using motif -M00294_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00295_2.00 of width 9. +Using motif -M00295_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00296_2.00 of width 8. +Using motif -M00296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00297_2.00 of width 10. +Using motif -M00297_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00298_2.00 of width 9. +Using motif -M00298_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00299_2.00 of width 9. +Using motif -M00299_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00300_2.00 of width 8. +Using motif -M00300_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03141_2.00 of width 10. +Using motif -M03141_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03142_2.00 of width 11. +Using motif -M03142_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05122_2.00 of width 11. +Using motif -M05122_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991 +# Estimated pi_0=0.999397 +Using motif +M05123_2.00 of width 11. +Using motif -M05123_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05124_2.00 of width 11. +Using motif -M05124_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05125_2.00 of width 11. +Using motif -M05125_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03143_2.00 of width 10. +Using motif -M03143_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05126_2.00 of width 8. +Using motif -M05126_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05127_2.00 of width 8. +Using motif -M05127_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05128_2.00 of width 8. +Using motif -M05128_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05129_2.00 of width 11. +Using motif -M05129_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05130_2.00 of width 11. +Using motif -M05130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05131_2.00 of width 11. +Using motif -M05131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03144_2.00 of width 17. +Using motif -M03144_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03145_2.00 of width 8. +Using motif -M03145_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05133_2.00 of width 8. +Using motif -M05133_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00106 +# Estimated pi_0=1 +Using motif +M03146_2.00 of width 10. +Using motif -M03146_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05134_2.00 of width 8. +Using motif -M05134_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05135_2.00 of width 8. +Using motif -M05135_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05136_2.00 of width 8. +Using motif -M05136_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05137_2.00 of width 8. +Using motif -M05137_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M02740_2.00 of width 11. +Using motif -M02740_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96576 +# Estimated pi_0=0.981404 +Using motif +M02741_2.00 of width 8. +Using motif -M02741_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938018 +# Estimated pi_0=0.939636 +Using motif +M03147_2.00 of width 14. +Using motif -M03147_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99563 +# Estimated pi_0=1 +Using motif +M03148_2.00 of width 8. +Using motif -M03148_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972358 +# Estimated pi_0=0.980578 +Using motif +M05138_2.00 of width 12. +Using motif -M05138_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983761 +# Estimated pi_0=0.995155 +Using motif +M05139_2.00 of width 12. +Using motif -M05139_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958347 +# Estimated pi_0=0.970263 +Using motif +M05140_2.00 of width 12. +Using motif -M05140_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960777 +# Estimated pi_0=0.973377 +Using motif +M05141_2.00 of width 12. +Using motif -M05141_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950286 +# Estimated pi_0=0.973333 +Using motif +M09143_2.00 of width 13. +Using motif -M09143_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995393 +# Estimated pi_0=0.998191 +Using motif +M03149_2.00 of width 10. +Using motif -M03149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05142_2.00 of width 8. +Using motif -M05142_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05143_2.00 of width 8. +Using motif -M05143_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05144_2.00 of width 8. +Using motif -M05144_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03150_2.00 of width 15. +Using motif -M03150_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09144_2.00 of width 15. +Using motif -M09144_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M10690_2.00 of width 15. +Using motif -M10690_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M10693_2.00 of width 17. +Using motif -M10693_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02088_2.00 of width 10. +Using motif -M02088_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03151_2.00 of width 10. +Using motif -M03151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03152_2.00 of width 14. +Using motif -M03152_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05145_2.00 of width 8. +Using motif -M05145_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05146_2.00 of width 8. +Using motif -M05146_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05147_2.00 of width 8. +Using motif -M05147_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05148_2.00 of width 8. +Using motif -M05148_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00301_2.00 of width 8. +Using motif -M00301_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993946 +# Estimated pi_0=0.99809 +Using motif +M00302_2.00 of width 8. +Using motif -M00302_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990449 +# Estimated pi_0=0.992917 +Using motif +M05149_2.00 of width 10. +Using motif -M05149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987921 +# Estimated pi_0=0.999397 +Using motif +M05150_2.00 of width 14. +Using motif -M05150_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998679 +# Estimated pi_0=1 +Using motif +M05151_2.00 of width 10. +Using motif -M05151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992549 +# Estimated pi_0=0.999394 +Using motif +M05152_2.00 of width 9. +Using motif -M05152_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.958 +Using motif +M09145_2.00 of width 9. +Using motif -M09145_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9558 +# Estimated pi_0=0.956505 +Using motif +M09146_2.00 of width 10. +Using motif -M09146_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969272 +# Estimated pi_0=0.972738 +Using motif +M02089_2.00 of width 10. +Using motif -M02089_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00485_2.00 of width 7. +Using motif -M00485_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03153_2.00 of width 8. +Using motif -M03153_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03154_2.00 of width 8. +Using motif -M03154_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05153_2.00 of width 11. +Using motif -M05153_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05154_2.00 of width 11. +Using motif -M05154_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05155_2.00 of width 8. +Using motif -M05155_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05156_2.00 of width 11. +Using motif -M05156_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05157_2.00 of width 8. +Using motif -M05157_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05158_2.00 of width 11. +Using motif -M05158_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05159_2.00 of width 10. +Using motif -M05159_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9816 +# Estimated pi_0=1 +Using motif +M05160_2.00 of width 10. +Using motif -M05160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9772 +# Estimated pi_0=1 +Using motif +M02090_2.00 of width 8. +Using motif -M02090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03155_2.00 of width 18. +Using motif -M03155_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03156_2.00 of width 8. +Using motif -M03156_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05161_2.00 of width 8. +Using motif -M05161_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05162_2.00 of width 8. +Using motif -M05162_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05163_2.00 of width 8. +Using motif -M05163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M09147_2.00 of width 9. +Using motif -M09147_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999482 +# Estimated pi_0=0.999899 +Using motif +M03157_2.00 of width 10. +Using motif -M03157_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M03158_2.00 of width 10. +Using motif -M03158_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03159_2.00 of width 16. +Using motif -M03159_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03160_2.00 of width 10. +Using motif -M03160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03161_2.00 of width 10. +Using motif -M03161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M03162_2.00 of width 16. +Using motif -M03162_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05164_2.00 of width 9. +Using motif -M05164_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05165_2.00 of width 9. +Using motif -M05165_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05166_2.00 of width 9. +Using motif -M05166_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05167_2.00 of width 8. +Using motif -M05167_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M05168_2.00 of width 8. +Using motif -M05168_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05169_2.00 of width 8. +Using motif -M05169_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M01505_2.00 of width 9. +Using motif -M01505_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M01506_2.00 of width 9. +Using motif -M01506_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03163_2.00 of width 8. +Using motif -M03163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03164_2.00 of width 13. +Using motif -M03164_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05170_2.00 of width 8. +Using motif -M05170_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05171_2.00 of width 8. +Using motif -M05171_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05172_2.00 of width 8. +Using motif -M05172_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05173_2.00 of width 8. +Using motif -M05173_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05174_2.00 of width 8. +Using motif -M05174_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05175_2.00 of width 8. +Using motif -M05175_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03165_2.00 of width 7. +Using motif -M03165_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978974 +# Estimated pi_0=0.993016 +Using motif +M09148_2.00 of width 12. +Using motif -M09148_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M10709_2.00 of width 12. +Using motif -M10709_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978182 +# Estimated pi_0=0.981522 +Using motif +M03166_2.00 of width 10. +Using motif -M03166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05176_2.00 of width 8. +Using motif -M05176_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05177_2.00 of width 8. +Using motif -M05177_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05178_2.00 of width 8. +Using motif -M05178_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05179_2.00 of width 8. +Using motif -M05179_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03167_2.00 of width 10. +Using motif -M03167_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05180_2.00 of width 9. +Using motif -M05180_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05181_2.00 of width 9. +Using motif -M05181_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03168_2.00 of width 8. +Using motif -M03168_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05182_2.00 of width 8. +Using motif -M05182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05183_2.00 of width 8. +Using motif -M05183_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05184_2.00 of width 8. +Using motif -M05184_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05185_2.00 of width 8. +Using motif -M05185_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05186_2.00 of width 8. +Using motif -M05186_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05187_2.00 of width 8. +Using motif -M05187_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00303_2.00 of width 11. +Using motif -M00303_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00304_2.00 of width 9. +Using motif -M00304_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00305_2.00 of width 8. +Using motif -M00305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M02091_2.00 of width 8. +Using motif -M02091_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03169_2.00 of width 9. +Using motif -M03169_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03170_2.00 of width 21. +Using motif -M03170_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05188_2.00 of width 9. +Using motif -M05188_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05189_2.00 of width 10. +Using motif -M05189_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05190_2.00 of width 10. +Using motif -M05190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M02184_2.00 of width 10. +Using motif -M02184_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977 +# Estimated pi_0=1 +Using motif +M03171_2.00 of width 12. +Using motif -M03171_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983101 +# Estimated pi_0=0.992766 +Using motif +M05191_2.00 of width 12. +Using motif -M05191_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965385 +# Estimated pi_0=0.97865 +Using motif +M05192_2.00 of width 12. +Using motif -M05192_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963725 +# Estimated pi_0=0.977632 +Using motif +M05193_2.00 of width 12. +Using motif -M05193_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971552 +# Estimated pi_0=0.986816 +Using motif +M05194_2.00 of width 12. +Using motif -M05194_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954653 +# Estimated pi_0=0.96176 +Using motif +M03172_2.00 of width 10. +Using motif -M03172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03173_2.00 of width 10. +Using motif -M03173_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03174_2.00 of width 13. +Using motif -M03174_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05195_2.00 of width 8. +Using motif -M05195_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05196_2.00 of width 8. +Using motif -M05196_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05197_2.00 of width 8. +Using motif -M05197_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05198_2.00 of width 8. +Using motif -M05198_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05199_2.00 of width 8. +Using motif -M05199_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05200_2.00 of width 8. +Using motif -M05200_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M02092_2.00 of width 10. +Using motif -M02092_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03175_2.00 of width 10. +Using motif -M03175_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03176_2.00 of width 11. +Using motif -M03176_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05201_2.00 of width 11. +Using motif -M05201_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05202_2.00 of width 11. +Using motif -M05202_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05203_2.00 of width 11. +Using motif -M05203_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09149_2.00 of width 10. +Using motif -M09149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M06305_2.00 of width 8. +Using motif -M06305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998283 +# Estimated pi_0=1 +Using motif +M03177_2.00 of width 8. +Using motif -M03177_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05204_2.00 of width 8. +Using motif -M05204_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05205_2.00 of width 8. +Using motif -M05205_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03178_2.00 of width 12. +Using motif -M03178_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974032 +# Estimated pi_0=0.980241 +Using motif +M05206_2.00 of width 12. +Using motif -M05206_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955769 +# Estimated pi_0=0.967785 +Using motif +M05207_2.00 of width 12. +Using motif -M05207_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9348 +# Estimated pi_0=0.953125 +Using motif +M05208_2.00 of width 12. +Using motif -M05208_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98019 +# Estimated pi_0=0.989783 +Using motif +M05209_2.00 of width 12. +Using motif -M05209_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971287 +# Estimated pi_0=0.986477 +Using motif +M09150_2.00 of width 11. +Using motif -M09150_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931569 +# Estimated pi_0=0.945839 +Using motif +M05210_2.00 of width 8. +Using motif -M05210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05211_2.00 of width 12. +Using motif -M05211_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05212_2.00 of width 8. +Using motif -M05212_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05213_2.00 of width 8. +Using motif -M05213_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00392 +# Estimated pi_0=1 +Using motif +M05214_2.00 of width 12. +Using motif -M05214_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03179_2.00 of width 8. +Using motif -M03179_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05215_2.00 of width 8. +Using motif -M05215_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05216_2.00 of width 8. +Using motif -M05216_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05217_2.00 of width 8. +Using motif -M05217_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03180_2.00 of width 10. +Using motif -M03180_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03181_2.00 of width 14. +Using motif -M03181_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03182_2.00 of width 8. +Using motif -M03182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03183_2.00 of width 14. +Using motif -M03183_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05218_2.00 of width 8. +Using motif -M05218_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05219_2.00 of width 8. +Using motif -M05219_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05220_2.00 of width 8. +Using motif -M05220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05871_2.00 of width 8. +Using motif -M05871_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M10714_2.00 of width 7. +Using motif -M10714_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03184_2.00 of width 18. +Using motif -M03184_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03185_2.00 of width 8. +Using motif -M03185_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03186_2.00 of width 8. +Using motif -M03186_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05221_2.00 of width 8. +Using motif -M05221_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05222_2.00 of width 8. +Using motif -M05222_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05223_2.00 of width 8. +Using motif -M05223_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05224_2.00 of width 8. +Using motif -M05224_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05225_2.00 of width 8. +Using motif -M05225_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05226_2.00 of width 8. +Using motif -M05226_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M10715_2.00 of width 9. +Using motif -M10715_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09151_2.00 of width 19. +Using motif -M09151_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10718_2.00 of width 13. +Using motif -M10718_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M00306_2.00 of width 8. +Using motif -M00306_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00307_2.00 of width 8. +Using motif -M00307_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00308_2.00 of width 9. +Using motif -M00308_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00309_2.00 of width 9. +Using motif -M00309_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03187_2.00 of width 10. +Using motif -M03187_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03188_2.00 of width 15. +Using motif -M03188_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05227_2.00 of width 8. +Using motif -M05227_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05228_2.00 of width 8. +Using motif -M05228_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00310_2.00 of width 8. +Using motif -M00310_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00311_2.00 of width 8. +Using motif -M00311_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00312_2.00 of width 13. +Using motif -M00312_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00313_2.00 of width 8. +Using motif -M00313_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00314_2.00 of width 8. +Using motif -M00314_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05229_2.00 of width 8. +Using motif -M05229_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990784 +# Estimated pi_0=1 +Using motif +M05230_2.00 of width 8. +Using motif -M05230_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99629 +# Estimated pi_0=1 +Using motif +M05231_2.00 of width 8. +Using motif -M05231_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05232_2.00 of width 8. +Using motif -M05232_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05234_2.00 of width 23. +Using motif -M05234_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05235_2.00 of width 22. +Using motif -M05235_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05236_2.00 of width 21. +Using motif -M05236_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03189_2.00 of width 10. +Using motif -M03189_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03190_2.00 of width 10. +Using motif -M03190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9864 +# Estimated pi_0=1 +Using motif +M05237_2.00 of width 8. +Using motif -M05237_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05238_2.00 of width 9. +Using motif -M05238_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05239_2.00 of width 8. +Using motif -M05239_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03191_2.00 of width 13. +Using motif -M03191_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03192_2.00 of width 8. +Using motif -M03192_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05240_2.00 of width 8. +Using motif -M05240_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05241_2.00 of width 8. +Using motif -M05241_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96129 +# Estimated pi_0=0.968217 +Using motif +M03193_2.00 of width 10. +Using motif -M03193_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05242_2.00 of width 9. +Using motif -M05242_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05243_2.00 of width 9. +Using motif -M05243_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M02093_2.00 of width 8. +Using motif -M02093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00205 +# Estimated pi_0=1 +Using motif +M01246_2.00 of width 7. +Using motif -M01246_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997037 +# Estimated pi_0=0.999497 +Using motif +M03194_2.00 of width 11. +Using motif -M03194_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05244_2.00 of width 8. +Using motif -M05244_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05245_2.00 of width 8. +Using motif -M05245_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03195_2.00 of width 12. +Using motif -M03195_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981408 +# Estimated pi_0=0.991323 +Using motif +M05246_2.00 of width 12. +Using motif -M05246_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971504 +# Estimated pi_0=0.977962 +Using motif +M05247_2.00 of width 12. +Using motif -M05247_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9532 +# Estimated pi_0=0.968243 +Using motif +M03196_2.00 of width 9. +Using motif -M03196_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05248_2.00 of width 10. +Using motif -M05248_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05249_2.00 of width 9. +Using motif -M05249_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05250_2.00 of width 12. +Using motif -M05250_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9784 +# Estimated pi_0=1 +Using motif +M08131_2.00 of width 11. +Using motif -M08131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997563 +# Estimated pi_0=1 +Using motif +M09152_2.00 of width 12. +Using motif -M09152_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998782 +# Estimated pi_0=0.998894 +Using motif +M03197_2.00 of width 15. +Using motif -M03197_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03198_2.00 of width 8. +Using motif -M03198_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05251_2.00 of width 8. +Using motif -M05251_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05252_2.00 of width 8. +Using motif -M05252_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09153_2.00 of width 11. +Using motif -M09153_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03199_2.00 of width 13. +Using motif -M03199_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05253_2.00 of width 8. +Using motif -M05253_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05254_2.00 of width 8. +Using motif -M05254_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05255_2.00 of width 10. +Using motif -M05255_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995301 +# Estimated pi_0=0.997677 +Using motif +M05256_2.00 of width 10. +Using motif -M05256_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999592 +# Estimated pi_0=1 +Using motif +M05257_2.00 of width 8. +Using motif -M05257_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996952 +# Estimated pi_0=1 +Using motif +M09154_2.00 of width 12. +Using motif -M09154_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997895 +# Estimated pi_0=1 +Using motif +M09559_2.00 of width 10. +Using motif -M09559_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992427 +# Estimated pi_0=0.998081 +Using motif +M10730_2.00 of width 12. +Using motif -M10730_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M07971_2.00 of width 15. +Using motif -M07971_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938627 +# Estimated pi_0=0.954286 +Using motif +M08132_2.00 of width 17. +Using motif -M08132_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945345 +# Estimated pi_0=0.955867 +Using motif +M09155_2.00 of width 11. +Using motif -M09155_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956607 +# Estimated pi_0=0.965389 +Using motif +M09560_2.00 of width 12. +Using motif -M09560_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976242 +# Estimated pi_0=0.980659 +Using motif +M03200_2.00 of width 8. +Using motif -M03200_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05258_2.00 of width 8. +Using motif -M05258_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9624 +# Estimated pi_0=0.97037 +Using motif +M05259_2.00 of width 8. +Using motif -M05259_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05260_2.00 of width 8. +Using motif -M05260_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9792 +# Estimated pi_0=1 +Using motif +M05261_2.00 of width 8. +Using motif -M05261_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05262_2.00 of width 8. +Using motif -M05262_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05263_2.00 of width 8. +Using motif -M05263_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05264_2.00 of width 8. +Using motif -M05264_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M09156_2.00 of width 7. +Using motif -M09156_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03201_2.00 of width 10. +Using motif -M03201_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03202_2.00 of width 14. +Using motif -M03202_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03203_2.00 of width 10. +Using motif -M03203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05265_2.00 of width 8. +Using motif -M05265_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05266_2.00 of width 8. +Using motif -M05266_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05267_2.00 of width 8. +Using motif -M05267_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05268_2.00 of width 8. +Using motif -M05268_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05269_2.00 of width 8. +Using motif -M05269_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05270_2.00 of width 8. +Using motif -M05270_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03204_2.00 of width 8. +Using motif -M03204_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03205_2.00 of width 10. +Using motif -M03205_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05271_2.00 of width 8. +Using motif -M05271_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05272_2.00 of width 8. +Using motif -M05272_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05273_2.00 of width 8. +Using motif -M05273_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05274_2.00 of width 8. +Using motif -M05274_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05275_2.00 of width 8. +Using motif -M05275_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05276_2.00 of width 8. +Using motif -M05276_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05277_2.00 of width 8. +Using motif -M05277_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05278_2.00 of width 8. +Using motif -M05278_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03206_2.00 of width 9. +Using motif -M03206_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03207_2.00 of width 11. +Using motif -M03207_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03208_2.00 of width 11. +Using motif -M03208_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03209_2.00 of width 9. +Using motif -M03209_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05279_2.00 of width 10. +Using motif -M05279_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05280_2.00 of width 10. +Using motif -M05280_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M03210_2.00 of width 10. +Using motif -M03210_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03211_2.00 of width 14. +Using motif -M03211_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05281_2.00 of width 8. +Using motif -M05281_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05282_2.00 of width 8. +Using motif -M05282_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05283_2.00 of width 18. +Using motif -M05283_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05284_2.00 of width 18. +Using motif -M05284_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941748 +# Estimated pi_0=0.95479 +Using motif +M03212_2.00 of width 12. +Using motif -M03212_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05285_2.00 of width 10. +Using motif -M05285_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9092 +# Estimated pi_0=0.911881 +Using motif +M05286_2.00 of width 10. +Using motif -M05286_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9354 +# Estimated pi_0=0.948037 +Using motif +M05287_2.00 of width 10. +Using motif -M05287_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9172 +# Estimated pi_0=0.926981 +Using motif +M05288_2.00 of width 10. +Using motif -M05288_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9414 +# Estimated pi_0=0.953645 +Using motif +M08133_2.00 of width 16. +Using motif -M08133_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998586 +# Estimated pi_0=0.999598 +Using motif +M09157_2.00 of width 13. +Using motif -M09157_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999378 +# Estimated pi_0=1 +Using motif +M05289_2.00 of width 10. +Using motif -M05289_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05290_2.00 of width 10. +Using motif -M05290_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05291_2.00 of width 10. +Using motif -M05291_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M00417_2.00 of width 8. +Using motif -M00417_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00422_2.00 of width 7. +Using motif -M00422_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03213_2.00 of width 10. +Using motif -M03213_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05292_2.00 of width 8. +Using motif -M05292_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05293_2.00 of width 8. +Using motif -M05293_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05294_2.00 of width 8. +Using motif -M05294_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03214_2.00 of width 8. +Using motif -M03214_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03215_2.00 of width 10. +Using motif -M03215_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05295_2.00 of width 8. +Using motif -M05295_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05296_2.00 of width 8. +Using motif -M05296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05297_2.00 of width 8. +Using motif -M05297_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09158_2.00 of width 13. +Using motif -M09158_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00315_2.00 of width 8. +Using motif -M00315_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00316_2.00 of width 8. +Using motif -M00316_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00317_2.00 of width 10. +Using motif -M00317_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03216_2.00 of width 11. +Using motif -M03216_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03217_2.00 of width 11. +Using motif -M03217_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05298_2.00 of width 11. +Using motif -M05298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05299_2.00 of width 11. +Using motif -M05299_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05300_2.00 of width 11. +Using motif -M05300_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05301_2.00 of width 11. +Using motif -M05301_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05302_2.00 of width 11. +Using motif -M05302_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05303_2.00 of width 11. +Using motif -M05303_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00318_2.00 of width 8. +Using motif -M00318_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M00319_2.00 of width 8. +Using motif -M00319_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03218_2.00 of width 13. +Using motif -M03218_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03219_2.00 of width 8. +Using motif -M03219_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03220_2.00 of width 8. +Using motif -M03220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05304_2.00 of width 8. +Using motif -M05304_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05305_2.00 of width 8. +Using motif -M05305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05306_2.00 of width 8. +Using motif -M05306_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05307_2.00 of width 8. +Using motif -M05307_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05308_2.00 of width 8. +Using motif -M05308_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05309_2.00 of width 8. +Using motif -M05309_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03221_2.00 of width 10. +Using motif -M03221_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05310_2.00 of width 8. +Using motif -M05310_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05311_2.00 of width 8. +Using motif -M05311_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05312_2.00 of width 8. +Using motif -M05312_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M05313_2.00 of width 12. +Using motif -M05313_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963301 +# Estimated pi_0=0.97113 +Using motif +M05314_2.00 of width 12. +Using motif -M05314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94781 +# Estimated pi_0=0.947885 +Using motif +M03222_2.00 of width 12. +Using motif -M03222_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05315_2.00 of width 15. +Using motif -M05315_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05316_2.00 of width 15. +Using motif -M05316_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M08208_2.00 of width 10. +Using motif -M08208_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M08209_2.00 of width 8. +Using motif -M08209_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967673 +# Estimated pi_0=0.971954 +Using motif +M03223_2.00 of width 12. +Using motif -M03223_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987258 +# Estimated pi_0=0.996041 +Using motif +M05317_2.00 of width 12. +Using motif -M05317_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979219 +# Estimated pi_0=0.995361 +Using motif +M05318_2.00 of width 12. +Using motif -M05318_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987669 +# Estimated pi_0=0.996633 +Using motif +M09159_2.00 of width 7. +Using motif -M09159_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962649 +# Estimated pi_0=0.969425 +Using motif +M10740_2.00 of width 11. +Using motif -M10740_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996963 +# Estimated pi_0=0.998894 +Using motif +M05319_2.00 of width 18. +Using motif -M05319_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05320_2.00 of width 18. +Using motif -M05320_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03224_2.00 of width 13. +Using motif -M03224_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96396 +# Estimated pi_0=0.9642 +Using motif +M03225_2.00 of width 10. +Using motif -M03225_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05321_2.00 of width 8. +Using motif -M05321_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05322_2.00 of width 12. +Using motif -M05322_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05323_2.00 of width 10. +Using motif -M05323_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05324_2.00 of width 8. +Using motif -M05324_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05325_2.00 of width 12. +Using motif -M05325_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05326_2.00 of width 10. +Using motif -M05326_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00496_2.00 of width 10. +Using motif -M00496_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M03226_2.00 of width 13. +Using motif -M03226_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05327_2.00 of width 11. +Using motif -M05327_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05328_2.00 of width 11. +Using motif -M05328_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09160_2.00 of width 12. +Using motif -M09160_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10741_2.00 of width 18. +Using motif -M10741_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03227_2.00 of width 10. +Using motif -M03227_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05329_2.00 of width 8. +Using motif -M05329_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05330_2.00 of width 8. +Using motif -M05330_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05331_2.00 of width 10. +Using motif -M05331_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05332_2.00 of width 10. +Using motif -M05332_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05333_2.00 of width 10. +Using motif -M05333_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05334_2.00 of width 10. +Using motif -M05334_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09161_2.00 of width 10. +Using motif -M09161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03228_2.00 of width 10. +Using motif -M03228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M03229_2.00 of width 10. +Using motif -M03229_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M03230_2.00 of width 10. +Using motif -M03230_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05335_2.00 of width 11. +Using motif -M05335_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05336_2.00 of width 11. +Using motif -M05336_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05337_2.00 of width 11. +Using motif -M05337_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05338_2.00 of width 11. +Using motif -M05338_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05339_2.00 of width 11. +Using motif -M05339_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05340_2.00 of width 11. +Using motif -M05340_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05341_2.00 of width 11. +Using motif -M05341_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00112 +# Estimated pi_0=1 +Using motif +M05342_2.00 of width 11. +Using motif -M05342_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05343_2.00 of width 8. +Using motif -M05343_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05344_2.00 of width 8. +Using motif -M05344_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05345_2.00 of width 8. +Using motif -M05345_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09162_2.00 of width 8. +Using motif -M09162_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00320_2.00 of width 9. +Using motif -M00320_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997579 +# Estimated pi_0=0.999397 +Using motif +M00321_2.00 of width 10. +Using motif -M00321_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999482 +# Estimated pi_0=0.999698 +Using motif +M00322_2.00 of width 7. +Using motif -M00322_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958658 +# Estimated pi_0=0.962839 +Using motif +M00323_2.00 of width 11. +Using motif -M00323_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M00324_2.00 of width 7. +Using motif -M00324_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M01507_2.00 of width 7. +Using motif -M01507_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M01508_2.00 of width 7. +Using motif -M01508_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999798 +# Estimated pi_0=1 +Using motif +M05346_2.00 of width 10. +Using motif -M05346_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9708 +# Estimated pi_0=0.989939 +Using motif +M05347_2.00 of width 10. +Using motif -M05347_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988447 +# Estimated pi_0=0.996429 +Using motif +M05348_2.00 of width 9. +Using motif -M05348_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9598 +# Estimated pi_0=0.963364 +Using motif +M09163_2.00 of width 8. +Using motif -M09163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972761 +# Estimated pi_0=0.976196 +Using motif +M10742_2.00 of width 7. +Using motif -M10742_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M10743_2.00 of width 8. +Using motif -M10743_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M02094_2.00 of width 10. +Using motif -M02094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05349_2.00 of width 19. +Using motif -M05349_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05350_2.00 of width 14. +Using motif -M05350_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05351_2.00 of width 10. +Using motif -M05351_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05352_2.00 of width 13. +Using motif -M05352_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05353_2.00 of width 14. +Using motif -M05353_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05354_2.00 of width 10. +Using motif -M05354_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999381 +# Estimated pi_0=1 +Using motif +M05355_2.00 of width 14. +Using motif -M05355_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M05356_2.00 of width 16. +Using motif -M05356_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M10746_2.00 of width 11. +Using motif -M10746_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M00325_2.00 of width 8. +Using motif -M00325_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00326_2.00 of width 7. +Using motif -M00326_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998654 +# Estimated pi_0=1 +Using motif +M00327_2.00 of width 9. +Using motif -M00327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996505 +# Estimated pi_0=1 +Using motif +M05357_2.00 of width 10. +Using motif -M05357_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9276 +# Estimated pi_0=0.936571 +Using motif +M05358_2.00 of width 10. +Using motif -M05358_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8784 +# Estimated pi_0=0.880777 +Using motif +M03231_2.00 of width 10. +Using motif -M03231_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05359_2.00 of width 11. +Using motif -M05359_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05360_2.00 of width 11. +Using motif -M05360_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05361_2.00 of width 11. +Using motif -M05361_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M00499_2.00 of width 7. +Using motif -M00499_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M02685_2.00 of width 12. +Using motif -M02685_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05362_2.00 of width 10. +Using motif -M05362_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990601 +# Estimated pi_0=0.995052 +Using motif +M05363_2.00 of width 10. +Using motif -M05363_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944356 +# Estimated pi_0=0.953333 +Using motif +M05364_2.00 of width 10. +Using motif -M05364_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972522 +# Estimated pi_0=0.980523 +Using motif +M05365_2.00 of width 10. +Using motif -M05365_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9996 +# Estimated pi_0=1 +Using motif +M09164_2.00 of width 10. +Using motif -M09164_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9514 +# Estimated pi_0=0.961224 +Using motif +M09561_2.00 of width 12. +Using motif -M09561_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964776 +# Estimated pi_0=0.977386 +Using motif +M10748_2.00 of width 9. +Using motif -M10748_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M10749_2.00 of width 15. +Using motif -M10749_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M02095_2.00 of width 8. +Using motif -M02095_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03232_2.00 of width 8. +Using motif -M03232_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05366_2.00 of width 8. +Using motif -M05366_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05367_2.00 of width 8. +Using motif -M05367_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05368_2.00 of width 8. +Using motif -M05368_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05369_2.00 of width 10. +Using motif -M05369_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05370_2.00 of width 13. +Using motif -M05370_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05371_2.00 of width 10. +Using motif -M05371_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05372_2.00 of width 13. +Using motif -M05372_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03233_2.00 of width 11. +Using motif -M03233_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M10754_2.00 of width 10. +Using motif -M10754_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947727 +# Estimated pi_0=0.954143 +Using motif +M03234_2.00 of width 11. +Using motif -M03234_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05373_2.00 of width 9. +Using motif -M05373_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9642 +# Estimated pi_0=0.977576 +Using motif +M05374_2.00 of width 9. +Using motif -M05374_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05375_2.00 of width 10. +Using motif -M05375_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05376_2.00 of width 10. +Using motif -M05376_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03235_2.00 of width 8. +Using motif -M03235_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05377_2.00 of width 8. +Using motif -M05377_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.943564 +Using motif +M05378_2.00 of width 8. +Using motif -M05378_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05379_2.00 of width 8. +Using motif -M05379_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05380_2.00 of width 8. +Using motif -M05380_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05381_2.00 of width 8. +Using motif -M05381_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05382_2.00 of width 8. +Using motif -M05382_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03236_2.00 of width 10. +Using motif -M03236_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9978 +# Estimated pi_0=1 +Using motif +M05383_2.00 of width 8. +Using motif -M05383_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9788 +# Estimated pi_0=1 +Using motif +M05384_2.00 of width 8. +Using motif -M05384_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00392_2.00 of width 8. +Using motif -M00392_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00328_2.00 of width 8. +Using motif -M00328_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M00329_2.00 of width 9. +Using motif -M00329_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00330_2.00 of width 8. +Using motif -M00330_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05385_2.00 of width 8. +Using motif -M05385_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05386_2.00 of width 8. +Using motif -M05386_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05387_2.00 of width 8. +Using motif -M05387_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08134_2.00 of width 12. +Using motif -M08134_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982416 +# Estimated pi_0=0.995751 +Using motif +M09165_2.00 of width 15. +Using motif -M09165_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03237_2.00 of width 10. +Using motif -M03237_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03238_2.00 of width 11. +Using motif -M03238_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9984 +# Estimated pi_0=1 +Using motif +M05388_2.00 of width 10. +Using motif -M05388_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05389_2.00 of width 10. +Using motif -M05389_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M01509_2.00 of width 7. +Using motif -M01509_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M01510_2.00 of width 7. +Using motif -M01510_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03239_2.00 of width 9. +Using motif -M03239_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M03240_2.00 of width 12. +Using motif -M03240_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03241_2.00 of width 10. +Using motif -M03241_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05390_2.00 of width 8. +Using motif -M05390_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M05391_2.00 of width 8. +Using motif -M05391_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03242_2.00 of width 12. +Using motif -M03242_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03243_2.00 of width 11. +Using motif -M03243_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05392_2.00 of width 9. +Using motif -M05392_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05393_2.00 of width 9. +Using motif -M05393_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M02096_2.00 of width 8. +Using motif -M02096_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00503_2.00 of width 9. +Using motif -M00503_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00442_2.00 of width 10. +Using motif -M00442_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05394_2.00 of width 11. +Using motif -M05394_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05395_2.00 of width 11. +Using motif -M05395_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05396_2.00 of width 11. +Using motif -M05396_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05397_2.00 of width 11. +Using motif -M05397_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05398_2.00 of width 11. +Using motif -M05398_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05399_2.00 of width 11. +Using motif -M05399_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05400_2.00 of width 11. +Using motif -M05400_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05401_2.00 of width 11. +Using motif -M05401_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9654 +# Estimated pi_0=0.9654 +Using motif +M01247_2.00 of width 9. +Using motif -M01247_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03244_2.00 of width 13. +Using motif -M03244_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05402_2.00 of width 11. +Using motif -M05402_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05403_2.00 of width 11. +Using motif -M05403_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M00331_2.00 of width 7. +Using motif -M00331_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997398 +# Estimated pi_0=0.999598 +Using motif +M00332_2.00 of width 10. +Using motif -M00332_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00333_2.00 of width 10. +Using motif -M00333_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00334_2.00 of width 7. +Using motif -M00334_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05404_2.00 of width 8. +Using motif -M05404_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05405_2.00 of width 8. +Using motif -M05405_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05406_2.00 of width 8. +Using motif -M05406_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05407_2.00 of width 10. +Using motif -M05407_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05408_2.00 of width 10. +Using motif -M05408_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05409_2.00 of width 8. +Using motif -M05409_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M09166_2.00 of width 10. +Using motif -M09166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M08135_2.00 of width 11. +Using motif -M08135_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M09167_2.00 of width 11. +Using motif -M09167_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05410_2.00 of width 8. +Using motif -M05410_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05411_2.00 of width 8. +Using motif -M05411_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M02097_2.00 of width 10. +Using motif -M02097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03245_2.00 of width 13. +Using motif -M03245_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03246_2.00 of width 15. +Using motif -M03246_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09168_2.00 of width 15. +Using motif -M09168_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00335_2.00 of width 8. +Using motif -M00335_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8542 +# Estimated pi_0=0.863077 +Using motif +M00336_2.00 of width 8. +Using motif -M00336_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968 +# Estimated pi_0=0.983836 +Using motif +M00337_2.00 of width 11. +Using motif -M00337_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00338_2.00 of width 8. +Using motif -M00338_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94419 +# Estimated pi_0=0.954416 +Using motif +M00339_2.00 of width 13. +Using motif -M00339_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8394 +# Estimated pi_0=0.841942 +Using motif +M00340_2.00 of width 7. +Using motif -M00340_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945 +# Estimated pi_0=0.953273 +Using motif +M00341_2.00 of width 10. +Using motif -M00341_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.855534 +# Estimated pi_0=0.862393 +Using motif +M00342_2.00 of width 14. +Using motif -M00342_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00343_2.00 of width 13. +Using motif -M00343_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8428 +# Estimated pi_0=0.846355 +Using motif +M00344_2.00 of width 12. +Using motif -M00344_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911176 +# Estimated pi_0=0.91876 +Using motif +M02689_2.00 of width 14. +Using motif -M02689_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00056 +# Estimated pi_0=1 +Using motif +M03291_2.00 of width 19. +Using motif -M03291_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99 +# Estimated pi_0=0.998485 +Using motif +M05412_2.00 of width 17. +Using motif -M05412_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905347 +# Estimated pi_0=0.918036 +Using motif +M05413_2.00 of width 17. +Using motif -M05413_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9336 +# Estimated pi_0=0.9425 +Using motif +M05414_2.00 of width 17. +Using motif -M05414_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879208 +# Estimated pi_0=0.885283 +Using motif +M05415_2.00 of width 17. +Using motif -M05415_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911 +# Estimated pi_0=0.916792 +Using motif +M09216_2.00 of width 12. +Using motif -M09216_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9718 +# Estimated pi_0=0.985833 +Using motif +M10806_2.00 of width 21. +Using motif -M10806_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00345_2.00 of width 10. +Using motif -M00345_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8952 +# Estimated pi_0=0.903208 +Using motif +M00346_2.00 of width 10. +Using motif -M00346_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971154 +# Estimated pi_0=0.987194 +Using motif +M03292_2.00 of width 10. +Using motif -M03292_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03293_2.00 of width 10. +Using motif -M03293_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05416_2.00 of width 15. +Using motif -M05416_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9004 +# Estimated pi_0=0.914237 +Using motif +M05417_2.00 of width 16. +Using motif -M05417_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9584 +# Estimated pi_0=0.973279 +Using motif +M05418_2.00 of width 12. +Using motif -M05418_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05419_2.00 of width 15. +Using motif -M05419_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948868 +# Estimated pi_0=0.963529 +Using motif +M05420_2.00 of width 16. +Using motif -M05420_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05421_2.00 of width 12. +Using motif -M05421_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05422_2.00 of width 15. +Using motif -M05422_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944528 +# Estimated pi_0=0.95641 +Using motif +M05423_2.00 of width 16. +Using motif -M05423_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9812 +# Estimated pi_0=1 +Using motif +M05424_2.00 of width 12. +Using motif -M05424_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05425_2.00 of width 15. +Using motif -M05425_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937282 +# Estimated pi_0=0.949643 +Using motif +M05426_2.00 of width 16. +Using motif -M05426_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9894 +# Estimated pi_0=1 +Using motif +M05427_2.00 of width 12. +Using motif -M05427_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05874_2.00 of width 13. +Using motif -M05874_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03294_2.00 of width 18. +Using motif -M03294_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9184 +# Estimated pi_0=0.923962 +Using motif +M05428_2.00 of width 20. +Using motif -M05428_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893333 +# Estimated pi_0=0.893592 +Using motif +M05429_2.00 of width 20. +Using motif -M05429_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931961 +# Estimated pi_0=0.94672 +Using motif +M10808_2.00 of width 19. +Using motif -M10808_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991154 +# Estimated pi_0=1 +Using motif +M10809_2.00 of width 9. +Using motif -M10809_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M00347_2.00 of width 7. +Using motif -M00347_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00348_2.00 of width 8. +Using motif -M00348_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00349_2.00 of width 9. +Using motif -M00349_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00350_2.00 of width 8. +Using motif -M00350_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00351_2.00 of width 8. +Using motif -M00351_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03295_2.00 of width 8. +Using motif -M03295_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03296_2.00 of width 8. +Using motif -M03296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M03883_2.00 of width 15. +Using motif -M03883_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9358 +# Estimated pi_0=0.940556 +Using motif +M03884_2.00 of width 16. +Using motif -M03884_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05430_2.00 of width 20. +Using motif -M05430_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05431_2.00 of width 21. +Using motif -M05431_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986337 +# Estimated pi_0=1 +Using motif +M05432_2.00 of width 18. +Using motif -M05432_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9416 +# Estimated pi_0=0.947478 +Using motif +M05433_2.00 of width 13. +Using motif -M05433_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05434_2.00 of width 14. +Using motif -M05434_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05435_2.00 of width 21. +Using motif -M05435_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9848 +# Estimated pi_0=1 +Using motif +M05436_2.00 of width 18. +Using motif -M05436_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971589 +# Estimated pi_0=0.980625 +Using motif +M05437_2.00 of width 13. +Using motif -M05437_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M10811_2.00 of width 21. +Using motif -M10811_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.825149 +# Estimated pi_0=0.825149 +Using motif +M10812_2.00 of width 11. +Using motif -M10812_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10813_2.00 of width 12. +Using motif -M10813_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936822 +# Estimated pi_0=0.949625 +Using motif +M10814_2.00 of width 30. +Using motif -M10814_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986272 +# Estimated pi_0=0.991385 +Using motif +M00352_2.00 of width 8. +Using motif -M00352_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00353_2.00 of width 9. +Using motif -M00353_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=1 +Using motif +M00354_2.00 of width 7. +Using motif -M00354_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9652 +# Estimated pi_0=0.972453 +Using motif +M00355_2.00 of width 9. +Using motif -M00355_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M00356_2.00 of width 11. +Using motif -M00356_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00357_2.00 of width 9. +Using motif -M00357_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9808 +# Estimated pi_0=1 +Using motif +M00358_2.00 of width 7. +Using motif -M00358_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M00359_2.00 of width 8. +Using motif -M00359_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03297_2.00 of width 10. +Using motif -M03297_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03885_2.00 of width 14. +Using motif -M03885_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03886_2.00 of width 13. +Using motif -M03886_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00139 +# Estimated pi_0=1 +Using motif +M05438_2.00 of width 16. +Using motif -M05438_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9748 +# Estimated pi_0=1 +Using motif +M05439_2.00 of width 12. +Using motif -M05439_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05440_2.00 of width 16. +Using motif -M05440_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96819 +# Estimated pi_0=0.976514 +Using motif +M05441_2.00 of width 16. +Using motif -M05441_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.001 +# Estimated pi_0=1 +Using motif +M05442_2.00 of width 12. +Using motif -M05442_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05443_2.00 of width 16. +Using motif -M05443_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9702 +# Estimated pi_0=0.978302 +Using motif +M09585_2.00 of width 15. +Using motif -M09585_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10816_2.00 of width 13. +Using motif -M10816_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972593 +# Estimated pi_0=0.99779 +Using motif +M02742_2.00 of width 10. +Using motif -M02742_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M02743_2.00 of width 10. +Using motif -M02743_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03298_2.00 of width 11. +Using motif -M03298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03299_2.00 of width 14. +Using motif -M03299_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00091 +# Estimated pi_0=1 +Using motif +M05444_2.00 of width 15. +Using motif -M05444_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05445_2.00 of width 12. +Using motif -M05445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00152 +# Estimated pi_0=1 +Using motif +M05446_2.00 of width 11. +Using motif -M05446_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05447_2.00 of width 12. +Using motif -M05447_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05448_2.00 of width 12. +Using motif -M05448_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986923 +# Estimated pi_0=1 +Using motif +M05449_2.00 of width 11. +Using motif -M05449_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05450_2.00 of width 12. +Using motif -M05450_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05451_2.00 of width 12. +Using motif -M05451_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05452_2.00 of width 12. +Using motif -M05452_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M07972_2.00 of width 10. +Using motif -M07972_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M07973_2.00 of width 10. +Using motif -M07973_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M07974_2.00 of width 13. +Using motif -M07974_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00031 +# Estimated pi_0=1 +Using motif +M07975_2.00 of width 13. +Using motif -M07975_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M08140_2.00 of width 13. +Using motif -M08140_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M09218_2.00 of width 11. +Using motif -M09218_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03300_2.00 of width 17. +Using motif -M03300_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03301_2.00 of width 14. +Using motif -M03301_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05453_2.00 of width 14. +Using motif -M05453_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05454_2.00 of width 13. +Using motif -M05454_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05455_2.00 of width 14. +Using motif -M05455_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05456_2.00 of width 14. +Using motif -M05456_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05457_2.00 of width 13. +Using motif -M05457_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05458_2.00 of width 14. +Using motif -M05458_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M00360_2.00 of width 7. +Using motif -M00360_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00361_2.00 of width 8. +Using motif -M00361_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03302_2.00 of width 16. +Using motif -M03302_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05459_2.00 of width 13. +Using motif -M05459_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05460_2.00 of width 13. +Using motif -M05460_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05461_2.00 of width 13. +Using motif -M05461_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05462_2.00 of width 10. +Using motif -M05462_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05463_2.00 of width 13. +Using motif -M05463_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05464_2.00 of width 13. +Using motif -M05464_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05465_2.00 of width 13. +Using motif -M05465_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05466_2.00 of width 13. +Using motif -M05466_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00362_2.00 of width 10. +Using motif -M00362_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00363_2.00 of width 8. +Using motif -M00363_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03303_2.00 of width 10. +Using motif -M03303_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03304_2.00 of width 16. +Using motif -M03304_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03305_2.00 of width 10. +Using motif -M03305_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03306_2.00 of width 9. +Using motif -M03306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M03307_2.00 of width 12. +Using motif -M03307_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05467_2.00 of width 11. +Using motif -M05467_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973889 +# Estimated pi_0=1 +Using motif +M05468_2.00 of width 11. +Using motif -M05468_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05469_2.00 of width 12. +Using motif -M05469_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00377_2.00 of width 10. +Using motif -M00377_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03308_2.00 of width 12. +Using motif -M03308_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03309_2.00 of width 14. +Using motif -M03309_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09219_2.00 of width 16. +Using motif -M09219_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M10826_2.00 of width 19. +Using motif -M10826_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M10827_2.00 of width 15. +Using motif -M10827_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M10828_2.00 of width 13. +Using motif -M10828_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M10829_2.00 of width 23. +Using motif -M10829_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M10830_2.00 of width 14. +Using motif -M10830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M10831_2.00 of width 14. +Using motif -M10831_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M10832_2.00 of width 12. +Using motif -M10832_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M10835_2.00 of width 15. +Using motif -M10835_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999492 +# Estimated pi_0=1 +Using motif +M03310_2.00 of width 16. +Using motif -M03310_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03311_2.00 of width 16. +Using motif -M03311_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03312_2.00 of width 14. +Using motif -M03312_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05470_2.00 of width 13. +Using motif -M05470_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05471_2.00 of width 13. +Using motif -M05471_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05472_2.00 of width 13. +Using motif -M05472_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03313_2.00 of width 13. +Using motif -M03313_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03314_2.00 of width 12. +Using motif -M03314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05473_2.00 of width 14. +Using motif -M05473_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05474_2.00 of width 13. +Using motif -M05474_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05475_2.00 of width 12. +Using motif -M05475_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05476_2.00 of width 13. +Using motif -M05476_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05477_2.00 of width 18. +Using motif -M05477_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05478_2.00 of width 14. +Using motif -M05478_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05479_2.00 of width 13. +Using motif -M05479_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05480_2.00 of width 12. +Using motif -M05480_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05481_2.00 of width 13. +Using motif -M05481_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05482_2.00 of width 17. +Using motif -M05482_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M09220_2.00 of width 17. +Using motif -M09220_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998889 +# Estimated pi_0=0.998995 +Using motif +M10843_2.00 of width 16. +Using motif -M10843_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10844_2.00 of width 14. +Using motif -M10844_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M10845_2.00 of width 10. +Using motif -M10845_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03315_2.00 of width 12. +Using motif -M03315_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03316_2.00 of width 12. +Using motif -M03316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05483_2.00 of width 17. +Using motif -M05483_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05484_2.00 of width 14. +Using motif -M05484_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05485_2.00 of width 13. +Using motif -M05485_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05486_2.00 of width 13. +Using motif -M05486_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05487_2.00 of width 14. +Using motif -M05487_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05488_2.00 of width 13. +Using motif -M05488_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05489_2.00 of width 12. +Using motif -M05489_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05490_2.00 of width 13. +Using motif -M05490_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05491_2.00 of width 17. +Using motif -M05491_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M09221_2.00 of width 14. +Using motif -M09221_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M10849_2.00 of width 15. +Using motif -M10849_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00364_2.00 of width 7. +Using motif -M00364_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00365_2.00 of width 7. +Using motif -M00365_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00366_2.00 of width 7. +Using motif -M00366_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M03317_2.00 of width 9. +Using motif -M03317_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03318_2.00 of width 11. +Using motif -M03318_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05492_2.00 of width 12. +Using motif -M05492_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05493_2.00 of width 14. +Using motif -M05493_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05494_2.00 of width 13. +Using motif -M05494_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05495_2.00 of width 12. +Using motif -M05495_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05496_2.00 of width 13. +Using motif -M05496_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05497_2.00 of width 14. +Using motif -M05497_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05498_2.00 of width 13. +Using motif -M05498_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05499_2.00 of width 12. +Using motif -M05499_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05500_2.00 of width 13. +Using motif -M05500_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03319_2.00 of width 13. +Using motif -M03319_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03320_2.00 of width 12. +Using motif -M03320_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03321_2.00 of width 12. +Using motif -M03321_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05501_2.00 of width 16. +Using motif -M05501_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05502_2.00 of width 12. +Using motif -M05502_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05503_2.00 of width 16. +Using motif -M05503_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05504_2.00 of width 15. +Using motif -M05504_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05875_2.00 of width 12. +Using motif -M05875_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M07976_2.00 of width 17. +Using motif -M07976_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M08141_2.00 of width 11. +Using motif -M08141_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09222_2.00 of width 16. +Using motif -M09222_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M02744_2.00 of width 12. +Using motif -M02744_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03324_2.00 of width 13. +Using motif -M03324_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05505_2.00 of width 15. +Using motif -M05505_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973137 +# Estimated pi_0=1 +Using motif +M05506_2.00 of width 15. +Using motif -M05506_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998587 +# Estimated pi_0=1 +Using motif +M05507_2.00 of width 15. +Using motif -M05507_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9964 +# Estimated pi_0=1 +Using motif +M05508_2.00 of width 15. +Using motif -M05508_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990939 +# Estimated pi_0=0.997172 +Using motif +M09229_2.00 of width 14. +Using motif -M09229_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977658 +# Estimated pi_0=1 +Using motif +M10859_2.00 of width 10. +Using motif -M10859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982574 +# Estimated pi_0=1 +Using motif +M03325_2.00 of width 13. +Using motif -M03325_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05509_2.00 of width 15. +Using motif -M05509_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971 +# Estimated pi_0=1 +Using motif +M05510_2.00 of width 15. +Using motif -M05510_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989282 +# Estimated pi_0=0.994227 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03327_2.00 of width 9. +Using motif -M03327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03328_2.00 of width 15. +Using motif -M03328_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M05511_2.00 of width 19. +Using motif -M05511_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05512_2.00 of width 17. +Using motif -M05512_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05513_2.00 of width 19. +Using motif -M05513_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05514_2.00 of width 17. +Using motif -M05514_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05515_2.00 of width 19. +Using motif -M05515_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05516_2.00 of width 17. +Using motif -M05516_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05517_2.00 of width 19. +Using motif -M05517_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05518_2.00 of width 17. +Using motif -M05518_2.00 of width 17. diff --git a/logs/fimo.out b/logs/fimo.out new file mode 100644 index 0000000..7659109 --- /dev/null +++ b/logs/fimo.out @@ -0,0 +1,26750 @@ +Using motif +M02753_2.00 of width 12. +Using motif -M02753_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878537 +Using motif +M02754_2.00 of width 11. +Using motif -M02754_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886131 +Using motif +M02755_2.00 of width 13. +Using motif -M02755_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877778 +Using motif +M04046_2.00 of width 11. +Using motif -M04046_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.875758 +Using motif +M04047_2.00 of width 11. +Using motif -M04047_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899424 +Using motif +M08701_2.00 of width 10. +Using motif -M08701_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.891293 +Using motif +M00111_2.00 of width 10. +Using motif -M00111_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894262 +Using motif +M02756_2.00 of width 12. +Using motif -M02756_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.880678 +Using motif +M02757_2.00 of width 11. +Using motif -M02757_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.857686 +Using motif +M02758_2.00 of width 13. +Using motif -M02758_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.852397 +Using motif +M02759_2.00 of width 12. +Using motif -M02759_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87248 +Using motif +M02760_2.00 of width 13. +Using motif -M02760_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877302 +Using motif +M02761_2.00 of width 11. +Using motif -M02761_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.887805 +Using motif +M04048_2.00 of width 11. +Using motif -M04048_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909155 +Using motif +M04049_2.00 of width 11. +Using motif -M04049_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896444 +Using motif +M07783_2.00 of width 15. +Using motif -M07783_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.865091 +Using motif +M08702_2.00 of width 14. +Using motif -M08702_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.900584 +Using motif +M09451_2.00 of width 12. +Using motif -M09451_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881416 +Using motif +M09751_2.00 of width 9. +Using motif -M09751_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854095 +Using motif +M04050_2.00 of width 11. +Using motif -M04050_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.868095 +Using motif +M04051_2.00 of width 12. +Using motif -M04051_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864 +Using motif +M04052_2.00 of width 11. +Using motif -M04052_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908296 +Using motif +M04053_2.00 of width 12. +Using motif -M04053_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863894 +Using motif +M02762_2.00 of width 12. +Using motif -M02762_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.867857 +Using motif +M02763_2.00 of width 11. +Using motif -M02763_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863636 +Using motif +M02764_2.00 of width 13. +Using motif -M02764_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877842 +Using motif +M02765_2.00 of width 11. +Using motif -M02765_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.870741 +Using motif +M02766_2.00 of width 12. +Using motif -M02766_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.875075 +Using motif +M02767_2.00 of width 13. +Using motif -M02767_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.867797 +Using motif +M04054_2.00 of width 11. +Using motif -M04054_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.858261 +Using motif +M04055_2.00 of width 11. +Using motif -M04055_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90375 +Using motif +M07784_2.00 of width 15. +Using motif -M07784_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884308 +Using motif +M08703_2.00 of width 15. +Using motif -M08703_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935948 +Using motif +M09755_2.00 of width 9. +Using motif -M09755_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871667 +Using motif +M08707_2.00 of width 13. +Using motif -M08707_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09767_2.00 of width 14. +Using motif -M09767_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01659_2.00 of width 11. +Using motif -M01659_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00116_2.00 of width 11. +Using motif -M00116_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00115_2.00 of width 9. +Using motif -M00115_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02771_2.00 of width 13. +Using motif -M02771_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04056_2.00 of width 12. +Using motif -M04056_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04057_2.00 of width 12. +Using motif -M04057_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02772_2.00 of width 10. +Using motif -M02772_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08754_2.00 of width 15. +Using motif -M08754_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8605 +Using motif +M02773_2.00 of width 10. +Using motif -M02773_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958102 +Using motif +M04058_2.00 of width 11. +Using motif -M04058_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906316 +Using motif +M04059_2.00 of width 11. +Using motif -M04059_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976761 +Using motif +M08709_2.00 of width 10. +Using motif -M08709_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951532 +Using motif +M04060_2.00 of width 10. +Using motif -M04060_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886111 +Using motif +M04061_2.00 of width 10. +Using motif -M04061_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909916 +Using motif +M02774_2.00 of width 10. +Using motif -M02774_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966853 +Using motif +M02775_2.00 of width 10. +Using motif -M02775_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956988 +Using motif +M08710_2.00 of width 11. +Using motif -M08710_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909643 +Using motif +M09794_2.00 of width 15. +Using motif -M09794_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915725 +Using motif +M09795_2.00 of width 16. +Using motif -M09795_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963333 +Using motif +M04062_2.00 of width 10. +Using motif -M04062_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.890784 +Using motif +M04063_2.00 of width 10. +Using motif -M04063_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.900577 +Using motif +M04064_2.00 of width 10. +Using motif -M04064_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941488 +Using motif +M04065_2.00 of width 10. +Using motif -M04065_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975302 +Using motif +M08048_2.00 of width 10. +Using motif -M08048_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945133 +Using motif +M08711_2.00 of width 11. +Using motif -M08711_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944024 +Using motif +M09797_2.00 of width 11. +Using motif -M09797_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989006 +Using motif +M09798_2.00 of width 11. +Using motif -M09798_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947009 +Using motif +M02776_2.00 of width 10. +Using motif -M02776_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99375 +Using motif +M02777_2.00 of width 10. +Using motif -M02777_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996985 +Using motif +M04066_2.00 of width 10. +Using motif -M04066_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M04067_2.00 of width 10. +Using motif -M04067_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983094 +Using motif +M04068_2.00 of width 10. +Using motif -M04068_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M04069_2.00 of width 10. +Using motif -M04069_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986923 +Using motif +M04070_2.00 of width 10. +Using motif -M04070_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M04071_2.00 of width 10. +Using motif -M04071_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96 +Using motif +M04072_2.00 of width 10. +Using motif -M04072_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997259 +Using motif +M04073_2.00 of width 10. +Using motif -M04073_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979773 +Using motif +M08712_2.00 of width 10. +Using motif -M08712_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948679 +Using motif +M09453_2.00 of width 10. +Using motif -M09453_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975169 +Using motif +M09802_2.00 of width 18. +Using motif -M09802_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945342 +Using motif +M09803_2.00 of width 10. +Using motif -M09803_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950922 +Using motif +M09806_2.00 of width 10. +Using motif -M09806_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947383 +Using motif +M08049_2.00 of width 10. +Using motif -M08049_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898252 +Using motif +M08713_2.00 of width 8. +Using motif -M08713_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950185 +Using motif +M09454_2.00 of width 8. +Using motif -M09454_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935534 +Using motif +M04074_2.00 of width 10. +Using motif -M04074_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88198 +Using motif +M04075_2.00 of width 10. +Using motif -M04075_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.888704 +Using motif +M08714_2.00 of width 14. +Using motif -M08714_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936207 +Using motif +M04076_2.00 of width 11. +Using motif -M04076_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94354 +Using motif +M04077_2.00 of width 11. +Using motif -M04077_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07785_2.00 of width 15. +Using motif -M07785_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902975 +Using motif +M07786_2.00 of width 16. +Using motif -M07786_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907193 +Using motif +M07787_2.00 of width 11. +Using motif -M07787_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977823 +Using motif +M07788_2.00 of width 13. +Using motif -M07788_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929677 +Using motif +M07789_2.00 of width 13. +Using motif -M07789_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9028 +Using motif +M08050_2.00 of width 16. +Using motif -M08050_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920769 +Using motif +M08715_2.00 of width 19. +Using motif -M08715_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.851028 +Using motif +M02778_2.00 of width 10. +Using motif -M02778_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962714 +Using motif +M04078_2.00 of width 11. +Using motif -M04078_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926422 +Using motif +M04079_2.00 of width 11. +Using motif -M04079_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M08716_2.00 of width 9. +Using motif -M08716_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916471 +Using motif +M09816_2.00 of width 18. +Using motif -M09816_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88099 +Using motif +M02779_2.00 of width 10. +Using motif -M02779_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982459 +Using motif +M04080_2.00 of width 8. +Using motif -M04080_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962237 +Using motif +M04081_2.00 of width 8. +Using motif -M04081_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951818 +Using motif +M04082_2.00 of width 11. +Using motif -M04082_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930841 +Using motif +M04083_2.00 of width 11. +Using motif -M04083_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987619 +Using motif +M02780_2.00 of width 10. +Using motif -M02780_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998788 +Using motif +M04084_2.00 of width 12. +Using motif -M04084_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941167 +Using motif +M04085_2.00 of width 12. +Using motif -M04085_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960979 +Using motif +M04086_2.00 of width 12. +Using motif -M04086_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977401 +Using motif +M04087_2.00 of width 12. +Using motif -M04087_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987527 +Using motif +M05831_2.00 of width 11. +Using motif -M05831_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M08717_2.00 of width 7. +Using motif -M08717_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948077 +Using motif +M02727_2.00 of width 10. +Using motif -M02727_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987531 +Using motif +M02781_2.00 of width 10. +Using motif -M02781_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981558 +Using motif +M04088_2.00 of width 11. +Using motif -M04088_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944912 +Using motif +M04089_2.00 of width 11. +Using motif -M04089_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993125 +Using motif +M08718_2.00 of width 9. +Using motif -M08718_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986014 +Using motif +M08769_2.00 of width 17. +Using motif -M08769_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977017 +Using motif +M04090_2.00 of width 10. +Using motif -M04090_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902772 +Using motif +M04091_2.00 of width 10. +Using motif -M04091_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95635 +Using motif +M08719_2.00 of width 9. +Using motif -M08719_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946957 +Using motif +M09455_2.00 of width 10. +Using motif -M09455_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952909 +Using motif +M04092_2.00 of width 12. +Using motif -M04092_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951801 +Using motif +M04093_2.00 of width 12. +Using motif -M04093_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962832 +Using motif +M04094_2.00 of width 12. +Using motif -M04094_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976121 +Using motif +M04095_2.00 of width 12. +Using motif -M04095_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07790_2.00 of width 11. +Using motif -M07790_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947737 +Using motif +M08051_2.00 of width 13. +Using motif -M08051_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854118 +Using motif +M08181_2.00 of width 15. +Using motif -M08181_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966154 +Using motif +M08182_2.00 of width 8. +Using motif -M08182_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.845941 +Using motif +M08720_2.00 of width 15. +Using motif -M08720_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.862759 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943308 +Using motif +M04096_2.00 of width 10. +Using motif -M04096_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939339 +Using motif +M04097_2.00 of width 10. +Using motif -M04097_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04098_2.00 of width 12. +Using motif -M04098_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966923 +Using motif +M04099_2.00 of width 12. +Using motif -M04099_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981667 +Using motif +M04100_2.00 of width 12. +Using motif -M04100_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968645 +Using motif +M04101_2.00 of width 12. +Using motif -M04101_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992577 +Using motif +M04102_2.00 of width 12. +Using motif -M04102_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98321 +Using motif +M04103_2.00 of width 12. +Using motif -M04103_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984111 +Using motif +M04104_2.00 of width 12. +Using motif -M04104_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98213 +Using motif +M04105_2.00 of width 12. +Using motif -M04105_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990526 +Using motif +M08721_2.00 of width 13. +Using motif -M08721_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915862 +Using motif +M08052_2.00 of width 13. +Using motif -M08052_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989149 +Using motif +M08722_2.00 of width 17. +Using motif -M08722_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998041 +Using motif +M02782_2.00 of width 10. +Using motif -M02782_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950986 +Using motif +M02783_2.00 of width 10. +Using motif -M02783_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953582 +Using motif +M04106_2.00 of width 10. +Using motif -M04106_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944296 +Using motif +M04107_2.00 of width 10. +Using motif -M04107_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961014 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02784_2.00 of width 10. +Using motif -M02784_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04108_2.00 of width 10. +Using motif -M04108_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04109_2.00 of width 10. +Using motif -M04109_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04110_2.00 of width 10. +Using motif -M04110_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949515 +Using motif +M04111_2.00 of width 10. +Using motif -M04111_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959804 +Using motif +M00938_2.00 of width 8. +Using motif -M00938_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956452 +Using motif +M01497_2.00 of width 9. +Using motif -M01497_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999596 +Using motif +M01498_2.00 of width 8. +Using motif -M01498_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962937 +Using motif +M02785_2.00 of width 17. +Using motif -M02785_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939658 +Using motif +M02786_2.00 of width 10. +Using motif -M02786_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953857 +Using motif +M04112_2.00 of width 19. +Using motif -M04112_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892079 +Using motif +M04113_2.00 of width 19. +Using motif -M04113_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937615 +Using motif +M04114_2.00 of width 10. +Using motif -M04114_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931961 +Using motif +M04115_2.00 of width 10. +Using motif -M04115_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969767 +Using motif +M05832_2.00 of width 6. +Using motif -M05832_2.00 of width 6. +Computing q-values. +Using motif +M07791_2.00 of width 13. +Using motif -M07791_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929298 +Using motif +M07792_2.00 of width 11. +Using motif -M07792_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936757 +Using motif +M07793_2.00 of width 11. +Using motif -M07793_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939344 +Using motif +M07794_2.00 of width 13. +Using motif -M07794_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910182 +Using motif +M07795_2.00 of width 10. +Using motif -M07795_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934953 +Using motif +M07796_2.00 of width 10. +Using motif -M07796_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925946 +Using motif +M07797_2.00 of width 11. +Using motif -M07797_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951915 +Using motif +M08723_2.00 of width 10. +Using motif -M08723_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925152 +Using motif +M09835_2.00 of width 14. +Using motif -M09835_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978462 +Using motif +M04116_2.00 of width 12. +Using motif -M04116_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948 +Using motif +M04117_2.00 of width 12. +Using motif -M04117_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906602 +Using motif +M04118_2.00 of width 12. +Using motif -M04118_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958485 +Using motif +M04119_2.00 of width 12. +Using motif -M04119_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910088 +Using motif +M04120_2.00 of width 12. +Using motif -M04120_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978764 +Using motif +M04121_2.00 of width 12. +Using motif -M04121_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947302 +Using motif +M04122_2.00 of width 12. +Using motif -M04122_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972387 +Using motif +M04123_2.00 of width 12. +Using motif -M04123_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939254 +Using motif +M08724_2.00 of width 15. +Using motif -M08724_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914724 +Using motif +M09838_2.00 of width 12. +Using motif -M09838_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96 +Using motif +M09840_2.00 of width 10. +Using motif -M09840_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968235 +Using motif +M02787_2.00 of width 10. +Using motif -M02787_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971064 +Using motif +M04124_2.00 of width 10. +Using motif -M04124_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95626 +Using motif +M04125_2.00 of width 10. +Using motif -M04125_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979521 +Using motif +M08725_2.00 of width 11. +Using motif -M08725_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962979 +Using motif +M02788_2.00 of width 10. +Using motif -M02788_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96031 +Using motif +M04126_2.00 of width 10. +Using motif -M04126_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929126 +Using motif +M04127_2.00 of width 10. +Using motif -M04127_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947705 +Using motif +M07798_2.00 of width 10. +Using motif -M07798_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943303 +Using motif +M08726_2.00 of width 10. +Using motif -M08726_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965493 +Using motif +M09456_2.00 of width 10. +Using motif -M09456_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98648 +Using motif +M04128_2.00 of width 10. +Using motif -M04128_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923333 +Using motif +M08053_2.00 of width 12. +Using motif -M08053_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881887 +Using motif +M08727_2.00 of width 12. +Using motif -M08727_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915 +Using motif +M09846_2.00 of width 12. +Using motif -M09846_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923495 +Using motif +M02789_2.00 of width 10. +Using motif -M02789_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972296 +Using motif +M04129_2.00 of width 10. +Using motif -M04129_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946496 +Using motif +M04130_2.00 of width 10. +Using motif -M04130_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08728_2.00 of width 14. +Using motif -M08728_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943556 +Using motif +M02790_2.00 of width 10. +Using motif -M02790_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.888288 +Using motif +M02791_2.00 of width 10. +Using motif -M02791_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897228 +Using motif +M04131_2.00 of width 10. +Using motif -M04131_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881188 +Using motif +M04132_2.00 of width 10. +Using motif -M04132_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906476 +Using motif +M04133_2.00 of width 10. +Using motif -M04133_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898095 +Using motif +M04134_2.00 of width 10. +Using motif -M04134_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893458 +Using motif +M07799_2.00 of width 10. +Using motif -M07799_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917248 +Using motif +M07800_2.00 of width 11. +Using motif -M07800_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904466 +Using motif +M07801_2.00 of width 11. +Using motif -M07801_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932571 +Using motif +M07802_2.00 of width 11. +Using motif -M07802_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925641 +Using motif +M07803_2.00 of width 8. +Using motif -M07803_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946723 +Using motif +M07804_2.00 of width 11. +Using motif -M07804_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9194 +Using motif +M08054_2.00 of width 12. +Using motif -M08054_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896098 +Using motif +M08729_2.00 of width 11. +Using motif -M08729_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928667 +Using motif +M09457_2.00 of width 8. +Using motif -M09457_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914231 +Using motif +M04135_2.00 of width 10. +Using motif -M04135_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934384 +Using motif +M04136_2.00 of width 10. +Using motif -M04136_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949041 +Using motif +M08055_2.00 of width 13. +Using motif -M08055_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909697 +Using motif +M08730_2.00 of width 14. +Using motif -M08730_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917483 +Using motif +M04137_2.00 of width 8. +Using motif -M04137_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953497 +Using motif +M04138_2.00 of width 8. +Using motif -M04138_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9548 +Using motif +M07805_2.00 of width 11. +Using motif -M07805_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915143 +Using motif +M07806_2.00 of width 8. +Using motif -M07806_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946857 +Using motif +M08731_2.00 of width 10. +Using motif -M08731_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91904 +Using motif +M09458_2.00 of width 10. +Using motif -M09458_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941491 +Using motif +M08732_2.00 of width 9. +Using motif -M08732_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964167 +Using motif +M09459_2.00 of width 8. +Using motif -M09459_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99121 +Using motif +M09863_2.00 of width 16. +Using motif -M09863_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97021 +Using motif +M09864_2.00 of width 20. +Using motif -M09864_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9272 +Using motif +M04139_2.00 of width 10. +Using motif -M04139_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938727 +Using motif +M04140_2.00 of width 14. +Using motif -M04140_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93664 +Using motif +M04141_2.00 of width 14. +Using motif -M04141_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956863 +Using motif +M04142_2.00 of width 14. +Using motif -M04142_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963205 +Using motif +M04143_2.00 of width 14. +Using motif -M04143_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97963 +Using motif +M04144_2.00 of width 12. +Using motif -M04144_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993439 +Using motif +M04145_2.00 of width 12. +Using motif -M04145_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M02792_2.00 of width 10. +Using motif -M02792_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986509 +Using motif +M04146_2.00 of width 11. +Using motif -M04146_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92752 +Using motif +M04147_2.00 of width 11. +Using motif -M04147_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M07807_2.00 of width 15. +Using motif -M07807_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88 +Using motif +M07808_2.00 of width 15. +Using motif -M07808_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88887 +Using motif +M07809_2.00 of width 15. +Using motif -M07809_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911273 +Using motif +M07810_2.00 of width 11. +Using motif -M07810_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946116 +Using motif +M07811_2.00 of width 14. +Using motif -M07811_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904 +Using motif +M08056_2.00 of width 11. +Using motif -M08056_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977986 +Using motif +M08733_2.00 of width 12. +Using motif -M08733_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9148 +Using motif +M09460_2.00 of width 10. +Using motif -M09460_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957226 +Using motif +M09867_2.00 of width 14. +Using motif -M09867_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973893 +Using motif +M09868_2.00 of width 14. +Using motif -M09868_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951688 +Using motif +M09869_2.00 of width 8. +Using motif -M09869_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955172 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97747 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04149_2.00 of width 19. +Using motif -M04149_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08057_2.00 of width 13. +Using motif -M08057_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967657 +Using motif +M08735_2.00 of width 10. +Using motif -M08735_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965917 +Using motif +M01722_2.00 of width 9. +Using motif -M01722_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921739 +Using motif +M04150_2.00 of width 10. +Using motif -M04150_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962179 +Using motif +M04151_2.00 of width 10. +Using motif -M04151_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961098 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937547 +Using motif +M02793_2.00 of width 10. +Using motif -M02793_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879 +Using motif +M04152_2.00 of width 10. +Using motif -M04152_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879608 +Using motif +M04153_2.00 of width 10. +Using motif -M04153_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899245 +Using motif +M04154_2.00 of width 10. +Using motif -M04154_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893922 +Using motif +M04155_2.00 of width 10. +Using motif -M04155_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.880762 +Using motif +M02794_2.00 of width 10. +Using motif -M02794_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937711 +Using motif +M04156_2.00 of width 12. +Using motif -M04156_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97175 +Using motif +M04157_2.00 of width 12. +Using motif -M04157_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981371 +Using motif +M08736_2.00 of width 18. +Using motif -M08736_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91359 +Using motif +M04158_2.00 of width 10. +Using motif -M04158_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945109 +Using motif +M04159_2.00 of width 10. +Using motif -M04159_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958148 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970435 +Using motif +M04160_2.00 of width 10. +Using motif -M04160_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98366 +Using motif +M04161_2.00 of width 10. +Using motif -M04161_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08737_2.00 of width 9. +Using motif -M08737_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95 +Using motif +M02796_2.00 of width 10. +Using motif -M02796_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929367 +Using motif +M02797_2.00 of width 10. +Using motif -M02797_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923459 +Using motif +M04162_2.00 of width 18. +Using motif -M04162_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915441 +Using motif +M04163_2.00 of width 18. +Using motif -M04163_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940645 +Using motif +M04164_2.00 of width 18. +Using motif -M04164_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911618 +Using motif +M04165_2.00 of width 18. +Using motif -M04165_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931489 +Using motif +M09875_2.00 of width 22. +Using motif -M09875_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921898 +Using motif +M09876_2.00 of width 22. +Using motif -M09876_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902063 +Using motif +M04166_2.00 of width 10. +Using motif -M04166_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983135 +Using motif +M04167_2.00 of width 10. +Using motif -M04167_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972923 +Using motif +M04168_2.00 of width 10. +Using motif -M04168_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976235 +Using motif +M04169_2.00 of width 10. +Using motif -M04169_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982319 +Using motif +M08738_2.00 of width 9. +Using motif -M08738_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952583 +Using motif +M04170_2.00 of width 10. +Using motif -M04170_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96125 +Using motif +M01743_2.00 of width 8. +Using motif -M01743_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961224 +Using motif +M02798_2.00 of width 10. +Using motif -M02798_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M04171_2.00 of width 10. +Using motif -M04171_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04172_2.00 of width 10. +Using motif -M04172_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997475 +Using motif +M04173_2.00 of width 10. +Using motif -M04173_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04174_2.00 of width 10. +Using motif -M04174_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992581 +Using motif +M04175_2.00 of width 10. +Using motif -M04175_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04176_2.00 of width 10. +Using motif -M04176_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04177_2.00 of width 18. +Using motif -M04177_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909048 +Using motif +M04178_2.00 of width 18. +Using motif -M04178_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923788 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02800_2.00 of width 10. +Using motif -M02800_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04179_2.00 of width 10. +Using motif -M04179_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04180_2.00 of width 10. +Using motif -M04180_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04181_2.00 of width 10. +Using motif -M04181_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898235 +Using motif +M04182_2.00 of width 10. +Using motif -M04182_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889412 +Using motif +M02801_2.00 of width 10. +Using motif -M02801_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984505 +Using motif +M04183_2.00 of width 12. +Using motif -M04183_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971026 +Using motif +M04184_2.00 of width 12. +Using motif -M04184_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932807 +Using motif +M04185_2.00 of width 12. +Using motif -M04185_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952455 +Using motif +M04186_2.00 of width 12. +Using motif -M04186_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04187_2.00 of width 12. +Using motif -M04187_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932676 +Using motif +M04188_2.00 of width 12. +Using motif -M04188_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944444 +Using motif +M04189_2.00 of width 12. +Using motif -M04189_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982034 +Using motif +M04190_2.00 of width 12. +Using motif -M04190_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02802_2.00 of width 12. +Using motif -M02802_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907154 +Using motif +M04191_2.00 of width 10. +Using motif -M04191_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878679 +Using motif +M04192_2.00 of width 10. +Using motif -M04192_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9138 +Using motif +M04193_2.00 of width 10. +Using motif -M04193_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986258 +Using motif +M04194_2.00 of width 10. +Using motif -M04194_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02803_2.00 of width 10. +Using motif -M02803_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04195_2.00 of width 10. +Using motif -M04195_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970795 +Using motif +M04196_2.00 of width 10. +Using motif -M04196_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95563 +Using motif +M04197_2.00 of width 10. +Using motif -M04197_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973068 +Using motif +M04198_2.00 of width 10. +Using motif -M04198_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997157 +Using motif +M08739_2.00 of width 11. +Using motif -M08739_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938902 +Using motif +M02804_2.00 of width 12. +Using motif -M02804_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04199_2.00 of width 10. +Using motif -M04199_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04200_2.00 of width 10. +Using motif -M04200_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04201_2.00 of width 10. +Using motif -M04201_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988228 +Using motif +M04202_2.00 of width 10. +Using motif -M04202_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M04203_2.00 of width 10. +Using motif -M04203_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04204_2.00 of width 10. +Using motif -M04204_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02805_2.00 of width 10. +Using motif -M02805_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965065 +Using motif +M04205_2.00 of width 9. +Using motif -M04205_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953117 +Using motif +M04206_2.00 of width 9. +Using motif -M04206_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965294 +Using motif +M04207_2.00 of width 10. +Using motif -M04207_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916571 +Using motif +M04208_2.00 of width 10. +Using motif -M04208_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955698 +Using motif +M04209_2.00 of width 10. +Using motif -M04209_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919478 +Using motif +M04210_2.00 of width 10. +Using motif -M04210_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932318 +Using motif +M04211_2.00 of width 10. +Using motif -M04211_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935782 +Using motif +M02806_2.00 of width 10. +Using motif -M02806_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984103 +Using motif +M08058_2.00 of width 18. +Using motif -M08058_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997474 +Using motif +M08740_2.00 of width 10. +Using motif -M08740_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951339 +Using motif +M04212_2.00 of width 12. +Using motif -M04212_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04213_2.00 of width 12. +Using motif -M04213_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01726_2.00 of width 10. +Using motif -M01726_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913455 +Using motif +M02728_2.00 of width 7. +Using motif -M02728_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954479 +Using motif +M02807_2.00 of width 10. +Using motif -M02807_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935105 +Using motif +M02808_2.00 of width 10. +Using motif -M02808_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948228 +Using motif +M04214_2.00 of width 8. +Using motif -M04214_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954825 +Using motif +M04215_2.00 of width 8. +Using motif -M04215_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969945 +Using motif +M09461_2.00 of width 12. +Using motif -M09461_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99533 +Using motif +M02809_2.00 of width 12. +Using motif -M02809_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948921 +Using motif +M02810_2.00 of width 12. +Using motif -M02810_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899833 +Using motif +M04216_2.00 of width 10. +Using motif -M04216_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.874808 +Using motif +M04217_2.00 of width 10. +Using motif -M04217_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893396 +Using motif +M04218_2.00 of width 8. +Using motif -M04218_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9466 +Using motif +M02811_2.00 of width 10. +Using motif -M02811_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M03591_2.00 of width 12. +Using motif -M03591_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971471 +Using motif +M04219_2.00 of width 10. +Using motif -M04219_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952358 +Using motif +M04220_2.00 of width 10. +Using motif -M04220_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936415 +Using motif +M04221_2.00 of width 10. +Using motif -M04221_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954959 +Using motif +M04222_2.00 of width 10. +Using motif -M04222_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965082 +Using motif +M08059_2.00 of width 10. +Using motif -M08059_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993895 +Using motif +M08741_2.00 of width 13. +Using motif -M08741_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919687 +Using motif +M09462_2.00 of width 12. +Using motif -M09462_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927538 +Using motif +M02812_2.00 of width 10. +Using motif -M02812_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02813_2.00 of width 10. +Using motif -M02813_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04223_2.00 of width 10. +Using motif -M04223_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04224_2.00 of width 10. +Using motif -M04224_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04225_2.00 of width 10. +Using motif -M04225_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994388 +Using motif +M04226_2.00 of width 10. +Using motif -M04226_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08742_2.00 of width 18. +Using motif -M08742_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955636 +Using motif +M01718_2.00 of width 10. +Using motif -M01718_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921985 +Using motif +M02821_2.00 of width 8. +Using motif -M02821_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992757 +Using motif +M02822_2.00 of width 18. +Using motif -M02822_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998883 +Using motif +M02823_2.00 of width 18. +Using motif -M02823_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99131 +Using motif +M01131_2.00 of width 8. +Using motif -M01131_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998769 +Using motif +M05834_2.00 of width 10. +Using motif -M05834_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05835_2.00 of width 8. +Using motif -M05835_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07812_2.00 of width 14. +Using motif -M07812_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977551 +Using motif +M08064_2.00 of width 11. +Using motif -M08064_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995155 +Using motif +M08781_2.00 of width 12. +Using motif -M08781_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04008_2.00 of width 9. +Using motif -M04008_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M08782_2.00 of width 7. +Using motif -M08782_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09926_2.00 of width 13. +Using motif -M09926_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02729_2.00 of width 10. +Using motif -M02729_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M02824_2.00 of width 10. +Using motif -M02824_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04009_2.00 of width 12. +Using motif -M04009_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995684 +Using motif +M04227_2.00 of width 12. +Using motif -M04227_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04228_2.00 of width 12. +Using motif -M04228_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08783_2.00 of width 12. +Using motif -M08783_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04229_2.00 of width 12. +Using motif -M04229_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940396 +Using motif +M04230_2.00 of width 12. +Using motif -M04230_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966055 +Using motif +M08784_2.00 of width 11. +Using motif -M08784_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968254 +Using motif +M02730_2.00 of width 11. +Using motif -M02730_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954151 +Using motif +M02825_2.00 of width 12. +Using motif -M02825_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948522 +Using motif +M02826_2.00 of width 14. +Using motif -M02826_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991389 +Using motif +M04010_2.00 of width 12. +Using motif -M04010_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970476 +Using motif +M04011_2.00 of width 12. +Using motif -M04011_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934074 +Using motif +M04012_2.00 of width 13. +Using motif -M04012_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981418 +Using motif +M04231_2.00 of width 12. +Using motif -M04231_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925149 +Using motif +M04232_2.00 of width 12. +Using motif -M04232_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945841 +Using motif +M04233_2.00 of width 12. +Using motif -M04233_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934107 +Using motif +M04234_2.00 of width 12. +Using motif -M04234_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93875 +Using motif +M09932_2.00 of width 24. +Using motif -M09932_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09933_2.00 of width 17. +Using motif -M09933_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02827_2.00 of width 12. +Using motif -M02827_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02828_2.00 of width 12. +Using motif -M02828_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04013_2.00 of width 10. +Using motif -M04013_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04235_2.00 of width 12. +Using motif -M04235_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04236_2.00 of width 12. +Using motif -M04236_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04237_2.00 of width 12. +Using motif -M04237_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04238_2.00 of width 12. +Using motif -M04238_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08785_2.00 of width 11. +Using motif -M08785_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M02829_2.00 of width 14. +Using motif -M02829_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975929 +Using motif +M02830_2.00 of width 14. +Using motif -M02830_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974963 +Using motif +M04014_2.00 of width 12. +Using motif -M04014_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921197 +Using motif +M04015_2.00 of width 12. +Using motif -M04015_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960862 +Using motif +M04239_2.00 of width 12. +Using motif -M04239_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949748 +Using motif +M04240_2.00 of width 12. +Using motif -M04240_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931619 +Using motif +M04241_2.00 of width 12. +Using motif -M04241_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04242_2.00 of width 12. +Using motif -M04242_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902476 +Using motif +M04243_2.00 of width 12. +Using motif -M04243_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97977 +Using motif +M04244_2.00 of width 12. +Using motif -M04244_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M02831_2.00 of width 12. +Using motif -M02831_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04016_2.00 of width 10. +Using motif -M04016_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04245_2.00 of width 12. +Using motif -M04245_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04246_2.00 of width 12. +Using motif -M04246_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08786_2.00 of width 13. +Using motif -M08786_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09937_2.00 of width 10. +Using motif -M09937_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03649_2.00 of width 10. +Using motif -M03649_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04247_2.00 of width 19. +Using motif -M04247_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04248_2.00 of width 20. +Using motif -M04248_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04249_2.00 of width 19. +Using motif -M04249_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M08065_2.00 of width 14. +Using motif -M08065_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997487 +Using motif +M08787_2.00 of width 11. +Using motif -M08787_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988324 +Using motif +M09484_2.00 of width 10. +Using motif -M09484_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993684 +Using motif +M09938_2.00 of width 11. +Using motif -M09938_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960893 +Using motif +M04017_2.00 of width 10. +Using motif -M04017_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04250_2.00 of width 12. +Using motif -M04250_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959231 +Using motif +M04251_2.00 of width 12. +Using motif -M04251_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04252_2.00 of width 12. +Using motif -M04252_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947477 +Using motif +M04253_2.00 of width 12. +Using motif -M04253_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08788_2.00 of width 11. +Using motif -M08788_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09941_2.00 of width 8. +Using motif -M09941_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09942_2.00 of width 12. +Using motif -M09942_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938909 +Using motif +M08789_2.00 of width 14. +Using motif -M08789_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M04018_2.00 of width 12. +Using motif -M04018_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944262 +Using motif +M04019_2.00 of width 11. +Using motif -M04019_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967273 +Using motif +M04020_2.00 of width 14. +Using motif -M04020_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964667 +Using motif +M04021_2.00 of width 11. +Using motif -M04021_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04254_2.00 of width 14. +Using motif -M04254_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945128 +Using motif +M04255_2.00 of width 12. +Using motif -M04255_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964182 +Using motif +M04256_2.00 of width 14. +Using motif -M04256_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947227 +Using motif +M04257_2.00 of width 12. +Using motif -M04257_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996284 +Using motif +M08790_2.00 of width 12. +Using motif -M08790_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9055 +Using motif +M09947_2.00 of width 8. +Using motif -M09947_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963582 +Using motif +M04022_2.00 of width 12. +Using motif -M04022_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973445 +Using motif +M04023_2.00 of width 10. +Using motif -M04023_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964538 +Using motif +M04258_2.00 of width 12. +Using motif -M04258_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977121 +Using motif +M04259_2.00 of width 9. +Using motif -M04259_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959412 +Using motif +M04260_2.00 of width 12. +Using motif -M04260_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968527 +Using motif +M04261_2.00 of width 11. +Using motif -M04261_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04262_2.00 of width 12. +Using motif -M04262_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04263_2.00 of width 9. +Using motif -M04263_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04264_2.00 of width 12. +Using motif -M04264_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04265_2.00 of width 12. +Using motif -M04265_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M08066_2.00 of width 12. +Using motif -M08066_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96087 +Using motif +M08791_2.00 of width 11. +Using motif -M08791_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M09948_2.00 of width 8. +Using motif -M09948_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96 +Using motif +M09949_2.00 of width 12. +Using motif -M09949_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933267 +Using motif +M09952_2.00 of width 12. +Using motif -M09952_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975862 +Using motif +M09953_2.00 of width 12. +Using motif -M09953_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952593 +Using motif +M09954_2.00 of width 15. +Using motif -M09954_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90902 +Using motif +M09955_2.00 of width 15. +Using motif -M09955_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966375 +Using motif +M08792_2.00 of width 11. +Using motif -M08792_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984768 +Using motif +M09485_2.00 of width 10. +Using motif -M09485_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02832_2.00 of width 11. +Using motif -M02832_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M04024_2.00 of width 9. +Using motif -M04024_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04266_2.00 of width 11. +Using motif -M04266_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999192 +Using motif +M04267_2.00 of width 11. +Using motif -M04267_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M07813_2.00 of width 17. +Using motif -M07813_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M08793_2.00 of width 13. +Using motif -M08793_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955079 +Using motif +M09486_2.00 of width 12. +Using motif -M09486_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993886 +Using motif +M09959_2.00 of width 11. +Using motif -M09959_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994842 +Using motif +M02833_2.00 of width 14. +Using motif -M02833_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04025_2.00 of width 12. +Using motif -M04025_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988143 +Using motif +M04026_2.00 of width 11. +Using motif -M04026_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959568 +Using motif +M04268_2.00 of width 12. +Using motif -M04268_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981053 +Using motif +M04269_2.00 of width 12. +Using motif -M04269_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04270_2.00 of width 12. +Using motif -M04270_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998256 +Using motif +M04271_2.00 of width 12. +Using motif -M04271_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08794_2.00 of width 17. +Using motif -M08794_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M04272_2.00 of width 12. +Using motif -M04272_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9632 +Using motif +M04273_2.00 of width 12. +Using motif -M04273_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08795_2.00 of width 9. +Using motif -M08795_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996768 +Using motif +M02834_2.00 of width 13. +Using motif -M02834_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M04027_2.00 of width 14. +Using motif -M04027_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94434 +Using motif +M04274_2.00 of width 14. +Using motif -M04274_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982642 +Using motif +M04275_2.00 of width 14. +Using motif -M04275_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M04276_2.00 of width 14. +Using motif -M04276_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982353 +Using motif +M08796_2.00 of width 12. +Using motif -M08796_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02835_2.00 of width 11. +Using motif -M02835_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998889 +Using motif +M04277_2.00 of width 13. +Using motif -M04277_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M04278_2.00 of width 13. +Using motif -M04278_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04279_2.00 of width 13. +Using motif -M04279_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04280_2.00 of width 13. +Using motif -M04280_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04281_2.00 of width 11. +Using motif -M04281_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956792 +Using motif +M04282_2.00 of width 12. +Using motif -M04282_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04283_2.00 of width 11. +Using motif -M04283_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04284_2.00 of width 11. +Using motif -M04284_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05836_2.00 of width 10. +Using motif -M05836_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05837_2.00 of width 11. +Using motif -M05837_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07814_2.00 of width 19. +Using motif -M07814_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07815_2.00 of width 11. +Using motif -M07815_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986629 +Using motif +M07816_2.00 of width 15. +Using motif -M07816_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M07817_2.00 of width 13. +Using motif -M07817_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984229 +Using motif +M07818_2.00 of width 15. +Using motif -M07818_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99899 +Using motif +M07819_2.00 of width 14. +Using motif -M07819_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99641 +Using motif +M07820_2.00 of width 15. +Using motif -M07820_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975252 +Using motif +M08067_2.00 of width 11. +Using motif -M08067_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993333 +Using motif +M08068_2.00 of width 15. +Using motif -M08068_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08797_2.00 of width 11. +Using motif -M08797_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99665 +Using motif +M09487_2.00 of width 12. +Using motif -M09487_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M01813_2.00 of width 8. +Using motif -M01813_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02836_2.00 of width 9. +Using motif -M02836_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02837_2.00 of width 12. +Using motif -M02837_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02838_2.00 of width 9. +Using motif -M02838_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M02839_2.00 of width 12. +Using motif -M02839_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04285_2.00 of width 11. +Using motif -M04285_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04286_2.00 of width 12. +Using motif -M04286_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04287_2.00 of width 11. +Using motif -M04287_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04288_2.00 of width 12. +Using motif -M04288_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M04289_2.00 of width 11. +Using motif -M04289_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962857 +Using motif +M04290_2.00 of width 12. +Using motif -M04290_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972456 +Using motif +M04291_2.00 of width 11. +Using motif -M04291_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992069 +Using motif +M04292_2.00 of width 12. +Using motif -M04292_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04293_2.00 of width 12. +Using motif -M04293_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935167 +Using motif +M04294_2.00 of width 12. +Using motif -M04294_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928103 +Using motif +M04295_2.00 of width 12. +Using motif -M04295_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916415 +Using motif +M04296_2.00 of width 12. +Using motif -M04296_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985068 +Using motif +M04297_2.00 of width 12. +Using motif -M04297_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924 +Using motif +M04298_2.00 of width 12. +Using motif -M04298_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969576 +Using motif +M04299_2.00 of width 12. +Using motif -M04299_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957321 +Using motif +M04300_2.00 of width 12. +Using motif -M04300_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04301_2.00 of width 12. +Using motif -M04301_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04302_2.00 of width 12. +Using motif -M04302_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02840_2.00 of width 10. +Using motif -M02840_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M02841_2.00 of width 10. +Using motif -M02841_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M04028_2.00 of width 12. +Using motif -M04028_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04303_2.00 of width 12. +Using motif -M04303_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04304_2.00 of width 12. +Using motif -M04304_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04305_2.00 of width 12. +Using motif -M04305_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04306_2.00 of width 12. +Using motif -M04306_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08798_2.00 of width 12. +Using motif -M08798_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04307_2.00 of width 10. +Using motif -M04307_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.870962 +Using motif +M04308_2.00 of width 10. +Using motif -M04308_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925965 +Using motif +M07821_2.00 of width 15. +Using motif -M07821_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08799_2.00 of width 18. +Using motif -M08799_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M08184_2.00 of width 8. +Using motif -M08184_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988939 +Using motif +M08185_2.00 of width 10. +Using motif -M08185_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991959 +Using motif +M08186_2.00 of width 9. +Using motif -M08186_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998693 +Using motif +M08800_2.00 of width 13. +Using motif -M08800_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892941 +Using motif +M09488_2.00 of width 15. +Using motif -M09488_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M09975_2.00 of width 15. +Using motif -M09975_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M02842_2.00 of width 14. +Using motif -M02842_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962969 +Using motif +M02843_2.00 of width 12. +Using motif -M02843_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921584 +Using motif +M02844_2.00 of width 12. +Using motif -M02844_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950355 +Using motif +M02845_2.00 of width 14. +Using motif -M02845_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9625 +Using motif +M02846_2.00 of width 12. +Using motif -M02846_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945088 +Using motif +M02847_2.00 of width 14. +Using motif -M02847_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968194 +Using motif +M04029_2.00 of width 13. +Using motif -M04029_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953178 +Using motif +M04030_2.00 of width 13. +Using motif -M04030_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926731 +Using motif +M04031_2.00 of width 12. +Using motif -M04031_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965333 +Using motif +M04309_2.00 of width 14. +Using motif -M04309_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924 +Using motif +M04310_2.00 of width 12. +Using motif -M04310_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930476 +Using motif +M04311_2.00 of width 12. +Using motif -M04311_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9218 +Using motif +M04312_2.00 of width 14. +Using motif -M04312_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916792 +Using motif +M04313_2.00 of width 12. +Using motif -M04313_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936535 +Using motif +M04314_2.00 of width 12. +Using motif -M04314_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954495 +Using motif +M04315_2.00 of width 12. +Using motif -M04315_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913208 +Using motif +M04316_2.00 of width 12. +Using motif -M04316_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881698 +Using motif +M04317_2.00 of width 13. +Using motif -M04317_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928438 +Using motif +M04318_2.00 of width 12. +Using motif -M04318_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95374 +Using motif +M04319_2.00 of width 12. +Using motif -M04319_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962958 +Using motif +M04320_2.00 of width 13. +Using motif -M04320_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94065 +Using motif +M04032_2.00 of width 10. +Using motif -M04032_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04033_2.00 of width 9. +Using motif -M04033_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M04321_2.00 of width 12. +Using motif -M04321_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04322_2.00 of width 12. +Using motif -M04322_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04323_2.00 of width 12. +Using motif -M04323_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997968 +Using motif +M04324_2.00 of width 12. +Using motif -M04324_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07822_2.00 of width 11. +Using motif -M07822_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941695 +Using motif +M07823_2.00 of width 11. +Using motif -M07823_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946462 +Using motif +M07824_2.00 of width 11. +Using motif -M07824_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9058 +Using motif +M07825_2.00 of width 10. +Using motif -M07825_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967068 +Using motif +M08187_2.00 of width 8. +Using motif -M08187_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.876822 +Using motif +M08188_2.00 of width 10. +Using motif -M08188_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08801_2.00 of width 11. +Using motif -M08801_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92434 +Using motif +M09489_2.00 of width 12. +Using motif -M09489_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M02658_2.00 of width 11. +Using motif -M02658_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02848_2.00 of width 12. +Using motif -M02848_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04034_2.00 of width 10. +Using motif -M04034_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M09982_2.00 of width 12. +Using motif -M09982_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02849_2.00 of width 12. +Using motif -M02849_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02850_2.00 of width 12. +Using motif -M02850_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04325_2.00 of width 12. +Using motif -M04325_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04326_2.00 of width 12. +Using motif -M04326_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09986_2.00 of width 10. +Using motif -M09986_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04327_2.00 of width 12. +Using motif -M04327_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04328_2.00 of width 12. +Using motif -M04328_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M07826_2.00 of width 13. +Using motif -M07826_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998586 +Using motif +M07827_2.00 of width 11. +Using motif -M07827_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997688 +Using motif +M07828_2.00 of width 15. +Using motif -M07828_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984944 +Using motif +M08069_2.00 of width 11. +Using motif -M08069_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M08802_2.00 of width 9. +Using motif -M08802_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992784 +Using motif +M04329_2.00 of width 12. +Using motif -M04329_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973821 +Using motif +M04330_2.00 of width 11. +Using motif -M04330_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982314 +Using motif +M05838_2.00 of width 8. +Using motif -M05838_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05839_2.00 of width 10. +Using motif -M05839_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07829_2.00 of width 15. +Using motif -M07829_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983234 +Using motif +M08070_2.00 of width 11. +Using motif -M08070_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M08803_2.00 of width 11. +Using motif -M08803_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996224 +Using motif +M02851_2.00 of width 10. +Using motif -M02851_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M02852_2.00 of width 10. +Using motif -M02852_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04331_2.00 of width 12. +Using motif -M04331_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04332_2.00 of width 12. +Using motif -M04332_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05840_2.00 of width 11. +Using motif -M05840_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07830_2.00 of width 14. +Using motif -M07830_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M07831_2.00 of width 11. +Using motif -M07831_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M07832_2.00 of width 14. +Using motif -M07832_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M08804_2.00 of width 12. +Using motif -M08804_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M09993_2.00 of width 14. +Using motif -M09993_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997789 +Using motif +M09994_2.00 of width 14. +Using motif -M09994_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04035_2.00 of width 10. +Using motif -M04035_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04036_2.00 of width 12. +Using motif -M04036_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967642 +Using motif +M04333_2.00 of width 11. +Using motif -M04333_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M04334_2.00 of width 12. +Using motif -M04334_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97 +Using motif +M04335_2.00 of width 9. +Using motif -M04335_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04336_2.00 of width 11. +Using motif -M04336_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04337_2.00 of width 12. +Using motif -M04337_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04338_2.00 of width 9. +Using motif -M04338_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07833_2.00 of width 11. +Using motif -M07833_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993128 +Using motif +M08071_2.00 of width 11. +Using motif -M08071_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998889 +Using motif +M08805_2.00 of width 12. +Using motif -M08805_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998247 +Using motif +M09490_2.00 of width 12. +Using motif -M09490_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M04037_2.00 of width 9. +Using motif -M04037_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998173 +Using motif +M04038_2.00 of width 10. +Using motif -M04038_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997419 +Using motif +M04339_2.00 of width 12. +Using motif -M04339_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04340_2.00 of width 11. +Using motif -M04340_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04341_2.00 of width 11. +Using motif -M04341_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04342_2.00 of width 11. +Using motif -M04342_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M05841_2.00 of width 11. +Using motif -M05841_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05842_2.00 of width 17. +Using motif -M05842_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07834_2.00 of width 20. +Using motif -M07834_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983187 +Using motif +M07835_2.00 of width 11. +Using motif -M07835_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M07836_2.00 of width 15. +Using motif -M07836_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998154 +Using motif +M07837_2.00 of width 15. +Using motif -M07837_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07838_2.00 of width 11. +Using motif -M07838_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997576 +Using motif +M08072_2.00 of width 13. +Using motif -M08072_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M08073_2.00 of width 14. +Using motif -M08073_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M08806_2.00 of width 11. +Using motif -M08806_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M04343_2.00 of width 9. +Using motif -M04343_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977225 +Using motif +M04344_2.00 of width 9. +Using motif -M04344_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04345_2.00 of width 14. +Using motif -M04345_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998081 +Using motif +M04346_2.00 of width 15. +Using motif -M04346_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995736 +Using motif +M04347_2.00 of width 16. +Using motif -M04347_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M04348_2.00 of width 14. +Using motif -M04348_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977959 +Using motif +M04349_2.00 of width 15. +Using motif -M04349_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971049 +Using motif +M04350_2.00 of width 16. +Using motif -M04350_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996465 +Using motif +M08807_2.00 of width 19. +Using motif -M08807_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990103 +Using motif +M01812_2.00 of width 9. +Using motif -M01812_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930088 +Using motif +M04351_2.00 of width 13. +Using motif -M04351_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M04352_2.00 of width 15. +Using motif -M04352_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987742 +Using motif +M04353_2.00 of width 16. +Using motif -M04353_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971842 +Using motif +M04354_2.00 of width 15. +Using motif -M04354_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98011 +Using motif +M04355_2.00 of width 16. +Using motif -M04355_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957554 +Using motif +M04356_2.00 of width 13. +Using motif -M04356_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02853_2.00 of width 15. +Using motif -M02853_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996181 +Using motif +M04357_2.00 of width 15. +Using motif -M04357_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9775 +Using motif +M04358_2.00 of width 16. +Using motif -M04358_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985896 +Using motif +M04359_2.00 of width 15. +Using motif -M04359_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990682 +Using motif +M04360_2.00 of width 16. +Using motif -M04360_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964878 +Using motif +M04361_2.00 of width 15. +Using motif -M04361_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987283 +Using motif +M04362_2.00 of width 16. +Using motif -M04362_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967077 +Using motif +M04363_2.00 of width 15. +Using motif -M04363_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987609 +Using motif +M04364_2.00 of width 16. +Using motif -M04364_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970645 +Using motif +M07839_2.00 of width 15. +Using motif -M07839_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994948 +Using motif +M08074_2.00 of width 21. +Using motif -M08074_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997889 +Using motif +M08808_2.00 of width 18. +Using motif -M08808_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966296 +Using motif +M02854_2.00 of width 21. +Using motif -M02854_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M04039_2.00 of width 17. +Using motif -M04039_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991505 +Using motif +M04365_2.00 of width 13. +Using motif -M04365_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04366_2.00 of width 15. +Using motif -M04366_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978129 +Using motif +M04367_2.00 of width 16. +Using motif -M04367_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977935 +Using motif +M04368_2.00 of width 13. +Using motif -M04368_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04369_2.00 of width 15. +Using motif -M04369_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984 +Using motif +M04370_2.00 of width 16. +Using motif -M04370_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989689 +Using motif +M04371_2.00 of width 10. +Using motif -M04371_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M04372_2.00 of width 13. +Using motif -M04372_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970395 +Using motif +M04373_2.00 of width 10. +Using motif -M04373_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04374_2.00 of width 13. +Using motif -M04374_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975802 +Using motif +M08809_2.00 of width 18. +Using motif -M08809_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995258 +Using motif +M02855_2.00 of width 12. +Using motif -M02855_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02856_2.00 of width 21. +Using motif -M02856_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999192 +Using motif +M02857_2.00 of width 12. +Using motif -M02857_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M02858_2.00 of width 15. +Using motif -M02858_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997387 +Using motif +M07840_2.00 of width 13. +Using motif -M07840_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988705 +Using motif +M07841_2.00 of width 15. +Using motif -M07841_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M08075_2.00 of width 19. +Using motif -M08075_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M08810_2.00 of width 18. +Using motif -M08810_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M04040_2.00 of width 18. +Using motif -M04040_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993439 +Using motif +M08811_2.00 of width 11. +Using motif -M08811_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990052 +Using motif +M04041_2.00 of width 12. +Using motif -M04041_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953611 +Using motif +M04042_2.00 of width 12. +Using motif -M04042_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911683 +Using motif +M04043_2.00 of width 11. +Using motif -M04043_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97952 +Using motif +M04044_2.00 of width 10. +Using motif -M04044_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04375_2.00 of width 12. +Using motif -M04375_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955878 +Using motif +M04376_2.00 of width 12. +Using motif -M04376_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949421 +Using motif +M04377_2.00 of width 12. +Using motif -M04377_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917 +Using motif +M04378_2.00 of width 12. +Using motif -M04378_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922593 +Using motif +M04379_2.00 of width 12. +Using motif -M04379_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957519 +Using motif +M02859_2.00 of width 10. +Using motif -M02859_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04380_2.00 of width 12. +Using motif -M04380_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04381_2.00 of width 12. +Using motif -M04381_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08812_2.00 of width 11. +Using motif -M08812_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992953 +Using motif +M04045_2.00 of width 12. +Using motif -M04045_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08076_2.00 of width 11. +Using motif -M08076_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08813_2.00 of width 12. +Using motif -M08813_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M10023_2.00 of width 13. +Using motif -M10023_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10024_2.00 of width 14. +Using motif -M10024_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10028_2.00 of width 18. +Using motif -M10028_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10029_2.00 of width 14. +Using motif -M10029_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04382_2.00 of width 12. +Using motif -M04382_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04383_2.00 of width 12. +Using motif -M04383_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01849_2.00 of width 9. +Using motif -M01849_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07842_2.00 of width 8. +Using motif -M07842_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960779 +Using motif +M08080_2.00 of width 11. +Using motif -M08080_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92094 +Using motif +M08190_2.00 of width 10. +Using motif -M08190_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970256 +Using motif +M08848_2.00 of width 10. +Using motif -M08848_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972527 +Using motif +M10089_2.00 of width 13. +Using motif -M10089_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985638 +Using motif +M10090_2.00 of width 12. +Using motif -M10090_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989319 +Using motif +M10091_2.00 of width 12. +Using motif -M10091_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932066 +Using motif +M10092_2.00 of width 9. +Using motif -M10092_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993412 +Using motif +M10093_2.00 of width 11. +Using motif -M10093_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991383 +Using motif +M01165_2.00 of width 10. +Using motif -M01165_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04384_2.00 of width 20. +Using motif -M04384_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9292 +Using motif +M04385_2.00 of width 20. +Using motif -M04385_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976522 +Using motif +M08271_2.00 of width 12. +Using motif -M08271_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96637 +Using motif +M07562_2.00 of width 9. +Using motif -M07562_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995657 +Using motif +M08850_2.00 of width 10. +Using motif -M08850_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894766 +Using motif +M04386_2.00 of width 10. +Using motif -M04386_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945032 +Using motif +M04387_2.00 of width 10. +Using motif -M04387_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934872 +Using motif +M07563_2.00 of width 9. +Using motif -M07563_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921457 +Using motif +M07843_2.00 of width 21. +Using motif -M07843_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.880606 +Using motif +M07844_2.00 of width 15. +Using motif -M07844_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897386 +Using motif +M08082_2.00 of width 21. +Using motif -M08082_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906036 +Using motif +M08272_2.00 of width 21. +Using motif -M08272_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878406 +Using motif +M08851_2.00 of width 20. +Using motif -M08851_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.869281 +Using motif +M00217_2.00 of width 11. +Using motif -M00217_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00218_2.00 of width 10. +Using motif -M00218_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986096 +Using motif +M00219_2.00 of width 10. +Using motif -M00219_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982905 +Using motif +M08232_2.00 of width 6. +Using motif -M08232_2.00 of width 6. +Computing q-values. +Using motif +M08273_2.00 of width 12. +Using motif -M08273_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905862 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04389_2.00 of width 11. +Using motif -M04389_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04390_2.00 of width 19. +Using motif -M04390_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04391_2.00 of width 19. +Using motif -M04391_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07564_2.00 of width 28. +Using motif -M07564_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907304 +Using motif +M08274_2.00 of width 27. +Using motif -M08274_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936076 +Using motif +M08852_2.00 of width 24. +Using motif -M08852_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996263 +Using motif +M00220_2.00 of width 8. +Using motif -M00220_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975 +Using motif +M00221_2.00 of width 7. +Using motif -M00221_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997085 +Using motif +M00222_2.00 of width 10. +Using motif -M00222_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996566 +Using motif +M02873_2.00 of width 9. +Using motif -M02873_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980552 +Using motif +M04392_2.00 of width 10. +Using motif -M04392_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960779 +Using motif +M04393_2.00 of width 10. +Using motif -M04393_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957467 +Using motif +M04394_2.00 of width 10. +Using motif -M04394_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978486 +Using motif +M04395_2.00 of width 10. +Using motif -M04395_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964943 +Using motif +M08853_2.00 of width 10. +Using motif -M08853_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952544 +Using motif +M08275_2.00 of width 15. +Using motif -M08275_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.81215 +Using motif +M08233_2.00 of width 11. +Using motif -M08233_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896066 +Using motif +M10107_2.00 of width 9. +Using motif -M10107_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913088 +Using motif +M02731_2.00 of width 12. +Using motif -M02731_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M02874_2.00 of width 15. +Using motif -M02874_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988854 +Using motif +M04396_2.00 of width 12. +Using motif -M04396_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971751 +Using motif +M04397_2.00 of width 12. +Using motif -M04397_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981398 +Using motif +M07845_2.00 of width 15. +Using motif -M07845_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986875 +Using motif +M08083_2.00 of width 10. +Using motif -M08083_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997374 +Using motif +M08191_2.00 of width 8. +Using motif -M08191_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993403 +Using motif +M08192_2.00 of width 9. +Using motif -M08192_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M08276_2.00 of width 15. +Using motif -M08276_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963687 +Using motif +M08854_2.00 of width 14. +Using motif -M08854_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970164 +Using motif +M09500_2.00 of width 12. +Using motif -M09500_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988969 +Using motif +M08277_2.00 of width 21. +Using motif -M08277_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 1.1e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973542 +Using motif +M08855_2.00 of width 13. +Using motif -M08855_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983878 +Using motif +M04398_2.00 of width 21. +Using motif -M04398_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959255 +Using motif +M04399_2.00 of width 21. +Using motif -M04399_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M08278_2.00 of width 21. +Using motif -M08278_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.895574 +Using motif +M08856_2.00 of width 22. +Using motif -M08856_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937703 +Using motif +M04400_2.00 of width 11. +Using motif -M04400_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902185 +Using motif +M04401_2.00 of width 11. +Using motif -M04401_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897196 +Using motif +M08857_2.00 of width 19. +Using motif -M08857_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896265 +Using motif +M08100_2.00 of width 14. +Using motif -M08100_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.867222 +Using motif +M02641_2.00 of width 11. +Using motif -M02641_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942313 +Using motif +M02732_2.00 of width 10. +Using motif -M02732_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942207 +Using motif +M02875_2.00 of width 14. +Using motif -M02875_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927586 +Using motif +M02876_2.00 of width 12. +Using motif -M02876_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921441 +Using motif +M04402_2.00 of width 15. +Using motif -M04402_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914872 +Using motif +M04403_2.00 of width 15. +Using motif -M04403_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941449 +Using motif +M08279_2.00 of width 9. +Using motif -M08279_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931647 +Using motif +M07565_2.00 of width 18. +Using motif -M07565_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923433 +Using motif +M07566_2.00 of width 15. +Using motif -M07566_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967602 +Using motif +M08280_2.00 of width 15. +Using motif -M08280_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99267 +Using motif +M08858_2.00 of width 23. +Using motif -M08858_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914228 +Using motif +M07567_2.00 of width 12. +Using motif -M07567_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985902 +Using motif +M08234_2.00 of width 18. +Using motif -M08234_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951698 +Using motif +M08281_2.00 of width 12. +Using motif -M08281_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916975 +Using motif +M08859_2.00 of width 20. +Using motif -M08859_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983053 +Using motif +M07568_2.00 of width 30. +Using motif -M07568_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897586 +Using motif +M08235_2.00 of width 15. +Using motif -M08235_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991751 +Using motif +M08282_2.00 of width 12. +Using motif -M08282_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992147 +Using motif +M08860_2.00 of width 24. +Using motif -M08860_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991713 +Using motif +M07846_2.00 of width 14. +Using motif -M07846_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956815 +Using motif +M07847_2.00 of width 15. +Using motif -M07847_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947943 +Using motif +M07848_2.00 of width 16. +Using motif -M07848_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949928 +Using motif +M07849_2.00 of width 15. +Using motif -M07849_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937293 +Using motif +M07850_2.00 of width 15. +Using motif -M07850_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946835 +Using motif +M07851_2.00 of width 16. +Using motif -M07851_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923101 +Using motif +M07852_2.00 of width 21. +Using motif -M07852_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937185 +Using motif +M07853_2.00 of width 15. +Using motif -M07853_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930299 +Using motif +M07854_2.00 of width 15. +Using motif -M07854_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957073 +Using motif +M08084_2.00 of width 21. +Using motif -M08084_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937447 +Using motif +M08861_2.00 of width 22. +Using motif -M08861_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907438 +Using motif +M09501_2.00 of width 20. +Using motif -M09501_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938356 +Using motif +M10117_2.00 of width 21. +Using motif -M10117_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930219 +Using motif +M08283_2.00 of width 12. +Using motif -M08283_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M08862_2.00 of width 16. +Using motif -M08862_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M10125_2.00 of width 16. +Using motif -M10125_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10126_2.00 of width 11. +Using motif -M10126_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10127_2.00 of width 11. +Using motif -M10127_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10128_2.00 of width 15. +Using motif -M10128_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M10129_2.00 of width 11. +Using motif -M10129_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10130_2.00 of width 9. +Using motif -M10130_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08284_2.00 of width 15. +Using motif -M08284_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.861356 +Using motif +M04404_2.00 of width 17. +Using motif -M04404_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93254 +Using motif +M04405_2.00 of width 17. +Using motif -M04405_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914175 +Using motif +M07569_2.00 of width 9. +Using motif -M07569_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932609 +Using motif +M07570_2.00 of width 18. +Using motif -M07570_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985549 +Using motif +M07571_2.00 of width 27. +Using motif -M07571_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983568 +Using motif +M08285_2.00 of width 9. +Using motif -M08285_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981497 +Using motif +M02663_2.00 of width 6. +Using motif -M02663_2.00 of width 6. +Computing q-values. +Using motif +M02664_2.00 of width 10. +Using motif -M02664_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969942 +Using motif +M08236_2.00 of width 7. +Using motif -M08236_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08286_2.00 of width 15. +Using motif -M08286_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981104 +Using motif +M08863_2.00 of width 13. +Using motif -M08863_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958613 +Using motif +M10133_2.00 of width 8. +Using motif -M10133_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939518 +Using motif +M10134_2.00 of width 13. +Using motif -M10134_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960989 +Using motif +M08287_2.00 of width 8. +Using motif -M08287_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936506 +Using motif +M08864_2.00 of width 22. +Using motif -M08864_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.5e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.2e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871975 +Using motif +M10137_2.00 of width 13. +Using motif -M10137_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903896 +Using motif +M02877_2.00 of width 11. +Using motif -M02877_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996907 +Using motif +M04406_2.00 of width 23. +Using motif -M04406_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M04407_2.00 of width 14. +Using motif -M04407_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04408_2.00 of width 23. +Using motif -M04408_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04409_2.00 of width 14. +Using motif -M04409_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05845_2.00 of width 13. +Using motif -M05845_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07855_2.00 of width 15. +Using motif -M07855_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944928 +Using motif +M07856_2.00 of width 11. +Using motif -M07856_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942407 +Using motif +M07857_2.00 of width 15. +Using motif -M07857_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921967 +Using motif +M07858_2.00 of width 15. +Using motif -M07858_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92354 +Using motif +M07859_2.00 of width 15. +Using motif -M07859_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933125 +Using motif +M07860_2.00 of width 15. +Using motif -M07860_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951942 +Using motif +M07861_2.00 of width 13. +Using motif -M07861_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931552 +Using motif +M08085_2.00 of width 12. +Using motif -M08085_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979333 +Using motif +M08237_2.00 of width 15. +Using motif -M08237_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9036 +Using motif +M08288_2.00 of width 12. +Using motif -M08288_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918413 +Using motif +M08865_2.00 of width 12. +Using motif -M08865_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945818 +Using motif +M10138_2.00 of width 17. +Using motif -M10138_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998579 +Using motif +M10139_2.00 of width 20. +Using motif -M10139_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939398 +Using motif +M08866_2.00 of width 10. +Using motif -M08866_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931111 +Using motif +M08867_2.00 of width 9. +Using motif -M08867_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937589 +Using motif +M04410_2.00 of width 10. +Using motif -M04410_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936265 +Using motif +M04411_2.00 of width 10. +Using motif -M04411_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941259 +Using motif +M08086_2.00 of width 10. +Using motif -M08086_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938588 +Using motif +M08868_2.00 of width 14. +Using motif -M08868_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924167 +Using motif +M09502_2.00 of width 10. +Using motif -M09502_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935 +Using motif +M10151_2.00 of width 14. +Using motif -M10151_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953191 +Using motif +M02878_2.00 of width 17. +Using motif -M02878_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955867 +Using motif +M05846_2.00 of width 17. +Using motif -M05846_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897415 +Using motif +M07550_2.00 of width 15. +Using motif -M07550_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93 +Using motif +M07551_2.00 of width 11. +Using motif -M07551_2.00 of width 11. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.9e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995377 +Using motif +M07552_2.00 of width 19. +Using motif -M07552_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991282 +Using motif +M07862_2.00 of width 21. +Using motif -M07862_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878779 +Using motif +M07863_2.00 of width 16. +Using motif -M07863_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894225 +Using motif +M07864_2.00 of width 15. +Using motif -M07864_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919231 +Using motif +M07865_2.00 of width 16. +Using motif -M07865_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.900423 +Using motif +M07866_2.00 of width 15. +Using motif -M07866_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922302 +Using motif +M07867_2.00 of width 13. +Using motif -M07867_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950909 +Using motif +M07868_2.00 of width 15. +Using motif -M07868_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927712 +Using motif +M07869_2.00 of width 15. +Using motif -M07869_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.890853 +Using motif +M07870_2.00 of width 15. +Using motif -M07870_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894032 +Using motif +M07871_2.00 of width 15. +Using motif -M07871_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884167 +Using motif +M07872_2.00 of width 15. +Using motif -M07872_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.895188 +Using motif +M07873_2.00 of width 21. +Using motif -M07873_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871736 +Using motif +M07874_2.00 of width 15. +Using motif -M07874_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893387 +Using motif +M07875_2.00 of width 15. +Using motif -M07875_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87252 +Using motif +M07876_2.00 of width 15. +Using motif -M07876_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.895252 +Using motif +M07877_2.00 of width 15. +Using motif -M07877_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898779 +Using motif +M07878_2.00 of width 15. +Using motif -M07878_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889635 +Using motif +M07879_2.00 of width 15. +Using motif -M07879_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886364 +Using motif +M07880_2.00 of width 15. +Using motif -M07880_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.891927 +Using motif +M07881_2.00 of width 15. +Using motif -M07881_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909173 +Using motif +M07882_2.00 of width 12. +Using motif -M07882_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941519 +Using motif +M07883_2.00 of width 17. +Using motif -M07883_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.876036 +Using motif +M07884_2.00 of width 15. +Using motif -M07884_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901077 +Using motif +M07885_2.00 of width 15. +Using motif -M07885_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889474 +Using motif +M07886_2.00 of width 15. +Using motif -M07886_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893582 +Using motif +M07887_2.00 of width 18. +Using motif -M07887_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892374 +Using motif +M07888_2.00 of width 15. +Using motif -M07888_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927771 +Using motif +M07889_2.00 of width 14. +Using motif -M07889_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923514 +Using motif +M07890_2.00 of width 13. +Using motif -M07890_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931733 +Using motif +M07891_2.00 of width 18. +Using motif -M07891_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892598 +Using motif +M07892_2.00 of width 15. +Using motif -M07892_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924414 +Using motif +M07893_2.00 of width 15. +Using motif -M07893_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914453 +Using motif +M07894_2.00 of width 17. +Using motif -M07894_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.873043 +Using motif +M07895_2.00 of width 18. +Using motif -M07895_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912162 +Using motif +M07896_2.00 of width 15. +Using motif -M07896_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915676 +Using motif +M07897_2.00 of width 15. +Using motif -M07897_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938462 +Using motif +M07898_2.00 of width 14. +Using motif -M07898_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92518 +Using motif +M07899_2.00 of width 20. +Using motif -M07899_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899091 +Using motif +M07900_2.00 of width 17. +Using motif -M07900_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.869667 +Using motif +M07901_2.00 of width 11. +Using motif -M07901_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93493 +Using motif +M07902_2.00 of width 15. +Using motif -M07902_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929929 +Using motif +M07903_2.00 of width 13. +Using motif -M07903_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922636 +Using motif +M07904_2.00 of width 15. +Using motif -M07904_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907077 +Using motif +M07905_2.00 of width 12. +Using motif -M07905_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950414 +Using motif +M07906_2.00 of width 18. +Using motif -M07906_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903248 +Using motif +M07907_2.00 of width 18. +Using motif -M07907_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919857 +Using motif +M07908_2.00 of width 21. +Using motif -M07908_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871206 +Using motif +M07909_2.00 of width 15. +Using motif -M07909_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960121 +Using motif +M07910_2.00 of width 20. +Using motif -M07910_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864317 +Using motif +M07911_2.00 of width 18. +Using motif -M07911_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881185 +Using motif +M07912_2.00 of width 15. +Using motif -M07912_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916993 +Using motif +M07913_2.00 of width 15. +Using motif -M07913_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893919 +Using motif +M07914_2.00 of width 15. +Using motif -M07914_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908243 +Using motif +M07915_2.00 of width 21. +Using motif -M07915_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944103 +Using motif +M07916_2.00 of width 17. +Using motif -M07916_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926197 +Using motif +M07917_2.00 of width 15. +Using motif -M07917_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923404 +Using motif +M07918_2.00 of width 14. +Using motif -M07918_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892444 +Using motif +M07919_2.00 of width 15. +Using motif -M07919_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907114 +Using motif +M07920_2.00 of width 18. +Using motif -M07920_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903158 +Using motif +M07921_2.00 of width 15. +Using motif -M07921_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917343 +Using motif +M07922_2.00 of width 13. +Using motif -M07922_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924865 +Using motif +M07923_2.00 of width 15. +Using motif -M07923_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90803 +Using motif +M07924_2.00 of width 15. +Using motif -M07924_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912057 +Using motif +M07925_2.00 of width 15. +Using motif -M07925_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897258 +Using motif +M08087_2.00 of width 19. +Using motif -M08087_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924681 +Using motif +M08238_2.00 of width 15. +Using motif -M08238_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864463 +Using motif +M08289_2.00 of width 15. +Using motif -M08289_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.866716 +Using motif +M08869_2.00 of width 19. +Using motif -M08869_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904898 +Using motif +M09503_2.00 of width 20. +Using motif -M09503_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904444 +Using motif +M09504_2.00 of width 20. +Using motif -M09504_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990778 +Using motif +M04412_2.00 of width 15. +Using motif -M04412_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04413_2.00 of width 15. +Using motif -M04413_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985946 +Using motif +M04414_2.00 of width 16. +Using motif -M04414_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94 +Using motif +M04415_2.00 of width 16. +Using motif -M04415_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939823 +Using motif +M08290_2.00 of width 9. +Using motif -M08290_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911195 +Using motif +M08870_2.00 of width 22. +Using motif -M08870_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.6e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.875491 +Using motif +M09505_2.00 of width 8. +Using motif -M09505_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879441 +Using motif +M07572_2.00 of width 21. +Using motif -M07572_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952129 +Using motif +M08291_2.00 of width 21. +Using motif -M08291_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980535 +Using motif +M08292_2.00 of width 21. +Using motif -M08292_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997889 +Using motif +M00223_2.00 of width 9. +Using motif -M00223_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940364 +Using motif +M00224_2.00 of width 8. +Using motif -M00224_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897778 +Using motif +M00225_2.00 of width 12. +Using motif -M00225_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00226_2.00 of width 8. +Using motif -M00226_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88785 +Using motif +M08293_2.00 of width 15. +Using motif -M08293_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90962 +Using motif +M08871_2.00 of width 14. +Using motif -M08871_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897517 +Using motif +M08239_2.00 of width 20. +Using motif -M08239_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.890092 +Using motif +M08294_2.00 of width 12. +Using motif -M08294_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928852 +Using motif +M07573_2.00 of width 18. +Using motif -M07573_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974251 +Using motif +M08295_2.00 of width 15. +Using motif -M08295_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992746 +Using motif +M08872_2.00 of width 20. +Using motif -M08872_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996465 +Using motif +M02879_2.00 of width 17. +Using motif -M02879_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935616 +Using motif +M08296_2.00 of width 15. +Using motif -M08296_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918089 +Using motif +M08873_2.00 of width 20. +Using motif -M08873_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892129 +Using motif +M05847_2.00 of width 13. +Using motif -M05847_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994643 +Using motif +M08874_2.00 of width 19. +Using motif -M08874_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964444 +Using motif +M02642_2.00 of width 11. +Using motif -M02642_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943077 +Using motif +M04416_2.00 of width 15. +Using motif -M04416_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914161 +Using motif +M04417_2.00 of width 15. +Using motif -M04417_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916557 +Using motif +M08875_2.00 of width 11. +Using motif -M08875_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959456 +Using motif +M02880_2.00 of width 14. +Using motif -M02880_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929618 +Using motif +M04418_2.00 of width 11. +Using motif -M04418_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923478 +Using motif +M04419_2.00 of width 11. +Using motif -M04419_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906462 +Using motif +M04420_2.00 of width 12. +Using motif -M04420_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894032 +Using motif +M08876_2.00 of width 19. +Using motif -M08876_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.5e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879456 +Using motif +M02881_2.00 of width 13. +Using motif -M02881_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973665 +Using motif +M04421_2.00 of width 20. +Using motif -M04421_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948056 +Using motif +M04422_2.00 of width 20. +Using motif -M04422_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930957 +Using motif +M04423_2.00 of width 20. +Using motif -M04423_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932523 +Using motif +M02643_2.00 of width 11. +Using motif -M02643_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931486 +Using motif +M00227_2.00 of width 10. +Using motif -M00227_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00228_2.00 of width 10. +Using motif -M00228_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04424_2.00 of width 16. +Using motif -M04424_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04425_2.00 of width 16. +Using motif -M04425_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04426_2.00 of width 16. +Using motif -M04426_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04427_2.00 of width 16. +Using motif -M04427_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08877_2.00 of width 13. +Using motif -M08877_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997056 +Using motif +M08878_2.00 of width 19. +Using motif -M08878_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9096 +Using motif +M04428_2.00 of width 18. +Using motif -M04428_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965486 +Using motif +M04429_2.00 of width 18. +Using motif -M04429_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965244 +Using motif +M07574_2.00 of width 9. +Using motif -M07574_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957647 +Using motif +M08240_2.00 of width 14. +Using motif -M08240_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949296 +Using motif +M08297_2.00 of width 15. +Using motif -M08297_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961098 +Using motif +M08298_2.00 of width 11. +Using motif -M08298_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914172 +Using motif +M00130_2.00 of width 10. +Using motif -M00130_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922909 +Using motif +M07575_2.00 of width 18. +Using motif -M07575_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934247 +Using motif +M04430_2.00 of width 11. +Using motif -M04430_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929041 +Using motif +M04431_2.00 of width 12. +Using motif -M04431_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879083 +Using motif +M04432_2.00 of width 11. +Using motif -M04432_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932239 +Using motif +M08299_2.00 of width 15. +Using motif -M08299_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.85854 +Using motif +M08879_2.00 of width 11. +Using motif -M08879_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913165 +Using motif +M10186_2.00 of width 7. +Using motif -M10186_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98337 +Using motif +M08088_2.00 of width 13. +Using motif -M08088_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886667 +Using motif +M08880_2.00 of width 15. +Using motif -M08880_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884769 +Using motif +M04433_2.00 of width 11. +Using motif -M04433_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04434_2.00 of width 11. +Using motif -M04434_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98688 +Using motif +M02882_2.00 of width 17. +Using motif -M02882_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04435_2.00 of width 14. +Using motif -M04435_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04436_2.00 of width 14. +Using motif -M04436_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00983_2.00 of width 10. +Using motif -M00983_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953226 +Using motif +M00984_2.00 of width 8. +Using motif -M00984_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937196 +Using motif +M00985_2.00 of width 10. +Using motif -M00985_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863883 +Using motif +M07926_2.00 of width 15. +Using motif -M07926_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970333 +Using motif +M08881_2.00 of width 17. +Using motif -M08881_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959892 +Using motif +M02883_2.00 of width 14. +Using motif -M02883_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9275 +Using motif +M02884_2.00 of width 14. +Using motif -M02884_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889811 +Using motif +M04437_2.00 of width 13. +Using motif -M04437_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916774 +Using motif +M04438_2.00 of width 13. +Using motif -M04438_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924667 +Using motif +M07927_2.00 of width 20. +Using motif -M07927_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871094 +Using motif +M07928_2.00 of width 15. +Using motif -M07928_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878487 +Using motif +M07929_2.00 of width 20. +Using motif -M07929_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.869037 +Using motif +M08882_2.00 of width 17. +Using motif -M08882_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920599 +Using motif +M09506_2.00 of width 10. +Using motif -M09506_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95103 +Using motif +M10188_2.00 of width 12. +Using motif -M10188_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926609 +Using motif +M07576_2.00 of width 15. +Using motif -M07576_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973837 +Using motif +M08300_2.00 of width 13. +Using motif -M08300_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981183 +Using motif +M08883_2.00 of width 20. +Using motif -M08883_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988796 +Using motif +M07577_2.00 of width 9. +Using motif -M07577_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08301_2.00 of width 9. +Using motif -M08301_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947875 +Using motif +M07578_2.00 of width 9. +Using motif -M07578_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956463 +Using motif +M00229_2.00 of width 9. +Using motif -M00229_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940556 +Using motif +M00230_2.00 of width 10. +Using motif -M00230_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935114 +Using motif +M00231_2.00 of width 8. +Using motif -M00231_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929116 +Using motif +M00232_2.00 of width 10. +Using motif -M00232_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963536 +Using motif +M00233_2.00 of width 10. +Using motif -M00233_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931667 +Using motif +M02885_2.00 of width 11. +Using motif -M02885_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918824 +Using motif +M02886_2.00 of width 15. +Using motif -M02886_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938868 +Using motif +M04439_2.00 of width 13. +Using motif -M04439_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922446 +Using motif +M04440_2.00 of width 13. +Using motif -M04440_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94 +Using motif +M08302_2.00 of width 9. +Using motif -M08302_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914658 +Using motif +M08884_2.00 of width 18. +Using motif -M08884_2.00 of width 18. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91907 +Using motif +M10197_2.00 of width 12. +Using motif -M10197_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957647 +Using motif +M07930_2.00 of width 15. +Using motif -M07930_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.842075 +Using motif +M08089_2.00 of width 14. +Using motif -M08089_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863538 +Using motif +M08885_2.00 of width 17. +Using motif -M08885_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881022 +Using motif +M09507_2.00 of width 20. +Using motif -M09507_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.872615 +Using motif +M04441_2.00 of width 10. +Using motif -M04441_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960503 +Using motif +M04442_2.00 of width 10. +Using motif -M04442_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957412 +Using motif +M04443_2.00 of width 10. +Using motif -M04443_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961899 +Using motif +M04444_2.00 of width 10. +Using motif -M04444_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961084 +Using motif +M08303_2.00 of width 15. +Using motif -M08303_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925378 +Using motif +M08886_2.00 of width 8. +Using motif -M08886_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947516 +Using motif +M07579_2.00 of width 11. +Using motif -M07579_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972609 +Using motif +M08241_2.00 of width 15. +Using motif -M08241_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08304_2.00 of width 9. +Using motif -M08304_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99134 +Using motif +M02665_2.00 of width 20. +Using motif -M02665_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935515 +Using motif +M10203_2.00 of width 14. +Using motif -M10203_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951977 +Using motif +M07580_2.00 of width 30. +Using motif -M07580_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M00234_2.00 of width 10. +Using motif -M00234_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00235_2.00 of width 10. +Using motif -M00235_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04445_2.00 of width 13. +Using motif -M04445_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985 +Using motif +M04446_2.00 of width 13. +Using motif -M04446_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04447_2.00 of width 13. +Using motif -M04447_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988264 +Using motif +M04448_2.00 of width 13. +Using motif -M04448_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08305_2.00 of width 15. +Using motif -M08305_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948029 +Using motif +M08887_2.00 of width 24. +Using motif -M08887_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943067 +Using motif +M01171_2.00 of width 10. +Using motif -M01171_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864286 +Using motif +M02887_2.00 of width 14. +Using motif -M02887_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904242 +Using motif +M04449_2.00 of width 14. +Using motif -M04449_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905273 +Using motif +M04450_2.00 of width 14. +Using motif -M04450_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941944 +Using motif +M04451_2.00 of width 22. +Using motif -M04451_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996667 +Using motif +M04452_2.00 of width 22. +Using motif -M04452_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M08090_2.00 of width 12. +Using motif -M08090_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993939 +Using motif +M08306_2.00 of width 9. +Using motif -M08306_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988513 +Using motif +M08888_2.00 of width 12. +Using motif -M08888_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997462 +Using motif +M00775_2.00 of width 10. +Using motif -M00775_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912308 +Using motif +M07581_2.00 of width 21. +Using motif -M07581_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911636 +Using motif +M00986_2.00 of width 9. +Using motif -M00986_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959712 +Using motif +M00987_2.00 of width 10. +Using motif -M00987_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95181 +Using motif +M00988_2.00 of width 9. +Using motif -M00988_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952364 +Using motif +M04453_2.00 of width 19. +Using motif -M04453_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04454_2.00 of width 19. +Using motif -M04454_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04455_2.00 of width 11. +Using motif -M04455_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903673 +Using motif +M04456_2.00 of width 11. +Using motif -M04456_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.859259 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974149 +Using motif +M08889_2.00 of width 12. +Using motif -M08889_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984896 +Using motif +M02888_2.00 of width 11. +Using motif -M02888_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915924 +Using motif +M04457_2.00 of width 10. +Using motif -M04457_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913906 +Using motif +M04458_2.00 of width 10. +Using motif -M04458_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925691 +Using motif +M07582_2.00 of width 6. +Using motif -M07582_2.00 of width 6. +Computing q-values. +Using motif +M07583_2.00 of width 15. +Using motif -M07583_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07584_2.00 of width 24. +Using motif -M07584_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08242_2.00 of width 9. +Using motif -M08242_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08308_2.00 of width 12. +Using motif -M08308_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991852 +Using motif +M08890_2.00 of width 20. +Using motif -M08890_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07585_2.00 of width 12. +Using motif -M07585_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969071 +Using motif +M08309_2.00 of width 27. +Using motif -M08309_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949294 +Using motif +M08891_2.00 of width 20. +Using motif -M08891_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952982 +Using motif +M08310_2.00 of width 21. +Using motif -M08310_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.858289 +Using motif +M08892_2.00 of width 22. +Using motif -M08892_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864472 +Using motif +M07586_2.00 of width 24. +Using motif -M07586_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.844237 +Using motif +M07587_2.00 of width 24. +Using motif -M07587_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.1e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.845303 +Using motif +M04459_2.00 of width 18. +Using motif -M04459_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940435 +Using motif +M04460_2.00 of width 18. +Using motif -M04460_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980326 +Using motif +M07588_2.00 of width 15. +Using motif -M07588_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912 +Using motif +M02889_2.00 of width 16. +Using motif -M02889_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893504 +Using motif +M02890_2.00 of width 16. +Using motif -M02890_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930909 +Using motif +M04461_2.00 of width 13. +Using motif -M04461_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8824 +Using motif +M04462_2.00 of width 13. +Using motif -M04462_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915286 +Using motif +M10216_2.00 of width 12. +Using motif -M10216_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942133 +Using motif +M01172_2.00 of width 10. +Using motif -M01172_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905379 +Using motif +M08893_2.00 of width 22. +Using motif -M08893_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.9e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879048 +Using motif +M04463_2.00 of width 11. +Using motif -M04463_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924605 +Using motif +M04464_2.00 of width 11. +Using motif -M04464_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918496 +Using motif +M08091_2.00 of width 11. +Using motif -M08091_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943419 +Using motif +M08243_2.00 of width 10. +Using motif -M08243_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924658 +Using motif +M08894_2.00 of width 10. +Using motif -M08894_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922424 +Using motif +M07589_2.00 of width 12. +Using motif -M07589_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917226 +Using motif +M08244_2.00 of width 7. +Using motif -M08244_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988556 +Using motif +M08311_2.00 of width 18. +Using motif -M08311_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91803 +Using motif +M04465_2.00 of width 20. +Using motif -M04465_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M04466_2.00 of width 20. +Using motif -M04466_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998586 +Using motif +M08986_2.00 of width 15. +Using motif -M08986_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927308 +Using motif +M02891_2.00 of width 10. +Using motif -M02891_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908903 +Using motif +M02892_2.00 of width 10. +Using motif -M02892_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896522 +Using motif +M04467_2.00 of width 13. +Using motif -M04467_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896959 +Using motif +M04468_2.00 of width 13. +Using motif -M04468_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899036 +Using motif +M04469_2.00 of width 13. +Using motif -M04469_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904596 +Using motif +M04470_2.00 of width 13. +Using motif -M04470_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.872029 +Using motif +M04471_2.00 of width 16. +Using motif -M04471_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899441 +Using motif +M04472_2.00 of width 16. +Using motif -M04472_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927919 +Using motif +M07590_2.00 of width 33. +Using motif -M07590_2.00 of width 33. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.9e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973641 +Using motif +M07591_2.00 of width 12. +Using motif -M07591_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992143 +Using motif +M08312_2.00 of width 18. +Using motif -M08312_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990515 +Using motif +M08895_2.00 of width 19. +Using motif -M08895_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993333 +Using motif +M05848_2.00 of width 17. +Using motif -M05848_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965549 +Using motif +M07592_2.00 of width 15. +Using motif -M07592_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932814 +Using motif +M04473_2.00 of width 20. +Using motif -M04473_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967792 +Using motif +M04474_2.00 of width 15. +Using motif -M04474_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973413 +Using motif +M04475_2.00 of width 20. +Using motif -M04475_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955338 +Using motif +M04476_2.00 of width 15. +Using motif -M04476_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993814 +Using motif +M07593_2.00 of width 21. +Using motif -M07593_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975029 +Using motif +M04477_2.00 of width 10. +Using motif -M04477_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990543 +Using motif +M04478_2.00 of width 10. +Using motif -M04478_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9875 +Using motif +M04479_2.00 of width 18. +Using motif -M04479_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994974 +Using motif +M04480_2.00 of width 18. +Using motif -M04480_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974465 +Using motif +M07594_2.00 of width 29. +Using motif -M07594_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928696 +Using motif +M07595_2.00 of width 15. +Using motif -M07595_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911462 +Using motif +M07596_2.00 of width 24. +Using motif -M07596_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970769 +Using motif +M07597_2.00 of width 12. +Using motif -M07597_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.5e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987347 +Using motif +M08245_2.00 of width 15. +Using motif -M08245_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974863 +Using motif +M08313_2.00 of width 33. +Using motif -M08313_2.00 of width 33. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931081 +Using motif +M08896_2.00 of width 24. +Using motif -M08896_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947636 +Using motif +M09508_2.00 of width 8. +Using motif -M09508_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9 +Using motif +M08897_2.00 of width 14. +Using motif -M08897_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995417 +Using motif +M09509_2.00 of width 12. +Using motif -M09509_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999381 +Using motif +M07598_2.00 of width 21. +Using motif -M07598_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995773 +Using motif +M07599_2.00 of width 20. +Using motif -M07599_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949831 +Using motif +M08314_2.00 of width 24. +Using motif -M08314_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08898_2.00 of width 22. +Using motif -M08898_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997245 +Using motif +M04481_2.00 of width 12. +Using motif -M04481_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92605 +Using motif +M04482_2.00 of width 12. +Using motif -M04482_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879029 +Using motif +M07600_2.00 of width 8. +Using motif -M07600_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981635 +Using motif +M08315_2.00 of width 15. +Using motif -M08315_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981758 +Using motif +M08899_2.00 of width 20. +Using motif -M08899_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971377 +Using motif +M07601_2.00 of width 21. +Using motif -M07601_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.866015 +Using motif +M04483_2.00 of width 17. +Using motif -M04483_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995625 +Using motif +M04484_2.00 of width 17. +Using motif -M04484_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97645 +Using motif +M02893_2.00 of width 14. +Using motif -M02893_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.883492 +Using motif +M04485_2.00 of width 15. +Using motif -M04485_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903846 +Using motif +M04486_2.00 of width 15. +Using motif -M04486_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9316 +Using motif +M04487_2.00 of width 15. +Using motif -M04487_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916061 +Using motif +M04488_2.00 of width 15. +Using motif -M04488_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951 +Using motif +M08900_2.00 of width 9. +Using motif -M08900_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894923 +Using motif +M10226_2.00 of width 9. +Using motif -M10226_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.883486 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975269 +Using motif +M07602_2.00 of width 15. +Using motif -M07602_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971828 +Using motif +M08316_2.00 of width 12. +Using motif -M08316_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968621 +Using motif +M08901_2.00 of width 12. +Using motif -M08901_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978506 +Using motif +M04489_2.00 of width 22. +Using motif -M04489_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91 +Using motif +M04490_2.00 of width 22. +Using motif -M04490_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90473 +Using motif +M08246_2.00 of width 10. +Using motif -M08246_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923165 +Using motif +M08317_2.00 of width 15. +Using motif -M08317_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.891053 +Using motif +M02894_2.00 of width 15. +Using motif -M02894_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889481 +Using motif +M04491_2.00 of width 16. +Using motif -M04491_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916622 +Using motif +M04492_2.00 of width 16. +Using motif -M04492_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91007 +Using motif +M04493_2.00 of width 15. +Using motif -M04493_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906259 +Using motif +M04494_2.00 of width 15. +Using motif -M04494_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934966 +Using motif +M08902_2.00 of width 15. +Using motif -M08902_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919747 +Using motif +M10229_2.00 of width 9. +Using motif -M10229_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922239 +Using motif +M07603_2.00 of width 18. +Using motif -M07603_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959091 +Using motif +M04495_2.00 of width 25. +Using motif -M04495_2.00 of width 25. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04496_2.00 of width 26. +Using motif -M04496_2.00 of width 26. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07604_2.00 of width 9. +Using motif -M07604_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93664 +Using motif +M07605_2.00 of width 30. +Using motif -M07605_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.1e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984365 +Using motif +M07606_2.00 of width 12. +Using motif -M07606_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958483 +Using motif +M02895_2.00 of width 12. +Using motif -M02895_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949859 +Using motif +M04497_2.00 of width 10. +Using motif -M04497_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9 +Using motif +M04498_2.00 of width 10. +Using motif -M04498_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933007 +Using motif +M08318_2.00 of width 20. +Using motif -M08318_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91303 +Using motif +M08903_2.00 of width 20. +Using motif -M08903_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988265 +Using motif +M07607_2.00 of width 9. +Using motif -M07607_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930462 +Using motif +M07608_2.00 of width 21. +Using motif -M07608_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994564 +Using motif +M08247_2.00 of width 15. +Using motif -M08247_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M08319_2.00 of width 24. +Using motif -M08319_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982826 +Using motif +M08904_2.00 of width 22. +Using motif -M08904_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993229 +Using motif +M08320_2.00 of width 9. +Using motif -M08320_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95814 +Using motif +M08248_2.00 of width 24. +Using motif -M08248_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02896_2.00 of width 17. +Using motif -M02896_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04499_2.00 of width 16. +Using motif -M04499_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M04500_2.00 of width 16. +Using motif -M04500_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04501_2.00 of width 16. +Using motif -M04501_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04502_2.00 of width 16. +Using motif -M04502_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02897_2.00 of width 12. +Using motif -M02897_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97848 +Using motif +M04503_2.00 of width 14. +Using motif -M04503_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98186 +Using motif +M04504_2.00 of width 14. +Using motif -M04504_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96439 +Using motif +M04505_2.00 of width 14. +Using motif -M04505_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980112 +Using motif +M04506_2.00 of width 14. +Using motif -M04506_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987253 +Using motif +M00236_2.00 of width 9. +Using motif -M00236_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00237_2.00 of width 8. +Using motif -M00237_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00238_2.00 of width 9. +Using motif -M00238_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04507_2.00 of width 21. +Using motif -M04507_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997766 +Using motif +M04508_2.00 of width 21. +Using motif -M04508_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995204 +Using motif +M08905_2.00 of width 10. +Using motif -M08905_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M10235_2.00 of width 24. +Using motif -M10235_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M04509_2.00 of width 12. +Using motif -M04509_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919302 +Using motif +M04510_2.00 of width 12. +Using motif -M04510_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932317 +Using motif +M08321_2.00 of width 12. +Using motif -M08321_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.5e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.892298 +Using motif +M08906_2.00 of width 15. +Using motif -M08906_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908916 +Using motif +M07609_2.00 of width 30. +Using motif -M07609_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961985 +Using motif +M08322_2.00 of width 15. +Using motif -M08322_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975529 +Using motif +M04511_2.00 of width 12. +Using motif -M04511_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901606 +Using motif +M04512_2.00 of width 12. +Using motif -M04512_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911586 +Using motif +M04513_2.00 of width 12. +Using motif -M04513_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937059 +Using motif +M04514_2.00 of width 12. +Using motif -M04514_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941071 +Using motif +M08323_2.00 of width 15. +Using motif -M08323_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899871 +Using motif +M08907_2.00 of width 19. +Using motif -M08907_2.00 of width 19. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.890419 +Using motif +M00142_2.00 of width 8. +Using motif -M00142_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97947 +Using motif +M07610_2.00 of width 24. +Using motif -M07610_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.867651 +Using motif +M04515_2.00 of width 21. +Using motif -M04515_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04516_2.00 of width 21. +Using motif -M04516_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970448 +Using motif +M07611_2.00 of width 18. +Using motif -M07611_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.866015 +Using motif +M02898_2.00 of width 12. +Using motif -M02898_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929938 +Using motif +M04517_2.00 of width 12. +Using motif -M04517_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923333 +Using motif +M04518_2.00 of width 12. +Using motif -M04518_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923385 +Using motif +M04519_2.00 of width 12. +Using motif -M04519_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.83211 +Using motif +M04520_2.00 of width 12. +Using motif -M04520_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8375 +Using motif +M04521_2.00 of width 10. +Using motif -M04521_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976517 +Using motif +M04522_2.00 of width 10. +Using motif -M04522_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987021 +Using motif +M08324_2.00 of width 11. +Using motif -M08324_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930336 +Using motif +M08908_2.00 of width 16. +Using motif -M08908_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934304 +Using motif +M08325_2.00 of width 9. +Using motif -M08325_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984718 +Using motif +M00239_2.00 of width 11. +Using motif -M00239_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00240_2.00 of width 11. +Using motif -M00240_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04523_2.00 of width 21. +Using motif -M04523_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994667 +Using motif +M04524_2.00 of width 21. +Using motif -M04524_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M08909_2.00 of width 10. +Using motif -M08909_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995838 +Using motif +M07612_2.00 of width 8. +Using motif -M07612_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917397 +Using motif +M02899_2.00 of width 16. +Using motif -M02899_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M07931_2.00 of width 15. +Using motif -M07931_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992865 +Using motif +M08910_2.00 of width 22. +Using motif -M08910_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956032 +Using motif +M09510_2.00 of width 15. +Using motif -M09510_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957255 +Using motif +M10247_2.00 of width 22. +Using motif -M10247_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983258 +Using motif +M10248_2.00 of width 21. +Using motif -M10248_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94439 +Using motif +M07613_2.00 of width 15. +Using motif -M07613_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M04525_2.00 of width 17. +Using motif -M04525_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939758 +Using motif +M04526_2.00 of width 17. +Using motif -M04526_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933614 +Using motif +M07932_2.00 of width 15. +Using motif -M07932_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903906 +Using motif +M08092_2.00 of width 15. +Using motif -M08092_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898922 +Using motif +M08326_2.00 of width 9. +Using motif -M08326_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905556 +Using motif +M08911_2.00 of width 22. +Using motif -M08911_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.811258 +Using motif +M04527_2.00 of width 19. +Using motif -M04527_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877937 +Using motif +M04528_2.00 of width 19. +Using motif -M04528_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877042 +Using motif +M07614_2.00 of width 21. +Using motif -M07614_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.3e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.811496 +Using motif +M07615_2.00 of width 9. +Using motif -M07615_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909211 +Using motif +M07616_2.00 of width 9. +Using motif -M07616_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.887642 +Using motif +M08327_2.00 of width 9. +Using motif -M08327_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932276 +Using motif +M07617_2.00 of width 15. +Using motif -M07617_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965663 +Using motif +M08249_2.00 of width 14. +Using motif -M08249_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990313 +Using motif +M08328_2.00 of width 12. +Using motif -M08328_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988449 +Using motif +M08912_2.00 of width 20. +Using motif -M08912_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966424 +Using motif +M07618_2.00 of width 12. +Using motif -M07618_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942156 +Using motif +M08329_2.00 of width 15. +Using motif -M08329_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08913_2.00 of width 24. +Using motif -M08913_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07619_2.00 of width 24. +Using motif -M07619_2.00 of width 24. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.829353 +Using motif +M04529_2.00 of width 16. +Using motif -M04529_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.89195 +Using motif +M04530_2.00 of width 16. +Using motif -M04530_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.887613 +Using motif +M07620_2.00 of width 27. +Using motif -M07620_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M02900_2.00 of width 19. +Using motif -M02900_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04531_2.00 of width 14. +Using motif -M04531_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980144 +Using motif +M04532_2.00 of width 14. +Using motif -M04532_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985033 +Using motif +M07621_2.00 of width 15. +Using motif -M07621_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.836752 +Using motif +M08330_2.00 of width 21. +Using motif -M08330_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884112 +Using motif +M02901_2.00 of width 17. +Using motif -M02901_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944088 +Using motif +M07622_2.00 of width 15. +Using motif -M07622_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08331_2.00 of width 15. +Using motif -M08331_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08914_2.00 of width 24. +Using motif -M08914_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997172 +Using motif +M04533_2.00 of width 20. +Using motif -M04533_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969461 +Using motif +M04534_2.00 of width 20. +Using motif -M04534_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970886 +Using motif +M08332_2.00 of width 24. +Using motif -M08332_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999293 +Using motif +M02902_2.00 of width 9. +Using motif -M02902_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946423 +Using motif +M04535_2.00 of width 15. +Using motif -M04535_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04536_2.00 of width 15. +Using motif -M04536_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02903_2.00 of width 18. +Using motif -M02903_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967568 +Using motif +M04537_2.00 of width 13. +Using motif -M04537_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911127 +Using motif +M04538_2.00 of width 13. +Using motif -M04538_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903438 +Using motif +M04539_2.00 of width 13. +Using motif -M04539_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923046 +Using motif +M04540_2.00 of width 13. +Using motif -M04540_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930519 +Using motif +M07623_2.00 of width 21. +Using motif -M07623_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986778 +Using motif +M08333_2.00 of width 15. +Using motif -M08333_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966742 +Using motif +M08915_2.00 of width 20. +Using motif -M08915_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957829 +Using motif +M08250_2.00 of width 21. +Using motif -M08250_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08334_2.00 of width 30. +Using motif -M08334_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994472 +Using motif +M07624_2.00 of width 15. +Using motif -M07624_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956 +Using motif +M08335_2.00 of width 12. +Using motif -M08335_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971948 +Using motif +M07625_2.00 of width 30. +Using motif -M07625_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936 +Using motif +M02904_2.00 of width 17. +Using motif -M02904_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979763 +Using motif +M04541_2.00 of width 14. +Using motif -M04541_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961075 +Using motif +M04542_2.00 of width 14. +Using motif -M04542_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94529 +Using motif +M05849_2.00 of width 11. +Using motif -M05849_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924533 +Using motif +M07626_2.00 of width 6. +Using motif -M07626_2.00 of width 6. +Computing q-values. +Using motif +M08251_2.00 of width 15. +Using motif -M08251_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936149 +Using motif +M08336_2.00 of width 18. +Using motif -M08336_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972552 +Using motif +M04543_2.00 of width 11. +Using motif -M04543_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963053 +Using motif +M04544_2.00 of width 11. +Using motif -M04544_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941429 +Using motif +M05850_2.00 of width 16. +Using motif -M05850_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979 +Using motif +M08252_2.00 of width 15. +Using motif -M08252_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925758 +Using motif +M08337_2.00 of width 15. +Using motif -M08337_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933986 +Using motif +M08253_2.00 of width 13. +Using motif -M08253_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.868067 +Using motif +M08338_2.00 of width 13. +Using motif -M08338_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87719 +Using motif +M07627_2.00 of width 14. +Using motif -M07627_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947534 +Using motif +M07628_2.00 of width 15. +Using motif -M07628_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988511 +Using motif +M02905_2.00 of width 12. +Using motif -M02905_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922222 +Using motif +M02906_2.00 of width 14. +Using motif -M02906_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928288 +Using motif +M04545_2.00 of width 12. +Using motif -M04545_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937121 +Using motif +M04546_2.00 of width 12. +Using motif -M04546_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906038 +Using motif +M08339_2.00 of width 15. +Using motif -M08339_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.900168 +Using motif +M04547_2.00 of width 12. +Using motif -M04547_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04548_2.00 of width 12. +Using motif -M04548_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M08340_2.00 of width 21. +Using motif -M08340_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.814909 +Using motif +M07629_2.00 of width 17. +Using motif -M07629_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M07630_2.00 of width 7. +Using motif -M07630_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986321 +Using motif +M07631_2.00 of width 21. +Using motif -M07631_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04549_2.00 of width 16. +Using motif -M04549_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998469 +Using motif +M04550_2.00 of width 16. +Using motif -M04550_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99373 +Using motif +M07632_2.00 of width 15. +Using motif -M07632_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M07933_2.00 of width 17. +Using motif -M07933_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990785 +Using motif +M07934_2.00 of width 15. +Using motif -M07934_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989688 +Using motif +M07935_2.00 of width 15. +Using motif -M07935_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M07936_2.00 of width 15. +Using motif -M07936_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998586 +Using motif +M08916_2.00 of width 20. +Using motif -M08916_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996615 +Using motif +M07633_2.00 of width 21. +Using motif -M07633_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985397 +Using motif +M07634_2.00 of width 11. +Using motif -M07634_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07635_2.00 of width 21. +Using motif -M07635_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998191 +Using motif +M04551_2.00 of width 15. +Using motif -M04551_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92961 +Using motif +M04552_2.00 of width 12. +Using motif -M04552_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951977 +Using motif +M04553_2.00 of width 15. +Using motif -M04553_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931677 +Using motif +M08341_2.00 of width 21. +Using motif -M08341_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924237 +Using motif +M08917_2.00 of width 20. +Using motif -M08917_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927034 +Using motif +M00241_2.00 of width 8. +Using motif -M00241_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931687 +Using motif +M00242_2.00 of width 8. +Using motif -M00242_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901944 +Using motif +M04554_2.00 of width 22. +Using motif -M04554_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.2e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87536 +Using motif +M04555_2.00 of width 22. +Using motif -M04555_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908645 +Using motif +M02907_2.00 of width 12. +Using motif -M02907_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.853455 +Using motif +M02908_2.00 of width 20. +Using motif -M02908_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.828224 +Using motif +M02909_2.00 of width 19. +Using motif -M02909_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.821982 +Using motif +M08918_2.00 of width 16. +Using motif -M08918_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911 +Using motif +M08093_2.00 of width 13. +Using motif -M08093_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08342_2.00 of width 15. +Using motif -M08342_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.906144 +Using motif +M04556_2.00 of width 14. +Using motif -M04556_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04557_2.00 of width 14. +Using motif -M04557_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08919_2.00 of width 9. +Using motif -M08919_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02910_2.00 of width 11. +Using motif -M02910_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92686 +Using motif +M04558_2.00 of width 13. +Using motif -M04558_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920146 +Using motif +M04559_2.00 of width 13. +Using motif -M04559_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912562 +Using motif +M08920_2.00 of width 20. +Using motif -M08920_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.3e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.829155 +Using motif +M07636_2.00 of width 15. +Using motif -M07636_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992984 +Using motif +M08254_2.00 of width 15. +Using motif -M08254_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935588 +Using motif +M07637_2.00 of width 21. +Using motif -M07637_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977377 +Using motif +M08343_2.00 of width 21. +Using motif -M08343_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990154 +Using motif +M08921_2.00 of width 20. +Using motif -M08921_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986702 +Using motif +M07638_2.00 of width 15. +Using motif -M07638_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935827 +Using motif +M04560_2.00 of width 15. +Using motif -M04560_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941667 +Using motif +M04561_2.00 of width 10. +Using motif -M04561_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.885357 +Using motif +M04562_2.00 of width 10. +Using motif -M04562_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907846 +Using motif +M04563_2.00 of width 15. +Using motif -M04563_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91931 +Using motif +M08344_2.00 of width 21. +Using motif -M08344_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91625 +Using motif +M08922_2.00 of width 11. +Using motif -M08922_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952625 +Using motif +M08445_2.00 of width 12. +Using motif -M08445_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960227 +Using motif +M08923_2.00 of width 12. +Using motif -M08923_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942706 +Using motif +M07639_2.00 of width 21. +Using motif -M07639_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889333 +Using motif +M07640_2.00 of width 24. +Using motif -M07640_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951241 +Using motif +M02911_2.00 of width 16. +Using motif -M02911_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929381 +Using motif +M04564_2.00 of width 15. +Using motif -M04564_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909559 +Using motif +M04565_2.00 of width 15. +Using motif -M04565_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913636 +Using motif +M08345_2.00 of width 14. +Using motif -M08345_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893538 +Using motif +M07641_2.00 of width 15. +Using motif -M07641_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964321 +Using motif +M02912_2.00 of width 15. +Using motif -M02912_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90058 +Using motif +M04566_2.00 of width 15. +Using motif -M04566_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911942 +Using motif +M04567_2.00 of width 15. +Using motif -M04567_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935524 +Using motif +M07642_2.00 of width 15. +Using motif -M07642_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.846202 +Using motif +M08255_2.00 of width 10. +Using motif -M08255_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951579 +Using motif +M07643_2.00 of width 29. +Using motif -M07643_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07644_2.00 of width 7. +Using motif -M07644_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986878 +Using motif +M07645_2.00 of width 6. +Using motif -M07645_2.00 of width 6. +Computing q-values. +Using motif +M07646_2.00 of width 9. +Using motif -M07646_2.00 of width 9. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 4.6e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997085 +Using motif +M05851_2.00 of width 15. +Using motif -M05851_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995313 +Using motif +M07647_2.00 of width 9. +Using motif -M07647_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951481 +Using motif +M04568_2.00 of width 10. +Using motif -M04568_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945068 +Using motif +M04569_2.00 of width 10. +Using motif -M04569_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940741 +Using motif +M08924_2.00 of width 9. +Using motif -M08924_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9475 +Using motif +M04570_2.00 of width 20. +Using motif -M04570_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08094_2.00 of width 15. +Using motif -M08094_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.883301 +Using motif +M08925_2.00 of width 15. +Using motif -M08925_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.888224 +Using motif +M09511_2.00 of width 15. +Using motif -M09511_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884112 +Using motif +M07648_2.00 of width 29. +Using motif -M07648_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M07649_2.00 of width 23. +Using motif -M07649_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.858548 +Using motif +M02666_2.00 of width 6. +Using motif -M02666_2.00 of width 6. +Computing q-values. +Using motif +M07650_2.00 of width 28. +Using motif -M07650_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.856381 +Using motif +M04571_2.00 of width 15. +Using motif -M04571_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.889043 +Using motif +M04572_2.00 of width 14. +Using motif -M04572_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8684 +Using motif +M04573_2.00 of width 15. +Using motif -M04573_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897818 +Using motif +M04574_2.00 of width 14. +Using motif -M04574_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.88708 +Using motif +M07651_2.00 of width 29. +Using motif -M07651_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.859339 +Using motif +M08256_2.00 of width 7. +Using motif -M08256_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989841 +Using motif +M08346_2.00 of width 27. +Using motif -M08346_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925563 +Using motif +M07652_2.00 of width 12. +Using motif -M07652_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.847213 +Using motif +M08347_2.00 of width 12. +Using motif -M08347_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87168 +Using motif +M07653_2.00 of width 12. +Using motif -M07653_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07654_2.00 of width 14. +Using motif -M07654_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929919 +Using motif +M02913_2.00 of width 17. +Using motif -M02913_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04575_2.00 of width 19. +Using motif -M04575_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993503 +Using motif +M04576_2.00 of width 19. +Using motif -M04576_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959091 +Using motif +M04577_2.00 of width 19. +Using motif -M04577_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94 +Using motif +M04578_2.00 of width 19. +Using motif -M04578_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984821 +Using motif +M07655_2.00 of width 11. +Using motif -M07655_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908544 +Using motif +M02914_2.00 of width 12. +Using motif -M02914_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90193 +Using motif +M04579_2.00 of width 11. +Using motif -M04579_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921345 +Using motif +M04580_2.00 of width 11. +Using motif -M04580_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916762 +Using motif +M07937_2.00 of width 15. +Using motif -M07937_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.860313 +Using motif +M08095_2.00 of width 13. +Using motif -M08095_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.879672 +Using motif +M08926_2.00 of width 9. +Using motif -M08926_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913431 +Using motif +M04581_2.00 of width 15. +Using motif -M04581_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979456 +Using motif +M04582_2.00 of width 15. +Using motif -M04582_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994891 +Using motif +M08348_2.00 of width 12. +Using motif -M08348_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969726 +Using motif +M08927_2.00 of width 12. +Using motif -M08927_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966974 +Using motif +M02915_2.00 of width 15. +Using motif -M02915_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.909683 +Using motif +M04583_2.00 of width 13. +Using motif -M04583_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93269 +Using motif +M04584_2.00 of width 13. +Using motif -M04584_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911311 +Using motif +M08349_2.00 of width 9. +Using motif -M08349_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920927 +Using motif +M10279_2.00 of width 12. +Using motif -M10279_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928112 +Using motif +M02916_2.00 of width 13. +Using motif -M02916_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994124 +Using motif +M02917_2.00 of width 13. +Using motif -M02917_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991354 +Using motif +M04585_2.00 of width 13. +Using motif -M04585_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990157 +Using motif +M04586_2.00 of width 13. +Using motif -M04586_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976848 +Using motif +M08257_2.00 of width 12. +Using motif -M08257_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943125 +Using motif +M08350_2.00 of width 9. +Using motif -M08350_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930093 +Using motif +M08928_2.00 of width 11. +Using motif -M08928_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956025 +Using motif +M10282_2.00 of width 12. +Using motif -M10282_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986444 +Using motif +M08351_2.00 of width 9. +Using motif -M08351_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928615 +Using motif +M07656_2.00 of width 15. +Using motif -M07656_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M02918_2.00 of width 10. +Using motif -M02918_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972739 +Using motif +M04587_2.00 of width 11. +Using motif -M04587_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975385 +Using motif +M04588_2.00 of width 11. +Using motif -M04588_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981486 +Using motif +M08352_2.00 of width 9. +Using motif -M08352_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985629 +Using motif +M04589_2.00 of width 15. +Using motif -M04589_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997857 +Using motif +M04590_2.00 of width 15. +Using motif -M04590_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994759 +Using motif +M04591_2.00 of width 15. +Using motif -M04591_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04592_2.00 of width 15. +Using motif -M04592_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969404 +Using motif +M07657_2.00 of width 21. +Using motif -M07657_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911519 +Using motif +M08353_2.00 of width 18. +Using motif -M08353_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931728 +Using motif +M08929_2.00 of width 21. +Using motif -M08929_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93976 +Using motif +M07658_2.00 of width 15. +Using motif -M07658_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.81456 +Using motif +M02919_2.00 of width 15. +Using motif -M02919_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996768 +Using motif +M04593_2.00 of width 17. +Using motif -M04593_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974765 +Using motif +M04594_2.00 of width 17. +Using motif -M04594_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98234 +Using motif +M05852_2.00 of width 13. +Using motif -M05852_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973072 +Using motif +M08354_2.00 of width 15. +Using motif -M08354_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90816 +Using motif +M08258_2.00 of width 15. +Using motif -M08258_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993927 +Using motif +M08355_2.00 of width 18. +Using motif -M08355_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991263 +Using motif +M07659_2.00 of width 21. +Using motif -M07659_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975529 +Using motif +M07660_2.00 of width 30. +Using motif -M07660_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.802273 +Using motif +M05853_2.00 of width 18. +Using motif -M05853_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981648 +Using motif +M08356_2.00 of width 21. +Using motif -M08356_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997387 +Using motif +M08930_2.00 of width 24. +Using motif -M08930_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981466 +Using motif +M07661_2.00 of width 15. +Using motif -M07661_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938113 +Using motif +M08259_2.00 of width 17. +Using motif -M08259_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948333 +Using motif +M08357_2.00 of width 21. +Using motif -M08357_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.872252 +Using motif +M08931_2.00 of width 20. +Using motif -M08931_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935385 +Using motif +M08260_2.00 of width 21. +Using motif -M08260_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.3e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.874321 +Using motif +M08358_2.00 of width 12. +Using motif -M08358_2.00 of width 12. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910357 +Using motif +M08932_2.00 of width 22. +Using motif -M08932_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.1e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.878742 +Using motif +M04595_2.00 of width 12. +Using motif -M04595_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04596_2.00 of width 12. +Using motif -M04596_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971139 +Using motif +M04598_2.00 of width 15. +Using motif -M04598_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976258 +Using motif +M07662_2.00 of width 21. +Using motif -M07662_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.895032 +Using motif +M06465_2.00 of width 14. +Using motif -M06465_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902238 +Using motif +M04599_2.00 of width 12. +Using motif -M04599_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04600_2.00 of width 12. +Using motif -M04600_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08359_2.00 of width 21. +Using motif -M08359_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90626 +Using motif +M08933_2.00 of width 20. +Using motif -M08933_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968272 +Using motif +M07663_2.00 of width 15. +Using motif -M07663_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935526 +Using motif +M07664_2.00 of width 15. +Using motif -M07664_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996162 +Using motif +M07665_2.00 of width 12. +Using motif -M07665_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956306 +Using motif +M08360_2.00 of width 21. +Using motif -M08360_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963353 +Using motif +M08934_2.00 of width 20. +Using motif -M08934_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968362 +Using motif +M08261_2.00 of width 15. +Using motif -M08261_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939125 +Using motif +M08361_2.00 of width 18. +Using motif -M08361_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87969 +Using motif +M08935_2.00 of width 16. +Using motif -M08935_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921765 +Using motif +M07666_2.00 of width 18. +Using motif -M07666_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920952 +Using motif +M07667_2.00 of width 21. +Using motif -M07667_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907702 +Using motif +M08362_2.00 of width 24. +Using motif -M08362_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893208 +Using motif +M08936_2.00 of width 20. +Using motif -M08936_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907532 +Using motif +M07668_2.00 of width 18. +Using motif -M07668_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90585 +Using motif +M07669_2.00 of width 9. +Using motif -M07669_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940645 +Using motif +M04601_2.00 of width 15. +Using motif -M04601_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04602_2.00 of width 15. +Using motif -M04602_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M05854_2.00 of width 15. +Using motif -M05854_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987644 +Using motif +M05855_2.00 of width 15. +Using motif -M05855_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07670_2.00 of width 28. +Using motif -M07670_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M07671_2.00 of width 39. +Using motif -M07671_2.00 of width 39. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.868722 +Using motif +M02920_2.00 of width 12. +Using motif -M02920_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916333 +Using motif +M00243_2.00 of width 7. +Using motif -M00243_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.885042 +Using motif +M00244_2.00 of width 12. +Using motif -M00244_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00245_2.00 of width 7. +Using motif -M00245_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.885167 +Using motif +M00246_2.00 of width 7. +Using motif -M00246_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.891111 +Using motif +M00247_2.00 of width 11. +Using motif -M00247_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00248_2.00 of width 13. +Using motif -M00248_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00249_2.00 of width 11. +Using motif -M00249_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901538 +Using motif +M08937_2.00 of width 20. +Using motif -M08937_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 7.5e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.874847 +Using motif +M07672_2.00 of width 12. +Using motif -M07672_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998384 +Using motif +M07673_2.00 of width 18. +Using motif -M07673_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934516 +Using motif +M07674_2.00 of width 29. +Using motif -M07674_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997588 +Using motif +M07675_2.00 of width 21. +Using motif -M07675_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04603_2.00 of width 17. +Using motif -M04603_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923697 +Using motif +M04604_2.00 of width 17. +Using motif -M04604_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.842157 +Using motif +M02921_2.00 of width 11. +Using motif -M02921_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947051 +Using motif +M04605_2.00 of width 15. +Using motif -M04605_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933947 +Using motif +M04606_2.00 of width 15. +Using motif -M04606_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956914 +Using motif +M08096_2.00 of width 11. +Using motif -M08096_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917037 +Using motif +M08363_2.00 of width 15. +Using motif -M08363_2.00 of width 15. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898734 +Using motif +M08938_2.00 of width 22. +Using motif -M08938_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.6e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.2e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.82869 +Using motif +M10294_2.00 of width 10. +Using motif -M10294_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928105 +Using motif +M10300_2.00 of width 13. +Using motif -M10300_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.894094 +Using motif +M04607_2.00 of width 10. +Using motif -M04607_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967871 +Using motif +M04608_2.00 of width 10. +Using motif -M04608_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950633 +Using motif +M00152_2.00 of width 9. +Using motif -M00152_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973626 +Using motif +M08939_2.00 of width 8. +Using motif -M08939_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976522 +Using motif +M10301_2.00 of width 13. +Using motif -M10301_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10302_2.00 of width 12. +Using motif -M10302_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993011 +Using motif +M10303_2.00 of width 13. +Using motif -M10303_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10306_2.00 of width 9. +Using motif -M10306_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969036 +Using motif +M07676_2.00 of width 21. +Using motif -M07676_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.836794 +Using motif +M07677_2.00 of width 21. +Using motif -M07677_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.848682 +Using motif +M07678_2.00 of width 27. +Using motif -M07678_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 9e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854079 +Using motif +M07679_2.00 of width 9. +Using motif -M07679_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08364_2.00 of width 15. +Using motif -M08364_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923733 +Using motif +M08940_2.00 of width 13. +Using motif -M08940_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951138 +Using motif +M10307_2.00 of width 13. +Using motif -M10307_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953333 +Using motif +M07680_2.00 of width 9. +Using motif -M07680_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961067 +Using motif +M07681_2.00 of width 30. +Using motif -M07681_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910407 +Using motif +M07682_2.00 of width 30. +Using motif -M07682_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.860161 +Using motif +M07683_2.00 of width 12. +Using motif -M07683_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978478 +Using motif +M04609_2.00 of width 15. +Using motif -M04609_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.860943 +Using motif +M04610_2.00 of width 15. +Using motif -M04610_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863048 +Using motif +M07684_2.00 of width 15. +Using motif -M07684_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922857 +Using motif +M04611_2.00 of width 11. +Using motif -M04611_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04612_2.00 of width 11. +Using motif -M04612_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08365_2.00 of width 15. +Using motif -M08365_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.880972 +Using motif +M07685_2.00 of width 12. +Using motif -M07685_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94882 +Using motif +M07686_2.00 of width 18. +Using motif -M07686_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983548 +Using motif +M04613_2.00 of width 19. +Using motif -M04613_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04614_2.00 of width 19. +Using motif -M04614_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08366_2.00 of width 15. +Using motif -M08366_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96578 +Using motif +M08941_2.00 of width 24. +Using motif -M08941_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M07687_2.00 of width 27. +Using motif -M07687_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959487 +Using motif +M07688_2.00 of width 12. +Using motif -M07688_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937481 +Using motif +M07689_2.00 of width 27. +Using motif -M07689_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854305 +Using motif +M07690_2.00 of width 15. +Using motif -M07690_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908288 +Using motif +M08262_2.00 of width 7. +Using motif -M08262_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96537 +Using motif +M08367_2.00 of width 27. +Using motif -M08367_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886863 +Using motif +M04615_2.00 of width 23. +Using motif -M04615_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977099 +Using motif +M04616_2.00 of width 23. +Using motif -M04616_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07691_2.00 of width 21. +Using motif -M07691_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.7e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.840781 +Using motif +M02922_2.00 of width 14. +Using motif -M02922_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979653 +Using motif +M04617_2.00 of width 17. +Using motif -M04617_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961651 +Using motif +M04618_2.00 of width 17. +Using motif -M04618_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964928 +Using motif +M08942_2.00 of width 17. +Using motif -M08942_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901345 +Using motif +M08368_2.00 of width 18. +Using motif -M08368_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930184 +Using motif +M08943_2.00 of width 21. +Using motif -M08943_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930602 +Using motif +M07692_2.00 of width 30. +Using motif -M07692_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993299 +Using motif +M03682_2.00 of width 14. +Using motif -M03682_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92082 +Using motif +M07693_2.00 of width 21. +Using motif -M07693_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9385 +Using motif +M07694_2.00 of width 21. +Using motif -M07694_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912949 +Using motif +M07695_2.00 of width 6. +Using motif -M07695_2.00 of width 6. +Computing q-values. +Using motif +M08263_2.00 of width 22. +Using motif -M08263_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924462 +Using motif +M08369_2.00 of width 9. +Using motif -M08369_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985503 +Using motif +M02923_2.00 of width 14. +Using motif -M02923_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969167 +Using motif +M07696_2.00 of width 9. +Using motif -M07696_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925113 +Using motif +M04619_2.00 of width 10. +Using motif -M04619_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926667 +Using motif +M04620_2.00 of width 10. +Using motif -M04620_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981436 +Using motif +M08264_2.00 of width 15. +Using motif -M08264_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08370_2.00 of width 9. +Using motif -M08370_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921439 +Using motif +M08944_2.00 of width 20. +Using motif -M08944_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07697_2.00 of width 18. +Using motif -M07697_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M07698_2.00 of width 12. +Using motif -M07698_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930677 +Using motif +M07699_2.00 of width 7. +Using motif -M07699_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01173_2.00 of width 11. +Using motif -M01173_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M05856_2.00 of width 17. +Using motif -M05856_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998782 +Using motif +M07700_2.00 of width 18. +Using motif -M07700_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999188 +Using motif +M08371_2.00 of width 15. +Using motif -M08371_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970279 +Using motif +M04621_2.00 of width 14. +Using motif -M04621_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989326 +Using motif +M04622_2.00 of width 14. +Using motif -M04622_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997436 +Using motif +M08372_2.00 of width 24. +Using motif -M08372_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947826 +Using motif +M08945_2.00 of width 24. +Using motif -M08945_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990421 +Using motif +M08373_2.00 of width 18. +Using motif -M08373_2.00 of width 18. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927861 +Using motif +M07701_2.00 of width 18. +Using motif -M07701_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.86063 +Using motif +M07702_2.00 of width 12. +Using motif -M07702_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961326 +Using motif +M05857_2.00 of width 19. +Using motif -M05857_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968992 +Using motif +M07703_2.00 of width 7. +Using motif -M07703_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.848515 +Using motif +M07704_2.00 of width 21. +Using motif -M07704_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949176 +Using motif +M07705_2.00 of width 27. +Using motif -M07705_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992063 +Using motif +M07706_2.00 of width 30. +Using motif -M07706_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996061 +Using motif +M07707_2.00 of width 6. +Using motif -M07707_2.00 of width 6. +Computing q-values. +Using motif +M07708_2.00 of width 27. +Using motif -M07708_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08265_2.00 of width 21. +Using motif -M08265_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08374_2.00 of width 18. +Using motif -M08374_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08946_2.00 of width 24. +Using motif -M08946_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07709_2.00 of width 9. +Using motif -M07709_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967333 +Using motif +M08375_2.00 of width 27. +Using motif -M08375_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967816 +Using motif +M08947_2.00 of width 20. +Using motif -M08947_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07710_2.00 of width 21. +Using motif -M07710_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M07711_2.00 of width 15. +Using motif -M07711_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907241 +Using motif +M07712_2.00 of width 30. +Using motif -M07712_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997071 +Using motif +M08376_2.00 of width 15. +Using motif -M08376_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975301 +Using motif +M08948_2.00 of width 17. +Using motif -M08948_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982211 +Using motif +M02924_2.00 of width 18. +Using motif -M02924_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M04623_2.00 of width 19. +Using motif -M04623_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04624_2.00 of width 19. +Using motif -M04624_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M04625_2.00 of width 19. +Using motif -M04625_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998041 +Using motif +M04626_2.00 of width 19. +Using motif -M04626_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997846 +Using motif +M08377_2.00 of width 24. +Using motif -M08377_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08378_2.00 of width 33. +Using motif -M08378_2.00 of width 33. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979686 +Using motif +M08949_2.00 of width 20. +Using motif -M08949_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989846 +Using motif +M07713_2.00 of width 9. +Using motif -M07713_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998191 +Using motif +M07714_2.00 of width 21. +Using motif -M07714_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924925 +Using motif +M07715_2.00 of width 15. +Using motif -M07715_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864779 +Using motif +M07716_2.00 of width 14. +Using motif -M07716_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932849 +Using motif +M07717_2.00 of width 12. +Using motif -M07717_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.887164 +Using motif +M05858_2.00 of width 11. +Using motif -M05858_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920645 +Using motif +M07718_2.00 of width 21. +Using motif -M07718_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.847541 +Using motif +M07719_2.00 of width 9. +Using motif -M07719_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972961 +Using motif +M08379_2.00 of width 13. +Using motif -M08379_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972118 +Using motif +M08950_2.00 of width 12. +Using motif -M08950_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968095 +Using motif +M07720_2.00 of width 29. +Using motif -M07720_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962061 +Using motif +M00250_2.00 of width 10. +Using motif -M00250_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00251_2.00 of width 9. +Using motif -M00251_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00252_2.00 of width 10. +Using motif -M00252_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08380_2.00 of width 21. +Using motif -M08380_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980314 +Using motif +M07721_2.00 of width 7. +Using motif -M07721_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924189 +Using motif +M07722_2.00 of width 27. +Using motif -M07722_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901678 +Using motif +M08266_2.00 of width 20. +Using motif -M08266_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M08381_2.00 of width 21. +Using motif -M08381_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977514 +Using motif +M07723_2.00 of width 15. +Using motif -M07723_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905103 +Using motif +M07724_2.00 of width 7. +Using motif -M07724_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98254 +Using motif +M07725_2.00 of width 12. +Using motif -M07725_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07726_2.00 of width 15. +Using motif -M07726_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04627_2.00 of width 13. +Using motif -M04627_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877355 +Using motif +M04628_2.00 of width 13. +Using motif -M04628_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90038 +Using motif +M07727_2.00 of width 12. +Using motif -M07727_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.852101 +Using motif +M07728_2.00 of width 18. +Using motif -M07728_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986748 +Using motif +M07729_2.00 of width 24. +Using motif -M07729_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901528 +Using motif +M07730_2.00 of width 24. +Using motif -M07730_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965399 +Using motif +M07731_2.00 of width 30. +Using motif -M07731_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.864561 +Using motif +M07732_2.00 of width 8. +Using motif -M07732_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927863 +Using motif +M08382_2.00 of width 21. +Using motif -M08382_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987795 +Using motif +M07733_2.00 of width 15. +Using motif -M07733_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942727 +Using motif +M07734_2.00 of width 21. +Using motif -M07734_2.00 of width 21. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.814955 +Using motif +M08267_2.00 of width 14. +Using motif -M08267_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994949 +Using motif +M08383_2.00 of width 24. +Using motif -M08383_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925989 +Using motif +M08384_2.00 of width 24. +Using motif -M08384_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.861833 +Using motif +M08951_2.00 of width 20. +Using motif -M08951_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930884 +Using motif +M08952_2.00 of width 22. +Using motif -M08952_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917351 +Using motif +M07735_2.00 of width 9. +Using motif -M07735_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977814 +Using motif +M08268_2.00 of width 17. +Using motif -M08268_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908857 +Using motif +M08385_2.00 of width 12. +Using motif -M08385_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971294 +Using motif +M07736_2.00 of width 21. +Using motif -M07736_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930833 +Using motif +M07737_2.00 of width 27. +Using motif -M07737_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951892 +Using motif +M08386_2.00 of width 18. +Using motif -M08386_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972 +Using motif +M08953_2.00 of width 19. +Using motif -M08953_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927407 +Using motif +M04629_2.00 of width 16. +Using motif -M04629_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.873269 +Using motif +M04630_2.00 of width 16. +Using motif -M04630_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910667 +Using motif +M04631_2.00 of width 16. +Using motif -M04631_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912281 +Using motif +M04632_2.00 of width 16. +Using motif -M04632_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904034 +Using motif +M08387_2.00 of width 9. +Using motif -M08387_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8134 +Using motif +M08954_2.00 of width 9. +Using motif -M08954_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902759 +Using motif +M07738_2.00 of width 6. +Using motif -M07738_2.00 of width 6. +Computing q-values. +Using motif +M07739_2.00 of width 21. +Using motif -M07739_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M08388_2.00 of width 11. +Using motif -M08388_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939766 +Using motif +M08955_2.00 of width 22. +Using motif -M08955_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.4e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.873935 +Using motif +M07740_2.00 of width 28. +Using motif -M07740_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M07741_2.00 of width 15. +Using motif -M07741_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985297 +Using motif +M07742_2.00 of width 29. +Using motif -M07742_2.00 of width 29. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985946 +Using motif +M07743_2.00 of width 7. +Using motif -M07743_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988555 +Using motif +M07744_2.00 of width 11. +Using motif -M07744_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950738 +Using motif +M07745_2.00 of width 12. +Using motif -M07745_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958683 +Using motif +M07746_2.00 of width 24. +Using motif -M07746_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908 +Using motif +M07747_2.00 of width 21. +Using motif -M07747_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989529 +Using motif +M07748_2.00 of width 18. +Using motif -M07748_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.868657 +Using motif +M07749_2.00 of width 18. +Using motif -M07749_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M07750_2.00 of width 12. +Using motif -M07750_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952727 +Using motif +M07751_2.00 of width 15. +Using motif -M07751_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897183 +Using motif +M02938_2.00 of width 13. +Using motif -M02938_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07752_2.00 of width 15. +Using motif -M07752_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04633_2.00 of width 14. +Using motif -M04633_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886481 +Using motif +M04634_2.00 of width 14. +Using motif -M04634_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905303 +Using motif +M04635_2.00 of width 14. +Using motif -M04635_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901062 +Using motif +M04636_2.00 of width 14. +Using motif -M04636_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935044 +Using motif +M08269_2.00 of width 17. +Using motif -M08269_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08389_2.00 of width 9. +Using motif -M08389_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986901 +Using motif +M07753_2.00 of width 18. +Using motif -M07753_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927536 +Using motif +M08390_2.00 of width 15. +Using motif -M08390_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987802 +Using motif +M07754_2.00 of width 12. +Using motif -M07754_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921419 +Using motif +M07755_2.00 of width 15. +Using motif -M07755_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958057 +Using motif +M07756_2.00 of width 6. +Using motif -M07756_2.00 of width 6. +Computing q-values. +Using motif +M08391_2.00 of width 11. +Using motif -M08391_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963372 +Using motif +M08956_2.00 of width 12. +Using motif -M08956_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947515 +Using motif +M07757_2.00 of width 8. +Using motif -M07757_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.859817 +Using motif +M07758_2.00 of width 7. +Using motif -M07758_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915484 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04637_2.00 of width 18. +Using motif -M04637_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937966 +Using motif +M04638_2.00 of width 18. +Using motif -M04638_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962581 +Using motif +M04639_2.00 of width 19. +Using motif -M04639_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959218 +Using motif +M04640_2.00 of width 23. +Using motif -M04640_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04641_2.00 of width 19. +Using motif -M04641_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964828 +Using motif +M04642_2.00 of width 23. +Using motif -M04642_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07759_2.00 of width 18. +Using motif -M07759_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.82016 +Using motif +M07760_2.00 of width 12. +Using motif -M07760_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952857 +Using motif +M08392_2.00 of width 15. +Using motif -M08392_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961529 +Using motif +M08957_2.00 of width 22. +Using motif -M08957_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954932 +Using motif +M07761_2.00 of width 15. +Using motif -M07761_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988526 +Using motif +M07762_2.00 of width 21. +Using motif -M07762_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.862162 +Using motif +M07763_2.00 of width 11. +Using motif -M07763_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02925_2.00 of width 13. +Using motif -M02925_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992609 +Using motif +M04643_2.00 of width 11. +Using motif -M04643_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M04644_2.00 of width 11. +Using motif -M04644_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98 +Using motif +M04645_2.00 of width 12. +Using motif -M04645_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913121 +Using motif +M04646_2.00 of width 12. +Using motif -M04646_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93375 +Using motif +M07764_2.00 of width 27. +Using motif -M07764_2.00 of width 27. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.891733 +Using motif +M07765_2.00 of width 18. +Using motif -M07765_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978105 +Using motif +M02926_2.00 of width 11. +Using motif -M02926_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984932 +Using motif +M02927_2.00 of width 11. +Using motif -M02927_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976709 +Using motif +M02928_2.00 of width 12. +Using motif -M02928_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965833 +Using motif +M04647_2.00 of width 14. +Using motif -M04647_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974016 +Using motif +M04648_2.00 of width 14. +Using motif -M04648_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07766_2.00 of width 39. +Using motif -M07766_2.00 of width 39. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996985 +Using motif +M07767_2.00 of width 12. +Using motif -M07767_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992188 +Using motif +M04649_2.00 of width 19. +Using motif -M04649_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977605 +Using motif +M04650_2.00 of width 19. +Using motif -M04650_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974788 +Using motif +M08270_2.00 of width 16. +Using motif -M08270_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08393_2.00 of width 15. +Using motif -M08393_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991875 +Using motif +M08958_2.00 of width 18. +Using motif -M08958_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915143 +Using motif +M04651_2.00 of width 17. +Using motif -M04651_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04652_2.00 of width 17. +Using motif -M04652_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M07768_2.00 of width 30. +Using motif -M07768_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944 +Using motif +M07769_2.00 of width 21. +Using motif -M07769_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968639 +Using motif +M07770_2.00 of width 9. +Using motif -M07770_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08394_2.00 of width 21. +Using motif -M08394_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953803 +Using motif +M07771_2.00 of width 12. +Using motif -M07771_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.885273 +Using motif +M04653_2.00 of width 22. +Using motif -M04653_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976506 +Using motif +M04654_2.00 of width 22. +Using motif -M04654_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97071 +Using motif +M07772_2.00 of width 18. +Using motif -M07772_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953056 +Using motif +M08395_2.00 of width 15. +Using motif -M08395_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92775 +Using motif +M08959_2.00 of width 24. +Using motif -M08959_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07773_2.00 of width 30. +Using motif -M07773_2.00 of width 30. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 2.7e-05 have been dropped to reclaim memory. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 6.7e-06 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.870839 +Using motif +M07774_2.00 of width 9. +Using motif -M07774_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947386 +Using motif +M07775_2.00 of width 13. +Using motif -M07775_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886727 +Using motif +M07776_2.00 of width 28. +Using motif -M07776_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991414 +Using motif +M08396_2.00 of width 21. +Using motif -M08396_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987204 +Using motif +M08960_2.00 of width 18. +Using motif -M08960_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99202 +Using motif +M02929_2.00 of width 15. +Using motif -M02929_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99 +Using motif +M04655_2.00 of width 11. +Using motif -M04655_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991623 +Using motif +M04656_2.00 of width 11. +Using motif -M04656_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985635 +Using motif +M08397_2.00 of width 12. +Using motif -M08397_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988962 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96446 +Using motif +M02930_2.00 of width 14. +Using motif -M02930_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935075 +Using motif +M04657_2.00 of width 13. +Using motif -M04657_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926347 +Using motif +M04658_2.00 of width 13. +Using motif -M04658_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92 +Using motif +M08398_2.00 of width 14. +Using motif -M08398_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910811 +Using motif +M07777_2.00 of width 21. +Using motif -M07777_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07778_2.00 of width 36. +Using motif -M07778_2.00 of width 36. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995077 +Using motif +M08399_2.00 of width 21. +Using motif -M08399_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995025 +Using motif +M07779_2.00 of width 30. +Using motif -M07779_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.883399 +Using motif +M08400_2.00 of width 12. +Using motif -M08400_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07780_2.00 of width 9. +Using motif -M07780_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.888571 +Using motif +M07781_2.00 of width 12. +Using motif -M07781_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96521 +Using motif +M08401_2.00 of width 18. +Using motif -M08401_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986571 +Using motif +M08961_2.00 of width 22. +Using motif -M08961_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08104_2.00 of width 11. +Using motif -M08104_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989186 +Using motif +M09018_2.00 of width 14. +Using motif -M09018_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10405_2.00 of width 14. +Using motif -M10405_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M04659_2.00 of width 12. +Using motif -M04659_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94982 +Using motif +M04660_2.00 of width 12. +Using motif -M04660_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973373 +Using motif +M02939_2.00 of width 15. +Using motif -M02939_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956975 +Using motif +M01205_2.00 of width 9. +Using motif -M01205_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01203_2.00 of width 9. +Using motif -M01203_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01204_2.00 of width 10. +Using motif -M01204_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991381 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992513 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998163 +Using motif +M04662_2.00 of width 9. +Using motif -M04662_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997677 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944228 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921481 +Using motif +M04664_2.00 of width 15. +Using motif -M04664_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913451 +Using motif +M09519_2.00 of width 10. +Using motif -M09519_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976292 +Using motif +M00735_2.00 of width 10. +Using motif -M00735_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982283 +Using motif +M00736_2.00 of width 10. +Using motif -M00736_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981436 +Using motif +M00737_2.00 of width 10. +Using motif -M00737_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983298 +Using motif +M00738_2.00 of width 8. +Using motif -M00738_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981158 +Using motif +M00739_2.00 of width 9. +Using motif -M00739_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984944 +Using motif +M00740_2.00 of width 10. +Using motif -M00740_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984127 +Using motif +M00741_2.00 of width 10. +Using motif -M00741_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974066 +Using motif +M00742_2.00 of width 9. +Using motif -M00742_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972948 +Using motif +M08105_2.00 of width 10. +Using motif -M08105_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985792 +Using motif +M09020_2.00 of width 11. +Using motif -M09020_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944336 +Using motif +M01499_2.00 of width 9. +Using motif -M01499_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01500_2.00 of width 9. +Using motif -M01500_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02940_2.00 of width 18. +Using motif -M02940_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02941_2.00 of width 10. +Using motif -M02941_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04665_2.00 of width 12. +Using motif -M04665_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04666_2.00 of width 12. +Using motif -M04666_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02942_2.00 of width 14. +Using motif -M02942_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03714_2.00 of width 14. +Using motif -M03714_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04667_2.00 of width 10. +Using motif -M04667_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04668_2.00 of width 10. +Using motif -M04668_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04669_2.00 of width 12. +Using motif -M04669_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04670_2.00 of width 11. +Using motif -M04670_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04671_2.00 of width 12. +Using motif -M04671_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04672_2.00 of width 11. +Using motif -M04672_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02943_2.00 of width 14. +Using motif -M02943_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02944_2.00 of width 14. +Using motif -M02944_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03715_2.00 of width 11. +Using motif -M03715_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04673_2.00 of width 10. +Using motif -M04673_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04674_2.00 of width 10. +Using motif -M04674_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04675_2.00 of width 12. +Using motif -M04675_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04676_2.00 of width 12. +Using motif -M04676_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09022_2.00 of width 11. +Using motif -M09022_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02945_2.00 of width 14. +Using motif -M02945_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04677_2.00 of width 10. +Using motif -M04677_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04678_2.00 of width 10. +Using motif -M04678_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01501_2.00 of width 9. +Using motif -M01501_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01502_2.00 of width 9. +Using motif -M01502_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02946_2.00 of width 17. +Using motif -M02946_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02947_2.00 of width 18. +Using motif -M02947_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02948_2.00 of width 10. +Using motif -M02948_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04679_2.00 of width 10. +Using motif -M04679_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04680_2.00 of width 10. +Using motif -M04680_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09023_2.00 of width 14. +Using motif -M09023_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10422_2.00 of width 12. +Using motif -M10422_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10423_2.00 of width 15. +Using motif -M10423_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10424_2.00 of width 10. +Using motif -M10424_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951176 +Using motif +M10425_2.00 of width 15. +Using motif -M10425_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949143 +Using motif +M10426_2.00 of width 10. +Using motif -M10426_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953 +Using motif +M01911_2.00 of width 9. +Using motif -M01911_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946043 +Using motif +M01916_2.00 of width 8. +Using motif -M01916_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926324 +Using motif +M01912_2.00 of width 10. +Using motif -M01912_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8278 +Using motif +M01913_2.00 of width 10. +Using motif -M01913_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.808039 +Using motif +M01914_2.00 of width 9. +Using motif -M01914_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.886929 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980774 +Using motif +M01915_2.00 of width 10. +Using motif -M01915_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90566 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985823 +Using motif +M01927_2.00 of width 11. +Using motif -M01927_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04681_2.00 of width 13. +Using motif -M04681_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04682_2.00 of width 13. +Using motif -M04682_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01928_2.00 of width 10. +Using motif -M01928_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01929_2.00 of width 8. +Using motif -M01929_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04683_2.00 of width 12. +Using motif -M04683_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04684_2.00 of width 12. +Using motif -M04684_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04685_2.00 of width 10. +Using motif -M04685_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04686_2.00 of width 12. +Using motif -M04686_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04687_2.00 of width 12. +Using motif -M04687_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04688_2.00 of width 10. +Using motif -M04688_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04689_2.00 of width 12. +Using motif -M04689_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04690_2.00 of width 10. +Using motif -M04690_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04691_2.00 of width 12. +Using motif -M04691_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01930_2.00 of width 10. +Using motif -M01930_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04692_2.00 of width 12. +Using motif -M04692_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04693_2.00 of width 12. +Using motif -M04693_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09028_2.00 of width 16. +Using motif -M09028_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M01931_2.00 of width 10. +Using motif -M01931_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04694_2.00 of width 12. +Using motif -M04694_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04695_2.00 of width 12. +Using motif -M04695_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01961_2.00 of width 10. +Using motif -M01961_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.877851 +Using motif +M02949_2.00 of width 18. +Using motif -M02949_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02950_2.00 of width 18. +Using motif -M02950_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02951_2.00 of width 16. +Using motif -M02951_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04696_2.00 of width 11. +Using motif -M04696_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.855726 +Using motif +M04697_2.00 of width 16. +Using motif -M04697_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933271 +Using motif +M04698_2.00 of width 11. +Using motif -M04698_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.820179 +Using motif +M04699_2.00 of width 16. +Using motif -M04699_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09029_2.00 of width 10. +Using motif -M09029_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897241 +Using motif +M02952_2.00 of width 12. +Using motif -M02952_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922115 +Using motif +M02953_2.00 of width 12. +Using motif -M02953_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935574 +Using motif +M02954_2.00 of width 14. +Using motif -M02954_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975514 +Using motif +M02955_2.00 of width 14. +Using motif -M02955_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04700_2.00 of width 16. +Using motif -M04700_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986 +Using motif +M07938_2.00 of width 15. +Using motif -M07938_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.804561 +Using motif +M09030_2.00 of width 14. +Using motif -M09030_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87069 +Using motif +M09521_2.00 of width 10. +Using motif -M09521_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.89792 +Using motif +M10444_2.00 of width 15. +Using motif -M10444_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10445_2.00 of width 12. +Using motif -M10445_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87 +Using motif +M02956_2.00 of width 18. +Using motif -M02956_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02957_2.00 of width 18. +Using motif -M02957_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02958_2.00 of width 18. +Using motif -M02958_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04701_2.00 of width 16. +Using motif -M04701_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04702_2.00 of width 16. +Using motif -M04702_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09031_2.00 of width 11. +Using motif -M09031_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928533 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.899 +Using motif +M02959_2.00 of width 12. +Using motif -M02959_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986923 +Using motif +M04703_2.00 of width 14. +Using motif -M04703_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938983 +Using motif +M04704_2.00 of width 14. +Using motif -M04704_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999278 +Using motif +M09032_2.00 of width 10. +Using motif -M09032_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.897925 +Using motif +M02960_2.00 of width 14. +Using motif -M02960_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04705_2.00 of width 14. +Using motif -M04705_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04706_2.00 of width 14. +Using motif -M04706_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09033_2.00 of width 13. +Using motif -M09033_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.901944 +Using motif +M09522_2.00 of width 12. +Using motif -M09522_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893628 +Using motif +M07939_2.00 of width 11. +Using motif -M07939_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930676 +Using motif +M07940_2.00 of width 11. +Using motif -M07940_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926081 +Using motif +M07941_2.00 of width 11. +Using motif -M07941_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92719 +Using motif +M08107_2.00 of width 11. +Using motif -M08107_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931779 +Using motif +M09034_2.00 of width 13. +Using motif -M09034_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90702 +Using motif +M09523_2.00 of width 10. +Using motif -M09523_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8898 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.84396 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.898361 +Using motif +M09035_2.00 of width 14. +Using motif -M09035_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905616 +Using motif +M02961_2.00 of width 12. +Using motif -M02961_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02962_2.00 of width 12. +Using motif -M02962_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04707_2.00 of width 14. +Using motif -M04707_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960721 +Using motif +M07942_2.00 of width 15. +Using motif -M07942_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.858291 +Using motif +M07943_2.00 of width 15. +Using motif -M07943_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854513 +Using motif +M08109_2.00 of width 11. +Using motif -M08109_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87377 +Using motif +M09036_2.00 of width 13. +Using motif -M09036_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.874729 +Using motif +M09524_2.00 of width 10. +Using motif -M09524_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.856923 +Using motif +M02963_2.00 of width 14. +Using motif -M02963_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958797 +Using motif +M09044_2.00 of width 15. +Using motif -M09044_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958194 +Using motif +M02964_2.00 of width 10. +Using motif -M02964_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986 +Using motif +M04708_2.00 of width 11. +Using motif -M04708_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947194 +Using motif +M04709_2.00 of width 11. +Using motif -M04709_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941846 +Using motif +M04710_2.00 of width 11. +Using motif -M04710_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984286 +Using motif +M04711_2.00 of width 11. +Using motif -M04711_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965652 +Using motif +M09046_2.00 of width 11. +Using motif -M09046_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949296 +Using motif +M09528_2.00 of width 10. +Using motif -M09528_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96454 +Using motif +M04712_2.00 of width 16. +Using motif -M04712_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953214 +Using motif +M04713_2.00 of width 13. +Using motif -M04713_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04714_2.00 of width 16. +Using motif -M04714_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986441 +Using motif +M04715_2.00 of width 13. +Using motif -M04715_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M02965_2.00 of width 14. +Using motif -M02965_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995228 +Using motif +M07944_2.00 of width 19. +Using motif -M07944_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974759 +Using motif +M07945_2.00 of width 19. +Using motif -M07945_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975455 +Using motif +M07946_2.00 of width 15. +Using motif -M07946_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962147 +Using motif +M08026_2.00 of width 15. +Using motif -M08026_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966961 +Using motif +M09047_2.00 of width 17. +Using motif -M09047_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964972 +Using motif +M02966_2.00 of width 12. +Using motif -M02966_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967914 +Using motif +M04716_2.00 of width 12. +Using motif -M04716_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97859 +Using motif +M04717_2.00 of width 12. +Using motif -M04717_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970169 +Using motif +M04718_2.00 of width 12. +Using motif -M04718_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996181 +Using motif +M04719_2.00 of width 12. +Using motif -M04719_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97933 +Using motif +M02967_2.00 of width 11. +Using motif -M02967_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997172 +Using motif +M04720_2.00 of width 11. +Using motif -M04720_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968696 +Using motif +M04721_2.00 of width 11. +Using motif -M04721_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989158 +Using motif +M09048_2.00 of width 16. +Using motif -M09048_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947516 +Using motif +M02968_2.00 of width 10. +Using motif -M02968_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981818 +Using motif +M04722_2.00 of width 12. +Using motif -M04722_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967417 +Using motif +M04723_2.00 of width 12. +Using motif -M04723_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963472 +Using motif +M04724_2.00 of width 12. +Using motif -M04724_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986776 +Using motif +M04725_2.00 of width 12. +Using motif -M04725_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976023 +Using motif +M04726_2.00 of width 12. +Using motif -M04726_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984586 +Using motif +M04727_2.00 of width 12. +Using motif -M04727_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988757 +Using motif +M04728_2.00 of width 12. +Using motif -M04728_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992959 +Using motif +M04729_2.00 of width 12. +Using motif -M04729_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991934 +Using motif +M09049_2.00 of width 13. +Using motif -M09049_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942778 +Using motif +M02969_2.00 of width 10. +Using motif -M02969_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967083 +Using motif +M04730_2.00 of width 11. +Using motif -M04730_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94082 +Using motif +M04731_2.00 of width 12. +Using motif -M04731_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968393 +Using motif +M04732_2.00 of width 11. +Using motif -M04732_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953099 +Using motif +M04733_2.00 of width 10. +Using motif -M04733_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955161 +Using motif +M04734_2.00 of width 12. +Using motif -M04734_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04735_2.00 of width 11. +Using motif -M04735_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969548 +Using motif +M02970_2.00 of width 10. +Using motif -M02970_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987027 +Using motif +M04736_2.00 of width 16. +Using motif -M04736_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988592 +Using motif +M04737_2.00 of width 21. +Using motif -M04737_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96902 +Using motif +M04738_2.00 of width 16. +Using motif -M04738_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04739_2.00 of width 21. +Using motif -M04739_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99602 +Using motif +M02971_2.00 of width 12. +Using motif -M02971_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985442 +Using motif +M02972_2.00 of width 12. +Using motif -M02972_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04740_2.00 of width 12. +Using motif -M04740_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944356 +Using motif +M04741_2.00 of width 12. +Using motif -M04741_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972258 +Using motif +M04742_2.00 of width 12. +Using motif -M04742_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990316 +Using motif +M04743_2.00 of width 12. +Using motif -M04743_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969486 +Using motif +M04744_2.00 of width 12. +Using motif -M04744_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974065 +Using motif +M04745_2.00 of width 12. +Using motif -M04745_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984615 +Using motif +M04746_2.00 of width 12. +Using motif -M04746_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976988 +Using motif +M04747_2.00 of width 12. +Using motif -M04747_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985189 +Using motif +M07947_2.00 of width 15. +Using motif -M07947_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8975 +Using motif +M07948_2.00 of width 14. +Using motif -M07948_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912562 +Using motif +M07949_2.00 of width 11. +Using motif -M07949_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91875 +Using motif +M08198_2.00 of width 10. +Using motif -M08198_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988095 +Using motif +M09050_2.00 of width 14. +Using motif -M09050_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960585 +Using motif +M09529_2.00 of width 10. +Using motif -M09529_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932542 +Using motif +M02973_2.00 of width 11. +Using motif -M02973_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02974_2.00 of width 15. +Using motif -M02974_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97375 +Using motif +M02975_2.00 of width 16. +Using motif -M02975_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M02976_2.00 of width 11. +Using motif -M02976_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9704 +Using motif +M02977_2.00 of width 15. +Using motif -M02977_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984875 +Using motif +M02978_2.00 of width 16. +Using motif -M02978_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M04748_2.00 of width 12. +Using motif -M04748_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95563 +Using motif +M04749_2.00 of width 12. +Using motif -M04749_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99658 +Using motif +M04750_2.00 of width 12. +Using motif -M04750_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963774 +Using motif +M04751_2.00 of width 12. +Using motif -M04751_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09530_2.00 of width 10. +Using motif -M09530_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986879 +Using motif +M02979_2.00 of width 10. +Using motif -M02979_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978013 +Using motif +M02980_2.00 of width 10. +Using motif -M02980_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992376 +Using motif +M02981_2.00 of width 17. +Using motif -M02981_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.912605 +Using motif +M02982_2.00 of width 10. +Using motif -M02982_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94019 +Using motif +M04752_2.00 of width 11. +Using motif -M04752_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94729 +Using motif +M04753_2.00 of width 11. +Using motif -M04753_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943304 +Using motif +M04754_2.00 of width 11. +Using motif -M04754_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963597 +Using motif +M04755_2.00 of width 11. +Using motif -M04755_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967338 +Using motif +M09051_2.00 of width 11. +Using motif -M09051_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965031 +Using motif +M09531_2.00 of width 10. +Using motif -M09531_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956296 +Using motif +M10488_2.00 of width 14. +Using motif -M10488_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961203 +Using motif +M02645_2.00 of width 9. +Using motif -M02645_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988088 +Using motif +M02983_2.00 of width 10. +Using motif -M02983_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982545 +Using motif +M02984_2.00 of width 18. +Using motif -M02984_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954876 +Using motif +M02985_2.00 of width 10. +Using motif -M02985_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967899 +Using motif +M02986_2.00 of width 18. +Using motif -M02986_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944412 +Using motif +M07950_2.00 of width 21. +Using motif -M07950_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940444 +Using motif +M07951_2.00 of width 21. +Using motif -M07951_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939836 +Using motif +M09052_2.00 of width 13. +Using motif -M09052_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959518 +Using motif +M09532_2.00 of width 10. +Using motif -M09532_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977727 +Using motif +M10493_2.00 of width 10. +Using motif -M10493_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979506 +Using motif +M10494_2.00 of width 13. +Using motif -M10494_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M02733_2.00 of width 10. +Using motif -M02733_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989841 +Using motif +M02987_2.00 of width 12. +Using motif -M02987_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986548 +Using motif +M09053_2.00 of width 15. +Using motif -M09053_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978994 +Using motif +M09533_2.00 of width 10. +Using motif -M09533_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985989 +Using motif +M02988_2.00 of width 11. +Using motif -M02988_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99037 +Using motif +M02989_2.00 of width 11. +Using motif -M02989_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993474 +Using motif +M04756_2.00 of width 12. +Using motif -M04756_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966531 +Using motif +M04757_2.00 of width 12. +Using motif -M04757_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984479 +Using motif +M04758_2.00 of width 12. +Using motif -M04758_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972993 +Using motif +M04759_2.00 of width 12. +Using motif -M04759_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978011 +Using motif +M09054_2.00 of width 15. +Using motif -M09054_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96023 +Using motif +M09534_2.00 of width 10. +Using motif -M09534_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991263 +Using motif +M02990_2.00 of width 15. +Using motif -M02990_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922393 +Using motif +M02991_2.00 of width 10. +Using motif -M02991_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971235 +Using motif +M02992_2.00 of width 10. +Using motif -M02992_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953966 +Using motif +M02993_2.00 of width 14. +Using motif -M02993_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971376 +Using motif +M02994_2.00 of width 10. +Using motif -M02994_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971293 +Using motif +M02995_2.00 of width 14. +Using motif -M02995_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958286 +Using motif +M04760_2.00 of width 11. +Using motif -M04760_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972857 +Using motif +M04761_2.00 of width 16. +Using motif -M04761_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960165 +Using motif +M04762_2.00 of width 16. +Using motif -M04762_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97027 +Using motif +M04763_2.00 of width 11. +Using motif -M04763_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985257 +Using motif +M04764_2.00 of width 11. +Using motif -M04764_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975467 +Using motif +M04765_2.00 of width 16. +Using motif -M04765_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971024 +Using motif +M04766_2.00 of width 16. +Using motif -M04766_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967368 +Using motif +M04767_2.00 of width 11. +Using motif -M04767_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967482 +Using motif +M05864_2.00 of width 10. +Using motif -M05864_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M08111_2.00 of width 18. +Using motif -M08111_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948111 +Using motif +M09055_2.00 of width 18. +Using motif -M09055_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.910617 +Using motif +M02996_2.00 of width 10. +Using motif -M02996_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95625 +Using motif +M04768_2.00 of width 11. +Using motif -M04768_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960671 +Using motif +M04769_2.00 of width 11. +Using motif -M04769_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968696 +Using motif +M05865_2.00 of width 11. +Using motif -M05865_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973543 +Using motif +M07952_2.00 of width 11. +Using motif -M07952_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915596 +Using motif +M07953_2.00 of width 11. +Using motif -M07953_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.92928 +Using motif +M07954_2.00 of width 14. +Using motif -M07954_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.90082 +Using motif +M07955_2.00 of width 15. +Using motif -M07955_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881008 +Using motif +M07956_2.00 of width 13. +Using motif -M07956_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917612 +Using motif +M09056_2.00 of width 14. +Using motif -M09056_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923871 +Using motif +M09535_2.00 of width 10. +Using motif -M09535_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950492 +Using motif +M02734_2.00 of width 10. +Using motif -M02734_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975935 +Using motif +M02735_2.00 of width 9. +Using motif -M02735_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976258 +Using motif +M02997_2.00 of width 10. +Using motif -M02997_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977063 +Using motif +M02998_2.00 of width 14. +Using motif -M02998_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984204 +Using motif +M02999_2.00 of width 10. +Using motif -M02999_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998376 +Using motif +M03000_2.00 of width 14. +Using motif -M03000_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96624 +Using motif +M04770_2.00 of width 11. +Using motif -M04770_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979 +Using motif +M04771_2.00 of width 16. +Using motif -M04771_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982192 +Using motif +M04772_2.00 of width 11. +Using motif -M04772_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988984 +Using motif +M04773_2.00 of width 16. +Using motif -M04773_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991056 +Using motif +M04774_2.00 of width 11. +Using motif -M04774_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977765 +Using motif +M04775_2.00 of width 16. +Using motif -M04775_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974932 +Using motif +M04776_2.00 of width 16. +Using motif -M04776_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959417 +Using motif +M04777_2.00 of width 11. +Using motif -M04777_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980494 +Using motif +M09057_2.00 of width 13. +Using motif -M09057_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948276 +Using motif +M09536_2.00 of width 10. +Using motif -M09536_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985475 +Using motif +M04778_2.00 of width 20. +Using motif -M04778_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95009 +Using motif +M04779_2.00 of width 20. +Using motif -M04779_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985056 +Using motif +M09058_2.00 of width 13. +Using motif -M09058_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952069 +Using motif +M03001_2.00 of width 10. +Using motif -M03001_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973245 +Using motif +M04780_2.00 of width 11. +Using motif -M04780_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947869 +Using motif +M04781_2.00 of width 12. +Using motif -M04781_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04782_2.00 of width 11. +Using motif -M04782_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943429 +Using motif +M07957_2.00 of width 10. +Using motif -M07957_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947347 +Using motif +M07958_2.00 of width 11. +Using motif -M07958_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933504 +Using motif +M08112_2.00 of width 11. +Using motif -M08112_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926964 +Using motif +M09059_2.00 of width 12. +Using motif -M09059_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946331 +Using motif +M03002_2.00 of width 12. +Using motif -M03002_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994348 +Using motif +M03003_2.00 of width 13. +Using motif -M03003_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99533 +Using motif +M04783_2.00 of width 23. +Using motif -M04783_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M04784_2.00 of width 13. +Using motif -M04784_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951818 +Using motif +M04785_2.00 of width 13. +Using motif -M04785_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975436 +Using motif +M04786_2.00 of width 12. +Using motif -M04786_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M04787_2.00 of width 23. +Using motif -M04787_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998693 +Using motif +M04788_2.00 of width 23. +Using motif -M04788_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04789_2.00 of width 13. +Using motif -M04789_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94547 +Using motif +M04790_2.00 of width 23. +Using motif -M04790_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M09060_2.00 of width 14. +Using motif -M09060_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981675 +Using motif +M03004_2.00 of width 10. +Using motif -M03004_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982614 +Using motif +M04791_2.00 of width 11. +Using motif -M04791_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963175 +Using motif +M04792_2.00 of width 11. +Using motif -M04792_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967439 +Using motif +M09061_2.00 of width 10. +Using motif -M09061_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915455 +Using motif +M03005_2.00 of width 14. +Using motif -M03005_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M03006_2.00 of width 10. +Using motif -M03006_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980296 +Using motif +M04793_2.00 of width 11. +Using motif -M04793_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956336 +Using motif +M04794_2.00 of width 14. +Using motif -M04794_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976906 +Using motif +M04795_2.00 of width 11. +Using motif -M04795_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978375 +Using motif +M04796_2.00 of width 11. +Using motif -M04796_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961406 +Using motif +M04797_2.00 of width 12. +Using motif -M04797_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04798_2.00 of width 14. +Using motif -M04798_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977853 +Using motif +M04799_2.00 of width 11. +Using motif -M04799_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95968 +Using motif +M09062_2.00 of width 11. +Using motif -M09062_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962431 +Using motif +M03007_2.00 of width 10. +Using motif -M03007_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96112 +Using motif +M04800_2.00 of width 11. +Using motif -M04800_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928696 +Using motif +M04801_2.00 of width 14. +Using motif -M04801_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947229 +Using motif +M04802_2.00 of width 11. +Using motif -M04802_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967034 +Using motif +M09063_2.00 of width 14. +Using motif -M09063_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939543 +Using motif +M01477_2.00 of width 10. +Using motif -M01477_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998687 +Using motif +M02678_2.00 of width 7. +Using motif -M02678_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996985 +Using motif +M03008_2.00 of width 14. +Using motif -M03008_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984624 +Using motif +M04803_2.00 of width 12. +Using motif -M04803_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994315 +Using motif +M04804_2.00 of width 12. +Using motif -M04804_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993503 +Using motif +M04805_2.00 of width 13. +Using motif -M04805_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995102 +Using motif +M04806_2.00 of width 13. +Using motif -M04806_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992959 +Using motif +M09064_2.00 of width 17. +Using motif -M09064_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973904 +Using motif +M09537_2.00 of width 12. +Using motif -M09537_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983915 +Using motif +M03012_2.00 of width 7. +Using motif -M03012_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04807_2.00 of width 17. +Using motif -M04807_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04808_2.00 of width 17. +Using motif -M04808_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01227_2.00 of width 8. +Using motif -M01227_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00253_2.00 of width 11. +Using motif -M00253_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00254_2.00 of width 10. +Using motif -M00254_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00255_2.00 of width 12. +Using motif -M00255_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00256_2.00 of width 13. +Using motif -M00256_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00257_2.00 of width 10. +Using motif -M00257_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00258_2.00 of width 11. +Using motif -M00258_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M00259_2.00 of width 8. +Using motif -M00259_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03013_2.00 of width 13. +Using motif -M03013_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997789 +Using motif +M03014_2.00 of width 14. +Using motif -M03014_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03015_2.00 of width 11. +Using motif -M03015_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09083_2.00 of width 15. +Using motif -M09083_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M10526_2.00 of width 16. +Using motif -M10526_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03016_2.00 of width 14. +Using motif -M03016_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M03017_2.00 of width 8. +Using motif -M03017_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M03018_2.00 of width 13. +Using motif -M03018_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M04809_2.00 of width 14. +Using motif -M04809_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04810_2.00 of width 14. +Using motif -M04810_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09084_2.00 of width 10. +Using motif -M09084_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M10527_2.00 of width 18. +Using motif -M10527_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M10528_2.00 of width 14. +Using motif -M10528_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10530_2.00 of width 13. +Using motif -M10530_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09085_2.00 of width 12. +Using motif -M09085_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997259 +Using motif +M00836_2.00 of width 10. +Using motif -M00836_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00837_2.00 of width 9. +Using motif -M00837_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00838_2.00 of width 8. +Using motif -M00838_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00839_2.00 of width 11. +Using motif -M00839_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01976_2.00 of width 8. +Using motif -M01976_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04811_2.00 of width 30. +Using motif -M04811_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04812_2.00 of width 30. +Using motif -M04812_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08115_2.00 of width 12. +Using motif -M08115_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M09086_2.00 of width 9. +Using motif -M09086_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99066 +Using motif +M09541_2.00 of width 12. +Using motif -M09541_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03019_2.00 of width 14. +Using motif -M03019_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03020_2.00 of width 8. +Using motif -M03020_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03021_2.00 of width 11. +Using motif -M03021_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964575 +Using motif +M04813_2.00 of width 11. +Using motif -M04813_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970057 +Using motif +M04814_2.00 of width 11. +Using motif -M04814_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953372 +Using motif +M09087_2.00 of width 10. +Using motif -M09087_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M10537_2.00 of width 14. +Using motif -M10537_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M04815_2.00 of width 20. +Using motif -M04815_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04816_2.00 of width 20. +Using motif -M04816_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07959_2.00 of width 15. +Using motif -M07959_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09088_2.00 of width 12. +Using motif -M09088_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M09542_2.00 of width 12. +Using motif -M09542_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10540_2.00 of width 15. +Using motif -M10540_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M05866_2.00 of width 10. +Using motif -M05866_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08116_2.00 of width 11. +Using motif -M08116_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996382 +Using motif +M09089_2.00 of width 9. +Using motif -M09089_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M04817_2.00 of width 12. +Using motif -M04817_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04818_2.00 of width 12. +Using motif -M04818_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04819_2.00 of width 16. +Using motif -M04819_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04820_2.00 of width 20. +Using motif -M04820_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04821_2.00 of width 16. +Using motif -M04821_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04822_2.00 of width 20. +Using motif -M04822_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07960_2.00 of width 15. +Using motif -M07960_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07961_2.00 of width 11. +Using motif -M07961_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07962_2.00 of width 11. +Using motif -M07962_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08028_2.00 of width 10. +Using motif -M08028_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997085 +Using motif +M08117_2.00 of width 15. +Using motif -M08117_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08199_2.00 of width 13. +Using motif -M08199_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998081 +Using motif +M09090_2.00 of width 12. +Using motif -M09090_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M09543_2.00 of width 10. +Using motif -M09543_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09544_2.00 of width 16. +Using motif -M09544_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09545_2.00 of width 10. +Using motif -M09545_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998693 +Using motif +M00793_2.00 of width 9. +Using motif -M00793_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02680_2.00 of width 14. +Using motif -M02680_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10548_2.00 of width 16. +Using motif -M10548_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01011_2.00 of width 10. +Using motif -M01011_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920187 +Using motif +M08118_2.00 of width 11. +Using motif -M08118_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03022_2.00 of width 8. +Using motif -M03022_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03023_2.00 of width 14. +Using motif -M03023_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03024_2.00 of width 12. +Using motif -M03024_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971018 +Using motif +M09091_2.00 of width 12. +Using motif -M09091_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972809 +Using motif +M10549_2.00 of width 10. +Using motif -M10549_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M10550_2.00 of width 14. +Using motif -M10550_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M08119_2.00 of width 11. +Using motif -M08119_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09092_2.00 of width 9. +Using motif -M09092_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09546_2.00 of width 12. +Using motif -M09546_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04823_2.00 of width 12. +Using motif -M04823_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04824_2.00 of width 12. +Using motif -M04824_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09093_2.00 of width 12. +Using motif -M09093_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M01977_2.00 of width 10. +Using motif -M01977_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03025_2.00 of width 10. +Using motif -M03025_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04825_2.00 of width 11. +Using motif -M04825_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04826_2.00 of width 11. +Using motif -M04826_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08120_2.00 of width 14. +Using motif -M08120_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09094_2.00 of width 10. +Using motif -M09094_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03027_2.00 of width 17. +Using motif -M03027_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04827_2.00 of width 13. +Using motif -M04827_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04828_2.00 of width 13. +Using motif -M04828_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04829_2.00 of width 12. +Using motif -M04829_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04830_2.00 of width 12. +Using motif -M04830_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998593 +Using motif +M09095_2.00 of width 12. +Using motif -M09095_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M10556_2.00 of width 13. +Using motif -M10556_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997889 +Using motif +M04831_2.00 of width 16. +Using motif -M04831_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04832_2.00 of width 20. +Using motif -M04832_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04833_2.00 of width 16. +Using motif -M04833_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04834_2.00 of width 20. +Using motif -M04834_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09096_2.00 of width 13. +Using motif -M09096_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995306 +Using motif +M01007_2.00 of width 9. +Using motif -M01007_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963077 +Using motif +M04835_2.00 of width 19. +Using motif -M04835_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8488 +Using motif +M04836_2.00 of width 17. +Using motif -M04836_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988138 +Using motif +M03028_2.00 of width 14. +Using motif -M03028_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995876 +Using motif +M03029_2.00 of width 18. +Using motif -M03029_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03030_2.00 of width 11. +Using motif -M03030_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03031_2.00 of width 9. +Using motif -M03031_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04837_2.00 of width 20. +Using motif -M04837_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04838_2.00 of width 20. +Using motif -M04838_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01978_2.00 of width 8. +Using motif -M01978_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03032_2.00 of width 17. +Using motif -M03032_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M03033_2.00 of width 12. +Using motif -M03033_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998298 +Using motif +M04839_2.00 of width 11. +Using motif -M04839_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M04840_2.00 of width 11. +Using motif -M04840_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01008_2.00 of width 8. +Using motif -M01008_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03034_2.00 of width 7. +Using motif -M03034_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03035_2.00 of width 13. +Using motif -M03035_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M10558_2.00 of width 16. +Using motif -M10558_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03036_2.00 of width 14. +Using motif -M03036_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03037_2.00 of width 11. +Using motif -M03037_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M03038_2.00 of width 12. +Using motif -M03038_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M04841_2.00 of width 12. +Using motif -M04841_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997879 +Using motif +M04842_2.00 of width 12. +Using motif -M04842_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04843_2.00 of width 12. +Using motif -M04843_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04844_2.00 of width 12. +Using motif -M04844_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04846_2.00 of width 15. +Using motif -M04846_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M00164_2.00 of width 10. +Using motif -M00164_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04847_2.00 of width 11. +Using motif -M04847_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994973 +Using motif +M04848_2.00 of width 11. +Using motif -M04848_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03039_2.00 of width 14. +Using motif -M03039_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03040_2.00 of width 7. +Using motif -M03040_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03041_2.00 of width 12. +Using motif -M03041_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942 +Using motif +M04849_2.00 of width 10. +Using motif -M04849_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996224 +Using motif +M04850_2.00 of width 10. +Using motif -M04850_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09097_2.00 of width 9. +Using motif -M09097_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M10562_2.00 of width 11. +Using motif -M10562_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M10563_2.00 of width 14. +Using motif -M10563_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99799 +Using motif +M03042_2.00 of width 14. +Using motif -M03042_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03043_2.00 of width 7. +Using motif -M03043_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04851_2.00 of width 14. +Using motif -M04851_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04852_2.00 of width 13. +Using motif -M04852_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04853_2.00 of width 14. +Using motif -M04853_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04854_2.00 of width 13. +Using motif -M04854_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03044_2.00 of width 14. +Using motif -M03044_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03045_2.00 of width 7. +Using motif -M03045_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04855_2.00 of width 12. +Using motif -M04855_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04856_2.00 of width 12. +Using motif -M04856_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10565_2.00 of width 12. +Using motif -M10565_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04857_2.00 of width 17. +Using motif -M04857_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M04858_2.00 of width 17. +Using motif -M04858_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M02736_2.00 of width 12. +Using motif -M02736_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997789 +Using motif +M02737_2.00 of width 8. +Using motif -M02737_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997374 +Using motif +M03046_2.00 of width 8. +Using motif -M03046_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996382 +Using motif +M03047_2.00 of width 14. +Using motif -M03047_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M03048_2.00 of width 13. +Using motif -M03048_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04859_2.00 of width 10. +Using motif -M04859_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04860_2.00 of width 10. +Using motif -M04860_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09098_2.00 of width 13. +Using motif -M09098_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997387 +Using motif +M03049_2.00 of width 14. +Using motif -M03049_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03050_2.00 of width 7. +Using motif -M03050_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03051_2.00 of width 14. +Using motif -M03051_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967931 +Using motif +M01983_2.00 of width 10. +Using motif -M01983_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02681_2.00 of width 8. +Using motif -M02681_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02738_2.00 of width 8. +Using motif -M02738_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02739_2.00 of width 6. +Using motif -M02739_2.00 of width 6. +Computing q-values. +Using motif +M04861_2.00 of width 15. +Using motif -M04861_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04862_2.00 of width 15. +Using motif -M04862_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07963_2.00 of width 11. +Using motif -M07963_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07964_2.00 of width 18. +Using motif -M07964_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994787 +Using motif +M08202_2.00 of width 20. +Using motif -M08202_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957842 +Using motif +M08203_2.00 of width 10. +Using motif -M08203_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09115_2.00 of width 19. +Using motif -M09115_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996784 +Using motif +M09549_2.00 of width 10. +Using motif -M09549_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997273 +Using motif +M10577_2.00 of width 10. +Using motif -M10577_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956696 +Using motif +M10578_2.00 of width 14. +Using motif -M10578_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10579_2.00 of width 14. +Using motif -M10579_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10580_2.00 of width 13. +Using motif -M10580_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10581_2.00 of width 10. +Using motif -M10581_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10582_2.00 of width 10. +Using motif -M10582_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03063_2.00 of width 8. +Using motif -M03063_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03064_2.00 of width 8. +Using motif -M03064_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04863_2.00 of width 12. +Using motif -M04863_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04864_2.00 of width 11. +Using motif -M04864_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04865_2.00 of width 12. +Using motif -M04865_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04866_2.00 of width 11. +Using motif -M04866_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07965_2.00 of width 15. +Using motif -M07965_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09116_2.00 of width 11. +Using motif -M09116_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994242 +Using motif +M10586_2.00 of width 9. +Using motif -M10586_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10587_2.00 of width 10. +Using motif -M10587_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10588_2.00 of width 10. +Using motif -M10588_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03065_2.00 of width 8. +Using motif -M03065_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04867_2.00 of width 10. +Using motif -M04867_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04868_2.00 of width 12. +Using motif -M04868_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04869_2.00 of width 10. +Using motif -M04869_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04870_2.00 of width 12. +Using motif -M04870_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04871_2.00 of width 10. +Using motif -M04871_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04872_2.00 of width 12. +Using motif -M04872_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04873_2.00 of width 10. +Using motif -M04873_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04874_2.00 of width 12. +Using motif -M04874_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03066_2.00 of width 8. +Using motif -M03066_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04875_2.00 of width 10. +Using motif -M04875_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04876_2.00 of width 10. +Using motif -M04876_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09117_2.00 of width 10. +Using motif -M09117_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995204 +Using motif +M04877_2.00 of width 12. +Using motif -M04877_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04878_2.00 of width 12. +Using motif -M04878_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08124_2.00 of width 13. +Using motif -M08124_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M09118_2.00 of width 13. +Using motif -M09118_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M10592_2.00 of width 10. +Using motif -M10592_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04879_2.00 of width 9. +Using motif -M04879_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04880_2.00 of width 9. +Using motif -M04880_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07966_2.00 of width 11. +Using motif -M07966_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07967_2.00 of width 18. +Using motif -M07967_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995567 +Using motif +M07968_2.00 of width 18. +Using motif -M07968_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99899 +Using motif +M07969_2.00 of width 10. +Using motif -M07969_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07970_2.00 of width 10. +Using motif -M07970_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08125_2.00 of width 11. +Using motif -M08125_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09119_2.00 of width 19. +Using motif -M09119_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99551 +Using motif +M09550_2.00 of width 10. +Using motif -M09550_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999188 +Using motif +M10594_2.00 of width 10. +Using motif -M10594_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9464 +Using motif +M10595_2.00 of width 10. +Using motif -M10595_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10596_2.00 of width 10. +Using motif -M10596_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03067_2.00 of width 10. +Using motif -M03067_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948593 +Using motif +M04881_2.00 of width 9. +Using motif -M04881_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949524 +Using motif +M04882_2.00 of width 9. +Using motif -M04882_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981932 +Using motif +M02020_2.00 of width 9. +Using motif -M02020_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918699 +Using motif +M03068_2.00 of width 10. +Using motif -M03068_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975625 +Using motif +M03069_2.00 of width 16. +Using motif -M03069_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950365 +Using motif +M03070_2.00 of width 11. +Using motif -M03070_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926667 +Using motif +M04883_2.00 of width 9. +Using motif -M04883_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942754 +Using motif +M04884_2.00 of width 9. +Using motif -M04884_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968857 +Using motif +M05868_2.00 of width 9. +Using motif -M05868_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949116 +Using motif +M08129_2.00 of width 15. +Using motif -M08129_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M09125_2.00 of width 12. +Using motif -M09125_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986292 +Using motif +M09558_2.00 of width 20. +Using motif -M09558_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989198 +Using motif +M04885_2.00 of width 16. +Using motif -M04885_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946624 +Using motif +M04886_2.00 of width 16. +Using motif -M04886_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951948 +Using motif +M03071_2.00 of width 17. +Using motif -M03071_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984663 +Using motif +M03072_2.00 of width 10. +Using motif -M03072_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03073_2.00 of width 12. +Using motif -M03073_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04887_2.00 of width 10. +Using motif -M04887_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04888_2.00 of width 10. +Using motif -M04888_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03074_2.00 of width 10. +Using motif -M03074_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03075_2.00 of width 16. +Using motif -M03075_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04889_2.00 of width 18. +Using motif -M04889_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980491 +Using motif +M04890_2.00 of width 18. +Using motif -M04890_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973497 +Using motif +M04891_2.00 of width 18. +Using motif -M04891_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04892_2.00 of width 18. +Using motif -M04892_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10610_2.00 of width 11. +Using motif -M10610_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948289 +Using motif +M04893_2.00 of width 18. +Using motif -M04893_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945316 +Using motif +M04894_2.00 of width 18. +Using motif -M04894_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968902 +Using motif +M00260_2.00 of width 11. +Using motif -M00260_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00261_2.00 of width 9. +Using motif -M00261_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00262_2.00 of width 8. +Using motif -M00262_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00263_2.00 of width 10. +Using motif -M00263_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00264_2.00 of width 9. +Using motif -M00264_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00265_2.00 of width 8. +Using motif -M00265_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03076_2.00 of width 13. +Using motif -M03076_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04895_2.00 of width 8. +Using motif -M04895_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04896_2.00 of width 8. +Using motif -M04896_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04897_2.00 of width 11. +Using motif -M04897_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04898_2.00 of width 11. +Using motif -M04898_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04899_2.00 of width 11. +Using motif -M04899_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04900_2.00 of width 11. +Using motif -M04900_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03077_2.00 of width 10. +Using motif -M03077_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04901_2.00 of width 8. +Using motif -M04901_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970642 +Using motif +M04902_2.00 of width 8. +Using motif -M04902_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04903_2.00 of width 8. +Using motif -M04903_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04904_2.00 of width 8. +Using motif -M04904_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03078_2.00 of width 8. +Using motif -M03078_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04905_2.00 of width 8. +Using motif -M04905_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04906_2.00 of width 8. +Using motif -M04906_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04907_2.00 of width 8. +Using motif -M04907_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04908_2.00 of width 8. +Using motif -M04908_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04909_2.00 of width 8. +Using motif -M04909_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04910_2.00 of width 8. +Using motif -M04910_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04911_2.00 of width 8. +Using motif -M04911_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09128_2.00 of width 9. +Using motif -M09128_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04912_2.00 of width 8. +Using motif -M04912_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04913_2.00 of width 8. +Using motif -M04913_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04914_2.00 of width 8. +Using motif -M04914_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04915_2.00 of width 12. +Using motif -M04915_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04916_2.00 of width 12. +Using motif -M04916_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03079_2.00 of width 13. +Using motif -M03079_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04917_2.00 of width 8. +Using motif -M04917_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04918_2.00 of width 8. +Using motif -M04918_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04919_2.00 of width 8. +Using motif -M04919_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03080_2.00 of width 10. +Using motif -M03080_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04920_2.00 of width 8. +Using motif -M04920_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04921_2.00 of width 8. +Using motif -M04921_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01503_2.00 of width 8. +Using motif -M01503_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01504_2.00 of width 8. +Using motif -M01504_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03081_2.00 of width 8. +Using motif -M03081_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04922_2.00 of width 8. +Using motif -M04922_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04923_2.00 of width 8. +Using motif -M04923_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04924_2.00 of width 8. +Using motif -M04924_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04925_2.00 of width 8. +Using motif -M04925_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04926_2.00 of width 8. +Using motif -M04926_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04927_2.00 of width 8. +Using motif -M04927_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09129_2.00 of width 10. +Using motif -M09129_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03082_2.00 of width 9. +Using motif -M03082_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03083_2.00 of width 9. +Using motif -M03083_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03084_2.00 of width 8. +Using motif -M03084_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04928_2.00 of width 8. +Using motif -M04928_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04929_2.00 of width 8. +Using motif -M04929_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04930_2.00 of width 8. +Using motif -M04930_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04931_2.00 of width 8. +Using motif -M04931_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03085_2.00 of width 12. +Using motif -M03085_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03086_2.00 of width 11. +Using motif -M03086_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04932_2.00 of width 10. +Using motif -M04932_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04933_2.00 of width 10. +Using motif -M04933_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04934_2.00 of width 10. +Using motif -M04934_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04935_2.00 of width 10. +Using motif -M04935_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09130_2.00 of width 12. +Using motif -M09130_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09131_2.00 of width 11. +Using motif -M09131_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04936_2.00 of width 8. +Using motif -M04936_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04937_2.00 of width 8. +Using motif -M04937_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04938_2.00 of width 8. +Using motif -M04938_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04939_2.00 of width 10. +Using motif -M04939_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924727 +Using motif +M04940_2.00 of width 10. +Using motif -M04940_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932277 +Using motif +M00266_2.00 of width 9. +Using motif -M00266_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00267_2.00 of width 9. +Using motif -M00267_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00268_2.00 of width 9. +Using motif -M00268_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00269_2.00 of width 9. +Using motif -M00269_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03087_2.00 of width 8. +Using motif -M03087_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03088_2.00 of width 11. +Using motif -M03088_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04941_2.00 of width 8. +Using motif -M04941_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04942_2.00 of width 8. +Using motif -M04942_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04943_2.00 of width 8. +Using motif -M04943_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04944_2.00 of width 8. +Using motif -M04944_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04945_2.00 of width 8. +Using motif -M04945_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04946_2.00 of width 8. +Using motif -M04946_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05869_2.00 of width 10. +Using motif -M05869_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03089_2.00 of width 9. +Using motif -M03089_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03090_2.00 of width 8. +Using motif -M03090_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03091_2.00 of width 9. +Using motif -M03091_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03092_2.00 of width 8. +Using motif -M03092_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04947_2.00 of width 8. +Using motif -M04947_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04948_2.00 of width 8. +Using motif -M04948_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00270_2.00 of width 7. +Using motif -M00270_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00272_2.00 of width 8. +Using motif -M00272_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00273_2.00 of width 11. +Using motif -M00273_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00274_2.00 of width 10. +Using motif -M00274_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00275_2.00 of width 10. +Using motif -M00275_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04949_2.00 of width 8. +Using motif -M04949_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04950_2.00 of width 8. +Using motif -M04950_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09132_2.00 of width 13. +Using motif -M09132_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M03093_2.00 of width 8. +Using motif -M03093_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M03094_2.00 of width 12. +Using motif -M03094_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967097 +Using motif +M04951_2.00 of width 12. +Using motif -M04951_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98593 +Using motif +M04952_2.00 of width 12. +Using motif -M04952_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946496 +Using motif +M00276_2.00 of width 11. +Using motif -M00276_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00277_2.00 of width 10. +Using motif -M00277_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03095_2.00 of width 8. +Using motif -M03095_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04953_2.00 of width 8. +Using motif -M04953_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04954_2.00 of width 8. +Using motif -M04954_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04955_2.00 of width 8. +Using motif -M04955_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04956_2.00 of width 8. +Using motif -M04956_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04957_2.00 of width 8. +Using motif -M04957_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04958_2.00 of width 8. +Using motif -M04958_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03096_2.00 of width 10. +Using motif -M03096_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04959_2.00 of width 8. +Using motif -M04959_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04960_2.00 of width 8. +Using motif -M04960_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04961_2.00 of width 8. +Using motif -M04961_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09133_2.00 of width 10. +Using motif -M09133_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998384 +Using motif +M02083_2.00 of width 9. +Using motif -M02083_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03097_2.00 of width 10. +Using motif -M03097_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04962_2.00 of width 8. +Using motif -M04962_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04963_2.00 of width 8. +Using motif -M04963_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04964_2.00 of width 8. +Using motif -M04964_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04965_2.00 of width 8. +Using motif -M04965_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04966_2.00 of width 8. +Using motif -M04966_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04967_2.00 of width 8. +Using motif -M04967_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10651_2.00 of width 9. +Using motif -M10651_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04968_2.00 of width 8. +Using motif -M04968_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04969_2.00 of width 8. +Using motif -M04969_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04970_2.00 of width 8. +Using motif -M04970_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04971_2.00 of width 8. +Using motif -M04971_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04972_2.00 of width 8. +Using motif -M04972_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04973_2.00 of width 8. +Using motif -M04973_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08457_2.00 of width 8. +Using motif -M08457_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10652_2.00 of width 30. +Using motif -M10652_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M04974_2.00 of width 10. +Using motif -M04974_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04975_2.00 of width 10. +Using motif -M04975_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04976_2.00 of width 8. +Using motif -M04976_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03098_2.00 of width 10. +Using motif -M03098_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03099_2.00 of width 11. +Using motif -M03099_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03100_2.00 of width 10. +Using motif -M03100_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03101_2.00 of width 11. +Using motif -M03101_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04977_2.00 of width 11. +Using motif -M04977_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04978_2.00 of width 11. +Using motif -M04978_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04979_2.00 of width 11. +Using motif -M04979_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04980_2.00 of width 11. +Using motif -M04980_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04981_2.00 of width 11. +Using motif -M04981_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04982_2.00 of width 11. +Using motif -M04982_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04983_2.00 of width 11. +Using motif -M04983_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04984_2.00 of width 11. +Using motif -M04984_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09134_2.00 of width 11. +Using motif -M09134_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03102_2.00 of width 10. +Using motif -M03102_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04985_2.00 of width 8. +Using motif -M04985_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04986_2.00 of width 8. +Using motif -M04986_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04987_2.00 of width 8. +Using motif -M04987_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02084_2.00 of width 10. +Using motif -M02084_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09135_2.00 of width 9. +Using motif -M09135_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03103_2.00 of width 10. +Using motif -M03103_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03104_2.00 of width 17. +Using motif -M03104_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03105_2.00 of width 14. +Using motif -M03105_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04988_2.00 of width 8. +Using motif -M04988_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04989_2.00 of width 8. +Using motif -M04989_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04990_2.00 of width 8. +Using motif -M04990_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04991_2.00 of width 8. +Using motif -M04991_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02085_2.00 of width 10. +Using motif -M02085_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03106_2.00 of width 10. +Using motif -M03106_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03107_2.00 of width 15. +Using motif -M03107_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09136_2.00 of width 12. +Using motif -M09136_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02086_2.00 of width 8. +Using motif -M02086_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03108_2.00 of width 10. +Using motif -M03108_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03109_2.00 of width 16. +Using motif -M03109_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03110_2.00 of width 12. +Using motif -M03110_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04992_2.00 of width 8. +Using motif -M04992_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04993_2.00 of width 12. +Using motif -M04993_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04994_2.00 of width 8. +Using motif -M04994_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04995_2.00 of width 8. +Using motif -M04995_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04996_2.00 of width 12. +Using motif -M04996_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05870_2.00 of width 8. +Using motif -M05870_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09137_2.00 of width 13. +Using motif -M09137_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03111_2.00 of width 9. +Using motif -M03111_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04997_2.00 of width 8. +Using motif -M04997_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04998_2.00 of width 8. +Using motif -M04998_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M04999_2.00 of width 8. +Using motif -M04999_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05000_2.00 of width 8. +Using motif -M05000_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05001_2.00 of width 8. +Using motif -M05001_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03112_2.00 of width 8. +Using motif -M03112_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05002_2.00 of width 8. +Using motif -M05002_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05003_2.00 of width 8. +Using motif -M05003_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05004_2.00 of width 8. +Using motif -M05004_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05005_2.00 of width 8. +Using motif -M05005_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05006_2.00 of width 8. +Using motif -M05006_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05007_2.00 of width 8. +Using motif -M05007_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00278_2.00 of width 7. +Using motif -M00278_2.00 of width 7. +Computing q-values. +Using motif +M00279_2.00 of width 11. +Using motif -M00279_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00280_2.00 of width 13. +Using motif -M00280_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00281_2.00 of width 10. +Using motif -M00281_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932477 +Using motif +M00282_2.00 of width 8. +Using motif -M00282_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03113_2.00 of width 11. +Using motif -M03113_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03114_2.00 of width 11. +Using motif -M03114_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05008_2.00 of width 8. +Using motif -M05008_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05009_2.00 of width 8. +Using motif -M05009_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05010_2.00 of width 11. +Using motif -M05010_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05011_2.00 of width 11. +Using motif -M05011_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05012_2.00 of width 10. +Using motif -M05012_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99797 +Using motif +M05013_2.00 of width 10. +Using motif -M05013_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05014_2.00 of width 8. +Using motif -M05014_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968119 +Using motif +M09138_2.00 of width 10. +Using motif -M09138_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00451_2.00 of width 8. +Using motif -M00451_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05016_2.00 of width 7. +Using motif -M05016_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05017_2.00 of width 8. +Using motif -M05017_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09139_2.00 of width 17. +Using motif -M09139_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M00416_2.00 of width 8. +Using motif -M00416_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03115_2.00 of width 9. +Using motif -M03115_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05018_2.00 of width 11. +Using motif -M05018_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05019_2.00 of width 11. +Using motif -M05019_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09140_2.00 of width 8. +Using motif -M09140_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02087_2.00 of width 10. +Using motif -M02087_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05020_2.00 of width 8. +Using motif -M05020_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05021_2.00 of width 8. +Using motif -M05021_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10672_2.00 of width 10. +Using motif -M10672_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03116_2.00 of width 15. +Using motif -M03116_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03117_2.00 of width 8. +Using motif -M03117_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05022_2.00 of width 8. +Using motif -M05022_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05023_2.00 of width 8. +Using motif -M05023_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05024_2.00 of width 10. +Using motif -M05024_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05025_2.00 of width 10. +Using motif -M05025_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03118_2.00 of width 8. +Using motif -M03118_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05026_2.00 of width 8. +Using motif -M05026_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05027_2.00 of width 8. +Using motif -M05027_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05028_2.00 of width 8. +Using motif -M05028_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05029_2.00 of width 8. +Using motif -M05029_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00283_2.00 of width 10. +Using motif -M00283_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00284_2.00 of width 8. +Using motif -M00284_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03119_2.00 of width 8. +Using motif -M03119_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05030_2.00 of width 8. +Using motif -M05030_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05031_2.00 of width 8. +Using motif -M05031_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03120_2.00 of width 8. +Using motif -M03120_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03121_2.00 of width 8. +Using motif -M03121_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03122_2.00 of width 11. +Using motif -M03122_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03123_2.00 of width 12. +Using motif -M03123_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989362 +Using motif +M05032_2.00 of width 12. +Using motif -M05032_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984737 +Using motif +M05033_2.00 of width 12. +Using motif -M05033_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973906 +Using motif +M05034_2.00 of width 8. +Using motif -M05034_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994922 +Using motif +M05035_2.00 of width 8. +Using motif -M05035_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M00285_2.00 of width 12. +Using motif -M00285_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00286_2.00 of width 7. +Using motif -M00286_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00287_2.00 of width 10. +Using motif -M00287_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03124_2.00 of width 8. +Using motif -M03124_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05036_2.00 of width 8. +Using motif -M05036_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05037_2.00 of width 8. +Using motif -M05037_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05038_2.00 of width 8. +Using motif -M05038_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05039_2.00 of width 9. +Using motif -M05039_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05040_2.00 of width 8. +Using motif -M05040_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05041_2.00 of width 9. +Using motif -M05041_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10676_2.00 of width 14. +Using motif -M10676_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05042_2.00 of width 10. +Using motif -M05042_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990053 +Using motif +M05043_2.00 of width 10. +Using motif -M05043_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99224 +Using motif +M05044_2.00 of width 9. +Using motif -M05044_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914476 +Using motif +M05045_2.00 of width 10. +Using motif -M05045_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998687 +Using motif +M05046_2.00 of width 10. +Using motif -M05046_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985161 +Using motif +M05047_2.00 of width 9. +Using motif -M05047_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05048_2.00 of width 8. +Using motif -M05048_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05049_2.00 of width 8. +Using motif -M05049_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05050_2.00 of width 8. +Using motif -M05050_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09141_2.00 of width 11. +Using motif -M09141_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03125_2.00 of width 10. +Using motif -M03125_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05051_2.00 of width 8. +Using motif -M05051_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05052_2.00 of width 8. +Using motif -M05052_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05053_2.00 of width 8. +Using motif -M05053_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05054_2.00 of width 8. +Using motif -M05054_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05055_2.00 of width 8. +Using motif -M05055_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05056_2.00 of width 8. +Using motif -M05056_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03126_2.00 of width 10. +Using motif -M03126_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05057_2.00 of width 8. +Using motif -M05057_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05058_2.00 of width 8. +Using motif -M05058_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05059_2.00 of width 8. +Using motif -M05059_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00288_2.00 of width 8. +Using motif -M00288_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00289_2.00 of width 8. +Using motif -M00289_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00290_2.00 of width 8. +Using motif -M00290_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03127_2.00 of width 18. +Using motif -M03127_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03128_2.00 of width 8. +Using motif -M03128_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05060_2.00 of width 8. +Using motif -M05060_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05061_2.00 of width 8. +Using motif -M05061_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05062_2.00 of width 8. +Using motif -M05062_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05063_2.00 of width 8. +Using motif -M05063_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05064_2.00 of width 8. +Using motif -M05064_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05065_2.00 of width 8. +Using motif -M05065_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05066_2.00 of width 8. +Using motif -M05066_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05067_2.00 of width 8. +Using motif -M05067_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05068_2.00 of width 8. +Using motif -M05068_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05069_2.00 of width 8. +Using motif -M05069_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05070_2.00 of width 8. +Using motif -M05070_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05071_2.00 of width 8. +Using motif -M05071_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03129_2.00 of width 10. +Using motif -M03129_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03130_2.00 of width 11. +Using motif -M03130_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05072_2.00 of width 11. +Using motif -M05072_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05073_2.00 of width 11. +Using motif -M05073_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05074_2.00 of width 11. +Using motif -M05074_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03131_2.00 of width 11. +Using motif -M03131_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03132_2.00 of width 11. +Using motif -M03132_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03133_2.00 of width 11. +Using motif -M03133_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03134_2.00 of width 11. +Using motif -M03134_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05075_2.00 of width 11. +Using motif -M05075_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05076_2.00 of width 11. +Using motif -M05076_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05077_2.00 of width 11. +Using motif -M05077_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05078_2.00 of width 11. +Using motif -M05078_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05079_2.00 of width 11. +Using motif -M05079_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05080_2.00 of width 11. +Using motif -M05080_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05081_2.00 of width 11. +Using motif -M05081_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05082_2.00 of width 11. +Using motif -M05082_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03135_2.00 of width 9. +Using motif -M03135_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M03136_2.00 of width 11. +Using motif -M03136_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05083_2.00 of width 11. +Using motif -M05083_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05084_2.00 of width 11. +Using motif -M05084_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05085_2.00 of width 11. +Using motif -M05085_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05086_2.00 of width 11. +Using motif -M05086_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00291_2.00 of width 9. +Using motif -M00291_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00292_2.00 of width 9. +Using motif -M00292_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03137_2.00 of width 10. +Using motif -M03137_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03138_2.00 of width 10. +Using motif -M03138_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05087_2.00 of width 8. +Using motif -M05087_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05088_2.00 of width 8. +Using motif -M05088_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05089_2.00 of width 8. +Using motif -M05089_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05090_2.00 of width 8. +Using motif -M05090_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05091_2.00 of width 8. +Using motif -M05091_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05092_2.00 of width 8. +Using motif -M05092_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05093_2.00 of width 8. +Using motif -M05093_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00385_2.00 of width 8. +Using motif -M00385_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993909 +Using motif +M10681_2.00 of width 10. +Using motif -M10681_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05094_2.00 of width 10. +Using motif -M05094_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05095_2.00 of width 10. +Using motif -M05095_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05096_2.00 of width 10. +Using motif -M05096_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952427 +Using motif +M05097_2.00 of width 10. +Using motif -M05097_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967339 +Using motif +M08130_2.00 of width 11. +Using motif -M08130_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09142_2.00 of width 14. +Using motif -M09142_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05098_2.00 of width 8. +Using motif -M05098_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05099_2.00 of width 8. +Using motif -M05099_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05100_2.00 of width 8. +Using motif -M05100_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05101_2.00 of width 8. +Using motif -M05101_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05102_2.00 of width 8. +Using motif -M05102_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05103_2.00 of width 8. +Using motif -M05103_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05104_2.00 of width 10. +Using motif -M05104_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05105_2.00 of width 10. +Using motif -M05105_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05106_2.00 of width 10. +Using motif -M05106_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05107_2.00 of width 10. +Using motif -M05107_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05108_2.00 of width 10. +Using motif -M05108_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05109_2.00 of width 10. +Using motif -M05109_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05110_2.00 of width 10. +Using motif -M05110_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05111_2.00 of width 10. +Using motif -M05111_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05112_2.00 of width 10. +Using motif -M05112_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M05113_2.00 of width 10. +Using motif -M05113_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05114_2.00 of width 10. +Using motif -M05114_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05115_2.00 of width 10. +Using motif -M05115_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05116_2.00 of width 11. +Using motif -M05116_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05117_2.00 of width 11. +Using motif -M05117_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05118_2.00 of width 11. +Using motif -M05118_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05119_2.00 of width 11. +Using motif -M05119_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03139_2.00 of width 10. +Using motif -M03139_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03140_2.00 of width 10. +Using motif -M03140_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05120_2.00 of width 16. +Using motif -M05120_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05121_2.00 of width 16. +Using motif -M05121_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00293_2.00 of width 10. +Using motif -M00293_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00294_2.00 of width 8. +Using motif -M00294_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00295_2.00 of width 9. +Using motif -M00295_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00296_2.00 of width 8. +Using motif -M00296_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00297_2.00 of width 10. +Using motif -M00297_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00298_2.00 of width 9. +Using motif -M00298_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00299_2.00 of width 9. +Using motif -M00299_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00300_2.00 of width 8. +Using motif -M00300_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03141_2.00 of width 10. +Using motif -M03141_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03142_2.00 of width 11. +Using motif -M03142_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05122_2.00 of width 11. +Using motif -M05122_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M05123_2.00 of width 11. +Using motif -M05123_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05124_2.00 of width 11. +Using motif -M05124_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05125_2.00 of width 11. +Using motif -M05125_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03143_2.00 of width 10. +Using motif -M03143_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05126_2.00 of width 8. +Using motif -M05126_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05127_2.00 of width 8. +Using motif -M05127_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05128_2.00 of width 8. +Using motif -M05128_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05129_2.00 of width 11. +Using motif -M05129_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05130_2.00 of width 11. +Using motif -M05130_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05131_2.00 of width 11. +Using motif -M05131_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03144_2.00 of width 17. +Using motif -M03144_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03145_2.00 of width 8. +Using motif -M03145_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05133_2.00 of width 8. +Using motif -M05133_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03146_2.00 of width 10. +Using motif -M03146_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05134_2.00 of width 8. +Using motif -M05134_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05135_2.00 of width 8. +Using motif -M05135_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05136_2.00 of width 8. +Using motif -M05136_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05137_2.00 of width 8. +Using motif -M05137_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02740_2.00 of width 11. +Using motif -M02740_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981404 +Using motif +M02741_2.00 of width 8. +Using motif -M02741_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939636 +Using motif +M03147_2.00 of width 14. +Using motif -M03147_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03148_2.00 of width 8. +Using motif -M03148_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980578 +Using motif +M05138_2.00 of width 12. +Using motif -M05138_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995155 +Using motif +M05139_2.00 of width 12. +Using motif -M05139_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970263 +Using motif +M05140_2.00 of width 12. +Using motif -M05140_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973377 +Using motif +M05141_2.00 of width 12. +Using motif -M05141_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973333 +Using motif +M09143_2.00 of width 13. +Using motif -M09143_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998191 +Using motif +M03149_2.00 of width 10. +Using motif -M03149_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05142_2.00 of width 8. +Using motif -M05142_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05143_2.00 of width 8. +Using motif -M05143_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05144_2.00 of width 8. +Using motif -M05144_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03150_2.00 of width 15. +Using motif -M03150_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09144_2.00 of width 15. +Using motif -M09144_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10690_2.00 of width 15. +Using motif -M10690_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10693_2.00 of width 17. +Using motif -M10693_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02088_2.00 of width 10. +Using motif -M02088_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03151_2.00 of width 10. +Using motif -M03151_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03152_2.00 of width 14. +Using motif -M03152_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05145_2.00 of width 8. +Using motif -M05145_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05146_2.00 of width 8. +Using motif -M05146_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05147_2.00 of width 8. +Using motif -M05147_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05148_2.00 of width 8. +Using motif -M05148_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00301_2.00 of width 8. +Using motif -M00301_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M00302_2.00 of width 8. +Using motif -M00302_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992917 +Using motif +M05149_2.00 of width 10. +Using motif -M05149_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M05150_2.00 of width 14. +Using motif -M05150_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05151_2.00 of width 10. +Using motif -M05151_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999394 +Using motif +M05152_2.00 of width 9. +Using motif -M05152_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958 +Using motif +M09145_2.00 of width 9. +Using motif -M09145_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956505 +Using motif +M09146_2.00 of width 10. +Using motif -M09146_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972738 +Using motif +M02089_2.00 of width 10. +Using motif -M02089_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00485_2.00 of width 7. +Using motif -M00485_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03153_2.00 of width 8. +Using motif -M03153_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03154_2.00 of width 8. +Using motif -M03154_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05153_2.00 of width 11. +Using motif -M05153_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05154_2.00 of width 11. +Using motif -M05154_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05155_2.00 of width 8. +Using motif -M05155_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05156_2.00 of width 11. +Using motif -M05156_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05157_2.00 of width 8. +Using motif -M05157_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05158_2.00 of width 11. +Using motif -M05158_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05159_2.00 of width 10. +Using motif -M05159_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05160_2.00 of width 10. +Using motif -M05160_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02090_2.00 of width 8. +Using motif -M02090_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03155_2.00 of width 18. +Using motif -M03155_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03156_2.00 of width 8. +Using motif -M03156_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05161_2.00 of width 8. +Using motif -M05161_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05162_2.00 of width 8. +Using motif -M05162_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05163_2.00 of width 8. +Using motif -M05163_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09147_2.00 of width 9. +Using motif -M09147_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M03157_2.00 of width 10. +Using motif -M03157_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03158_2.00 of width 10. +Using motif -M03158_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03159_2.00 of width 16. +Using motif -M03159_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03160_2.00 of width 10. +Using motif -M03160_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03161_2.00 of width 10. +Using motif -M03161_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03162_2.00 of width 16. +Using motif -M03162_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05164_2.00 of width 9. +Using motif -M05164_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05165_2.00 of width 9. +Using motif -M05165_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05166_2.00 of width 9. +Using motif -M05166_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05167_2.00 of width 8. +Using motif -M05167_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05168_2.00 of width 8. +Using motif -M05168_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05169_2.00 of width 8. +Using motif -M05169_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01505_2.00 of width 9. +Using motif -M01505_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01506_2.00 of width 9. +Using motif -M01506_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03163_2.00 of width 8. +Using motif -M03163_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03164_2.00 of width 13. +Using motif -M03164_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05170_2.00 of width 8. +Using motif -M05170_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05171_2.00 of width 8. +Using motif -M05171_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05172_2.00 of width 8. +Using motif -M05172_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05173_2.00 of width 8. +Using motif -M05173_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05174_2.00 of width 8. +Using motif -M05174_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05175_2.00 of width 8. +Using motif -M05175_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03165_2.00 of width 7. +Using motif -M03165_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993016 +Using motif +M09148_2.00 of width 12. +Using motif -M09148_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10709_2.00 of width 12. +Using motif -M10709_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981522 +Using motif +M03166_2.00 of width 10. +Using motif -M03166_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05176_2.00 of width 8. +Using motif -M05176_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05177_2.00 of width 8. +Using motif -M05177_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05178_2.00 of width 8. +Using motif -M05178_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05179_2.00 of width 8. +Using motif -M05179_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03167_2.00 of width 10. +Using motif -M03167_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05180_2.00 of width 9. +Using motif -M05180_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05181_2.00 of width 9. +Using motif -M05181_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03168_2.00 of width 8. +Using motif -M03168_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05182_2.00 of width 8. +Using motif -M05182_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05183_2.00 of width 8. +Using motif -M05183_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05184_2.00 of width 8. +Using motif -M05184_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05185_2.00 of width 8. +Using motif -M05185_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05186_2.00 of width 8. +Using motif -M05186_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05187_2.00 of width 8. +Using motif -M05187_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00303_2.00 of width 11. +Using motif -M00303_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00304_2.00 of width 9. +Using motif -M00304_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00305_2.00 of width 8. +Using motif -M00305_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02091_2.00 of width 8. +Using motif -M02091_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03169_2.00 of width 9. +Using motif -M03169_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03170_2.00 of width 21. +Using motif -M03170_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05188_2.00 of width 9. +Using motif -M05188_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05189_2.00 of width 10. +Using motif -M05189_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05190_2.00 of width 10. +Using motif -M05190_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02184_2.00 of width 10. +Using motif -M02184_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03171_2.00 of width 12. +Using motif -M03171_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992766 +Using motif +M05191_2.00 of width 12. +Using motif -M05191_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97865 +Using motif +M05192_2.00 of width 12. +Using motif -M05192_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977632 +Using motif +M05193_2.00 of width 12. +Using motif -M05193_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986816 +Using motif +M05194_2.00 of width 12. +Using motif -M05194_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96176 +Using motif +M03172_2.00 of width 10. +Using motif -M03172_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03173_2.00 of width 10. +Using motif -M03173_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03174_2.00 of width 13. +Using motif -M03174_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05195_2.00 of width 8. +Using motif -M05195_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05196_2.00 of width 8. +Using motif -M05196_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05197_2.00 of width 8. +Using motif -M05197_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05198_2.00 of width 8. +Using motif -M05198_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05199_2.00 of width 8. +Using motif -M05199_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05200_2.00 of width 8. +Using motif -M05200_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02092_2.00 of width 10. +Using motif -M02092_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03175_2.00 of width 10. +Using motif -M03175_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03176_2.00 of width 11. +Using motif -M03176_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05201_2.00 of width 11. +Using motif -M05201_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05202_2.00 of width 11. +Using motif -M05202_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05203_2.00 of width 11. +Using motif -M05203_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09149_2.00 of width 10. +Using motif -M09149_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M06305_2.00 of width 8. +Using motif -M06305_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03177_2.00 of width 8. +Using motif -M03177_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05204_2.00 of width 8. +Using motif -M05204_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05205_2.00 of width 8. +Using motif -M05205_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03178_2.00 of width 12. +Using motif -M03178_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980241 +Using motif +M05206_2.00 of width 12. +Using motif -M05206_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967785 +Using motif +M05207_2.00 of width 12. +Using motif -M05207_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953125 +Using motif +M05208_2.00 of width 12. +Using motif -M05208_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989783 +Using motif +M05209_2.00 of width 12. +Using motif -M05209_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986477 +Using motif +M09150_2.00 of width 11. +Using motif -M09150_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945839 +Using motif +M05210_2.00 of width 8. +Using motif -M05210_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05211_2.00 of width 12. +Using motif -M05211_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05212_2.00 of width 8. +Using motif -M05212_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05213_2.00 of width 8. +Using motif -M05213_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05214_2.00 of width 12. +Using motif -M05214_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03179_2.00 of width 8. +Using motif -M03179_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05215_2.00 of width 8. +Using motif -M05215_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05216_2.00 of width 8. +Using motif -M05216_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05217_2.00 of width 8. +Using motif -M05217_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03180_2.00 of width 10. +Using motif -M03180_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03181_2.00 of width 14. +Using motif -M03181_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03182_2.00 of width 8. +Using motif -M03182_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03183_2.00 of width 14. +Using motif -M03183_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05218_2.00 of width 8. +Using motif -M05218_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05219_2.00 of width 8. +Using motif -M05219_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05220_2.00 of width 8. +Using motif -M05220_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05871_2.00 of width 8. +Using motif -M05871_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10714_2.00 of width 7. +Using motif -M10714_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03184_2.00 of width 18. +Using motif -M03184_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03185_2.00 of width 8. +Using motif -M03185_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03186_2.00 of width 8. +Using motif -M03186_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05221_2.00 of width 8. +Using motif -M05221_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05222_2.00 of width 8. +Using motif -M05222_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05223_2.00 of width 8. +Using motif -M05223_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05224_2.00 of width 8. +Using motif -M05224_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05225_2.00 of width 8. +Using motif -M05225_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05226_2.00 of width 8. +Using motif -M05226_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10715_2.00 of width 9. +Using motif -M10715_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09151_2.00 of width 19. +Using motif -M09151_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10718_2.00 of width 13. +Using motif -M10718_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00306_2.00 of width 8. +Using motif -M00306_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00307_2.00 of width 8. +Using motif -M00307_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00308_2.00 of width 9. +Using motif -M00308_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00309_2.00 of width 9. +Using motif -M00309_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03187_2.00 of width 10. +Using motif -M03187_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03188_2.00 of width 15. +Using motif -M03188_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05227_2.00 of width 8. +Using motif -M05227_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05228_2.00 of width 8. +Using motif -M05228_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00310_2.00 of width 8. +Using motif -M00310_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00311_2.00 of width 8. +Using motif -M00311_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00312_2.00 of width 13. +Using motif -M00312_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00313_2.00 of width 8. +Using motif -M00313_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00314_2.00 of width 8. +Using motif -M00314_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05229_2.00 of width 8. +Using motif -M05229_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05230_2.00 of width 8. +Using motif -M05230_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05231_2.00 of width 8. +Using motif -M05231_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05232_2.00 of width 8. +Using motif -M05232_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05234_2.00 of width 23. +Using motif -M05234_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05235_2.00 of width 22. +Using motif -M05235_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05236_2.00 of width 21. +Using motif -M05236_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03189_2.00 of width 10. +Using motif -M03189_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03190_2.00 of width 10. +Using motif -M03190_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05237_2.00 of width 8. +Using motif -M05237_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05238_2.00 of width 9. +Using motif -M05238_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05239_2.00 of width 8. +Using motif -M05239_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03191_2.00 of width 13. +Using motif -M03191_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03192_2.00 of width 8. +Using motif -M03192_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05240_2.00 of width 8. +Using motif -M05240_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05241_2.00 of width 8. +Using motif -M05241_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968217 +Using motif +M03193_2.00 of width 10. +Using motif -M03193_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05242_2.00 of width 9. +Using motif -M05242_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05243_2.00 of width 9. +Using motif -M05243_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02093_2.00 of width 8. +Using motif -M02093_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01246_2.00 of width 7. +Using motif -M01246_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M03194_2.00 of width 11. +Using motif -M03194_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05244_2.00 of width 8. +Using motif -M05244_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05245_2.00 of width 8. +Using motif -M05245_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03195_2.00 of width 12. +Using motif -M03195_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991323 +Using motif +M05246_2.00 of width 12. +Using motif -M05246_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977962 +Using motif +M05247_2.00 of width 12. +Using motif -M05247_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968243 +Using motif +M03196_2.00 of width 9. +Using motif -M03196_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05248_2.00 of width 10. +Using motif -M05248_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05249_2.00 of width 9. +Using motif -M05249_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05250_2.00 of width 12. +Using motif -M05250_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08131_2.00 of width 11. +Using motif -M08131_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09152_2.00 of width 12. +Using motif -M09152_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M03197_2.00 of width 15. +Using motif -M03197_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03198_2.00 of width 8. +Using motif -M03198_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05251_2.00 of width 8. +Using motif -M05251_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05252_2.00 of width 8. +Using motif -M05252_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09153_2.00 of width 11. +Using motif -M09153_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03199_2.00 of width 13. +Using motif -M03199_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05253_2.00 of width 8. +Using motif -M05253_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05254_2.00 of width 8. +Using motif -M05254_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05255_2.00 of width 10. +Using motif -M05255_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997677 +Using motif +M05256_2.00 of width 10. +Using motif -M05256_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05257_2.00 of width 8. +Using motif -M05257_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09154_2.00 of width 12. +Using motif -M09154_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09559_2.00 of width 10. +Using motif -M09559_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998081 +Using motif +M10730_2.00 of width 12. +Using motif -M10730_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07971_2.00 of width 15. +Using motif -M07971_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954286 +Using motif +M08132_2.00 of width 17. +Using motif -M08132_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955867 +Using motif +M09155_2.00 of width 11. +Using motif -M09155_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965389 +Using motif +M09560_2.00 of width 12. +Using motif -M09560_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980659 +Using motif +M03200_2.00 of width 8. +Using motif -M03200_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05258_2.00 of width 8. +Using motif -M05258_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97037 +Using motif +M05259_2.00 of width 8. +Using motif -M05259_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05260_2.00 of width 8. +Using motif -M05260_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05261_2.00 of width 8. +Using motif -M05261_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05262_2.00 of width 8. +Using motif -M05262_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05263_2.00 of width 8. +Using motif -M05263_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05264_2.00 of width 8. +Using motif -M05264_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09156_2.00 of width 7. +Using motif -M09156_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03201_2.00 of width 10. +Using motif -M03201_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03202_2.00 of width 14. +Using motif -M03202_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03203_2.00 of width 10. +Using motif -M03203_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05265_2.00 of width 8. +Using motif -M05265_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05266_2.00 of width 8. +Using motif -M05266_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05267_2.00 of width 8. +Using motif -M05267_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05268_2.00 of width 8. +Using motif -M05268_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05269_2.00 of width 8. +Using motif -M05269_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05270_2.00 of width 8. +Using motif -M05270_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03204_2.00 of width 8. +Using motif -M03204_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03205_2.00 of width 10. +Using motif -M03205_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05271_2.00 of width 8. +Using motif -M05271_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05272_2.00 of width 8. +Using motif -M05272_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05273_2.00 of width 8. +Using motif -M05273_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05274_2.00 of width 8. +Using motif -M05274_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05275_2.00 of width 8. +Using motif -M05275_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05276_2.00 of width 8. +Using motif -M05276_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05277_2.00 of width 8. +Using motif -M05277_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05278_2.00 of width 8. +Using motif -M05278_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03206_2.00 of width 9. +Using motif -M03206_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03207_2.00 of width 11. +Using motif -M03207_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03208_2.00 of width 11. +Using motif -M03208_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03209_2.00 of width 9. +Using motif -M03209_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05279_2.00 of width 10. +Using motif -M05279_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05280_2.00 of width 10. +Using motif -M05280_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03210_2.00 of width 10. +Using motif -M03210_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03211_2.00 of width 14. +Using motif -M03211_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05281_2.00 of width 8. +Using motif -M05281_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05282_2.00 of width 8. +Using motif -M05282_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05283_2.00 of width 18. +Using motif -M05283_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05284_2.00 of width 18. +Using motif -M05284_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95479 +Using motif +M03212_2.00 of width 12. +Using motif -M03212_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05285_2.00 of width 10. +Using motif -M05285_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911881 +Using motif +M05286_2.00 of width 10. +Using motif -M05286_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948037 +Using motif +M05287_2.00 of width 10. +Using motif -M05287_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926981 +Using motif +M05288_2.00 of width 10. +Using motif -M05288_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953645 +Using motif +M08133_2.00 of width 16. +Using motif -M08133_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M09157_2.00 of width 13. +Using motif -M09157_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05289_2.00 of width 10. +Using motif -M05289_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05290_2.00 of width 10. +Using motif -M05290_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05291_2.00 of width 10. +Using motif -M05291_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00417_2.00 of width 8. +Using motif -M00417_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00422_2.00 of width 7. +Using motif -M00422_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03213_2.00 of width 10. +Using motif -M03213_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05292_2.00 of width 8. +Using motif -M05292_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05293_2.00 of width 8. +Using motif -M05293_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05294_2.00 of width 8. +Using motif -M05294_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03214_2.00 of width 8. +Using motif -M03214_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03215_2.00 of width 10. +Using motif -M03215_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05295_2.00 of width 8. +Using motif -M05295_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05296_2.00 of width 8. +Using motif -M05296_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05297_2.00 of width 8. +Using motif -M05297_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09158_2.00 of width 13. +Using motif -M09158_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00315_2.00 of width 8. +Using motif -M00315_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00316_2.00 of width 8. +Using motif -M00316_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00317_2.00 of width 10. +Using motif -M00317_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03216_2.00 of width 11. +Using motif -M03216_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03217_2.00 of width 11. +Using motif -M03217_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05298_2.00 of width 11. +Using motif -M05298_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05299_2.00 of width 11. +Using motif -M05299_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05300_2.00 of width 11. +Using motif -M05300_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05301_2.00 of width 11. +Using motif -M05301_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05302_2.00 of width 11. +Using motif -M05302_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05303_2.00 of width 11. +Using motif -M05303_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00318_2.00 of width 8. +Using motif -M00318_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00319_2.00 of width 8. +Using motif -M00319_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03218_2.00 of width 13. +Using motif -M03218_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03219_2.00 of width 8. +Using motif -M03219_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03220_2.00 of width 8. +Using motif -M03220_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05304_2.00 of width 8. +Using motif -M05304_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05305_2.00 of width 8. +Using motif -M05305_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05306_2.00 of width 8. +Using motif -M05306_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05307_2.00 of width 8. +Using motif -M05307_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05308_2.00 of width 8. +Using motif -M05308_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05309_2.00 of width 8. +Using motif -M05309_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03221_2.00 of width 10. +Using motif -M03221_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05310_2.00 of width 8. +Using motif -M05310_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05311_2.00 of width 8. +Using motif -M05311_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05312_2.00 of width 8. +Using motif -M05312_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05313_2.00 of width 12. +Using motif -M05313_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97113 +Using motif +M05314_2.00 of width 12. +Using motif -M05314_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947885 +Using motif +M03222_2.00 of width 12. +Using motif -M03222_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05315_2.00 of width 15. +Using motif -M05315_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05316_2.00 of width 15. +Using motif -M05316_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08208_2.00 of width 10. +Using motif -M08208_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08209_2.00 of width 8. +Using motif -M08209_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971954 +Using motif +M03223_2.00 of width 12. +Using motif -M03223_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996041 +Using motif +M05317_2.00 of width 12. +Using motif -M05317_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995361 +Using motif +M05318_2.00 of width 12. +Using motif -M05318_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996633 +Using motif +M09159_2.00 of width 7. +Using motif -M09159_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969425 +Using motif +M10740_2.00 of width 11. +Using motif -M10740_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M05319_2.00 of width 18. +Using motif -M05319_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05320_2.00 of width 18. +Using motif -M05320_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03224_2.00 of width 13. +Using motif -M03224_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9642 +Using motif +M03225_2.00 of width 10. +Using motif -M03225_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05321_2.00 of width 8. +Using motif -M05321_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05322_2.00 of width 12. +Using motif -M05322_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05323_2.00 of width 10. +Using motif -M05323_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05324_2.00 of width 8. +Using motif -M05324_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05325_2.00 of width 12. +Using motif -M05325_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05326_2.00 of width 10. +Using motif -M05326_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00496_2.00 of width 10. +Using motif -M00496_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M03226_2.00 of width 13. +Using motif -M03226_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05327_2.00 of width 11. +Using motif -M05327_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05328_2.00 of width 11. +Using motif -M05328_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09160_2.00 of width 12. +Using motif -M09160_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10741_2.00 of width 18. +Using motif -M10741_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03227_2.00 of width 10. +Using motif -M03227_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05329_2.00 of width 8. +Using motif -M05329_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05330_2.00 of width 8. +Using motif -M05330_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05331_2.00 of width 10. +Using motif -M05331_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05332_2.00 of width 10. +Using motif -M05332_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05333_2.00 of width 10. +Using motif -M05333_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05334_2.00 of width 10. +Using motif -M05334_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09161_2.00 of width 10. +Using motif -M09161_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03228_2.00 of width 10. +Using motif -M03228_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M03229_2.00 of width 10. +Using motif -M03229_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03230_2.00 of width 10. +Using motif -M03230_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05335_2.00 of width 11. +Using motif -M05335_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05336_2.00 of width 11. +Using motif -M05336_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05337_2.00 of width 11. +Using motif -M05337_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05338_2.00 of width 11. +Using motif -M05338_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05339_2.00 of width 11. +Using motif -M05339_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05340_2.00 of width 11. +Using motif -M05340_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05341_2.00 of width 11. +Using motif -M05341_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05342_2.00 of width 11. +Using motif -M05342_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05343_2.00 of width 8. +Using motif -M05343_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05344_2.00 of width 8. +Using motif -M05344_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05345_2.00 of width 8. +Using motif -M05345_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09162_2.00 of width 8. +Using motif -M09162_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00320_2.00 of width 9. +Using motif -M00320_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M00321_2.00 of width 10. +Using motif -M00321_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M00322_2.00 of width 7. +Using motif -M00322_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962839 +Using motif +M00323_2.00 of width 11. +Using motif -M00323_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00324_2.00 of width 7. +Using motif -M00324_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01507_2.00 of width 7. +Using motif -M01507_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01508_2.00 of width 7. +Using motif -M01508_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05346_2.00 of width 10. +Using motif -M05346_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989939 +Using motif +M05347_2.00 of width 10. +Using motif -M05347_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996429 +Using motif +M05348_2.00 of width 9. +Using motif -M05348_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963364 +Using motif +M09163_2.00 of width 8. +Using motif -M09163_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976196 +Using motif +M10742_2.00 of width 7. +Using motif -M10742_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M10743_2.00 of width 8. +Using motif -M10743_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02094_2.00 of width 10. +Using motif -M02094_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05349_2.00 of width 19. +Using motif -M05349_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05350_2.00 of width 14. +Using motif -M05350_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05351_2.00 of width 10. +Using motif -M05351_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05352_2.00 of width 13. +Using motif -M05352_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05353_2.00 of width 14. +Using motif -M05353_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05354_2.00 of width 10. +Using motif -M05354_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05355_2.00 of width 14. +Using motif -M05355_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05356_2.00 of width 16. +Using motif -M05356_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10746_2.00 of width 11. +Using motif -M10746_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00325_2.00 of width 8. +Using motif -M00325_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00326_2.00 of width 7. +Using motif -M00326_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00327_2.00 of width 9. +Using motif -M00327_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05357_2.00 of width 10. +Using motif -M05357_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936571 +Using motif +M05358_2.00 of width 10. +Using motif -M05358_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.880777 +Using motif +M03231_2.00 of width 10. +Using motif -M03231_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05359_2.00 of width 11. +Using motif -M05359_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05360_2.00 of width 11. +Using motif -M05360_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05361_2.00 of width 11. +Using motif -M05361_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00499_2.00 of width 7. +Using motif -M00499_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02685_2.00 of width 12. +Using motif -M02685_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05362_2.00 of width 10. +Using motif -M05362_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995052 +Using motif +M05363_2.00 of width 10. +Using motif -M05363_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953333 +Using motif +M05364_2.00 of width 10. +Using motif -M05364_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980523 +Using motif +M05365_2.00 of width 10. +Using motif -M05365_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09164_2.00 of width 10. +Using motif -M09164_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961224 +Using motif +M09561_2.00 of width 12. +Using motif -M09561_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977386 +Using motif +M10748_2.00 of width 9. +Using motif -M10748_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10749_2.00 of width 15. +Using motif -M10749_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02095_2.00 of width 8. +Using motif -M02095_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03232_2.00 of width 8. +Using motif -M03232_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05366_2.00 of width 8. +Using motif -M05366_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05367_2.00 of width 8. +Using motif -M05367_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05368_2.00 of width 8. +Using motif -M05368_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05369_2.00 of width 10. +Using motif -M05369_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05370_2.00 of width 13. +Using motif -M05370_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05371_2.00 of width 10. +Using motif -M05371_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05372_2.00 of width 13. +Using motif -M05372_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03233_2.00 of width 11. +Using motif -M03233_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10754_2.00 of width 10. +Using motif -M10754_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954143 +Using motif +M03234_2.00 of width 11. +Using motif -M03234_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05373_2.00 of width 9. +Using motif -M05373_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977576 +Using motif +M05374_2.00 of width 9. +Using motif -M05374_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05375_2.00 of width 10. +Using motif -M05375_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05376_2.00 of width 10. +Using motif -M05376_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03235_2.00 of width 8. +Using motif -M03235_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05377_2.00 of width 8. +Using motif -M05377_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943564 +Using motif +M05378_2.00 of width 8. +Using motif -M05378_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05379_2.00 of width 8. +Using motif -M05379_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05380_2.00 of width 8. +Using motif -M05380_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05381_2.00 of width 8. +Using motif -M05381_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05382_2.00 of width 8. +Using motif -M05382_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03236_2.00 of width 10. +Using motif -M03236_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05383_2.00 of width 8. +Using motif -M05383_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05384_2.00 of width 8. +Using motif -M05384_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00392_2.00 of width 8. +Using motif -M00392_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00328_2.00 of width 8. +Using motif -M00328_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00329_2.00 of width 9. +Using motif -M00329_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00330_2.00 of width 8. +Using motif -M00330_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05385_2.00 of width 8. +Using motif -M05385_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05386_2.00 of width 8. +Using motif -M05386_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05387_2.00 of width 8. +Using motif -M05387_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08134_2.00 of width 12. +Using motif -M08134_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995751 +Using motif +M09165_2.00 of width 15. +Using motif -M09165_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03237_2.00 of width 10. +Using motif -M03237_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03238_2.00 of width 11. +Using motif -M03238_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05388_2.00 of width 10. +Using motif -M05388_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05389_2.00 of width 10. +Using motif -M05389_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01509_2.00 of width 7. +Using motif -M01509_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01510_2.00 of width 7. +Using motif -M01510_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03239_2.00 of width 9. +Using motif -M03239_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03240_2.00 of width 12. +Using motif -M03240_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03241_2.00 of width 10. +Using motif -M03241_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05390_2.00 of width 8. +Using motif -M05390_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05391_2.00 of width 8. +Using motif -M05391_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03242_2.00 of width 12. +Using motif -M03242_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03243_2.00 of width 11. +Using motif -M03243_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05392_2.00 of width 9. +Using motif -M05392_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05393_2.00 of width 9. +Using motif -M05393_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02096_2.00 of width 8. +Using motif -M02096_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00503_2.00 of width 9. +Using motif -M00503_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00442_2.00 of width 10. +Using motif -M00442_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05394_2.00 of width 11. +Using motif -M05394_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05395_2.00 of width 11. +Using motif -M05395_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05396_2.00 of width 11. +Using motif -M05396_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05397_2.00 of width 11. +Using motif -M05397_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05398_2.00 of width 11. +Using motif -M05398_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05399_2.00 of width 11. +Using motif -M05399_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05400_2.00 of width 11. +Using motif -M05400_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05401_2.00 of width 11. +Using motif -M05401_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9654 +Using motif +M01247_2.00 of width 9. +Using motif -M01247_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03244_2.00 of width 13. +Using motif -M03244_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05402_2.00 of width 11. +Using motif -M05402_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05403_2.00 of width 11. +Using motif -M05403_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00331_2.00 of width 7. +Using motif -M00331_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M00332_2.00 of width 10. +Using motif -M00332_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00333_2.00 of width 10. +Using motif -M00333_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00334_2.00 of width 7. +Using motif -M00334_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05404_2.00 of width 8. +Using motif -M05404_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05405_2.00 of width 8. +Using motif -M05405_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05406_2.00 of width 8. +Using motif -M05406_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05407_2.00 of width 10. +Using motif -M05407_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05408_2.00 of width 10. +Using motif -M05408_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05409_2.00 of width 8. +Using motif -M05409_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09166_2.00 of width 10. +Using motif -M09166_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08135_2.00 of width 11. +Using motif -M08135_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09167_2.00 of width 11. +Using motif -M09167_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05410_2.00 of width 8. +Using motif -M05410_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05411_2.00 of width 8. +Using motif -M05411_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02097_2.00 of width 10. +Using motif -M02097_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03245_2.00 of width 13. +Using motif -M03245_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03246_2.00 of width 15. +Using motif -M03246_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09168_2.00 of width 15. +Using motif -M09168_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00335_2.00 of width 8. +Using motif -M00335_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.863077 +Using motif +M00336_2.00 of width 8. +Using motif -M00336_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983836 +Using motif +M00337_2.00 of width 11. +Using motif -M00337_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00338_2.00 of width 8. +Using motif -M00338_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954416 +Using motif +M00339_2.00 of width 13. +Using motif -M00339_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.841942 +Using motif +M00340_2.00 of width 7. +Using motif -M00340_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953273 +Using motif +M00341_2.00 of width 10. +Using motif -M00341_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.862393 +Using motif +M00342_2.00 of width 14. +Using motif -M00342_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00343_2.00 of width 13. +Using motif -M00343_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.846355 +Using motif +M00344_2.00 of width 12. +Using motif -M00344_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91876 +Using motif +M02689_2.00 of width 14. +Using motif -M02689_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03291_2.00 of width 19. +Using motif -M03291_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998485 +Using motif +M05412_2.00 of width 17. +Using motif -M05412_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918036 +Using motif +M05413_2.00 of width 17. +Using motif -M05413_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9425 +Using motif +M05414_2.00 of width 17. +Using motif -M05414_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.885283 +Using motif +M05415_2.00 of width 17. +Using motif -M05415_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916792 +Using motif +M09216_2.00 of width 12. +Using motif -M09216_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985833 +Using motif +M10806_2.00 of width 21. +Using motif -M10806_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00345_2.00 of width 10. +Using motif -M00345_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903208 +Using motif +M00346_2.00 of width 10. +Using motif -M00346_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987194 +Using motif +M03292_2.00 of width 10. +Using motif -M03292_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03293_2.00 of width 10. +Using motif -M03293_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05416_2.00 of width 15. +Using motif -M05416_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.914237 +Using motif +M05417_2.00 of width 16. +Using motif -M05417_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973279 +Using motif +M05418_2.00 of width 12. +Using motif -M05418_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05419_2.00 of width 15. +Using motif -M05419_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963529 +Using motif +M05420_2.00 of width 16. +Using motif -M05420_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05421_2.00 of width 12. +Using motif -M05421_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05422_2.00 of width 15. +Using motif -M05422_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95641 +Using motif +M05423_2.00 of width 16. +Using motif -M05423_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05424_2.00 of width 12. +Using motif -M05424_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05425_2.00 of width 15. +Using motif -M05425_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949643 +Using motif +M05426_2.00 of width 16. +Using motif -M05426_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05427_2.00 of width 12. +Using motif -M05427_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05874_2.00 of width 13. +Using motif -M05874_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03294_2.00 of width 18. +Using motif -M03294_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923962 +Using motif +M05428_2.00 of width 20. +Using motif -M05428_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893592 +Using motif +M05429_2.00 of width 20. +Using motif -M05429_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94672 +Using motif +M10808_2.00 of width 19. +Using motif -M10808_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10809_2.00 of width 9. +Using motif -M10809_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00347_2.00 of width 7. +Using motif -M00347_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00348_2.00 of width 8. +Using motif -M00348_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00349_2.00 of width 9. +Using motif -M00349_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00350_2.00 of width 8. +Using motif -M00350_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00351_2.00 of width 8. +Using motif -M00351_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03295_2.00 of width 8. +Using motif -M03295_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03296_2.00 of width 8. +Using motif -M03296_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03883_2.00 of width 15. +Using motif -M03883_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.940556 +Using motif +M03884_2.00 of width 16. +Using motif -M03884_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05430_2.00 of width 20. +Using motif -M05430_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05431_2.00 of width 21. +Using motif -M05431_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05432_2.00 of width 18. +Using motif -M05432_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947478 +Using motif +M05433_2.00 of width 13. +Using motif -M05433_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05434_2.00 of width 14. +Using motif -M05434_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05435_2.00 of width 21. +Using motif -M05435_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05436_2.00 of width 18. +Using motif -M05436_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980625 +Using motif +M05437_2.00 of width 13. +Using motif -M05437_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10811_2.00 of width 21. +Using motif -M10811_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.825149 +Using motif +M10812_2.00 of width 11. +Using motif -M10812_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10813_2.00 of width 12. +Using motif -M10813_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949625 +Using motif +M10814_2.00 of width 30. +Using motif -M10814_2.00 of width 30. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991385 +Using motif +M00352_2.00 of width 8. +Using motif -M00352_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00353_2.00 of width 9. +Using motif -M00353_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00354_2.00 of width 7. +Using motif -M00354_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972453 +Using motif +M00355_2.00 of width 9. +Using motif -M00355_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00356_2.00 of width 11. +Using motif -M00356_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00357_2.00 of width 9. +Using motif -M00357_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00358_2.00 of width 7. +Using motif -M00358_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00359_2.00 of width 8. +Using motif -M00359_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03297_2.00 of width 10. +Using motif -M03297_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03885_2.00 of width 14. +Using motif -M03885_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03886_2.00 of width 13. +Using motif -M03886_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05438_2.00 of width 16. +Using motif -M05438_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05439_2.00 of width 12. +Using motif -M05439_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05440_2.00 of width 16. +Using motif -M05440_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976514 +Using motif +M05441_2.00 of width 16. +Using motif -M05441_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05442_2.00 of width 12. +Using motif -M05442_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05443_2.00 of width 16. +Using motif -M05443_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978302 +Using motif +M09585_2.00 of width 15. +Using motif -M09585_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10816_2.00 of width 13. +Using motif -M10816_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99779 +Using motif +M02742_2.00 of width 10. +Using motif -M02742_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02743_2.00 of width 10. +Using motif -M02743_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03298_2.00 of width 11. +Using motif -M03298_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03299_2.00 of width 14. +Using motif -M03299_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05444_2.00 of width 15. +Using motif -M05444_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05445_2.00 of width 12. +Using motif -M05445_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05446_2.00 of width 11. +Using motif -M05446_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05447_2.00 of width 12. +Using motif -M05447_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05448_2.00 of width 12. +Using motif -M05448_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05449_2.00 of width 11. +Using motif -M05449_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05450_2.00 of width 12. +Using motif -M05450_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05451_2.00 of width 12. +Using motif -M05451_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05452_2.00 of width 12. +Using motif -M05452_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07972_2.00 of width 10. +Using motif -M07972_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07973_2.00 of width 10. +Using motif -M07973_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07974_2.00 of width 13. +Using motif -M07974_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07975_2.00 of width 13. +Using motif -M07975_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08140_2.00 of width 13. +Using motif -M08140_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M09218_2.00 of width 11. +Using motif -M09218_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M03300_2.00 of width 17. +Using motif -M03300_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03301_2.00 of width 14. +Using motif -M03301_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05453_2.00 of width 14. +Using motif -M05453_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05454_2.00 of width 13. +Using motif -M05454_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05455_2.00 of width 14. +Using motif -M05455_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05456_2.00 of width 14. +Using motif -M05456_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05457_2.00 of width 13. +Using motif -M05457_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05458_2.00 of width 14. +Using motif -M05458_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00360_2.00 of width 7. +Using motif -M00360_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00361_2.00 of width 8. +Using motif -M00361_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03302_2.00 of width 16. +Using motif -M03302_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05459_2.00 of width 13. +Using motif -M05459_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05460_2.00 of width 13. +Using motif -M05460_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05461_2.00 of width 13. +Using motif -M05461_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05462_2.00 of width 10. +Using motif -M05462_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05463_2.00 of width 13. +Using motif -M05463_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05464_2.00 of width 13. +Using motif -M05464_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05465_2.00 of width 13. +Using motif -M05465_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05466_2.00 of width 13. +Using motif -M05466_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00362_2.00 of width 10. +Using motif -M00362_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00363_2.00 of width 8. +Using motif -M00363_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03303_2.00 of width 10. +Using motif -M03303_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03304_2.00 of width 16. +Using motif -M03304_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03305_2.00 of width 10. +Using motif -M03305_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03306_2.00 of width 9. +Using motif -M03306_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03307_2.00 of width 12. +Using motif -M03307_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05467_2.00 of width 11. +Using motif -M05467_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05468_2.00 of width 11. +Using motif -M05468_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05469_2.00 of width 12. +Using motif -M05469_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00377_2.00 of width 10. +Using motif -M00377_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03308_2.00 of width 12. +Using motif -M03308_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03309_2.00 of width 14. +Using motif -M03309_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09219_2.00 of width 16. +Using motif -M09219_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M10826_2.00 of width 19. +Using motif -M10826_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10827_2.00 of width 15. +Using motif -M10827_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10828_2.00 of width 13. +Using motif -M10828_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10829_2.00 of width 23. +Using motif -M10829_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10830_2.00 of width 14. +Using motif -M10830_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10831_2.00 of width 14. +Using motif -M10831_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10832_2.00 of width 12. +Using motif -M10832_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10835_2.00 of width 15. +Using motif -M10835_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03310_2.00 of width 16. +Using motif -M03310_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03311_2.00 of width 16. +Using motif -M03311_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03312_2.00 of width 14. +Using motif -M03312_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05470_2.00 of width 13. +Using motif -M05470_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05471_2.00 of width 13. +Using motif -M05471_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05472_2.00 of width 13. +Using motif -M05472_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03313_2.00 of width 13. +Using motif -M03313_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03314_2.00 of width 12. +Using motif -M03314_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05473_2.00 of width 14. +Using motif -M05473_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05474_2.00 of width 13. +Using motif -M05474_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05475_2.00 of width 12. +Using motif -M05475_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05476_2.00 of width 13. +Using motif -M05476_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05477_2.00 of width 18. +Using motif -M05477_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05478_2.00 of width 14. +Using motif -M05478_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05479_2.00 of width 13. +Using motif -M05479_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05480_2.00 of width 12. +Using motif -M05480_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05481_2.00 of width 13. +Using motif -M05481_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05482_2.00 of width 17. +Using motif -M05482_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09220_2.00 of width 17. +Using motif -M09220_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998995 +Using motif +M10843_2.00 of width 16. +Using motif -M10843_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10844_2.00 of width 14. +Using motif -M10844_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10845_2.00 of width 10. +Using motif -M10845_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03315_2.00 of width 12. +Using motif -M03315_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03316_2.00 of width 12. +Using motif -M03316_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05483_2.00 of width 17. +Using motif -M05483_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05484_2.00 of width 14. +Using motif -M05484_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05485_2.00 of width 13. +Using motif -M05485_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05486_2.00 of width 13. +Using motif -M05486_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05487_2.00 of width 14. +Using motif -M05487_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05488_2.00 of width 13. +Using motif -M05488_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05489_2.00 of width 12. +Using motif -M05489_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05490_2.00 of width 13. +Using motif -M05490_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05491_2.00 of width 17. +Using motif -M05491_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09221_2.00 of width 14. +Using motif -M09221_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10849_2.00 of width 15. +Using motif -M10849_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00364_2.00 of width 7. +Using motif -M00364_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00365_2.00 of width 7. +Using motif -M00365_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00366_2.00 of width 7. +Using motif -M00366_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03317_2.00 of width 9. +Using motif -M03317_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03318_2.00 of width 11. +Using motif -M03318_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05492_2.00 of width 12. +Using motif -M05492_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05493_2.00 of width 14. +Using motif -M05493_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05494_2.00 of width 13. +Using motif -M05494_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05495_2.00 of width 12. +Using motif -M05495_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05496_2.00 of width 13. +Using motif -M05496_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05497_2.00 of width 14. +Using motif -M05497_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05498_2.00 of width 13. +Using motif -M05498_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05499_2.00 of width 12. +Using motif -M05499_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05500_2.00 of width 13. +Using motif -M05500_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03319_2.00 of width 13. +Using motif -M03319_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03320_2.00 of width 12. +Using motif -M03320_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03321_2.00 of width 12. +Using motif -M03321_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05501_2.00 of width 16. +Using motif -M05501_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05502_2.00 of width 12. +Using motif -M05502_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05503_2.00 of width 16. +Using motif -M05503_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05504_2.00 of width 15. +Using motif -M05504_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05875_2.00 of width 12. +Using motif -M05875_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07976_2.00 of width 17. +Using motif -M07976_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08141_2.00 of width 11. +Using motif -M08141_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09222_2.00 of width 16. +Using motif -M09222_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02744_2.00 of width 12. +Using motif -M02744_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03324_2.00 of width 13. +Using motif -M03324_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05505_2.00 of width 15. +Using motif -M05505_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05506_2.00 of width 15. +Using motif -M05506_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05507_2.00 of width 15. +Using motif -M05507_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05508_2.00 of width 15. +Using motif -M05508_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997172 +Using motif +M09229_2.00 of width 14. +Using motif -M09229_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10859_2.00 of width 10. +Using motif -M10859_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03325_2.00 of width 13. +Using motif -M03325_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05509_2.00 of width 15. +Using motif -M05509_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05510_2.00 of width 15. +Using motif -M05510_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994227 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03327_2.00 of width 9. +Using motif -M03327_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03328_2.00 of width 15. +Using motif -M03328_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05511_2.00 of width 19. +Using motif -M05511_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05512_2.00 of width 17. +Using motif -M05512_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05513_2.00 of width 19. +Using motif -M05513_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05514_2.00 of width 17. +Using motif -M05514_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05515_2.00 of width 19. +Using motif -M05515_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05516_2.00 of width 17. +Using motif -M05516_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05517_2.00 of width 19. +Using motif -M05517_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05518_2.00 of width 17. +Using motif -M05518_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05519_2.00 of width 16. +Using motif -M05519_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9576 +Using motif +M05520_2.00 of width 27. +Using motif -M05520_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91215 +Using motif +M05521_2.00 of width 16. +Using motif -M05521_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05522_2.00 of width 27. +Using motif -M05522_2.00 of width 27. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03329_2.00 of width 13. +Using motif -M03329_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03330_2.00 of width 13. +Using motif -M03330_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05523_2.00 of width 15. +Using motif -M05523_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98064 +Using motif +M05524_2.00 of width 15. +Using motif -M05524_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05525_2.00 of width 15. +Using motif -M05525_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05526_2.00 of width 15. +Using motif -M05526_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M07977_2.00 of width 18. +Using motif -M07977_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948189 +Using motif +M09230_2.00 of width 15. +Using motif -M09230_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998376 +Using motif +M09592_2.00 of width 20. +Using motif -M09592_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949697 +Using motif +M10863_2.00 of width 10. +Using motif -M10863_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05527_2.00 of width 20. +Using motif -M05527_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05528_2.00 of width 20. +Using motif -M05528_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05529_2.00 of width 9. +Using motif -M05529_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05530_2.00 of width 9. +Using motif -M05530_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94233 +Using motif +M08142_2.00 of width 21. +Using motif -M08142_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985436 +Using motif +M09233_2.00 of width 20. +Using motif -M09233_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989645 +Using motif +M09594_2.00 of width 12. +Using motif -M09594_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991633 +Using motif +M10879_2.00 of width 13. +Using motif -M10879_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M03331_2.00 of width 21. +Using motif -M03331_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05531_2.00 of width 23. +Using motif -M05531_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05532_2.00 of width 22. +Using motif -M05532_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M05533_2.00 of width 20. +Using motif -M05533_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996482 +Using motif +M05534_2.00 of width 20. +Using motif -M05534_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994694 +Using motif +M09234_2.00 of width 20. +Using motif -M09234_2.00 of width 20. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.6e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944545 +Using motif +M03332_2.00 of width 14. +Using motif -M03332_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03333_2.00 of width 11. +Using motif -M03333_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05535_2.00 of width 14. +Using motif -M05535_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05536_2.00 of width 14. +Using motif -M05536_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M05537_2.00 of width 14. +Using motif -M05537_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05538_2.00 of width 14. +Using motif -M05538_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M03334_2.00 of width 15. +Using motif -M03334_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05539_2.00 of width 15. +Using motif -M05539_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05540_2.00 of width 15. +Using motif -M05540_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M07978_2.00 of width 18. +Using motif -M07978_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989843 +Using motif +M09235_2.00 of width 18. +Using motif -M09235_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972406 +Using motif +M09595_2.00 of width 10. +Using motif -M09595_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03335_2.00 of width 14. +Using motif -M03335_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M03336_2.00 of width 14. +Using motif -M03336_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05541_2.00 of width 15. +Using motif -M05541_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05542_2.00 of width 15. +Using motif -M05542_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05543_2.00 of width 15. +Using motif -M05543_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05544_2.00 of width 15. +Using motif -M05544_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M09236_2.00 of width 20. +Using motif -M09236_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982947 +Using motif +M02691_2.00 of width 18. +Using motif -M02691_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05545_2.00 of width 12. +Using motif -M05545_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M05546_2.00 of width 12. +Using motif -M05546_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99697 +Using motif +M09237_2.00 of width 20. +Using motif -M09237_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993232 +Using motif +M09596_2.00 of width 12. +Using motif -M09596_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988632 +Using motif +M10892_2.00 of width 13. +Using motif -M10892_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03337_2.00 of width 14. +Using motif -M03337_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M03338_2.00 of width 17. +Using motif -M03338_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05547_2.00 of width 15. +Using motif -M05547_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05548_2.00 of width 15. +Using motif -M05548_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993333 +Using motif +M09238_2.00 of width 10. +Using motif -M09238_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996683 +Using motif +M10894_2.00 of width 18. +Using motif -M10894_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03339_2.00 of width 15. +Using motif -M03339_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05549_2.00 of width 15. +Using motif -M05549_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M05550_2.00 of width 15. +Using motif -M05550_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M09239_2.00 of width 12. +Using motif -M09239_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M01259_2.00 of width 10. +Using motif -M01259_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993646 +Using motif +M01260_2.00 of width 10. +Using motif -M01260_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954909 +Using motif +M03344_2.00 of width 12. +Using motif -M03344_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03340_2.00 of width 12. +Using motif -M03340_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07979_2.00 of width 15. +Using motif -M07979_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08212_2.00 of width 10. +Using motif -M08212_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08213_2.00 of width 8. +Using motif -M08213_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M09247_2.00 of width 13. +Using motif -M09247_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M10935_2.00 of width 16. +Using motif -M10935_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10936_2.00 of width 22. +Using motif -M10936_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10937_2.00 of width 22. +Using motif -M10937_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10938_2.00 of width 22. +Using motif -M10938_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10943_2.00 of width 16. +Using motif -M10943_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02745_2.00 of width 12. +Using motif -M02745_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05551_2.00 of width 10. +Using motif -M05551_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05552_2.00 of width 10. +Using motif -M05552_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07980_2.00 of width 15. +Using motif -M07980_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08149_2.00 of width 15. +Using motif -M08149_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998693 +Using motif +M09248_2.00 of width 13. +Using motif -M09248_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09597_2.00 of width 12. +Using motif -M09597_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03341_2.00 of width 12. +Using motif -M03341_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03342_2.00 of width 16. +Using motif -M03342_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05553_2.00 of width 10. +Using motif -M05553_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05554_2.00 of width 10. +Using motif -M05554_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07981_2.00 of width 19. +Using motif -M07981_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07982_2.00 of width 14. +Using motif -M07982_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M07983_2.00 of width 18. +Using motif -M07983_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M07984_2.00 of width 17. +Using motif -M07984_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09249_2.00 of width 18. +Using motif -M09249_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10947_2.00 of width 18. +Using motif -M10947_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10951_2.00 of width 15. +Using motif -M10951_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M10956_2.00 of width 14. +Using motif -M10956_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03343_2.00 of width 12. +Using motif -M03343_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05555_2.00 of width 10. +Using motif -M05555_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05556_2.00 of width 10. +Using motif -M05556_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09250_2.00 of width 12. +Using motif -M09250_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03344_2.00 of width 12. +Using motif -M03344_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05557_2.00 of width 10. +Using motif -M05557_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05558_2.00 of width 10. +Using motif -M05558_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05559_2.00 of width 15. +Using motif -M05559_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05560_2.00 of width 15. +Using motif -M05560_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09251_2.00 of width 14. +Using motif -M09251_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09256_2.00 of width 11. +Using motif -M09256_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.847748 +Using motif +M09257_2.00 of width 7. +Using motif -M09257_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.895938 +Using motif +M01272_2.00 of width 11. +Using motif -M01272_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11049_2.00 of width 12. +Using motif -M11049_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03345_2.00 of width 14. +Using motif -M03345_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9624 +Using motif +M03346_2.00 of width 16. +Using motif -M03346_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03347_2.00 of width 11. +Using motif -M03347_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03348_2.00 of width 15. +Using motif -M03348_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05561_2.00 of width 11. +Using motif -M05561_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980935 +Using motif +M05562_2.00 of width 11. +Using motif -M05562_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97623 +Using motif +M09600_2.00 of width 10. +Using motif -M09600_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08150_2.00 of width 10. +Using motif -M08150_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993403 +Using motif +M09260_2.00 of width 12. +Using motif -M09260_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987333 +Using motif +M11050_2.00 of width 18. +Using motif -M11050_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.929173 +Using motif +M11054_2.00 of width 10. +Using motif -M11054_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979315 +Using motif +M02323_2.00 of width 9. +Using motif -M02323_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996384 +Using motif +M00808_2.00 of width 10. +Using motif -M00808_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974118 +Using motif +M03349_2.00 of width 12. +Using motif -M03349_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03350_2.00 of width 12. +Using motif -M03350_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03351_2.00 of width 11. +Using motif -M03351_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03352_2.00 of width 17. +Using motif -M03352_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02385_2.00 of width 8. +Using motif -M02385_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944138 +Using motif +M00367_2.00 of width 13. +Using motif -M00367_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00368_2.00 of width 10. +Using motif -M00368_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985917 +Using motif +M05876_2.00 of width 11. +Using motif -M05876_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M08152_2.00 of width 11. +Using motif -M08152_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09263_2.00 of width 19. +Using motif -M09263_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881452 +Using motif +M09264_2.00 of width 19. +Using motif -M09264_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969708 +Using motif +M02705_2.00 of width 10. +Using motif -M02705_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02706_2.00 of width 14. +Using motif -M02706_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03353_2.00 of width 20. +Using motif -M03353_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991087 +Using motif +M03354_2.00 of width 19. +Using motif -M03354_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09265_2.00 of width 13. +Using motif -M09265_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949506 +Using motif +M11090_2.00 of width 13. +Using motif -M11090_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11091_2.00 of width 13. +Using motif -M11091_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03355_2.00 of width 16. +Using motif -M03355_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05563_2.00 of width 10. +Using motif -M05563_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993372 +Using motif +M05564_2.00 of width 16. +Using motif -M05564_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971678 +Using motif +M05565_2.00 of width 14. +Using motif -M05565_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951525 +Using motif +M05566_2.00 of width 10. +Using motif -M05566_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993333 +Using motif +M05567_2.00 of width 16. +Using motif -M05567_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971847 +Using motif +M05568_2.00 of width 14. +Using motif -M05568_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964 +Using motif +M03923_2.00 of width 15. +Using motif -M03923_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998579 +Using motif +M09266_2.00 of width 16. +Using motif -M09266_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991795 +Using motif +M09603_2.00 of width 15. +Using motif -M09603_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987083 +Using motif +M03356_2.00 of width 17. +Using motif -M03356_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956547 +Using motif +M05569_2.00 of width 17. +Using motif -M05569_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96755 +Using motif +M05570_2.00 of width 17. +Using motif -M05570_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927179 +Using motif +M05571_2.00 of width 17. +Using motif -M05571_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.964444 +Using motif +M05572_2.00 of width 17. +Using motif -M05572_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903738 +Using motif +M07985_2.00 of width 14. +Using motif -M07985_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935 +Using motif +M08217_2.00 of width 18. +Using motif -M08217_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96703 +Using motif +M08218_2.00 of width 10. +Using motif -M08218_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922353 +Using motif +M09267_2.00 of width 15. +Using motif -M09267_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953026 +Using motif +M09604_2.00 of width 15. +Using motif -M09604_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982295 +Using motif +M11102_2.00 of width 19. +Using motif -M11102_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954087 +Using motif +M03357_2.00 of width 16. +Using motif -M03357_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03358_2.00 of width 16. +Using motif -M03358_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984686 +Using motif +M03359_2.00 of width 16. +Using motif -M03359_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999485 +Using motif +M03360_2.00 of width 14. +Using motif -M03360_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980578 +Using motif +M03361_2.00 of width 16. +Using motif -M03361_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995648 +Using motif +M03362_2.00 of width 15. +Using motif -M03362_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999596 +Using motif +M05573_2.00 of width 15. +Using motif -M05573_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980488 +Using motif +M05574_2.00 of width 15. +Using motif -M05574_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980838 +Using motif +M05575_2.00 of width 15. +Using motif -M05575_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993838 +Using motif +M05576_2.00 of width 15. +Using motif -M05576_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996995 +Using motif +M05577_2.00 of width 15. +Using motif -M05577_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981111 +Using motif +M05578_2.00 of width 15. +Using motif -M05578_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980864 +Using motif +M05579_2.00 of width 15. +Using motif -M05579_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994922 +Using motif +M05580_2.00 of width 15. +Using motif -M05580_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986494 +Using motif +M08219_2.00 of width 15. +Using motif -M08219_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967929 +Using motif +M08220_2.00 of width 6. +Using motif -M08220_2.00 of width 6. +Computing q-values. +Using motif +M09268_2.00 of width 14. +Using motif -M09268_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974615 +Using motif +M09605_2.00 of width 16. +Using motif -M09605_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979524 +Using motif +M11105_2.00 of width 15. +Using motif -M11105_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981676 +Using motif +M11106_2.00 of width 19. +Using motif -M11106_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975367 +Using motif +M03363_2.00 of width 16. +Using motif -M03363_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999497 +Using motif +M05877_2.00 of width 8. +Using motif -M05877_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09269_2.00 of width 16. +Using motif -M09269_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.923824 +Using motif +M09606_2.00 of width 20. +Using motif -M09606_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95939 +Using motif +M05581_2.00 of width 16. +Using motif -M05581_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994375 +Using motif +M05582_2.00 of width 16. +Using motif -M05582_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97471 +Using motif +M05583_2.00 of width 16. +Using motif -M05583_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974972 +Using motif +M05584_2.00 of width 16. +Using motif -M05584_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.931318 +Using motif +M02394_2.00 of width 10. +Using motif -M02394_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982933 +Using motif +M03364_2.00 of width 9. +Using motif -M03364_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03365_2.00 of width 14. +Using motif -M03365_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05585_2.00 of width 9. +Using motif -M05585_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986623 +Using motif +M05586_2.00 of width 9. +Using motif -M05586_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996821 +Using motif +M03366_2.00 of width 17. +Using motif -M03366_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05587_2.00 of width 15. +Using motif -M05587_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05588_2.00 of width 15. +Using motif -M05588_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M07986_2.00 of width 14. +Using motif -M07986_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983087 +Using motif +M07987_2.00 of width 21. +Using motif -M07987_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9832 +Using motif +M09270_2.00 of width 15. +Using motif -M09270_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995127 +Using motif +M09607_2.00 of width 16. +Using motif -M09607_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98023 +Using motif +M11120_2.00 of width 16. +Using motif -M11120_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991023 +Using motif +M11124_2.00 of width 19. +Using motif -M11124_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992042 +Using motif +M05589_2.00 of width 18. +Using motif -M05589_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958033 +Using motif +M05590_2.00 of width 11. +Using motif -M05590_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98051 +Using motif +M05591_2.00 of width 19. +Using motif -M05591_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955814 +Using motif +M05592_2.00 of width 18. +Using motif -M05592_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933571 +Using motif +M05593_2.00 of width 11. +Using motif -M05593_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989 +Using motif +M05594_2.00 of width 19. +Using motif -M05594_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924571 +Using motif +M09271_2.00 of width 11. +Using motif -M09271_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979535 +Using motif +M00818_2.00 of width 8. +Using motif -M00818_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03367_2.00 of width 11. +Using motif -M03367_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05595_2.00 of width 11. +Using motif -M05595_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969593 +Using motif +M05596_2.00 of width 11. +Using motif -M05596_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999474 +Using motif +M09272_2.00 of width 9. +Using motif -M09272_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982022 +Using motif +M05597_2.00 of width 14. +Using motif -M05597_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93028 +Using motif +M05598_2.00 of width 9. +Using motif -M05598_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94541 +Using motif +M05599_2.00 of width 9. +Using motif -M05599_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977733 +Using motif +M05600_2.00 of width 14. +Using motif -M05600_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960138 +Using motif +M09273_2.00 of width 13. +Using motif -M09273_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971544 +Using motif +M05601_2.00 of width 10. +Using motif -M05601_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05602_2.00 of width 10. +Using motif -M05602_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05603_2.00 of width 10. +Using motif -M05603_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05604_2.00 of width 10. +Using motif -M05604_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08153_2.00 of width 10. +Using motif -M08153_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M09274_2.00 of width 9. +Using motif -M09274_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986703 +Using motif +M09608_2.00 of width 12. +Using motif -M09608_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M03368_2.00 of width 18. +Using motif -M03368_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99559 +Using motif +M09275_2.00 of width 17. +Using motif -M09275_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922158 +Using motif +M05605_2.00 of width 20. +Using motif -M05605_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99445 +Using motif +M05606_2.00 of width 20. +Using motif -M05606_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994286 +Using motif +M09276_2.00 of width 19. +Using motif -M09276_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954396 +Using motif +M09620_2.00 of width 16. +Using motif -M09620_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997778 +Using motif +M03369_2.00 of width 18. +Using motif -M03369_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983086 +Using motif +M03370_2.00 of width 18. +Using motif -M03370_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M03371_2.00 of width 17. +Using motif -M03371_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965891 +Using motif +M03372_2.00 of width 15. +Using motif -M03372_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03373_2.00 of width 18. +Using motif -M03373_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978057 +Using motif +M03374_2.00 of width 17. +Using motif -M03374_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987072 +Using motif +M05607_2.00 of width 16. +Using motif -M05607_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93021 +Using motif +M05608_2.00 of width 14. +Using motif -M05608_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944651 +Using motif +M05609_2.00 of width 10. +Using motif -M05609_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969125 +Using motif +M05610_2.00 of width 16. +Using motif -M05610_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948049 +Using motif +M05611_2.00 of width 14. +Using motif -M05611_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.924561 +Using motif +M05612_2.00 of width 10. +Using motif -M05612_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965513 +Using motif +M05878_2.00 of width 7. +Using motif -M05878_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980759 +Using motif +M09277_2.00 of width 20. +Using motif -M09277_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935152 +Using motif +M02707_2.00 of width 20. +Using motif -M02707_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968841 +Using motif +M09278_2.00 of width 17. +Using motif -M09278_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938851 +Using motif +M11138_2.00 of width 21. +Using motif -M11138_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994365 +Using motif +M11139_2.00 of width 23. +Using motif -M11139_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M11140_2.00 of width 17. +Using motif -M11140_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995052 +Using motif +M05613_2.00 of width 11. +Using motif -M05613_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966957 +Using motif +M05614_2.00 of width 11. +Using motif -M05614_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946338 +Using motif +M08154_2.00 of width 15. +Using motif -M08154_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949536 +Using motif +M09279_2.00 of width 15. +Using motif -M09279_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934286 +Using motif +M02746_2.00 of width 12. +Using motif -M02746_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M03375_2.00 of width 14. +Using motif -M03375_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990549 +Using motif +M03376_2.00 of width 14. +Using motif -M03376_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.953534 +Using motif +M03377_2.00 of width 14. +Using motif -M03377_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985955 +Using motif +M03378_2.00 of width 14. +Using motif -M03378_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943016 +Using motif +M05615_2.00 of width 15. +Using motif -M05615_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982935 +Using motif +M05616_2.00 of width 14. +Using motif -M05616_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946466 +Using motif +M05617_2.00 of width 15. +Using motif -M05617_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950173 +Using motif +M05618_2.00 of width 14. +Using motif -M05618_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.935246 +Using motif +M05879_2.00 of width 15. +Using motif -M05879_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979563 +Using motif +M09280_2.00 of width 13. +Using motif -M09280_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99868 +Using motif +M03924_2.00 of width 16. +Using motif -M03924_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956716 +Using motif +M05619_2.00 of width 9. +Using motif -M05619_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05620_2.00 of width 9. +Using motif -M05620_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09281_2.00 of width 18. +Using motif -M09281_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981781 +Using motif +M05621_2.00 of width 14. +Using motif -M05621_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.954375 +Using motif +M05622_2.00 of width 20. +Using motif -M05622_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980468 +Using motif +M05623_2.00 of width 14. +Using motif -M05623_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955944 +Using motif +M05624_2.00 of width 20. +Using motif -M05624_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05880_2.00 of width 12. +Using motif -M05880_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09282_2.00 of width 12. +Using motif -M09282_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997755 +Using motif +M03925_2.00 of width 15. +Using motif -M03925_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958978 +Using motif +M05625_2.00 of width 17. +Using motif -M05625_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956828 +Using motif +M05626_2.00 of width 17. +Using motif -M05626_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945658 +Using motif +M09283_2.00 of width 19. +Using motif -M09283_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985902 +Using motif +M05627_2.00 of width 17. +Using motif -M05627_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05628_2.00 of width 17. +Using motif -M05628_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09284_2.00 of width 13. +Using motif -M09284_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11148_2.00 of width 18. +Using motif -M11148_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M03379_2.00 of width 18. +Using motif -M03379_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968552 +Using motif +M03380_2.00 of width 19. +Using motif -M03380_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995722 +Using motif +M03381_2.00 of width 20. +Using motif -M03381_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993854 +Using motif +M05629_2.00 of width 15. +Using motif -M05629_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971716 +Using motif +M05630_2.00 of width 16. +Using motif -M05630_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951262 +Using motif +M05631_2.00 of width 15. +Using motif -M05631_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956273 +Using motif +M05632_2.00 of width 16. +Using motif -M05632_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944167 +Using motif +M05633_2.00 of width 19. +Using motif -M05633_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.915893 +Using motif +M05634_2.00 of width 19. +Using motif -M05634_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942623 +Using motif +M05635_2.00 of width 19. +Using motif -M05635_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930182 +Using motif +M05636_2.00 of width 19. +Using motif -M05636_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09285_2.00 of width 17. +Using motif -M09285_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927092 +Using motif +M02395_2.00 of width 9. +Using motif -M02395_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03382_2.00 of width 17. +Using motif -M03382_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05637_2.00 of width 15. +Using motif -M05637_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946842 +Using motif +M05638_2.00 of width 15. +Using motif -M05638_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977679 +Using motif +M03383_2.00 of width 17. +Using motif -M03383_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997938 +Using motif +M03384_2.00 of width 16. +Using motif -M03384_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03385_2.00 of width 11. +Using motif -M03385_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05639_2.00 of width 10. +Using motif -M05639_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05640_2.00 of width 10. +Using motif -M05640_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05881_2.00 of width 9. +Using motif -M05881_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08461_2.00 of width 8. +Using motif -M08461_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994872 +Using motif +M09286_2.00 of width 9. +Using motif -M09286_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997789 +Using motif +M02396_2.00 of width 8. +Using motif -M02396_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9528 +Using motif +M03386_2.00 of width 15. +Using motif -M03386_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986971 +Using motif +M03387_2.00 of width 14. +Using motif -M03387_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991828 +Using motif +M03388_2.00 of width 14. +Using motif -M03388_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972418 +Using motif +M05641_2.00 of width 15. +Using motif -M05641_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996308 +Using motif +M05642_2.00 of width 15. +Using motif -M05642_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.973514 +Using motif +M05643_2.00 of width 15. +Using motif -M05643_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976491 +Using motif +M05644_2.00 of width 15. +Using motif -M05644_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925794 +Using motif +M05645_2.00 of width 15. +Using motif -M05645_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.937761 +Using motif +M05646_2.00 of width 15. +Using motif -M05646_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949007 +Using motif +M05647_2.00 of width 15. +Using motif -M05647_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.942308 +Using motif +M05648_2.00 of width 15. +Using motif -M05648_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944118 +Using motif +M08155_2.00 of width 15. +Using motif -M08155_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978046 +Using motif +M09287_2.00 of width 14. +Using motif -M09287_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986283 +Using motif +M03389_2.00 of width 17. +Using motif -M03389_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990824 +Using motif +M03390_2.00 of width 17. +Using motif -M03390_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996082 +Using motif +M09288_2.00 of width 18. +Using motif -M09288_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996583 +Using motif +M09609_2.00 of width 10. +Using motif -M09609_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974023 +Using motif +M09610_2.00 of width 16. +Using motif -M09610_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983053 +Using motif +M02397_2.00 of width 8. +Using motif -M02397_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970943 +Using motif +M03391_2.00 of width 17. +Using motif -M03391_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998283 +Using motif +M03392_2.00 of width 18. +Using motif -M03392_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98358 +Using motif +M03393_2.00 of width 17. +Using motif -M03393_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977806 +Using motif +M03394_2.00 of width 16. +Using motif -M03394_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998061 +Using motif +M03395_2.00 of width 18. +Using motif -M03395_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979172 +Using motif +M03396_2.00 of width 17. +Using motif -M03396_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946504 +Using motif +M05649_2.00 of width 16. +Using motif -M05649_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995833 +Using motif +M05650_2.00 of width 14. +Using motif -M05650_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97988 +Using motif +M05651_2.00 of width 16. +Using motif -M05651_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962767 +Using motif +M05652_2.00 of width 14. +Using motif -M05652_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962276 +Using motif +M05653_2.00 of width 9. +Using motif -M05653_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949155 +Using motif +M05654_2.00 of width 9. +Using motif -M05654_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961972 +Using motif +M09289_2.00 of width 18. +Using motif -M09289_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.930213 +Using motif +M03397_2.00 of width 11. +Using motif -M03397_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996324 +Using motif +M03398_2.00 of width 17. +Using motif -M03398_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999796 +Using motif +M03399_2.00 of width 19. +Using motif -M03399_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.962238 +Using motif +M03400_2.00 of width 11. +Using motif -M03400_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995495 +Using motif +M03401_2.00 of width 17. +Using motif -M03401_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989894 +Using motif +M03402_2.00 of width 19. +Using motif -M03402_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985348 +Using motif +M05655_2.00 of width 11. +Using motif -M05655_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977914 +Using motif +M05656_2.00 of width 11. +Using motif -M05656_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98067 +Using motif +M05657_2.00 of width 11. +Using motif -M05657_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977987 +Using motif +M05658_2.00 of width 11. +Using motif -M05658_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984353 +Using motif +M07988_2.00 of width 15. +Using motif -M07988_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958344 +Using motif +M07989_2.00 of width 19. +Using motif -M07989_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950811 +Using motif +M09290_2.00 of width 15. +Using motif -M09290_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980341 +Using motif +M05659_2.00 of width 20. +Using motif -M05659_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M05660_2.00 of width 20. +Using motif -M05660_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979888 +Using motif +M03403_2.00 of width 15. +Using motif -M03403_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990164 +Using motif +M03404_2.00 of width 16. +Using motif -M03404_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991088 +Using motif +M03405_2.00 of width 8. +Using motif -M03405_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981034 +Using motif +M03406_2.00 of width 13. +Using motif -M03406_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958483 +Using motif +M05661_2.00 of width 15. +Using motif -M05661_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961557 +Using motif +M05662_2.00 of width 15. +Using motif -M05662_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.936694 +Using motif +M05663_2.00 of width 15. +Using motif -M05663_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979532 +Using motif +M05664_2.00 of width 15. +Using motif -M05664_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960153 +Using motif +M05665_2.00 of width 15. +Using motif -M05665_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977318 +Using motif +M05666_2.00 of width 15. +Using motif -M05666_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948932 +Using motif +M05667_2.00 of width 15. +Using motif -M05667_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956712 +Using motif +M05668_2.00 of width 15. +Using motif -M05668_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952857 +Using motif +M09291_2.00 of width 17. +Using motif -M09291_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928171 +Using motif +M03407_2.00 of width 14. +Using motif -M03407_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986811 +Using motif +M05669_2.00 of width 8. +Using motif -M05669_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948411 +Using motif +M05670_2.00 of width 8. +Using motif -M05670_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971447 +Using motif +M07990_2.00 of width 15. +Using motif -M07990_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.943358 +Using motif +M07991_2.00 of width 15. +Using motif -M07991_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946471 +Using motif +M07992_2.00 of width 15. +Using motif -M07992_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926261 +Using motif +M08156_2.00 of width 15. +Using motif -M08156_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933245 +Using motif +M08221_2.00 of width 15. +Using motif -M08221_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957829 +Using motif +M08222_2.00 of width 10. +Using motif -M08222_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983787 +Using motif +M09292_2.00 of width 12. +Using motif -M09292_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918349 +Using motif +M09611_2.00 of width 14. +Using motif -M09611_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955783 +Using motif +M08157_2.00 of width 11. +Using motif -M08157_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995979 +Using motif +M08223_2.00 of width 8. +Using motif -M08223_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980814 +Using motif +M08224_2.00 of width 10. +Using motif -M08224_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956133 +Using motif +M09293_2.00 of width 13. +Using motif -M09293_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979448 +Using motif +M11180_2.00 of width 16. +Using motif -M11180_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981648 +Using motif +M03408_2.00 of width 14. +Using motif -M03408_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981404 +Using motif +M03409_2.00 of width 14. +Using motif -M03409_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927642 +Using motif +M03410_2.00 of width 14. +Using motif -M03410_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984659 +Using motif +M03411_2.00 of width 14. +Using motif -M03411_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9632 +Using motif +M05671_2.00 of width 15. +Using motif -M05671_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979091 +Using motif +M05672_2.00 of width 15. +Using motif -M05672_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05882_2.00 of width 15. +Using motif -M05882_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978571 +Using motif +M07993_2.00 of width 8. +Using motif -M07993_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996495 +Using motif +M07994_2.00 of width 15. +Using motif -M07994_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.932925 +Using motif +M09294_2.00 of width 20. +Using motif -M09294_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.926228 +Using motif +M09295_2.00 of width 17. +Using motif -M09295_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959548 +Using motif +M11183_2.00 of width 20. +Using motif -M11183_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992103 +Using motif +M03412_2.00 of width 18. +Using motif -M03412_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982558 +Using motif +M03413_2.00 of width 17. +Using motif -M03413_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994639 +Using motif +M03414_2.00 of width 10. +Using motif -M03414_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99625 +Using motif +M05673_2.00 of width 11. +Using motif -M05673_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987432 +Using motif +M05674_2.00 of width 11. +Using motif -M05674_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969928 +Using motif +M05675_2.00 of width 11. +Using motif -M05675_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977842 +Using motif +M05676_2.00 of width 11. +Using motif -M05676_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978837 +Using motif +M09296_2.00 of width 9. +Using motif -M09296_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05677_2.00 of width 20. +Using motif -M05677_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983648 +Using motif +M05678_2.00 of width 22. +Using motif -M05678_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996477 +Using motif +M05679_2.00 of width 20. +Using motif -M05679_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985525 +Using motif +M05680_2.00 of width 22. +Using motif -M05680_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05883_2.00 of width 11. +Using motif -M05883_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M03415_2.00 of width 14. +Using motif -M03415_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992292 +Using motif +M05681_2.00 of width 15. +Using motif -M05681_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997766 +Using motif +M05682_2.00 of width 14. +Using motif -M05682_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956 +Using motif +M05683_2.00 of width 15. +Using motif -M05683_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969419 +Using motif +M05684_2.00 of width 14. +Using motif -M05684_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948062 +Using motif +M09297_2.00 of width 10. +Using motif -M09297_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959683 +Using motif +M00369_2.00 of width 10. +Using motif -M00369_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97939 +Using motif +M00370_2.00 of width 13. +Using motif -M00370_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M00371_2.00 of width 13. +Using motif -M00371_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00372_2.00 of width 8. +Using motif -M00372_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00373_2.00 of width 10. +Using motif -M00373_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09298_2.00 of width 14. +Using motif -M09298_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996345 +Using motif +M03437_2.00 of width 18. +Using motif -M03437_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09335_2.00 of width 19. +Using motif -M09335_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965357 +Using motif +M08035_2.00 of width 15. +Using motif -M08035_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974835 +Using motif +M08036_2.00 of width 19. +Using motif -M08036_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950657 +Using motif +M08037_2.00 of width 15. +Using motif -M08037_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961818 +Using motif +M09336_2.00 of width 19. +Using motif -M09336_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972514 +Using motif +M09337_2.00 of width 20. +Using motif -M09337_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.980112 +Using motif +M09621_2.00 of width 20. +Using motif -M09621_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978989 +Using motif +M11197_2.00 of width 20. +Using motif -M11197_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9524 +Using motif +M11198_2.00 of width 10. +Using motif -M11198_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989043 +Using motif +M05685_2.00 of width 20. +Using motif -M05685_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.91386 +Using motif +M05686_2.00 of width 20. +Using motif -M05686_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928037 +Using motif +M03442_2.00 of width 17. +Using motif -M03442_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9525 +Using motif +M05687_2.00 of width 17. +Using motif -M05687_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904865 +Using motif +M05688_2.00 of width 17. +Using motif -M05688_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.916667 +Using motif +M03443_2.00 of width 18. +Using motif -M03443_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927455 +Using motif +M07995_2.00 of width 15. +Using motif -M07995_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.893543 +Using motif +M07996_2.00 of width 15. +Using motif -M07996_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.903925 +Using motif +M07997_2.00 of width 15. +Using motif -M07997_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913719 +Using motif +M07998_2.00 of width 15. +Using motif -M07998_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.854087 +Using motif +M08160_2.00 of width 12. +Using motif -M08160_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.911193 +Using motif +M08227_2.00 of width 15. +Using motif -M08227_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907154 +Using motif +M08228_2.00 of width 10. +Using motif -M08228_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.884098 +Using motif +M09341_2.00 of width 21. +Using motif -M09341_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8704 +Using motif +M09622_2.00 of width 16. +Using motif -M09622_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921654 +Using motif +M09623_2.00 of width 14. +Using motif -M09623_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.904274 +Using motif +M11208_2.00 of width 28. +Using motif -M11208_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.881575 +Using motif +M11209_2.00 of width 28. +Using motif -M11209_2.00 of width 28. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96227 +Using motif +M03444_2.00 of width 17. +Using motif -M03444_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.921441 +Using motif +M05689_2.00 of width 17. +Using motif -M05689_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918198 +Using motif +M05690_2.00 of width 17. +Using motif -M05690_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922243 +Using motif +M01304_2.00 of width 8. +Using motif -M01304_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02433_2.00 of width 10. +Using motif -M02433_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03445_2.00 of width 12. +Using motif -M03445_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05691_2.00 of width 12. +Using motif -M05691_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968142 +Using motif +M05692_2.00 of width 12. +Using motif -M05692_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03445_2.00 of width 12. +Using motif -M03445_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02440_2.00 of width 10. +Using motif -M02440_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05693_2.00 of width 10. +Using motif -M05693_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05694_2.00 of width 14. +Using motif -M05694_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05695_2.00 of width 21. +Using motif -M05695_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998485 +Using motif +M05696_2.00 of width 10. +Using motif -M05696_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05697_2.00 of width 14. +Using motif -M05697_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05698_2.00 of width 21. +Using motif -M05698_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M09343_2.00 of width 9. +Using motif -M09343_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996447 +Using motif +M03446_2.00 of width 13. +Using motif -M03446_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.933167 +Using motif +M05699_2.00 of width 13. +Using motif -M05699_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960769 +Using motif +M05700_2.00 of width 13. +Using motif -M05700_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977669 +Using motif +M09344_2.00 of width 11. +Using motif -M09344_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.959 +Using motif +M01306_2.00 of width 10. +Using motif -M01306_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05701_2.00 of width 10. +Using motif -M05701_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M05702_2.00 of width 14. +Using motif -M05702_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05703_2.00 of width 10. +Using motif -M05703_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05704_2.00 of width 14. +Using motif -M05704_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09345_2.00 of width 10. +Using motif -M09345_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M01511_2.00 of width 10. +Using motif -M01511_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99623 +Using motif +M01512_2.00 of width 10. +Using motif -M01512_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998586 +Using motif +M02441_2.00 of width 10. +Using motif -M02441_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05705_2.00 of width 14. +Using motif -M05705_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05706_2.00 of width 18. +Using motif -M05706_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05707_2.00 of width 14. +Using motif -M05707_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05708_2.00 of width 18. +Using motif -M05708_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08462_2.00 of width 7. +Using motif -M08462_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992103 +Using motif +M09346_2.00 of width 9. +Using motif -M09346_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996531 +Using motif +M03447_2.00 of width 14. +Using motif -M03447_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M05709_2.00 of width 13. +Using motif -M05709_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967143 +Using motif +M05710_2.00 of width 13. +Using motif -M05710_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08161_2.00 of width 11. +Using motif -M08161_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971462 +Using motif +M09347_2.00 of width 12. +Using motif -M09347_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968451 +Using motif +M03448_2.00 of width 13. +Using motif -M03448_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.925882 +Using motif +M05887_2.00 of width 13. +Using motif -M05887_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947484 +Using motif +M09348_2.00 of width 8. +Using motif -M09348_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965444 +Using motif +M11228_2.00 of width 10. +Using motif -M11228_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.905739 +Using motif +M11229_2.00 of width 12. +Using motif -M11229_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974362 +Using motif +M11230_2.00 of width 14. +Using motif -M11230_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985 +Using motif +M02747_2.00 of width 11. +Using motif -M02747_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996304 +Using motif +M02748_2.00 of width 10. +Using motif -M02748_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999698 +Using motif +M02749_2.00 of width 9. +Using motif -M02749_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03449_2.00 of width 20. +Using motif -M03449_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03450_2.00 of width 15. +Using motif -M03450_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03451_2.00 of width 14. +Using motif -M03451_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05711_2.00 of width 14. +Using motif -M05711_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05712_2.00 of width 18. +Using motif -M05712_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05713_2.00 of width 14. +Using motif -M05713_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05714_2.00 of width 18. +Using motif -M05714_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09349_2.00 of width 15. +Using motif -M09349_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975079 +Using motif +M09625_2.00 of width 10. +Using motif -M09625_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M02710_2.00 of width 10. +Using motif -M02710_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09350_2.00 of width 15. +Using motif -M09350_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96538 +Using motif +M11235_2.00 of width 10. +Using motif -M11235_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M02711_2.00 of width 10. +Using motif -M02711_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970922 +Using motif +M07999_2.00 of width 14. +Using motif -M07999_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961119 +Using motif +M08000_2.00 of width 14. +Using motif -M08000_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974744 +Using motif +M08001_2.00 of width 15. +Using motif -M08001_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.970548 +Using motif +M08002_2.00 of width 15. +Using motif -M08002_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971656 +Using motif +M08003_2.00 of width 14. +Using motif -M08003_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.947121 +Using motif +M08004_2.00 of width 15. +Using motif -M08004_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99037 +Using motif +M08005_2.00 of width 15. +Using motif -M08005_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981395 +Using motif +M08006_2.00 of width 11. +Using motif -M08006_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979634 +Using motif +M08007_2.00 of width 13. +Using motif -M08007_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967237 +Using motif +M08008_2.00 of width 15. +Using motif -M08008_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95443 +Using motif +M09351_2.00 of width 14. +Using motif -M09351_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996205 +Using motif +M09626_2.00 of width 12. +Using motif -M09626_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.991204 +Using motif +M11237_2.00 of width 10. +Using motif -M11237_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967389 +Using motif +M02750_2.00 of width 10. +Using motif -M02750_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M03452_2.00 of width 16. +Using motif -M03452_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03453_2.00 of width 15. +Using motif -M03453_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968943 +Using motif +M05715_2.00 of width 16. +Using motif -M05715_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05716_2.00 of width 15. +Using motif -M05716_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984052 +Using motif +M05717_2.00 of width 16. +Using motif -M05717_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988906 +Using motif +M05718_2.00 of width 15. +Using motif -M05718_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967581 +Using motif +M09361_2.00 of width 14. +Using motif -M09361_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09627_2.00 of width 16. +Using motif -M09627_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988488 +Using motif +M02449_2.00 of width 10. +Using motif -M02449_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9875 +Using motif +M03454_2.00 of width 16. +Using motif -M03454_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03455_2.00 of width 15. +Using motif -M03455_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944463 +Using motif +M05719_2.00 of width 16. +Using motif -M05719_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05720_2.00 of width 16. +Using motif -M05720_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09362_2.00 of width 18. +Using motif -M09362_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968696 +Using motif +M09628_2.00 of width 15. +Using motif -M09628_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.978675 +Using motif +M03456_2.00 of width 16. +Using motif -M03456_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03457_2.00 of width 15. +Using motif -M03457_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956615 +Using motif +M05721_2.00 of width 16. +Using motif -M05721_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05722_2.00 of width 16. +Using motif -M05722_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05723_2.00 of width 16. +Using motif -M05723_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05724_2.00 of width 16. +Using motif -M05724_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09363_2.00 of width 22. +Using motif -M09363_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.855044 +Using motif +M11246_2.00 of width 17. +Using motif -M11246_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998469 +Using motif +M11247_2.00 of width 18. +Using motif -M11247_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03458_2.00 of width 16. +Using motif -M03458_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03459_2.00 of width 16. +Using motif -M03459_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03460_2.00 of width 16. +Using motif -M03460_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05725_2.00 of width 19. +Using motif -M05725_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97296 +Using motif +M05726_2.00 of width 17. +Using motif -M05726_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946535 +Using motif +M05727_2.00 of width 19. +Using motif -M05727_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9236 +Using motif +M05728_2.00 of width 17. +Using motif -M05728_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9142 +Using motif +M08009_2.00 of width 15. +Using motif -M08009_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976517 +Using motif +M08010_2.00 of width 19. +Using motif -M08010_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98 +Using motif +M08011_2.00 of width 15. +Using motif -M08011_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986667 +Using motif +M08012_2.00 of width 15. +Using motif -M08012_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97619 +Using motif +M09364_2.00 of width 20. +Using motif -M09364_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.927424 +Using motif +M09629_2.00 of width 12. +Using motif -M09629_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M05729_2.00 of width 9. +Using motif -M05729_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944425 +Using motif +M05730_2.00 of width 9. +Using motif -M05730_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949464 +Using motif +M09365_2.00 of width 11. +Using motif -M09365_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945223 +Using motif +M02454_2.00 of width 10. +Using motif -M02454_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975147 +Using motif +M02751_2.00 of width 8. +Using motif -M02751_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982159 +Using motif +M03464_2.00 of width 16. +Using motif -M03464_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.922957 +Using motif +M03465_2.00 of width 10. +Using motif -M03465_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979589 +Using motif +M03466_2.00 of width 18. +Using motif -M03466_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941983 +Using motif +M03467_2.00 of width 10. +Using motif -M03467_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984969 +Using motif +M05731_2.00 of width 20. +Using motif -M05731_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997868 +Using motif +M05732_2.00 of width 21. +Using motif -M05732_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998291 +Using motif +M09369_2.00 of width 10. +Using motif -M09369_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977356 +Using motif +M03468_2.00 of width 16. +Using motif -M03468_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928909 +Using motif +M03469_2.00 of width 18. +Using motif -M03469_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928772 +Using motif +M03470_2.00 of width 9. +Using motif -M03470_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987407 +Using motif +M05733_2.00 of width 20. +Using motif -M05733_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963399 +Using motif +M05734_2.00 of width 20. +Using motif -M05734_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998392 +Using motif +M09370_2.00 of width 14. +Using motif -M09370_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972291 +Using motif +M09630_2.00 of width 12. +Using motif -M09630_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966405 +Using motif +M09371_2.00 of width 11. +Using motif -M09371_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984 +Using motif +M09631_2.00 of width 12. +Using motif -M09631_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.972787 +Using motif +M09632_2.00 of width 10. +Using motif -M09632_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993538 +Using motif +M11258_2.00 of width 6. +Using motif -M11258_2.00 of width 6. +Computing q-values. +Using motif +M03471_2.00 of width 8. +Using motif -M03471_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.941062 +Using motif +M03472_2.00 of width 14. +Using motif -M03472_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03473_2.00 of width 15. +Using motif -M03473_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94466 +Using motif +M03474_2.00 of width 8. +Using motif -M03474_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.975091 +Using motif +M05735_2.00 of width 17. +Using motif -M05735_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907921 +Using motif +M05736_2.00 of width 17. +Using motif -M05736_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.871765 +Using motif +M09375_2.00 of width 18. +Using motif -M09375_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02460_2.00 of width 9. +Using motif -M02460_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.860784 +Using motif +M05737_2.00 of width 17. +Using motif -M05737_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05738_2.00 of width 16. +Using motif -M05738_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.945085 +Using motif +M05739_2.00 of width 14. +Using motif -M05739_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05740_2.00 of width 14. +Using motif -M05740_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03475_2.00 of width 15. +Using motif -M03475_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.958523 +Using motif +M03476_2.00 of width 9. +Using motif -M03476_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.95284 +Using motif +M03477_2.00 of width 9. +Using motif -M03477_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969444 +Using motif +M03478_2.00 of width 15. +Using motif -M03478_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.965541 +Using motif +M05741_2.00 of width 17. +Using motif -M05741_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.920696 +Using motif +M05742_2.00 of width 17. +Using motif -M05742_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94469 +Using motif +M05743_2.00 of width 10. +Using motif -M05743_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985547 +Using motif +M05744_2.00 of width 10. +Using motif -M05744_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966476 +Using motif +M09377_2.00 of width 13. +Using motif -M09377_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.966433 +Using motif +M09634_2.00 of width 10. +Using motif -M09634_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.955849 +Using motif +M05745_2.00 of width 17. +Using motif -M05745_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952215 +Using motif +M05746_2.00 of width 17. +Using motif -M05746_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950135 +Using motif +M08164_2.00 of width 11. +Using motif -M08164_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.976273 +Using motif +M09378_2.00 of width 17. +Using motif -M09378_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.9224 +Using motif +M09635_2.00 of width 8. +Using motif -M09635_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951592 +Using motif +M09636_2.00 of width 16. +Using motif -M09636_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950241 +Using motif +M03479_2.00 of width 15. +Using motif -M03479_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981421 +Using motif +M05747_2.00 of width 17. +Using motif -M05747_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.907925 +Using motif +M05748_2.00 of width 17. +Using motif -M05748_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949012 +Using motif +M03480_2.00 of width 15. +Using motif -M03480_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974805 +Using motif +M03481_2.00 of width 10. +Using motif -M03481_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977051 +Using motif +M09379_2.00 of width 15. +Using motif -M09379_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948759 +Using motif +M03482_2.00 of width 10. +Using motif -M03482_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09380_2.00 of width 12. +Using motif -M09380_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.946743 +Using motif +M03483_2.00 of width 16. +Using motif -M03483_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03484_2.00 of width 13. +Using motif -M03484_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03485_2.00 of width 17. +Using motif -M03485_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03486_2.00 of width 17. +Using motif -M03486_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03487_2.00 of width 14. +Using motif -M03487_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03488_2.00 of width 15. +Using motif -M03488_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03489_2.00 of width 13. +Using motif -M03489_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03490_2.00 of width 17. +Using motif -M03490_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05749_2.00 of width 10. +Using motif -M05749_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05750_2.00 of width 10. +Using motif -M05750_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05751_2.00 of width 16. +Using motif -M05751_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05752_2.00 of width 16. +Using motif -M05752_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05753_2.00 of width 8. +Using motif -M05753_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05754_2.00 of width 8. +Using motif -M05754_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M02487_2.00 of width 11. +Using motif -M02487_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00195_2.00 of width 10. +Using motif -M00195_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989529 +Using motif +M05755_2.00 of width 9. +Using motif -M05755_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05756_2.00 of width 15. +Using motif -M05756_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.951111 +Using motif +M05757_2.00 of width 9. +Using motif -M05757_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M05758_2.00 of width 15. +Using motif -M05758_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.944655 +Using motif +M05890_2.00 of width 13. +Using motif -M05890_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09386_2.00 of width 16. +Using motif -M09386_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979888 +Using motif +M03491_2.00 of width 15. +Using motif -M03491_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03492_2.00 of width 16. +Using motif -M03492_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03493_2.00 of width 15. +Using motif -M03493_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03494_2.00 of width 15. +Using motif -M03494_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03495_2.00 of width 14. +Using motif -M03495_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05759_2.00 of width 17. +Using motif -M05759_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M05760_2.00 of width 17. +Using motif -M05760_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05761_2.00 of width 18. +Using motif -M05761_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05762_2.00 of width 18. +Using motif -M05762_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05891_2.00 of width 10. +Using motif -M05891_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997172 +Using motif +M08165_2.00 of width 11. +Using motif -M08165_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996939 +Using motif +M09387_2.00 of width 16. +Using motif -M09387_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05892_2.00 of width 10. +Using motif -M05892_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05763_2.00 of width 13. +Using motif -M05763_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05764_2.00 of width 13. +Using motif -M05764_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M01026_2.00 of width 8. +Using motif -M01026_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994416 +Using motif +M03496_2.00 of width 16. +Using motif -M03496_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05765_2.00 of width 10. +Using motif -M05765_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995204 +Using motif +M05766_2.00 of width 10. +Using motif -M05766_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992371 +Using motif +M09388_2.00 of width 12. +Using motif -M09388_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.961011 +Using motif +M03497_2.00 of width 15. +Using motif -M03497_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M03498_2.00 of width 16. +Using motif -M03498_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03499_2.00 of width 13. +Using motif -M03499_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03500_2.00 of width 13. +Using motif -M03500_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02716_2.00 of width 9. +Using motif -M02716_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03501_2.00 of width 9. +Using motif -M03501_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03502_2.00 of width 16. +Using motif -M03502_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M03503_2.00 of width 16. +Using motif -M03503_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03504_2.00 of width 16. +Using motif -M03504_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03505_2.00 of width 17. +Using motif -M03505_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03506_2.00 of width 17. +Using motif -M03506_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03507_2.00 of width 17. +Using motif -M03507_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05767_2.00 of width 10. +Using motif -M05767_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05768_2.00 of width 10. +Using motif -M05768_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09389_2.00 of width 16. +Using motif -M09389_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993231 +Using motif +M11305_2.00 of width 14. +Using motif -M11305_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03508_2.00 of width 15. +Using motif -M03508_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03509_2.00 of width 15. +Using motif -M03509_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03510_2.00 of width 15. +Using motif -M03510_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05893_2.00 of width 10. +Using motif -M05893_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M05894_2.00 of width 10. +Using motif -M05894_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09390_2.00 of width 8. +Using motif -M09390_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11309_2.00 of width 10. +Using motif -M11309_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05769_2.00 of width 10. +Using motif -M05769_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05770_2.00 of width 10. +Using motif -M05770_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09391_2.00 of width 11. +Using motif -M09391_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982304 +Using motif +M03511_2.00 of width 15. +Using motif -M03511_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994404 +Using motif +M05771_2.00 of width 9. +Using motif -M05771_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05772_2.00 of width 15. +Using motif -M05772_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.938849 +Using motif +M05773_2.00 of width 9. +Using motif -M05773_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05774_2.00 of width 15. +Using motif -M05774_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.956515 +Using motif +M05775_2.00 of width 9. +Using motif -M05775_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05776_2.00 of width 15. +Using motif -M05776_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.950469 +Using motif +M05777_2.00 of width 9. +Using motif -M05777_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05778_2.00 of width 15. +Using motif -M05778_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.948296 +Using motif +M05895_2.00 of width 10. +Using motif -M05895_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09392_2.00 of width 14. +Using motif -M09392_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97116 +Using motif +M02487_2.00 of width 11. +Using motif -M02487_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08166_2.00 of width 11. +Using motif -M08166_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08167_2.00 of width 14. +Using motif -M08167_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997778 +Using motif +M09393_2.00 of width 10. +Using motif -M09393_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939079 +Using motif +M09394_2.00 of width 13. +Using motif -M09394_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989319 +Using motif +M09638_2.00 of width 10. +Using motif -M09638_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M03512_2.00 of width 12. +Using motif -M03512_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M05779_2.00 of width 9. +Using motif -M05779_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05780_2.00 of width 15. +Using motif -M05780_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.928618 +Using motif +M05781_2.00 of width 9. +Using motif -M05781_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05782_2.00 of width 15. +Using motif -M05782_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.913592 +Using motif +M09395_2.00 of width 13. +Using motif -M09395_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998173 +Using motif +M05896_2.00 of width 10. +Using motif -M05896_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09396_2.00 of width 11. +Using motif -M09396_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989637 +Using motif +M03513_2.00 of width 12. +Using motif -M03513_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03514_2.00 of width 15. +Using motif -M03514_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03515_2.00 of width 13. +Using motif -M03515_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05783_2.00 of width 10. +Using motif -M05783_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05784_2.00 of width 10. +Using motif -M05784_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05785_2.00 of width 10. +Using motif -M05785_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05786_2.00 of width 10. +Using motif -M05786_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03516_2.00 of width 16. +Using motif -M03516_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03517_2.00 of width 17. +Using motif -M03517_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03518_2.00 of width 17. +Using motif -M03518_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05787_2.00 of width 13. +Using motif -M05787_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05788_2.00 of width 13. +Using motif -M05788_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05789_2.00 of width 11. +Using motif -M05789_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05790_2.00 of width 11. +Using motif -M05790_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03519_2.00 of width 17. +Using motif -M03519_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03520_2.00 of width 15. +Using motif -M03520_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03521_2.00 of width 17. +Using motif -M03521_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03522_2.00 of width 17. +Using motif -M03522_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03523_2.00 of width 17. +Using motif -M03523_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03524_2.00 of width 15. +Using motif -M03524_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05897_2.00 of width 10. +Using motif -M05897_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09397_2.00 of width 13. +Using motif -M09397_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977514 +Using motif +M00214_2.00 of width 10. +Using motif -M00214_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M02717_2.00 of width 9. +Using motif -M02717_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03525_2.00 of width 15. +Using motif -M03525_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03526_2.00 of width 16. +Using motif -M03526_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03527_2.00 of width 13. +Using motif -M03527_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03528_2.00 of width 13. +Using motif -M03528_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05898_2.00 of width 8. +Using motif -M05898_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09398_2.00 of width 9. +Using motif -M09398_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M11331_2.00 of width 7. +Using motif -M11331_2.00 of width 7. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11332_2.00 of width 12. +Using motif -M11332_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999799 +Using motif +M03529_2.00 of width 15. +Using motif -M03529_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03530_2.00 of width 15. +Using motif -M03530_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03531_2.00 of width 15. +Using motif -M03531_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05791_2.00 of width 8. +Using motif -M05791_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.96069 +Using motif +M05792_2.00 of width 8. +Using motif -M05792_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08013_2.00 of width 21. +Using motif -M08013_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99798 +Using motif +M08014_2.00 of width 11. +Using motif -M08014_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08171_2.00 of width 11. +Using motif -M08171_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998894 +Using motif +M08229_2.00 of width 10. +Using motif -M08229_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08230_2.00 of width 10. +Using motif -M08230_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M09411_2.00 of width 19. +Using motif -M09411_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99809 +Using motif +M09642_2.00 of width 14. +Using motif -M09642_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11348_2.00 of width 21. +Using motif -M11348_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939074 +Using motif +M11350_2.00 of width 8. +Using motif -M11350_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M09412_2.00 of width 10. +Using motif -M09412_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11355_2.00 of width 15. +Using motif -M11355_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999196 +Using motif +M11356_2.00 of width 24. +Using motif -M11356_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M11358_2.00 of width 8. +Using motif -M11358_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989297 +Using motif +M09413_2.00 of width 11. +Using motif -M09413_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.992796 +Using motif +M11361_2.00 of width 8. +Using motif -M11361_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999095 +Using motif +M09414_2.00 of width 11. +Using motif -M09414_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994822 +Using motif +M11365_2.00 of width 8. +Using motif -M11365_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989897 +Using motif +M08015_2.00 of width 20. +Using motif -M08015_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996224 +Using motif +M08016_2.00 of width 13. +Using motif -M08016_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M08017_2.00 of width 15. +Using motif -M08017_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995102 +Using motif +M08018_2.00 of width 15. +Using motif -M08018_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997186 +Using motif +M08172_2.00 of width 11. +Using motif -M08172_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993846 +Using motif +M09415_2.00 of width 12. +Using motif -M09415_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997056 +Using motif +M11367_2.00 of width 21. +Using motif -M11367_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.963689 +Using motif +M11368_2.00 of width 8. +Using motif -M11368_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97907 +Using motif +M08019_2.00 of width 15. +Using motif -M08019_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996382 +Using motif +M09416_2.00 of width 19. +Using motif -M09416_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987795 +Using motif +M09417_2.00 of width 12. +Using motif -M09417_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11371_2.00 of width 15. +Using motif -M11371_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03546_2.00 of width 16. +Using motif -M03546_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.984828 +Using motif +M03547_2.00 of width 10. +Using motif -M03547_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997396 +Using motif +M03548_2.00 of width 19. +Using motif -M03548_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.949815 +Using motif +M03549_2.00 of width 16. +Using motif -M03549_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M03550_2.00 of width 10. +Using motif -M03550_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03551_2.00 of width 19. +Using motif -M03551_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989091 +Using motif +M05793_2.00 of width 10. +Using motif -M05793_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99288 +Using motif +M05794_2.00 of width 10. +Using motif -M05794_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09425_2.00 of width 13. +Using motif -M09425_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.94327 +Using motif +M03552_2.00 of width 8. +Using motif -M03552_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.979767 +Using motif +M03553_2.00 of width 20. +Using motif -M03553_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03554_2.00 of width 19. +Using motif -M03554_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03555_2.00 of width 8. +Using motif -M03555_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987807 +Using motif +M05795_2.00 of width 10. +Using motif -M05795_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.971852 +Using motif +M05796_2.00 of width 10. +Using motif -M05796_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974198 +Using motif +M05797_2.00 of width 12. +Using motif -M05797_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.983696 +Using motif +M05798_2.00 of width 12. +Using motif -M05798_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995269 +Using motif +M03556_2.00 of width 18. +Using motif -M03556_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957778 +Using motif +M03557_2.00 of width 11. +Using motif -M03557_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05799_2.00 of width 10. +Using motif -M05799_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.87945 +Using motif +M05800_2.00 of width 10. +Using motif -M05800_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.917925 +Using motif +M03558_2.00 of width 8. +Using motif -M03558_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997157 +Using motif +M03559_2.00 of width 20. +Using motif -M03559_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995714 +Using motif +M05801_2.00 of width 10. +Using motif -M05801_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.918889 +Using motif +M05802_2.00 of width 10. +Using motif -M05802_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.986744 +Using motif +M09426_2.00 of width 11. +Using motif -M09426_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.93519 +Using motif +M03560_2.00 of width 10. +Using motif -M03560_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998492 +Using motif +M03561_2.00 of width 11. +Using motif -M03561_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03562_2.00 of width 20. +Using motif -M03562_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05803_2.00 of width 21. +Using motif -M05803_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05804_2.00 of width 21. +Using motif -M05804_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05805_2.00 of width 10. +Using motif -M05805_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.967578 +Using motif +M05806_2.00 of width 10. +Using motif -M05806_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952475 +Using motif +M02752_2.00 of width 9. +Using motif -M02752_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997563 +Using motif +M03563_2.00 of width 13. +Using motif -M03563_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03564_2.00 of width 20. +Using motif -M03564_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.969344 +Using motif +M05807_2.00 of width 10. +Using motif -M05807_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993265 +Using motif +M05808_2.00 of width 10. +Using motif -M05808_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993506 +Using motif +M09647_2.00 of width 10. +Using motif -M09647_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M05809_2.00 of width 22. +Using motif -M05809_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05810_2.00 of width 18. +Using motif -M05810_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05811_2.00 of width 22. +Using motif -M05811_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05812_2.00 of width 18. +Using motif -M05812_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09427_2.00 of width 20. +Using motif -M09427_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.98266 +Using motif +M11384_2.00 of width 24. +Using motif -M11384_2.00 of width 24. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03565_2.00 of width 16. +Using motif -M03565_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993333 +Using motif +M03566_2.00 of width 19. +Using motif -M03566_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998872 +Using motif +M03567_2.00 of width 15. +Using motif -M03567_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.997245 +Using motif +M03568_2.00 of width 11. +Using motif -M03568_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994721 +Using motif +M03569_2.00 of width 16. +Using motif -M03569_2.00 of width 16. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982619 +Using motif +M05813_2.00 of width 18. +Using motif -M05813_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.981111 +Using motif +M05814_2.00 of width 21. +Using motif -M05814_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05815_2.00 of width 18. +Using motif -M05815_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974862 +Using motif +M05816_2.00 of width 21. +Using motif -M05816_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05817_2.00 of width 18. +Using motif -M05817_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.97087 +Using motif +M05818_2.00 of width 18. +Using motif -M05818_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05819_2.00 of width 18. +Using motif -M05819_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.957969 +Using motif +M05820_2.00 of width 18. +Using motif -M05820_2.00 of width 18. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999397 +Using motif +M00832_2.00 of width 11. +Using motif -M00832_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.996615 +Using motif +M03570_2.00 of width 20. +Using motif -M03570_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998693 +Using motif +M03571_2.00 of width 19. +Using motif -M03571_2.00 of width 19. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999598 +Using motif +M03572_2.00 of width 8. +Using motif -M03572_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.995692 +Using motif +M03573_2.00 of width 23. +Using motif -M03573_2.00 of width 23. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.985926 +Using motif +M03574_2.00 of width 20. +Using motif -M03574_2.00 of width 20. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.989508 +Using motif +M05821_2.00 of width 12. +Using motif -M05821_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.982989 +Using motif +M05822_2.00 of width 12. +Using motif -M05822_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.99533 +Using motif +M08231_2.00 of width 10. +Using motif -M08231_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09432_2.00 of width 10. +Using motif -M09432_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11388_2.00 of width 15. +Using motif -M11388_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11389_2.00 of width 10. +Using motif -M11389_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11390_2.00 of width 8. +Using motif -M11390_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11491_2.00 of width 15. +Using motif -M11491_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M00216_2.00 of width 7. +Using motif -M00216_2.00 of width 7. +Computing q-values. +Using motif +M02527_2.00 of width 9. +Using motif -M02527_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03575_2.00 of width 17. +Using motif -M03575_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03576_2.00 of width 8. +Using motif -M03576_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998794 +Using motif +M05823_2.00 of width 10. +Using motif -M05823_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05824_2.00 of width 10. +Using motif -M05824_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M08175_2.00 of width 13. +Using motif -M08175_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.987303 +Using motif +M03577_2.00 of width 10. +Using motif -M03577_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M03578_2.00 of width 17. +Using motif -M03578_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05825_2.00 of width 9. +Using motif -M05825_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999797 +Using motif +M05826_2.00 of width 9. +Using motif -M05826_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M09434_2.00 of width 12. +Using motif -M09434_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.994948 +Using motif +M03579_2.00 of width 10. +Using motif -M03579_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999296 +Using motif +M09435_2.00 of width 13. +Using motif -M09435_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.999899 +Using motif +M08020_2.00 of width 21. +Using motif -M08020_2.00 of width 21. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.977584 +Using motif +M08176_2.00 of width 9. +Using motif -M08176_2.00 of width 9. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.974362 +Using motif +M09439_2.00 of width 22. +Using motif -M09439_2.00 of width 22. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 3.8e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.771538 +Using motif +M02538_2.00 of width 12. +Using motif -M02538_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.908824 +Using motif +M09440_2.00 of width 22. +Using motif -M09440_2.00 of width 22. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.960759 +Using motif +M09442_2.00 of width 14. +Using motif -M09442_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.968286 +Using motif +M03580_2.00 of width 12. +Using motif -M03580_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.902692 +Using motif +M08177_2.00 of width 11. +Using motif -M08177_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.834423 +Using motif +M09443_2.00 of width 17. +Using motif -M09443_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.859811 +Using motif +M09655_2.00 of width 12. +Using motif -M09655_2.00 of width 12. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.8718 +Using motif +M00967_2.00 of width 10. +Using motif -M00967_2.00 of width 10. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.872248 +Using motif +M08178_2.00 of width 15. +Using motif -M08178_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.952982 +Using motif +M09444_2.00 of width 13. +Using motif -M09444_2.00 of width 13. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.939057 +Using motif +M05827_2.00 of width 11. +Using motif -M05827_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.934771 +Using motif +M05828_2.00 of width 11. +Using motif -M05828_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.919619 +Using motif +M00968_2.00 of width 11. +Using motif -M00968_2.00 of width 11. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.7838 +Using motif +M11433_2.00 of width 15. +Using motif -M11433_2.00 of width 15. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.896505 +Using motif +M11435_2.00 of width 14. +Using motif -M11435_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.993436 +Using motif +M05829_2.00 of width 14. +Using motif -M05829_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M05830_2.00 of width 14. +Using motif -M05830_2.00 of width 14. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=1 +Using motif +M11437_2.00 of width 17. +Using motif -M11437_2.00 of width 17. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.990588 +Using motif +M03581_2.00 of width 8. +Using motif -M03581_2.00 of width 8. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.998191 +Using motif +M07782_2.00 of width 29. +Using motif -M07782_2.00 of width 29. +Warning: Reached max stored scores (100000). +Motif matches with p-value >= 1.1e-05 have been dropped to reclaim memory. +Computing q-values. + Estimating pi_0 from a uniformly sampled set of 10000 p-values. + Estimating pi_0. + Estimated pi_0=0.988934 diff --git a/logs/gtf.out b/logs/gtf.out new file mode 100644 index 0000000..4fb3ada --- /dev/null +++ b/logs/gtf.out @@ -0,0 +1,3 @@ +100.00% (698) TFs have expresssion +92.05% (15856) genes have peak data +11,552 genes and 1,949,311 gene-TF combinations with at least 2 peaks. diff --git a/notebooks/enhancer_plot.ipynb b/notebooks/enhancer_plot.ipynb new file mode 100644 index 0000000..db17fbc --- /dev/null +++ b/notebooks/enhancer_plot.ipynb @@ -0,0 +1,1187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import scanpy as sc\n", + "from anndata import read_h5ad\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import Normalize\n", + "from tqdm.auto import tqdm\n", + "import seaborn as sns\n", + "import pyranges as pr\n", + "from scipy import sparse, stats" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import atac-metacell-utilities functions\n", + "sys.path = [\"../src\"] + sys.path\n", + "from pl import enhancer_plot, setup_cmap_and_legend, discretize_colors, get_peak_deltas\n", + "from util import load_module\n", + "from density_analysis import density_analysis as da" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data\n", + "\n", + "Uncomment the cell below to download the dataset used to generate this example." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#! aws s3 sync s3://fh-pi-setty-m-eco-public/mellon-tutorial/ ../data/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before running this notebook, make sure the `diff_acc` and `gp_corrs` rules of the `atac-metacell-utilities` pipeline have been run successfully. The output of these rules is required to run this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_dir = \"../data/\"\n", + "rna_annData_file = data_dir + \"preprocessed_t-cell-depleted-bm-rna.h5ad\" \n", + "atac_annData_file = data_dir + \"preprocessed_t-cell-depleted-bm-atac.h5ad\" " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rna_ad = read_h5ad(rna_annData_file)\n", + "atac_ad = read_h5ad(atac_annData_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 8627 × 17226\n", + " obs: 'sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'batch', 'DoubletScores', 'n_counts', 'leiden', 'phenograph', 'log_n_counts', 'celltype', 'palantir_pseudotime', 'selection', 'NaiveB_lineage', 'mellon_log_density', 'mellon_log_density_clipped'\n", + " var: 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'PeakCounts'\n", + " uns: 'DMEigenValues', 'DM_EigenValues', 'NaiveB_lineage_colors', 'celltype_colors', 'custom_branch_mask_columns', 'hvg', 'leiden', 'mellon_log_density_predictor', 'neighbors', 'pca', 'sample_colors', 'umap'\n", + " obsm: 'DM_EigenVectors', 'X_FDL', 'X_pca', 'X_umap', 'branch_masks', 'chromVAR_deviations', 'palantir_branch_probs', 'palantir_fate_probabilities', 'palantir_lineage_cells'\n", + " varm: 'PCs', 'geneXTF'\n", + " layers: 'Bcells_lineage_specific', 'Bcells_primed', 'MAGIC_imputed_data'\n", + " obsp: 'DM_Kernel', 'DM_Similarity', 'connectivities', 'distances', 'knn'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rna_ad" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 8627 × 216477\n", + " obs: 'Sample', 'TSSEnrichment', 'ReadsInTSS', 'ReadsInPromoter', 'ReadsInBlacklist', 'PromoterRatio', 'PassQC', 'NucleosomeRatio', 'nMultiFrags', 'nMonoFrags', 'nFrags', 'nDiFrags', 'BlacklistRatio', 'Clusters', 'ReadsInPeaks', 'FRIP', 'leiden', 'phenograph', 'celltype', 'SEACell'\n", + " var: 'seqnames', 'start', 'end', 'width', 'strand', 'score', 'replicateScoreQuantile', 'groupScoreQuantile', 'Reproducibility', 'GroupReplicate', 'nearestGene', 'distToGeneStart', 'peakType', 'distToTSS', 'nearestTSS', 'GC', 'idx', 'N', 'Bcells_primed', 'Bcells_lineage_specific'\n", + " uns: 'FIMOColumns', 'GeneScoresColumns', 'InSilicoChipColumns', 'celltype_colors', 'celltype_combined_colors', 'leiden', 'leiden_colors', 'neighbors', 'phenograph_colors', 'tab20', 'umap'\n", + " obsm: 'DM_EigenVectors', 'GeneScores', 'X_svd', 'X_umap'\n", + " varm: 'FIMO', 'InSilicoChip', 'InSilicoChip_Corrs', 'OpenPeaks'\n", + " layers: 'tf_idf'\n", + " obsp: 'ImputeWeights', 'connectivities', 'distances'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atac_ad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set plotting parameters and lineage of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "n_genes = 100\n", + "min_peaks = 4\n", + "target_lineage = 'NaiveB'\n", + "target_peak_lineage = 'Bcells'\n", + "target_fate = 'proB'\n", + "base_fate = 'HSC'\n", + "dorc_pval = 1.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load output from atac-metacells-utility pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#differential accessibility fille for target cell type vs. stem cell type\n", + "peak_file = f'../results/diff_acc/{target_fate}_{base_fate}_diff_acc.tsv'\n", + "#SEACells gene-peak correlations\n", + "gene_peak_scores_file = '../results/gp_corr/gp_corr.pickle'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/loc/scratch/24691136/ipykernel_7408/2688512778.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " peaks = peaks[atac_ad.var[f'{target_peak_lineage}_primed'] | atac_ad.var[f'{target_peak_lineage}_lineage_specific']]\n" + ] + }, + { + "data": { + "text/html": [ + "
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logFCpadj_fdrgroupA_NgroupB_Nmean_groupAmean_groupB
feature
chr2:231672408-231672908-8.4593.200000e-236.03.04.560.00
chr19:42129437-42129937-8.3928.960000e-226.03.04.380.00
chr5:139747147-139747647-5.5458.960000e-226.03.04.680.78
chr10:124601330-124601830-8.3391.530000e-206.03.04.580.00
chr12:123131799-123132299-8.3472.140000e-206.03.04.520.00
.....................
chrX:68432583-684330830.0001.000000e+00NaNNaN0.210.00
chrX:71112012-711125120.0001.000000e+00NaNNaN0.270.00
chrX:7146660-71471600.0001.000000e+00NaNNaN0.270.00
chrX:78331666-783321660.0001.000000e+00NaNNaN0.270.00
chrX:9472654-94731540.0001.000000e+00NaNNaN0.270.00
\n", + "

18589 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " logFC padj_fdr groupA_N groupB_N \\\n", + "feature \n", + "chr2:231672408-231672908 -8.459 3.200000e-23 6.0 3.0 \n", + "chr19:42129437-42129937 -8.392 8.960000e-22 6.0 3.0 \n", + "chr5:139747147-139747647 -5.545 8.960000e-22 6.0 3.0 \n", + "chr10:124601330-124601830 -8.339 1.530000e-20 6.0 3.0 \n", + "chr12:123131799-123132299 -8.347 2.140000e-20 6.0 3.0 \n", + "... ... ... ... ... \n", + "chrX:68432583-68433083 0.000 1.000000e+00 NaN NaN \n", + "chrX:71112012-71112512 0.000 1.000000e+00 NaN NaN \n", + "chrX:7146660-7147160 0.000 1.000000e+00 NaN NaN \n", + "chrX:78331666-78332166 0.000 1.000000e+00 NaN NaN \n", + "chrX:9472654-9473154 0.000 1.000000e+00 NaN NaN \n", + "\n", + " mean_groupA mean_groupB \n", + "feature \n", + "chr2:231672408-231672908 4.56 0.00 \n", + "chr19:42129437-42129937 4.38 0.00 \n", + "chr5:139747147-139747647 4.68 0.78 \n", + "chr10:124601330-124601830 4.58 0.00 \n", + "chr12:123131799-123132299 4.52 0.00 \n", + "... ... ... \n", + "chrX:68432583-68433083 0.21 0.00 \n", + "chrX:71112012-71112512 0.27 0.00 \n", + "chrX:7146660-7147160 0.27 0.00 \n", + "chrX:78331666-78332166 0.27 0.00 \n", + "chrX:9472654-9473154 0.27 0.00 \n", + "\n", + "[18589 rows x 6 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Subset peaks to only peaks annotated as primed or lineage-specific in the target lineage\"\n", + "peaks = pd.read_csv(peak_file, sep = \"\\t\", index_col= 0)\n", + "peaks = peaks[atac_ad.var[f'{target_peak_lineage}_primed'] | atac_ad.var[f'{target_peak_lineage}_lineage_specific']]\n", + "peaks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create dictionary of lineage masks from branch masks stored in rna_ad.obsm" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "branch_mask_df = pd.DataFrame(rna_ad.obsm['branch_masks'], index = rna_ad.obs_names, columns = rna_ad.uns['custom_branch_mask_columns'])\n", + "fate_dict=dict()\n", + "for col in branch_mask_df.columns:\n", + " fate_dict[col] = branch_mask_df[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/loc/scratch/24691136/ipykernel_7408/134065165.py:2: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", + " lineages = pd.Series(index = rna_ad.obs_names)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Add lineage information to rna_ad.obs\n", + "lineages = pd.Series(index = rna_ad.obs_names)\n", + "for lineage in fate_dict.keys():\n", + " lineages[fate_dict[lineage]] = lineage\n", + "rna_ad.obs['lineage'] = lineages\n", + "plt.rcParams[\"figure.figsize\"] = (4,4)\n", + "sc.pl.scatter(rna_ad, basis='umap', color='lineage', frameon = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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x3xpzyl3k++z8fVdXxvY8j4WOk+ipPZ5cj4WeSJJkb/y6ZJqXZn+Mtp4Ey2dkSU1VIYQQ4gNCgtRJYm9DiF3HgswsdTOz1MXG/d0oCiyfkY1FU2jsjLC3PoQ/nEoHmJqqpFeMOp48r4WOwNCgUcEcfk8NWPEpy2VBUxhzkOlxqETjekaQeybUlrhYUOU9s40QQgghxLiQIHUS0XUD3YC61jDbj5i1Ry+aa87kH9xTCZl5nAA2DXpXFKUkx5YuS6Vg9qT2BZGFWVZau820AZddIRwzr5HntWAYBv5QChTQp8iqpFYVqopczCx1Y7dKBosQQgjxQSATpyaJnnCSN3Z3EUuYkaEClOTayfFYqO+IDXvO9GInFfkOXt/ZSSLVH6ACGWWaDDKH8vsCVIBI3EBVzFSA7lCy/7gz8NHFGmzBEu0mklsDqnb8E3o57BrBaIpESpcgVQghhPiAkN/ok0R9RzQdoAKgQGNnjDd2deGyqQw3iX1fY5h12zvTq0nl+6xYemO7SHxsUaZhmAEqMKHD9T7n6G81JZUk++gbeFvex9l1eEzX7EvXDUZSNHfFONwSOdVmCiGEEGKSkCB1EtB1A6umkOOx4HNpFGfb0jVCO4NJ3tnnT094GjyTf2BKqlkJYHKWZOqJDB8B91UcMFSNpD0bQ1FJOrLGdM2kDnarQmmuHVWBzkAcfYw5ukIIIYSY3CQndRLY2xBk17EQFo30rPXCLCvBSIpwXEcbMLGpj6oCRmaQes7MLBo7Yxxrj05oewe2c1wZBhg6LqcVn9NCs3/0pVyHs2pJ/pCyXUIIIYSYeiQndRJw2sygamDg19qdwOs0tw9XClTXGZIC8M6+buyWk+9JddgUomNIE5iQABXMB1I0onGdcGz4ANWqKRnltoQQQgjxwSTD/ZOAy64NO0gfiIweDQ6XpxobobZpSY7tuO2Ixo1RlyudSE6bwvQic2WtkUbsLRqjBqg+l3bG2i+EEEKI8SW/0ieBps7oiJPpcz0W1BE6R7UTWBK0rxzV8fRNntJO8zsjEjc41DJ6mkJfD64aD6Gkhj5PTzjFq9s7SJzpgq1CCCGEOGUSpJ5h/lBi1NzLzmCSbLeZlTHwh2XVFBZUebCd4PD+SAHvYHne4/e8jqZvQtR4swWayNv/HLkHXwJ9aE9zJG7w7LtttPeceD6rEEIIISYPCVLPsIaOaHqN+sEz9/v0LXGqDPhpJVIGXaEEVQVO5pS5GeHUIcY6+b21+9SCvEjCmJA6A2oqjgIoqTiKMXyPqW6YdWeFEEIIMXXJxKkzKJHU0VQFh1UhmjCGzbe0WRTiI+SZNnTEiCcNPA4Nm1UlEp9cw9xjiYf7lngdq2hWJbpqJWVzY2jWYY+pKXFRXeg8gasKIYQQYrKRIPUMiSV0NuzupDts9qKOFKwNDFBTg2LQvuphwWgKt/3MdYo7bScfII8UoGa5tPRrk0FRiPtKR71maY4ddax5DUIIIYSYlKRO6hnQE07y6vsdwwZoDquC1aIed2Z/31KmAw2up2rVSK9GNdiJ9mCOxKoCysSUhRquPuzxuGwKHqeV7nCSc2dm4XZYZKlUIYQQYgqS396nSSKls/lAN9uPBOgMxDICxHxvf4f2vEovDPjcMNLEqOFySwcHdCMFqNAfoI7W39g3wz/HY8ExwkSohD56WahTMfCyCrCg0k1VgX3YYxXM9sZTZj5tLKGz6UA3z77bxrF2WS5VCCGEmGokSD1NmrrMlaAONIWHFDhtDyRRMANSh00lFDOHzlXFLPRfXeiYsHaNFl72pRd0BZNEEyceiOa4x+/tZdEUth8N0TJMJYR8nwWrRUE3IDkgso0nzAc4Xq+0EEIIISYfCVJPE2VAjOe2a8wud2fsNzBzTDfs9qd7SXUDusNJWsewPOjxZvc7bSrTik5uMtHJpnd2hcZvIley90WJJ42MnuZst4VE0iCeNBicuFJb7GJhlYdZZZmvtRBCCCEmPwlST5Msd/9M9L0NIRo6hhauH2l4PjyGSUkjjbh7HCo+p0YkrnO45eSGvQenFti1k7rMKTEMczjfc+wdCnY/ha2nATBXoaotGRqE5nut7GkM09gZQ5NJVEIIIcSUI7P7T5OBuaVtPYnTdt9gVB/XeqV2y8RMkhqNkoyhxQMkXfnYgq0oho4t1EbcV0Z7TxIIZxxfmGWlrdt8jaOJyVWWSwghhBBjI0HqabL1cCD9977809gI9U/BXMs+Eh+fYHA8Q8rR2jxRcg6vwxIPEixZRHjaeSj+ZsJ5M9L7zUC1X2cggYGZprCo2nuaWyuEEEKI8SDD/adJKNofSBkcP9gbrwB1Mhtpha3BlN5kU0M3CNvzCRXNx7D0zvI3dLwNm/EdfRMlZfaeJnVw2lV0A7bVBTImUwkhhBBiapAgdRzpusH6HZ2s3dxKd6h/SD+W0OkZYYa5265SmDX8ykknynoGckVPxXBpA872feTvfgpn+750mkLXtBV0VV9MZEDvaR9LtBunvw5HoBFnpDW9Pdn7ISCW0Fm7uZVWf2xCnkEIIYQQE0OC1HEUT+p0BRMkkgadwQSGYdDV++dwFAVCMT2dPzmQfYS6pKMZrS7qVGEPNKHqKezBJqy9eby61UHCmT+kdBdA0pFFJLuaqLeMkupKFlV7cdpUfC4LTpuZP2v0VkkQQgghxNQhOanjzG3XUBQoy3Owuz7E3oYQNouCVRtmwpGR8UeaAsRGqUuqYOZbTtVRbKfFLLo/XPsDJYvJDdYR8FSTShqoiSi++newhtvpKT+HWFZ5xvG2YAtKKkE4fxb13TpzvHDV0gKef6+NSNygKMtGYbaNaUWu0/R0QgghhBgPEqSOA8Mw2Hygh85gnHBvIf5IPIU/aPaQxkfIPx0pxjxe7GkwNMBz2BRicWPYgPdMxbKqCqqiDMkJjYzSqZlyZNHmWASAoqfIPfgSasocqrdGOtJBalGWjfZAHHfLDqyxbhRDp9t1AfsaQ+w8GsRhUynPszOn3IPHKW9zIYQQYqqR4f5xEE3o1HdECcd0fC4Ls8rchKMpOgJmkOqwKngcZsLoMCPWGfpKep5oac9YYmiACmcuQAXQ9cznON4jWQbl1GqqAYYZ9Ed95YQK5qT32W0q151dSDR3Okmbl0hONQDhmE4iZRCIpFg0zScBqhBCCDFFyW/wcZAY0FPaE06iqXCwKUSyt0RneZ6DBdU+XtvRQWcwOWrvZt/2wQX0j2eEtNczbmAv8vGa6LRp6SVMXTaV8gIfB+KXoyVCJNyFGcfaLObnq2RBLZ25NRn7XDaVeVXe9DFCCCGEmHokSB0HlkGllLqCmePZh1oiHGyOpHtRRwvWslwa/tDJz4DSRshVVZUTD3z7WLWJm5Q1MGAf+DrWlLgoyrbT2OEjmfKSGFSUP5ZI8dd3WrFZFJZM87HjaCCd8xuJ67R3x0nGE7jDTfiKy7C7ZWlUIYQQYiqRrqZxsPOoWahfAYqybRT4rNgGVJXSDTMQGylILPD1f1Y4lQAVRp5MdbIBKowtQLVbFeZWuCnNtY+5/qlKZsDuHxDc57gtvL6zk2A0lV41Shvwbm3uiqfrzTpsKhfMyU6nExjA4dYI+996g33rX2LPK8+OqT1CCCGEmDwkSD1B4ViKF7e289qOzvSEoKauOAB5Xgst/jhdoQTxEVY+7QukCrOs2C0KNg3aeqZeeaTBYWg8YaCpCkum+8acTzuwb9ThP0LuwRexdx8D4PVd/iHHp5IpLOEOMAzKcu14HBoKsOlAN0m9P+DN81qxamCxOwGwOmVmvxBCCDHVyHD/CeoKJghFU4RIEY6ncNs1sl0aHcEk7QEz2EwO6HkcnH/a9/dAJHXalxh1WCE6QvB8oga33AB2HQ2yrzF8Us/l7DyIFu3B2XWIWFYFAFUFTlK6QW2Ji62HA8TeX4c92IRRMov5Z68kEE2lPyzsbwxSke8gGtdZVuvDblWBQiLdC3F4s075eYUQQghxekmQegJSukFJjp3qQifhWIqWrhg7jgaHPVZTID/LSkqH9p6hkWE0rg/Z5rZDaIwLI3kcCsHoiQWDowWoHodKMNrfppMpXZUyIJUY+lxjESych6vzAOG8mQD4nBr7m8IA6IbB/CoPW7aZHwKi0QTRhI7LplKUZaOlO06LP8HZtS7K8x2EoiliCR2HTcOVnXtS7RFCCCHEmSVB6hht3OenoTPGWTU+ukMJukJJgtGhw/SKYs60TxnQ4k9Q4M18ifsmMA0XAI41QIXhA85TqYk6ePGAE72OzaJkzORXFLM9Y8mF7csOCOfPIuHKB8hYRrauNUooqhOZdgGhnlbi7iIUBXbXh2jpjqePs2gKR9sivHeoB01VuGJRHg7bFFsrVgghhBCA5KSOSSKp09lbmL8rmCDPZwOgLNeRUffUYQGHNfMlbQtkBrJ5XivF2baMbdpJ/BT68mEHpn8eLx4cKVfUqin4XBrWU4jnBi9YYNXGPlmr0t5DzpG/k314PVosMOwxuV4ry2bnYysopyzfid2iku+zZhyz/UiAdw/2mB8SdIPUyXXqCiGEEGISkJ7U49h1LMjehhBleXayXFamFTmxWVS6Q/H0cHQfs2N19Mgo223lSFsk/b2qmLmXh1oio5w1spHiQLddI5FKER8QI48UNCZSBh2BJAU+K23DpCacTPmqgfd12hQMlIwUh75e3+pCBzOyNLZuUTEUFafDSnDAvVQFVi3JT/eIWjWVhs44Fi3A0posmrpiNHTEKM+zp0t/VeY7qClx4XZIL6oQQggxVUmQOopESmd/Ywgwi/RPK3Rhs6gYhpExI3+kIC7LZSHbbeFYezS9/2BzOONYVeGkA9SRqIq5JGn8BNIHgGEDVDixANVtVzEUCPfmtyrAJfPzeHN3F9EBx/Vdsq41SmOnwrSLbqQ9lCSasKbraCl6ClJx2nriVOSbM/WdNpWuELjsZgAa701TUBSFi+bm4A8nKc62oSgKiaROPGlIsCqEEEJMQTLcP4pkykgHaNF4ijd2d3GwOUw4llk4tMBnG3bJz1nlbmaWuTOCvMEBX/Ikh6QH388+4OOGbpBeuel0u2BODrNK+wvnG8CWQ91UFzlHPCeeNNjbAR1RSzqNASD78Dry9v2NrroD5rUMg3BcR1XMtIlITw/5LZuZ42hn8TQvTrtGSY4dRVFI6QavvN/Bi1vbae46wWhdCCGEEGec9KSOIKUbNHfFWDrdR4s/RlNvoNPqj9HU2d8nOKfcTb7PSjSRojucQgEqCu1ku2yUZNvpCSewqCcfjI7EIHOiVGySlFpNpgwKszJzblv9CWqK+wNXbcDkseoCJ/WdUXTdwGXXyPFYUANtaJEuQqkwBpBjMSdHJXWD7nASw4CN+7txNGzF0b4flP1Mm1GNZcCqUoZhpPNkY+P94gshhBBiwimGMVlXfT9zmruibNzXTcrInLVekmNPB6sALrvK0ulZvLG7a9jreBwawejoPZoOq0I0Mfl+BC67Sjg2NLgbS36qpoCmZealWi0KNk0hHNcZ+I7LcmmcOzubzft76AgkKPRpGG//EcXQyZ+9gOyCAgpqZqJq5pB9iz9GW7eZD2wNtZFd95rZq1w6B++cs1lY7U1fuzts1rTt610VQgghxNQhw/3D2Hk0mF5eVFMVphc7yfVYmVXuwm3vf8ncdo3d9cPPRgeOG6BaNHMi1YkwJ1rZjn/gKRouQAUzJ/R4UkZmgAqQSBqEYpkBKkB3OMVLWzrS5bxau5MkHVkYikJuRRVFM+egahqNnVHae+IUZduZX+Vl+YwsckvLiPoqSFmcdNmKOFTvJxTsn8zmdVokQBVCCCGmKOlJHcbhlhBbD5tF+hdWe6gudKGpCk1dMaLxZHrfSL2NYzW4gP4HmaqYw/uGYaYp2G0K0biRsb+vh9aqGtQUOZhTlQ1Ac1eMt/b6AbhycX56IlTDgUMcfu05VLsbY/ZFsP1lLFYLS278KDHFzms7OrFZVFYsyMVqkc9jQgghxFQiOanDmFbkJsttZcuBHrbXBdlxJMiS6T7ePdgDwPQis2TU8QJUBagscNAVTGQUp+8zOEC1aOCxW/CHz1yCqaaaAaPHYUFVoCM4eluy3RbC0STxVP/5w9UnvWhuDroBf99lpkbEegPUHLcFVVVQlP6VuS6Ym4vNorF5+zGiTYcpnjUbVQGrRcWimb2ihmHQ2t6DAiipGLMKNfboSZKxJMl4jICukUgZJFLm6lMSpAohhBBTiwSpI9BUhZ7e4XrDgLZuMxdVVfrLHw02OF/TAOxWddgAtU9Zjo2Grt6JQSmIjrKs6KmsKDWSwW1O6ZACukJJlk7z0BEcftnXPv5QZhCb0mFGiYtIPEVBlo327jiVhU5yvWaKwpxyN3sbQul7Zrkt5HhsHGwKk+O2UJbnIMdjY/2OToztG7BFOmgJdXHVh65LB6oA3eEkTZ1RPKqGIzuf3IoqZl9+NbGUyqZ6KMhKsKjai82i4nHK21wIIYSYaqR7aQQ+pyUj//JYuxmk2ixqRpmkmaUuXL15qrZheuta/PERV3oC0gFqn9GC1L5exBPlso/8Yx5tEtTh1siQFbRcNnXY5xm4LRBJcvaMbPY2hDjWEaOp03zt4kmdFn8MPZUku+51cg69Sn1zD/sbQ/REkjhsGjN6y1e5Y+0ohvla5JaUYLeqxJMGHQHz9fI4LPh6DqHpKaLtzezZtovd9WHea3fQGUywryHMtCIn5fmOsbxEQgghhJhkJEgdgaIoXLW0gBULcrFoZg+mVVOYU2HWPvW5evMiO2LMLnNjsyik9KEBZnc4mQ4E7RaFs2q8OE5srlRaojc4HrwcvaqA0zo0crT2diC6bCq2k+hM7A6nSBkGhVn9DbbZVOzDTJ4aGOxmuS1s3Ocn0VsVIRxP8cJ7bby0tZ3OYBKfEcQWasUa6cTadQSvU8Xj0CjqXS62oSNKbMfrWKNdFM+Zz5xzl5NI6azb3sHrO7to6Ihi0RTmX3QhqtVGyuqidftmlD2vYWvaSZbLwtwymTAlhBBCTGUSpB5HttuKVVN7/26hutDVW9DfDEgTKZ2UbhakTxynfr7XZcGiKcSGX9hpzKyWzOBLN8wZ9XaLki7y77Qq6Xa3B5JDZtsPNNKbQDfMWfmt3f0N9geTRIfJxe0LglUFSrLtNHTGSKQMvE6N5q444bieLuWVX1qCJ78AAFf7ftp6kgSjKXYcDWIYBnsaQsTcRaCoZJdWmG3RDZRYALv/KBgpkvE4h995E81qJVR7KVjMANfduZ+CY6/T9uLvaN6zc5RXUQghhBCTmQSpYzCv0kOe18qsMg8Ae+pD6SH/RNJgx9GRy1AN1N6T4J19PRl5pTbLiff2hWJDx+jjSYNY0khfO5IwCMf0UVMN+pxofQFVJd2T3H9/808D2Nw7wcxuVdIrXymKOYns4rk5LKz2Ujx7PgAJZw45HgsKZoqFYYAlGSHlK6by2k+QVz0dgO1HgngPrierYSP+d1+lp7mRcFcHiXCIs4p1zv3wh1EtFtBT9DQ3AtB++MAJPpkQQgghJguZUTIGFfnO9NrxAHMr3BxqDpt1Pxl+NrvXqRKK6qPmfNoskOWy0NZzcl2rY1kIwO3QiCd14glj3CZdpXSoKXLSFUqhKhCNJ6nId7K1Lkgsoafrw8Z625bjsXLOzCw27+/mzb1+LpidTdGsuWwNZBFOaMzx2jhnRjYWTWHfm2+g7dmOG4NOSw8VRZcBZpCrW51oySj++qP4G47hys2jYPoMIl3tHH5zPcWz59O4Y2u6nd2N9UQDPTi8vnF6ciGEEEKcLtKTehJqS9xcvigPj2P4Wf4AqqKOGqA6bSrxJCcdoAJjWqkqEEkRO8kA1Try47HlcJAWf4xDLRGauhIcaYuS5x2abKv19rpqKnQEEiRTBu2BOLGEzkULijlnVjazytxYLSqKohCoP4zS29qs7P7VoxZWuKheupySBYvNDYZBuLODgpqZdNYdIhmLEgsGcOXkpc+x2B1YHQ5CHe30tDSdxCsghBBCiDNFivmfhJRuEIqm8LksvLazg86AOdY9o8RFiz9OTySJ06YSiY88kF6YZaO1Oz5ku9epUZbrIM9n42BziOauocf0sWhm2arjGVxmyuPQUDDI89ioa48e/wInwOvU0kP8brtKqDd/9eJ5OcQSOv5QgqZO8zU6b1Y2xTn2jPP9DcdoP3yAvGm15JRVpLfvXf8i7Qf3kz99Bs7sHI69txGApR/5OPFImPaD+yidvxhnVjYNO7ZR984bABTPXUjz7u1gGMy/5gaySsrG9XmFEEIIMTFkuP8kvLm7i/ZAgmlFznSAeun8XAKRJF3BBKoCkbiOz6WRSBrDBqvR2PAzmeZVeCjJdbDzaDAdoI5UH7UvQHXZVOJJneQIMXFfgKqp5rX6huMD0dEDVEVhyDKmYzG92El9e5TSPDsNHTFURSHLZcWiKRRn29nf2ArQuxRqZpCaXVZB9oDgdLBYMED7of1Y7HZmr7wKZ1Y2zqxssopLqd/2LpFuPyVzF+DJLyTU0UbzrvfNJFowc1aFEEIIMSXIb+0TFI6lCMf7i/zbLAqKotDij7G7PgSYwR1AT7i/m9OmkV6VKeOgATQVkimDjfv8tAf60wAyJ1qZpaVCA+LL8Cg9tgMNzJ0dy8IAJxKg9q3CFU3o1LVE0A3Y3xhJ7+8OJ8jz2ghGU+mgORJP0bhjG8H2VqrPuQCb0zXi9WsvXEnhjDkE21oItDYDCt6ikvT+YEcHRza/DYAnv5BF19/M+8/8iWBbKzUXXIqvqARnVvbYH0gIIYQQZ5QEqScgmTJ4dXsHiaRBjtuCPxinJNfGkdYYDR39UaNhmEPdkXj/xKl4ygwuE70dqJHY0HH6lA7b6npIpGCkVTzjSUYtJzVWhVkWglGdlG6MKbd1OLkeC8FoinyfjXyfjcMtEewWlZRqEI3r6SDYblXxOCwkkjoDs0sO1gfo2WMOy7ty8ihftHTEe2kWC/6GozRu30pORRXTz7sYVTOTZuORMPtffwkAq8NJTkUVbYf2E2htwe71UThjttRMFUIIIaYYCVJPkj+cxDDMgvcAPZEU2W5LepnQUEzH41AJRvu7L6sKnBxoMnsX+xaWsltg4Mh/ImX2cg4cuh9r7in095C67cqwpar6tHSPHOkO7GV121XCMR2XXWV2uZumzhh2q4aiwPwqL7pu8PZeP4dawqxckMsr2zsBKMyyEoykiCZ0ZpW5WLe9g1hCR1GgJMdGU1ccm91K0ay5BNtaya2qPu6zxQJmqS89lUrP2E/GY7z3p9+TipurWhXNmovD66PzaJ25PxYzPzVIkCqEEEJMKRKkngCLprByQR6BSBJ/MMHBlnC6zBJAKJpiXqWbPfUhDAOCUR1NhemFTjwuC20DJko5bCqluXZa/XESqVTGxKaBoWWBz5quAGC3Khn366P2RpXGgHNHC1BHo6lmj27vOgDpiU+hmE5Blp3Kgswh+Y5APJ2a4A/3pygUZtk5d5YLXTfYeSzYn5drwPQiF/MqvThtGhZtxZjbVnPBpWSXV5JbUZXeZqR09GT/fT0FhYQ6OyiZuwCby41mtRH2d+HOzRvukkIIIYSYpCRIPUEKsGl/N7phpHM8C7wW2gNJEimDVn88I/czpUNdWxSLqqBp/b15XodKKJJMT2LqCw4HctnVjBJVwwWowJBSV1aLkl6SdODMfpddBcNAN4aWr+prWl8bBrelJMeKc/B6rEC+z0Ztiav3vv05CrleK5qqoKkKZbl2DreYPcg1xU4Ks+1DrjMWVoeD4llz098bhkF3QmPWlddx5K3Xsdjt7Fv/MnoywdwrP4S3sIh3/+e3GLrOvKuvJ7u0/KTuK4QQQojTT4LUExRP6SR6V5vyujQC4RQdwSSLp3kJxVIUZdto2+XPOCeRMsxzBpREDUX1jAlPqqKQGjSVKTzM8qN9FKAsz04yZaDrBq29wWxxto3zZuewcb+fho4YDptGuDf/dbTrzS53U1vi5t2D3TR2xdAHHWpRVZ59txUFhSXTvbgdFrxOC6qisKDKrGeq6wbTi51oikKup79maq7XRoHPRiSeoqbYPWIbTtSuY0H2NYbJ87q4+CO3sevFv6V7VfVUEj2Vwuh9kI7DByRIFUIIIaYQCVJPUJbLygVzstF12HLIXP5TN6C1O06u10ognKI420azf+T6pjB0Rn7fMqsjUZXe4fzewwzA7bDQE05Snu9gWrGT9p4Exdl2Xt7aTo7HykVzc/A5Nf72bvuQ61k1hXkVbt4/EkRVFVIGbD7QTbbbQko3aBpUnzUYS/X25Bq8vbcbgEsX5JLt7g9GVVVhUfXQ1Z00VeHCuTmjPt/J6Osh7ntNkjFz8lpORRU2l5vDb72O3esjHgqSWzWdWDiE3TV+QbIQQgghJo4U8z9JjZ1R3tlnBmseh5Yetu8zr9LNoebIsDVSS7OtNPpPbqUpi9o/qWrghCqbReHqswp4e08XLd391871WOgM9k+SUhVw2TVqS1wUZtl5cevQAHawQp+VWCJFMKZjGGZQaGAW6M/z2k7qOcaDbhi0dcfRAq10Hd6HO6+QZCxKuLuLcEcbkW4/zqwcFn/4Vt586i8o3c0UL7uYmkULzlibhRBCCDE20pN6EpIpnU37zQDVqin4nOaQ+sDc0GAklc4LBXNp0MJsGwcaIxkBqtOm4nVptHcnRl1GNX3vATHvwBn/8aRBIJKktTsz+O0MJjPyUjVNIRhNUdcaIcdrxW5RSOgGmmJWFhgo32fF57RwqCWSsX1WmYvibDvZbiuNnVH2NYSYUeqmLM9x/AcYR6qiUJRt571X/k7E3wX792Tst7nclM5fiKGo6KFuNKDj0D6mL5wvJamEEEKISW6EapxiNIqi4LCZL10iZdDYFae2NHPWezJlUJBtNWfeYxb2PzIo2ANzZapW/9AA1eccOYgaZv4SYAZtFm3oeX3XLsu1M7/Cky6V9fedXcSSBrqeGaAqwIIqD0um+9LPaV4fKvMdVBU42bS/m2ffbWNfQ4iuUJIDTeER2zvRCmpmDl1NSlGoXLqcvOpaNFWhZPG5ACQ6m+lurD8DrRRCCCHEiZCe1JOw/UgAVQGvQyMUS6UD0YEaO2NDVnQa3FM5mp7IyN2q8RGus68xRG2JK73y1WANnTGa/bH+vFbDwGZRiCcz71VV6GR6sYv12zvpDidxWBWiCQOHmmJekULc6M+pLc6xY9HU9Ax/gNbuGHaLStaAfNWxSsbjHHhjHRabjZrzL0FR+4PkREpny8EerBaVRdO8qL29oRWLl1G+6CzqNr5pfr/oLLqbGtjz6vNYNr/F3Cuvw21JYvd4MQwDZ/b458cKIYQQYnxJTuoJ0nWDv25sHbI932slZRj0hJKkDLBbwWWzEIqmiB9nUtRoXDZ1zMue9h2f1PUxrUp18bxsjrRG6Q4nsWgKgXAivbBAdaGTeFKnsTNGRb6Dju4ovr0voEcCVF9yFfV6HtkuCzPL3BlD581dMd7a60dVYNWSfBwjdfsOI6UbtBw8wOHXXwRg0fW34MkvSO9v6IiysTfNYsWgSVt92g7uo7uxAXdePofeej3ztcnJY8mNH0VPJlFUNSMAFkIIIcTkIr+lT5CqKiye5qXAZwZIfb2oncEE4WiKvng0noDucHLYANU6zKvusinMKnVit2Z2y9qsSsaQ+7BtGnBKOD62ABWgoyfJkbYo/lASl13jisUF5HvN56prjaBicP7sbPJ9VlYuzIWkOXv+0NEOGjtjdAQTNHbG2HEkQDSWoqc32FUwc1/V4bqYR/Hajk42tjpxFldSUDsLV25uxv7CLBvFOXYq8h34nMMPAux//RVa9u0iGgxQMm9Rxj6LzU7r/r2887v/YsuTT5BKjsP6skIIIYSYENKTegoSSXN9+h1HAhxpiw57zMAlRk/GcEX+h2PTzDQApfeGFg3sVrPqgKqY19GNzGtZVPP4RApUFa5fXsSR1jDvHTKXH83zWugImIFcTbGTGl+cSLefFq2IfY1h5pT3rq6FOQEsEtdZUOWlJMeGRVOxDxeNj8AwDJ59t4140mBehYeZZSdWKiocS7H5QDf2o5vReprRrFbCXZ0Zxyiqmq6biqJw9kc/hU1KUgkhhBCTkuSkngKrRaW9J05rd2ZNUa9DJRDVUYBF07wEIkm6Qgk6A8fvuVMV86tvFv/govqjGRjQJlLgsveXoFKMocFu3z1UBeZXmgX5C7LsqEoAA3P50o6AWQvWMAzcufm4c/PJB+ZWeFAUha5Qks5A//PHkzpux4m/rRRF4aK5uXSHE5TlnniVgMbOKB2BBGQvZFlFMQ1b3snY78jKIdrdlf4+u7wKq8N5wvcRQgghxOkhPamn6IUtbRkrOSnApfNzafHH2NU7gSnXY6Ui3862uiCaAlarSvQE8kzHarhJUH2sKqiaiq7rw07g6huZL821k9TNnNR8r5W1m9sAmFfpYWZpf69jZyBBLKlTkmMucdrWE+Noa5S5FR6c9rHnoZ4swzDY/9rLhP2dzFq5CsXhZVtdAEegkeDWdQB4i0oItDSNfBFFYe6VHyKnvHLC2yuEEEKIEyM5qacoMmipUQPYdiRAT6S/17QzmGBbXRCAlMEJB6hjLenZF6Bah+nITOgQS+hD6oN67OZbQDfMr/qOGM1dMXYdC2K1qEwvcpLrsVKaa0+fE4mneH1XJ2/v9dPUFQNg55EgR9uj7DgaPKFnO1nJaJS2g/sIdbTTdewIDpvGOTOzmVFTgmY182oDLU1kl1eNfBHDINg2dBKcEEIIIc48CVJPQUo30vmmFXkOynLtOG0qnYEE9R2xMV1DG/QTsAzzExmur3u449LtGqXU1eCe1nBcp2+S+8D4NcdtRrqLpvm4ZH4ungFD+JqqYNUUFAXsvQ3xuSwZf040q9NJ9fLzKZwxm/zpM2g9sJdAWwuu7BwW/dPNKKqGqmnppVKHo9kdNO7cxra//pFU4uRWABNCCCHExJDh/lO0rzFEZyDBwmovb+3poidyAsVQx4k5AUohmTKDZpddzUhBGJoGYADmMlSKqvQH2vkOjrWbQd35s7Mpyu7vPY0ndYIRs2h/sz/G0hofeV4bzt4SU4ZhEE8aJzRZarw079nJwQ3rUTWNs29bQzTQjWaxoWgq7/7hsTFdY/GHP4o7N2+CWyqEEEKIsZKJU6doYJ5mclC5qYE1Tvtmvx/PaNUALGrmsqjp++pknJWRI6uYObJv7vETjKZ679EbmA4IUC2awrLaLKYXO9F6E1Q37vfjDyaxWRQSKSN9PsDR1gjlec4B91GGlM86XRxeHygKNreHhu1bqN+6mazScuZffT2a3U4qNnqvtjMri57mRhy+LLTBK1cJIYQQ4oyQ4f5xcrglnFF03+fSOG92dnpC0lgCVMgMUC0aVBX0z3SfX+nhnJlZQ1IERqKpMK3QicOmce6s/rYMDoKzXBYWVpmz+3M9NrJcVnYdDdLQESMUS9EVShLvjY77rlGeP3lmxmeXVXD2x1az+MMfTQ/bR3u6MXSd4plzj3t+pLubQ2+9TsP77010U4UQQggxRtJtNE56wslB36do9cfJ8VgJRpPEEieeVZFMkVF/9WBLBFVRxlQ3FcySU42dUZq6YkQTOlX5DuoGXM9jV5he7KYwy459wIIBda1hWrrj6V5dTQGHTSPfp7Gw2ksiaZy23NOxsjqcpBIJqs46h5Y9O4kFAzTu3EYiGsHu8xHr6TnuNWS5VCGEEGLymFyRxhQ2vcTFoZZIxrb2QMKs3TmMwcP6qnr8mqiBSAplmHNHE00Y6aNbumMZ+arBmMH7R4JAELtVZcX8HPY2hukIJDIma6UMMwjvCSdZVpuF03ZmhvVHs/ulv9F17Ai1F1+G3esl4u+iYftWEpFw+hhF0zAGzCpzZuUS6e7s3WchFY8Pua4QQgghzgwZ7h8nXoeFhdXejCVKm7pi6Vnwg/XFgH1BZ47LwlhWETUAh00h2zVyLVKnrf/vmgoVeeYEqEjcIJkysGjmPQemDcQSOht2+zncEqEnnMTj0KgssKNgDvEXZtmYV+lJ56tONqGOdgD89UeJBnqwOpwZASqAoeu4BkyOinR3YnE4ceXmY6SSHNn81mltsxBCCCFGJrP7x1lzV5Sth3qIDDO879b9XBj5M1HFxd+dN6MrE9eRbbUo2CMdRJMKSWfmMLbViFKUrKPFUk1pYTbFOXberwsMyZtdWuOjKMuMeDuCCbKcFjzOydn5Hmxvo7upAUVROPzOG8MfpKqoFgv6oB5T1WJBUVXK5i+hYsmy09BaIYQQQhzP5Iw4prAstxW7TSUyzLJO2akWXEYAlxHAaQQIKZnB44kM46tmBakR2cIduPa/ihOFztpVpOye9L6l0ZcpTR2gKTmdZvUWSnMd7BqmCP+eY0GqCgo40BRi+5EgCnDBnGwKsuxDjj0dQp0dHNywnuyyciqXnpOxz5NfgCe/AD2VwjAMooEeWvbuxBiYQ6EPXcwAQE+a+cQdRw7hKy4hq6RsQp9DCHGaBVvhwMsQD4E7H2asApvrTLdKjKeuQxBqAxTwlZlfYsqT4f5xtq8hhD80fK3URksth1znscV+GSF16CSdwTGn2z7y0LqmKmS7LSMW9TcUFQMFQ1Ux1P6D7BaFqGLOzI8pTtr8CbqCCUpybEOuEY7rpHQDW+9NDKCudeTi+BOt7cBeAq3N1G8deRa+qmmULVhMzfkXo1mHPlMqkUS1WIc9N9TRxtF33xm39gohzrCeRvjTGvjZXPjL/4ZnvwZ/XA0/mw0v/BskJvb/s+bmZr70pS8xffp07HY7FRUVXHfddbzyyisAVFdX8/Of/3zYc+vq6lAUJf2Vk5PDxRdfzGuvvTahbZ5ymrbAmz+Ddx+EPX+BPU/Bxl/CxvugY/+E3Xb16tUoisLnP//5Ifu+8IUvoCgKq1evnrD7D6e6ujr9fnG5XMyfP58HHnjgtLZhvEmQOs6Ksu3YLArleUN7Gw1FY6f1HOqsC9LbnLaRfwTF2SP3WCZSBv5QEk+ihbxUw5D9EWs2HTNW0Vm7CmNAj4Giwjb7Sl523m4Gy7EUr+3oZF9TZMg1fA4DdctvqNx/P4uKkuT7rNSUnLneh6LZc8mpqKZ6+fkjHlO38U12vvAMsVCQ+VdfT80Fl5BdVtl/gJ5CtQ4NUhVNw5mdQ9HseRPRdCHE6eY/Br+5Anb8GfRBE1ij3fDWL+F3H4Hk2FYHPFF1dXWcddZZvPrqq/zkJz9h+/btPP/886xYsYIvfvGLY77Oyy+/TFNTE6+99ho+n49rrrmGw4cPT0ibp5y612Dn/0C4bei+nnrY+gi0vD9ht6+oqOCJJ54gEun//RmNRnn88ceprKwc5cyJ873vfY+mpibef/99brjhBj7/+c/zhz/8Ydhj41NgsrAEqeOsOMfOtcsKOas2ixxPfzZFX59oclAna2W+g1pnG1eFfsNZ0ecz9h1sGf1Tvlv3c0nkD1wc+SMF+qBAVQHd5kG3uijNsWJRFTQVonEDFAVPfgkzyzxkuSwZPbiaCstrfVw6P4cVtRaUrkMQbmO6o5WL5uaS6xm+F/J0cPqymXvltZTOXzTs/kQ0QsP2Lfjrj7Jv/Usc3bKJnIpqimbOzjguOWhCFYBmtbH0ptsorJ01IW0XQpxmT33ODFRGU/d3WP9/J+T2fb1pGzdu5CMf+QgzZ85k3rx5fPWrX+Xtt98e83Xy8vIoLi5m4cKFPPDAA4TDYV588cUJafOU0n0UDrww+jGGDjv/CLHjlyA8GUuXLqWyspInn3wyve3JJ5+koqKCJUuWpLfFYjG+/OUvU1hYiMPh4MILL2TTpk3p/evXr0dRFF555RWWLVuGy+Xi/PPPZ+/evRn3u//++6mpqcFmszFr1ix++9vfDmmT1+uluLiY2tpafvCDHzBjxgz+8pe/AHDppZdy55138tWvfpX8/HyuuOIKAHbt2sU111yDx+OhqKiI22+/nfb29vF8qU6aBKkTxB9M0hVMHve4fU1hLP4DOI0g5cl9qKNkpQ5Op0xhIYWGjkrcMINHVTGXRbUPyDb2h1KoikFKJz1b32XXqC1xs3JhHr4BlQJSOmw80MP6HV0cCjhh5rUkyy+k2zv5gzerw0nZgiX4ikrpaW6k88gh3vvT79j/91fTQ/9WV39PsGbr76lORof2JAshpqjmHXBkw9iOfffRcR/27+zs5Pnnn+eLX/wibrd7yP7s7OyTuq6r9/+vRGL40ob/UI69xZhmcehJaNg4Yc244447ePjhh9PfP/TQQ6xZsybjmH/5l3/hz3/+M48++ijvvfcetbW1rFq1is7Ozozj/u3f/o2f/vSnbN68GYvFknGdp556iq985Sv88z//Mzt27OBzn/scd9xxB+vWrRu1fQ6HI+P98uijj2KxWNiwYQMPPPAATU1NXHLJJSxevJjNmzfz/PPP09LSwi233HIqL8u4kSB1gmS5LZTk2LBoCnaLufyoVYM5ZZnD5YoBh2yLOWhdxGb7KhZUe4fNM812a8woceFz9e+Mqh5edn2Kl1yfolvNJz95DFWPEY7pRAf8HxaK6cR7e3AdVgXdgIPNEdbv6CAQSdIT7u/eHRgHd4eTJMvO54Xg2by6I0Bz18QMi42n6uXnU3PRCmxuDza3Bz2ZRE8mSSV6hzV6J1LZ3R5szv6fha+4lNb9e/E3HqfnRQgx+e3409iPjXTCodF/0Z+oAwcOYBgGs2fPPv7BYxQKhfjGN76Bpmlccskl43bdKcnQoXXH2I9vnrgh/9tvv5033niDuro6jhw5woYNG/jEJz6R3h8Khbj//vu5++67ufrqq5k7dy4PPvggTqeT//qv/8q41g9/+EMuueQS5s6dy7/+67/y5ptvEo2aH6DuueceVq9ezRe+8AVmzpzJV7/6VW688UbuueeeYduVTCZ55JFH2L59O5dddll6e21tLT/5yU+YNWsWs2fP5v7772fp0qX86Ec/Yvbs2SxZsoSHHnqIdevWsW/fvgl4xU6MBKkTRFMVPA4LyZSBboDNouCwaexuCKMpfcfAubOzsDs9vG9fQYN1FtvqgkOWHHXZVTRVZV9jmJ5wZpmoqOohrGYxL76Bi6J/5rzI06O2a2BprHBMp607jtWI4tK7ATgnr4WLPTupLbKxoMqLYfRXEUiNVk5gEmndu4t4KEg8FKLq7PPIqajC4csCwFNQBJhBafGc+QAoqkr+tFr2v/4yO59/mlgodMbaLoQYB+GOEzs+NL5Dm32VHYerJnKizj//fDweD16vl2eeeYZHHnmEBQsWHP/ED7JkzOwhHavE0Oo14yU/P59rr72WRx99lIcffphrr72W/Pz89P6DBw+SSCS44IIL0tusVivLly9n9+7dGddauHBh+u8lJSUAtLa2ArB79+6MawBccMEFQ67x9a9/HY/Hg9Pp5Itf/CJ33XUXn/vc59L7ly3LLLP47rvvsm7dOjweT/qr78PVwYMHT/j1GG9SgmoCZbnNl9fj0PCHk8R7E1JThjnkfv7sHFK6QWGOnUBTf57k4Hr54ZieXiXq+Mz/HAeuLDUcpbd9WY4Ul4cfw25EeNv+IYqPrEXBwDndQWPnYnI9NlYsyCUa18n3DZ0tPxkV1M6iZd9ukrEo7Yf2s/iGWwl1dtB5tA53Xh4zL70SzWrl6HvmEJCqWfAUFKJqGlaHE22YiVVCiCnE5j2x4+0nePxxzJgxA0VR2L17NzfccMMpXesPf/gDc+fOJTs7m7y8vOOf8I9As3FCRRs1x0S2hjVr1nDnnXcCcN9992XsG+kDi2EYQ7ZZB/zu6dunDyijOJZr3HXXXaxevRqXy0VJScmQ/YPTT3Rd57rrruPHP/7xkOfqC5TPJOlJnUAV+U4+tKyA8nwHg5dM0A34+64u3tzjp7mzfxg912MZsrwqmP8c873Df6awarDPeSFvOG7kbec/oQBluaP/ozyrxoeqwqY9HViNGAoGFpIknUWksLCxycWWQwHe2N2Fx2GZMgEqgDs3j/JFSwFIxuIcfOt1tj39R46++za7X/wb4a4OFEWhbMESimbNpWrZOXgLijj7Y3ew5CMfx2KbOs8qhBjG7GvGfqzVBTUrxvX2ubm5rFq1ivvuu4/QMCMzfr9/zNeqqKigpqZGAtSBVA3yZ479+II5E9cW4KqrriIejxOPx1m1alXGvtraWmw2G2+80b/ITCKRYPPmzcyZM/Z2zZkzJ+MaAG+++eaQa+Tn51NbW0tpaemYevKXLl3Kzp07qa6upra2NuNruHzq0016UieY1aIyrchFSoc99cFhC/CHY2YPa4HPRlvPCCUhFGgPJNOfHV02hXjCIGmALdFFSZ6HA139JS+au8w8FodVJZoY2qO682iQSEIHnLzu+ig51jD2/NnsMhbR0BEmljI/v+RqAdj4P+DKh3k3mzWsJpm2g/voqDtEbmU1hTPMYYqyBUuIR8I0bt9K867tKH21YlUVq8NMp4j2dNOydxcA7rwCfEVn/lOjEGIcVF8IhXOhddfxj11wMziyxr0Jv/rVrzj//PNZvnw53/ve91i4cCHJZJKXXnqJ+++/Pz1M29DQwNatWzPOPVPli6aU8vOgfe/xj0OB8nMntCmapqV/npqWuWS52+3mf//v/81dd91Fbm4ulZWV/OQnPyEcDvPpT396zPe46667uOWWW1i6dCmXXXYZzzzzDE8++SQvv/zyKbX9i1/8Ig8++CAf+9jHuOuuu8jPz+fAgQM88cQTPPjgg0Oe53SbfBHHB5CmKswsdeGyZ/6wLSq4bP3z+XO9VhTAbR/6YzEMyE01cnZkLfnJYyys9pE0ICfVxBXhx5jX8CCzC/qD0UDU/HtsUIBqs5ifrCIDtvvVAg6nqgg17EQ79gYOJc5ZNT6cNhVX8IBZxqV5K6lw1zi8GuOrq/4I+9a/REfdQfa//gqdx46k9+VVTU8X7rf2TpKyWK04vD4ANKsVVTOXRN2+9kmObH7r9D+AEGJi3PCr4w/7582Ay78zIbefNm0a7733HitWrOCf//mfmT9/PldccQWvvPIK999/f/q4e+65hyVLlmR8Pf306HMLBJA/C8rOPv5xtavAXTDhzfH5fPh8vmH3/b//9/+46aabuP3221m6dCkHDhzghRdeICdn6KI+I7nhhhv4xS9+wd133828efN44IEHePjhh7n00ktPqd2lpaVs2LCBVCrFqlWrmD9/Pl/5ylfIyspCVc98iKgYxuCBaDFRUrpBIqmT1A2cNg1NVdh1LMDeBjMf1W5VuXJxHpqqsOlANw0dmbPpL4j8mcLUMTrVYryXfIm393ZB52Euiv4ZQ9GILf8qR95bj0fvYqtjJXFl7IX3s1ItrIg8jgLoqLTWruatZh9WI8oK21u0dULD0Q4qlpxN5dLl4/mynJJgexvvP/On9PKndo+PZbfeTlf9UXa/9Dfc+QWk4gkifrPUh8PrY+nNn0gPg8RCIfatf4me5gaySsuZf/X1Z+xZhBDjrGkb/PXOYWZ3KzBzFVx/n7lMqpiaDAMOvQxHN0BqUPUZmwemXw7l5wx/rpgSZLj/NNJUBc3W35saiiaxaiqaYk6m0hSwaOYnl+UzsnlP6+bIgGVIj1jm4dSDRLLnkKtHyXJasaWO0aqWE8dOy/s7OCthFghuS1bQ4l5COJ7Zk2rXQ5wVe4Gw4mOr/TJQFBQFKhJ70qkEKjpZaoDzZ1cSieu4Cm4l+NxfAQi2tUzsi3SCPPkFLPvop+g6doRDb/+d3Kpq4pEwwfY2DF0n1NFOVkkZEX8nRTPnUL38gow8HbvbzYyLV9J+aD/502ecwScRQoy7kkXw+b/DsU2w91mIh8xetQUfgdxpZ7p14lQpCtRcAVUXQ/M2CLWaKWm+UiicD6qEOFOd9KSeQa/t7KQzkMCiQlKHwiwb04ucHG2PMrvMjd2q8tx7maVRavX3mR9+laRi5w3Hh1kReSK9L4XCMcscfEqAd6xXott8JJJGxvzHqsQOlsbMHJYXXHcQVs1crMV53RQ1rsXq8qHZHKizbwC7J31etKeb9sMHyJ8+Iz1cPhk17tjG4XfeILdyGt6iYjz5hdhcbtoPH6B4znyad23H5nJTPMzypy37dtNRd4jqs8/FlSOTFIQQQogzST5mnEFZLgudgQQFWTZCUZ1pRU62Hw0SiqZQFLM39ZJ5ufhDcfY3higMvc+C2KsAWI0Y3lQnDVotuXoTTiOEhsFB6xISjgJ0QyGe7A9P+wLhLucMYs5j1IfdhJXe3EwVCium46r9/8Gb90B3JwSbzKXkFtwGBXNw+LIoX3TWGXmdxiIeCbPr+aeJ9y55Gu3ppmrZOSiqxu6XnyXi7yLQ2oy//igAWaXlOHtrpwbaWuiqP8qxLZvAMEjEYyy89sPjUuNQCCGEECdHgtQzaPE0H3MrPNgGLDFlbXgTb/gtovmXA+eQ67WS67USiCQxQubQvQEYKOTQjtWIsdW2gvNiawFwGEF6EgUMrh+3OL4OV7yViOLBntpPxHVZep1VPaVj2fIAxNoJ2opx04kR6UYlBV2HoWAOjZ1RbBb1jJeiSta/x+533ke3uJlz5YdQNQ2LzUawrYVQp1nAu2zBErxFxWx58glQFNx5Zs6ZOzefcFcndpcbm8ssrZGMRdn+t6cwUv2rbgVbmtj0+MMsv23N0AYIIYQQ4rSQIPUMsw1aA7Ugsgv0II6eHfDmG5CKc2zaHVg0N4cciwhb8klqLhKJJCsjv0MBlESKGA5sRFkYW88rWhXGgFJRBYk6ymPbUIC4YtZP9cUbwWGuWlKV3IEtZi4Hqhg9KIDfXoYtp4q3uufj3tNFsz+OAly5JH9IlYLTJtxOeOtf6fEXAiF2v/Q3gm0tzLj4cvJrailbuBTNaqVi8TKC7W3mOYaBnkyy9OZP4PRlUX32eRmXVDQLNqeLWDCA3esjFugBIBEZWqtWCCGEEKfPma8vIDLN/BCULIWSZRBuh1gPR+uOsK8xzNxyD9Nnz0PzFhFWfaSwYADNlunsd5yLAriMIOdEnubS8O8pT+yhMt+OTdV7J0Up2OZejzH9CuodC7AZEWqKnSyOv4YC4Cog5jCXDe1R8jngvpBAyk57IIGqgM2qYjHi0L4HUiPUc51INi/eHC/lOWHKZs8iHjKXugt1tqGqGtVnn0fFYnPJN09+ASVzzSXm3Ln56aH9wTSLhSU3fpSzP7Za6qQKIYQQk4hMnJrM6jeSTMR41T+bWMIgOWAlgByPhVAggMWI480tYHmNh4adb+Ho2klRysy7NBSV+LL/w3N7dHKTDcQUF3NmVlPe/QYcfgUDqC+/FXv7Ngqi5uz+1vxL2R4qR/EUUpTtoDOUoKbYRa7HiqYqWHf+Dtp2QfFimH/r6X9NDDPZAUUl2N5Gd1MDRbPmYrHZMpaIS8ZjtOzbjTsnj6zSchRFIdzVQTQQILeyethLdzc1sOeV50nGoqgWK+d96n+dvucSQgghRAYZ7p/MypdjAa4Ettf1cKDZHILWVFhWk0U0pJC197dYO+KQvYou7wIc/jZyaMJGAsXQObB7BwZz6dDKAAhGk+A/DJhLrcaa99KhFlPAHgAKbSGW1MwgEEny3qEAAEumW3D0lc7qK+lxOkt7GAa0vA+RLogHoXQpeEvx5BfgyTeLNB9663Wadu+g5oJLKZ41l6PvvkPTru04vD7OuuV2kvE4257+E3oyyYxLLqewdtaQ22SVlLHso5+kdf9efEXFp+/5hBBCCDGEBKmT0Z6/mhOW5t8C3lIA5lV6yfXZ8Ng17FYVh03DE2iESLN5zo7HWYSCgkECjR22C3AaYQ4aM0CBEleCJT1/xtZogzkfhl1/BKub0kgnrshWdIubUPYc6p0XUqQo5Hpt2K0qdgs4u3ZDVim48giXX8HBQ+ALl1F1ul6Plm2w4w/93/vr4Jw7Mw7pbmwAw6Bu4wbCne3pElLuvHy66o9i93ix2OzEk0ksdvuQWyRiUWKBgJkmMGf+RD6NEEIIcUIUReGpp57ihhtuGNPxq1evxu/385e//GXEYy699FIWL17Mz3/+83Fp40SQnNTJRk9B/TsQaoHWnenNatNmyg49Rlayqb9X0987rA8YqCjpGf0KocJzOJS1gpRixapBuaUZe7QZpecobLof7D5YugZX7xKsajLEW+F57G4xWL+jk0AkyTUzk1zmfg9t5+9h432w4w+07d5KT3sn9dveTa/yNC6SUehpyNx28CV45z/N3tOB8oYW3Z9xyWV4i4pJxeM07dpOT2tzb63UEna98Azb//Yki2+4laU3f5zciuoh57//1z+y7a//Q/PeMaz1LYSYUo4F2vjtrnU88P7z/GnfBrpjoQm/5+rVq1EUZcjXVVddNeH3/kejGzp14QNs7X6Hbd0baYgcnfB79v18P//5zw/Z94UvfAFFUVi9evWEt2Mkv/jFL3jkkUdO6JxLL700/T612+3MnDmTH/3oR6QGVL853aQndbJRNZh9vdlbOHA5t8OvQtQPx96CrEpzW9wcjg8qWbiIoBlxM0y1ulje+B/0WIoJp6xss19GQ0eU8r5r6XGM9j00tQUpmvcxYlt+R1DJIUh2+nZW/wE49Lv+YX09Ac1bKfLOIFheSVZxKcqprOurp+DYm2awXLwINj8AwWaovdq8p7fUfGYwVxE5/y7oOQa5tWBzD7mcJ7+QWStWUbfxTexuDw3btwBg93p7X1YLFrsdq9M55FzDMEilkr2PmTj5ZxJCTCpHe9r44Tv/w98bdqIPmH7xg7f/wHU15/Cvy2/CbXVM2P2vuuoqHn744Yxt9mFGcgASiQRWq3XC2vJBtbNnC1u63yGUCmRsz7bmsTz7Iqa5J24lwYqKCp544gnuvfdenL2/W6LRKI8//jiVlZUTdt+xyMoafrLw8Xz2s5/le9/7HtFolLVr1/LlL38ZTdP4+te/PuTYeDyOzTaxZSmlJ3UyKj/HnJRkH7Cy07SVkF0N5ef2bzPMnkzVSLG/9HaYdwvK7BuwJnpQ9CRZ8XpKUoepUfbRbq0gljUT3MUYQAqNcMMODvk1XlBv5E1lBdVFLi5w7eBK3xYKPL2F7A0DFnzcXAPZkY2jainzVl13/ML+wWZ4617Y+T/D72/dDvufhR1PmLmmfesut++Gfc/AjsfNgBRAs4Mr1wxmhwlQ+9jdHmatuJLKs86hoGYmuVXTqFx6DotvuJXFN9w6YlCtKAqLrvsIc1ddR8m8haM/lxBiSqjrbuVjf7ub1+p3ZASoANFUgj/ue4M7nv8F4URshCucOrvdTnFxccZXTk4OYP6/8+tf/5rrr78et9vND37wA2pra7nnnnsyrrFjxw5UVeXgwYMT1s6palPXG7zR+fKQABXAn+jgxba/sCewfcLuv3TpUiorK3nyySfT25588kkqKipYsmQJAI899hh5eXnEYpnvs5tuuolPfvKT6e+feeYZzjrrLBwOB9OnT+e73/0uyWRyxHtv376dlStX4nQ6ycvL43/9r/9FMNg/6rh69eqM1IBQKMQnP/lJPB4PJSUl/PSnPx32ui6Xi+LiYqqrq7nzzju57LLL0ikDfdf8v//3/1JaWsrMmTMBaGho4NZbbyUnJ4e8vDyuv/566urqxvQaHo8EqVNF2dmw7HOQXQWBRji8DooWYfgqoOoiZrpbwJlrrlecMx3sWWDPBsVKbWIrV+t/xm6EwepEASykqGl/mhL/W9iIke22sDg/SGHry7gbXzNTDrKrwUhhbP+92Yt74dfNWf29esJJ/r6rk70Nwwydte8xe0CbtgxfrspTAhYnuAvNwPOsz8HiT0HpMkABTxEUmXVcSYTM9vjrRnx5DF1n53NPs/H3DxP2dzLz0iuYc/k1WGw2FE3l0Nuv03n0cPr4RDSCv/EYfcUt7B4vOeWVssqUEB8QX//7w3REhwYvA73fXscvtjxzmlo01Le//W2uv/56tm/fzpo1a1izZs2QnteHHnqIiy66iJqamjPUysmpKVrPe91vHfe4v3e8SCDZM2HtuOOOOzJ+Zg899BBr1vQvBHPzzTeTSqV4+umn09va29tZu3Ytd9xxBwAvvPACn/jEJ/jyl7/Mrl27eOCBB3jkkUf44Q9/OOw9w+EwV111FTk5OWzatIk//vGPvPzyy9x5553DHg9w1113sW7dOp566ilefPFF1q9fz7vvvnvc53M6nSQGjDC+8sor7N69m5deeom1a9cSDodZsWIFHo+H119/nTfeeAOPx8NVV11FPH7qpSolSJ2K3nsIDr4IbTtRzvos7tZ3UPethc2/BkWDsz5r9kzG/GAkUOI9qOE26Kk3ey01G6gWFMDT8ApX57zPJfNzUdz5ZvAI0LmfKObwhYJhlp0a5GhbhPaeBLvrg0P2UXq2GXDOvsG832CeIrjk3+G8/5+535EF+bOh9Cxz++LVkFML7iLwVcCev8C7D0K0e9iXJJWI4288RiISpqe5CYBQZzv177/Hgddfpf3gfg5ueC19/I5n/8rO557m2NbNY3/dhRhHsVgbiYT/TDfjA2l7+xG2tdWN6din9r9JJDkxdZ/Xrl2Lx+PJ+Pr+97+f3n/bbbexZs0apk+fTlVVFXfccQd79+5l48aNgJkC8N///d8ZQY8w7eh5b0zH6ejsDmybsHbcfvvtvPHGG9TV1XHkyBE2bNjAJz7xifR+p9PJbbfdlhHI/u53v6O8vJxLL70UgB/+8If867/+K5/61KeYPn06V1xxBd///vd54IEHhr3n7373OyKRCI899hjz589n5cqV/PKXv+S3v/0tLS0tQ44PBoP813/9F/fccw9XXHEFCxYs4NFHHx0111TXdZ5//nleeOEFLrvssvR2t9vNb37zG+bNm8f8+fN54oknUFWV3/zmNyxYsIA5c+bw8MMPc/ToUdavX3+Cr+ZQkpM6hRiGgZKKmz2LAN1HIdgEkc7+g/Q44ICCudA06B+xajEDV0WD6hVw+GVzs/+ouUSqZoPlX4Ttj0O0iy5nDYlAipLUYSzuYtJ9jLoOzVuYbnPjzyqiMGuYINTmhrk3jf5AfatidR817+0phlgArE5znysXzvs/0H3MzFm1OocPeAGL3cGMSy4n3NVJ0cw5AOx5+TmigZ70ML/V5Rpwa/NppOdUnG6GYdDU9FfC4UOAQmHhVSSTASKRo2iak6KiVSiK/Nd8Kp4/fPweoj498QgbGnZxedXicW/HihUruP/++zO25ebmpv++bNmyjH0lJSVce+21PPTQQyxfvpy1a9cSjUa5+eabx71tU5k5UWr/mI8/GNrD8pyLJqQt+fn5XHvttTz66KMYhsG1115Lfn5+xjGf/exnOfvss2loaKCsrIyHH344PfEK4N1332XTpk0ZPaepVIpoNEo4HMY14HcXwO7du1m0aBFud3/62wUXXICu6+zdu5eioqKM4w8ePEg8Hue88/pXXMzNzWXWrKGlGH/1q1/xm9/8Jt0Levvtt/Ptb387vX/BggUZeajvvvsuBw4cwNs7/6NPNBodlxQV+Z9with2uIfDLRGWlSX7J0BpdvCWQdk5ZqBXeja07gabE2weqF1lTrSK9Q516L35LUYKDr/Sf/FYN7Tthvd/DxhmcFk4j+L130XpzXul64CZA7vzj9C8FQCXonLhhV+Hjj3w/h7zfq7Mf5zH5a8zA1BFM1fb2vs0eEvgnC+Z+bB6ErIqzFQDzQqWkSc5DK596s4vJBroIbu8imiPn2nLL0jvm3/19YS7OvHKKlPiNEsmA70BKoBBe/ur6Hp/vlp29lIcDnlfnooTnb3fHQtPSDvcbje1tbWj7h/sM5/5DLfffjv33nsvDz/8MLfeeuuQIOUfXUKPozP26jIxPTqBrYE1a9akh9rvu+++IfuXLFnCokWLeOyxx1i1ahXbt2/nmWf600x0Xee73/0uN95445BzHY6hv/MGLlwz2HDbT2TNpo9//OP827/9G3a7ndLSUjQtcxn0we9ZXdc566yz+N3vfjfkWgUFBWO+70gkSJ0iIh3HKE520xydQ3nuDAi3QeVF0L4X5twAnYdgy296V2TqlVVh9kz2UbTeyVa9qzYBOHLNuqltO8DoDWL3Pg0501E8JWb+q7fEPG/fs9AyIAndnmUGynufMdML7F6Y9U8n9mCqxew1VVQzWMYwe4YN3Uxr8B+GhR83e4ZHEQsGsDgcaJb+2bGVS5dTumAxvoKiIcdb7A58xaUn1lYhxoHV6kNVHei9vzgHBqhe73x6enbT1vYqRUVXYbPlnalmTmk++4kFdV7b0KofZ8o111yD2+3m/vvv57nnnuP1118/002adKyqDRV1zIGqXR2+osJ4GZh/uWrVqmGP+cxnPsO9995LQ0MDl19+ORUVFel9S5cuZe/evaN+oBlo7ty5PProo4RCoXTQuGHDBlRVTU9mGqi2thar1crbb7+drjrQ1dXFvn37uOSSSzKOzcrKGnM7+tr+hz/8gcLCQnw+3/FPOEGSkzoVxEOc0/0E50afYZGxyQzcLA7Y+QS8/1to3mYO+w/+tGTPIh2Mglm6qrB3MpLdZw7tX3gXNLwDLTvA1RvMJaOw60/mZCkMM1ANNsOxDf0VBzxl5vmGDpUXmEP1AyZVjf48QbM4/5E3wFcO5/4fOP+rMP0ymHMjLP2sGbT21JvX72kc9XLth/az+Q+P8f5f/5TeFvZ3seXJx9n+9J/Y8uTj6GewzpsQg2VlLQSG9njYbLn09GwlFmshGBz7cKbIdGXVkjEf67Y6uKBszoS0IxaL0dzcnPHV3t4+6jmaprF69Wq+8Y1vUFtbmzFEK0yqolLlGvtEsmmuocPa40nTNHbv3s3u3buH9Dz2+fjHP05DQwMPPvjgkBzjb33rWzz22GN85zvfYefOnezevZs//OEP/Pu///uI13I4HHzqU59ix44drFu3ji996UvcfvvtQ4b6ATweD5/+9Ke56667eOWVV9ixYwerV69GPZUykgPakp+fz/XXX8/f//53Dh8+zGuvvcZXvvIV6uvrT/n6EqROBaoFxeoEFKxxvzkEHmzu39+2C1KJIedQfi6oA3I4/Yeh75NnPAg2L9S9Bq07zDzXyID/PDsPQGKYIbBol/lnsAE23A1v/D8zOD33K3D0TXj9h0Nn4QdbzLSDZG+PUeO7ZsrA/r+ZAbG7ABzZZpvLzgZfbw/nktVm3dTqi0d9eWIhc2gvFg6lFxiIh0PpoD3c1UkyNrHDPUKMVTzeRU/PTjI+QAKK4qGjw+w1s9ny8PnmpfeFQoeJRvsnRKRSUXp6dpBMjj57/R/V4sLpzM2rOP6BwPU150xYrdTnn3+ekpKSjK8LL7zwuOd9+tOfJh6Py4SpUczzju2DiILCXO+iCW4N+Hy+UXsSfT4fN910Ex6PZ8iqUatWrWLt2rW89NJLnH322Zx77rn87Gc/o6pq+HUdXS4XL7zwAp2dnZx99tl85CMf4bLLLuOXv/zliPe/++67ufjii/mnf/onLr/8ci688ELOOus4pSTHwOVy8frrr1NZWcmNN97InDlzWLNmDZFIZFx6VhXjRJIVxJmz+0lo2GQWuc+baQaXvb/kEuWX0nL4CDlGPW6PI13kH9UKM64xV25K9gacvgqzKH5WlRkYtgyY9ajaeideAflzzJqlfexZULIYOg6Yvbao/ekBSz8LDW/3pwJMWwk1V/Sfu+Fucwi/4gKY9SGzNNWOP5jPcrzJVWNg6Drthw/gysnDnWsOjx597x2ObTFn7s+89EoKajILOvsb6wm2tVAydyGaFNAWp0EqFaW5eS3JZIBEomvUY0tKbsLtrkLXY7S1rSMQMKtrOJ2VOJ0VdHa+CRjY7SWUld2Mqkrm1mAH/E18/NmfjpqfOju3nP+++qt4JtFwP5hDt5deein19fXD9owJ01ud63i/Z/QKLRfmXs4839h71ifSFVdcwZw5c/iP//iPM92UKUP+Z5sqbL0z53Qd6l4n3Qsz/2Mc3bqL5sYELZY8zlr5KbOn8tDLZkpA6VIzX/TA82bPaiJsBqmufHMVqYFUDSw+8BRCXm1mkDrjmv7Vp6ouNINMMEtWWZ39AaqiQcX5mdd1F5hBqruw9/tCc2LUOFFUlYKazDycvOoaOo/U4SsuyQhQDcPASOnsfnEteiqFrutULjl73NoixEii0UYivcs1Op1VJBJdJEeo39jV9TZudxVdXZvSASpAJHKUeLyDvn//sVgTR48+QioVxTCSFBdfh8cj9TQBarNLePyar/Htt37PpubM1AmrqrGqeinfOvejkypAjcViHDt2jG9+85vccsstEqAex3m5K3BpHrZ2bySqZ478eS1ZnJ19ITM8o89nOB06Ozt58cUXefXVV0ft7RRDSZA6VUy/HArmQf1bEGoGFLPYftchPJYQYODxWM2c0emXmaszWXpLNlVfApUXmkHp6//XvF7eDHMyUtnZEGyDfU+baQTTVprD9b4BQ2VFi6BwHrz6TcAAV2+wiWL2qr7zn/3HFi8euirUok9C6y7o3A/hDnBN/GQQq8NJVmk5uZXVhLs6ObBhPb6iUkpieRjhOB5XLj2BNoKtQ2vKCTERXK5KfL6FKIqF/PyL6eraRGfnBsysq74JIApmAGrmq1os2QBYrYUkk34MI04qZfYMapqXVCqQEeiGQgckSB1genYxv736q+zvamTdse2EE1FynV6urj6LAtfJLRs5kR5//HE+/elPs3jxYn7729+e6eZMCYuyzma+bymHQ/voSnSgKir5tiIqndMnTYnBpUuX0tXVxY9//ONhyz6JkUmQOlUEm2H778HqIj3hwl0ADe9QBOQvnYeWU2lOogo0QbgdihdC8/uQP9PsRdWToFnMIX1FNUs65daaX+48c0h/431mMOvIMoPiYBNMu9QsW6VZzdWjwq0AGIqGYiTJyK3rOWbmx6bi/cGqoZtLncZ6zO3zbzW3N75rBsS1Vw0JbPVkElQFVR0+Cf14jm3ZRPOenbQf2o8zO4dASxORtnaKis5FURSyfYX0BNowkGwXcXooioXCwssBSCZDdHX1DVPq2GzFKAp4vfNob3+VRKILXU8QiRwBQFX7gtd+uh4jJ+d8/P6NGEYSVXWSm3sBYqgZOaXMyJn81TxWr17N6tWrz3QzphxN0aj1TMzkt/EwXkuE/iOSIHWq6NgHkQ7zq096uVEFrWMndOzMPCfYZM7Q7zxgBqkWO5Qth7r1cHRD/7KjYOa5gjmU374XSpaYM+9DrebErJzaIZOzlL6c1D4Wl9lTuu5b5vezrzfvu/spM0BVVDMQ3v6EWRrL31srsvMgXPgv6cuE/Z28//SfsdjtLP7wrVhsYy8fcvDN1+g6doSimXNQNA1XTi7RgJmjm9QTHOzcid3ipDXUSE5FNbUXrRjztYUYL/F4J4YRG/B9M9nZ5xCLtQAGuh7FMFLY7QUEg3uJxztRFCuG0f9v0DDi9PRsx+j9d6jrcY4de4yKio9jtU6+XkIhhDhREqROFYXzzFn4PQ3m9/NuhcI5ZiDpyof9z5oTpYLNZq8nQMlZ0LEX8ocZXkhGhr9PzZXmF4D/CLz3G7MH1rfTnGgV7SKhOLAavbPlVWt/bqvF3j9BC8y6qslY/2IChg5H+pcm7W9L5sz7SLefVCJOKhEnEY0MDVIDLfDkZyG7Aq77TxhQRqN13x70VJJkPI4rKxt/wzHyp9Xi9Pnoqj9Kd6wTYn3N0bE5pUi2OP3s9qFFrv3+d3r/puByTScUOkAodCQ9rK+qVsCGoigYRhwwSKUGzu5Poespurvfx+mswOks6z1HCCGmJglSp4qDL5p1Q8EMDL1FZr5pZe/wXl8g+ubPzEL/imb2itZcnnmd6ZebgW3WoPIsesrMGfVV9A+9N2zqX6Wqp7/emUVJ9o886gkzBUHR+stT9eXV6QlzwlZfm/UEaA5IDQhKKy4g7JmJo34jqqcQsqvJrZxGzYUrsNodOH3ZQ1+L/S/A4d5g96KvQe609K4ZF6/E31BP6fzFBNrMfFNXbh7ZZZV01R/tbZ4ChoFnmCL/QpwOiqKhKJb0ML2uD/zQqBAK7ScUMif7WCxeNM1FKhUGFAzDwGbLJx5v771WZg+r378Jv38TVms2lZV3TJq8PCGEOFESpE4VroE9Lwa8/Qtz4lTxYig9y1wW1VcBi26HvX+D3OnDLyGqamav7GD71kL922bwevYXzFSCgbVYB1ByZpg9tH2TPdL1VPsmgAyT59nX21p7hdm+3nOPNKWoP/gSea4wswu74ZJ/R7E4KJ7VOyNTT0LbHsiu7F9IYPaH4OCrZltzqjNukz99BvnTzdn881Z9iEi3H09+IXvXvwSAIyubWCCAYaTwNxyh6qzlwz6jEBNpYA+nufKUBqR697nR9f4eUrd7BslkgFBoP253DYpiQVXtgEI83oGiqNhsJb15rP0fABOJHnQ9gaYNqJUshBBTiASpU0XNFVB+DnTVwY7HzW3+OvOrYz+07YTC+VB1EXTuNb9ypg3tMR3oyBtmCkH5uVDfN9TYO3R+7G0INAx7WjSRxNEXoJacZU7WMpIweIk6d6GZ0zpQd33GcfFAp/lnSjMD6PXfM3dodrB7zEld9W+bNVXP+ZKZy6pZ4eZHRn4u4OiWTXQ31lNz/iW0Hz5I+8F9Ztu7/eljnFk5o15DiInkds8kGNyLGZz2r4g2cJnU7OzlWCxZdHe/B5jBbSLRQzS6J+P4WKxpmDvodHdvITf3nAl6AiGEmFgSpE4ldh90H8ncZvP21y+NBcxjLE5zxN3uHXqNWA/YPOYkprp1Zi+oxU6693P2DeafWRXmdfXkkEs4evajo6BimOc6sntXq+rtSVU0My82GRt0Ys6QwHe6t5msVJJsRxRSA4LcVBTCUYh2m98nY+bksW3/bQ7Xn/t/wDl8kGkYBse2bALDoPXAXrrqjwx7XNjfyYE31lFzwaUyJComXDTaQioVJBJpxO2uweEoIJHoJBYzRyw0zU1h4dU0NZnL+3o8c7BafbS1vZS+RjIZJhod/sPjcGy23PF9CCGEOI1kxamp5rXv9w+vl59r9rDu/gu09hbTn/8xMzh1F5jBaOcB2PlHKJgDzlzY/xwULoCFt5m9p01bzGDUXWjmtQ6cZGXoEOmCxs1mRYABDBSChefhrV5qBsrJMNh8cOAFaNw4tN2KxextLZwHrTuH7h+OzWMu32pxmJOr7L7+SVjnfgU8xSOe2rB9C/6GeqafdxFHNr9NR93BjP0W1YZNsxNOBFh688eHz30VYpzE450cPfrIgC0DS0ppWK0+EolA7zYD0Ckuvh7DiNPS8lz/WYrWu9qvjtWaQ27uueh6IiOQ7ZObeyG5uZLOIsRk953vfIe//OUvbN269Uw3ZdJRj3+ImFQGfqZIRMyST30BKpgB4Lv/X/+KUG17zMCuaatZOxX6/yw/xyxR1XXQHFLf+gi8/3szHzUegr3PmDP8a1fBzOsymqFg4M3OhY2/hLfvNSsMNL07fIAK/RUHwp2QN0y1AWe+2dM60Kx/Mle06pv9HxuwOk9fvm0iAm/dC2/8pL/XFShbsIR5c3JwbvkFpQWq2fua7i1VmFuwlDkFS6ietgiHV8r1iImlKBrp+sbAwLxtM/BMAknMYX9zRKG9/TUCgQPY7f31PQ0jRUnJP1FVdQdVVavxemf3lptSAA23ew6goGkusrMnx1KQk0F0717af/1rWn/6MzoeeYRES+vxTzpFq1evRlGUIV9XXXXVKV23uro6fS2Xy8X8+fN54IEHTrm9r7/+Otdddx2lpaUoisJf/vKXU77maZNMwv59sPEd2LQR6g6bqzNOoL6f7+c///kh+77whS+gKMpJ17z9zne+M+x7Z/bs2afY6qlHhvunnAFBass2c6g9d4aZzzlthVkgH8xc1UCTmaOqJ8w6qLk1Zp3SngZo2mYO0Scimddt3Q6eIkAxA1feMRcFsPsAtTcFIGEev7935n4yaua32j2ZTbVnQ8yfef2+ntHBpq80c2rf/2/oOGAuJrD9fzB/cQ+gaGaPsLW3dFSkoz/vNdRits3QzZ7htp2QjBJpPpwR3CuAopmLBBTPnCdD/WLCWa1Z5ORcSFfX34fss9ny0jmlBQVXpHtFk0k/yaQfAEWxYxgxFEXD7TarWcTjnbS0PE8i4aev99XtriQU2k0qFSaVCqGq2RP/cJNYbP9+mr7zXSLvvpuxvfWen+K78gqKv/1ttKyJ+5B61VVX8fDDD2dss9uHr/ucSCSwWsdWMux73/sen/3sZwkGgzzyyCN8/vOfJzs7m1tvvXXIsfF4HJvt+JPnQqEQixYt4o477uCmm24aUzsmha1bYNtWiGaWMsTjgbOXw8yJW+GpoqKCJ554gnvvvRen01xeNxqN8vjjj1NZWXlK1543bx4vv/xyxjaLZeSQbaw/56lGelKnmuzp5p+KZg53N20xS0dZXWB1mz2m0DsrfrcZ7CUj5pB/5yFo2Q7tu2Hn/8Chl83Vp/pYegO/zgPmKlWObChaaF5r95OAbh7flwM7sJi/O99s00CVF0DlRWYFAtUKKFBxvplCMNiup2Ddd8xcWfTeclaDAlRPMVz8TZj/UXOy1v7nzFSDWdfB9CvM4Patn5uVDwJNBPLOJ1G0DOeMC7DYHWSXVVC2YAkGBruaN6EsLkUrl8lT4vTQ9Z5BW8z/fhVFxedbSGHhlWRlLcDrXTjoGK238L+KxZJNT88OdD3GsWOPE4s1p2f0OxylGEb/BCxFsRAKHaah4UlCocMT+myTUXTvPuo+/okhASoAySQ9zz7HkU/cTioQGLp/nNjtdoqLizO+cnLM/3MUReHXv/41119/PW63mx/84AfU1tZyzz33ZFxjx44dqKrKwYP9KUter5fi4mJqa2v5wQ9+wIwZM9I9n5deeil33nknX/3qV8nPz+eKK64AYNeuXVxzzTV4PB6Kioq4/fbbaW9vT1/z6quv5gc/+AE33njjhL0e427DG/DO20MDVIBgENa9Ctvfn7DbL126lMrKSp588sn0tieffJKKigqWLDFHMh577DHy8vKIxTLnaNx000188pOfHPHaFotlyHsnPz8/vb+6upof/OAHrF69mqysLD772c+ycuVK7rzzzozrdHR0YLfbefXVV8fjkU87CVKnmjk3mMX2z/2ymZfZ13vZ9B68/fPMoNORbf7ZtgdSMah/y+xthN7JUgP4yvtrrvqPgLsILvw6LPgotO7qL/7vKhhQvN/85EjBPLMXtGAOqL2f5BQr7P8bHP27GRzrvcunHnwhc9Szj5EAdGjfP8zO3rdpsBk2/qeZYrD7STjyulk/1p4Nh14x72WkwNBp2vQK77/6Ou/vDuPvCpGMRfE31pNfMwOrw4mzIA9rUfZxX24hTlUyGaKx8SlCocET+MzhSIejjMLCy/H55gNQWLgSt7sGqzWPkpIbelMFzOMTiQ5aW1+ktfXljBWrLJYsiouvweEoRVWd2O1FhMNHaWp6ikikjs7ON0/Dk04ujd/4V/SewR8MMsX276ft3ntPU4uG+va3v83111/P9u3bWbNmDWvWrBnS8/rQQw9x0UUXUVNTM+J1HA4HiUR/rdxHH30Ui8XChg0beOCBB2hqauKSSy5h8eLFbN68meeff56WlhZuueWWCXu2CVdfDzu2H/+4t96E7u7jH3eS7rjjjoyf2UMPPcSaNWvS3998882kUimefvrp9Lb29nbWrl3LHXfccUr3vvvuu5k/fz7vvvsu3/zmN/nMZz7D73//+4yA+He/+x2lpaWsWDE1V1eUIHWqsXvNYX13ofl94fz+fXrS7Fm0OMxeTXeBOZw//6NQdbFZyN+ZY06OWv4FmPkhyJlu9l5WXQwV55qTsebeBPv+Bu/80hxKz5thHufIMSdtOfPM3lRvCSxeDfN7h5gsDihZBChmT2yfeA/4yvq/j/ozn6lv6B4YWIpnwIP1/zXSQWbKww6zV3hQ+SvDbwYEiWiU/JqZFM2cy4yLVuLJK2D5x9ew4EM3oqqDen6FmACh0H7C4cPpofs+Fks2DkcpWVnzM7YrikpJyfVUVX2Kzs63eleXymSWruqXTAZpbl5LILAbSBGPtxOJ9C5egUpW1j9Wfmr4vS3Edu0e07Hdf32aVDA0Ie1Yu3YtHo8n4+v73/9+ev9tt93GmjVrmD59OlVVVdxxxx3s3buXjRvN3P5EIsF///d/ZwQ9AyWTSR555BG2b9/OZZddlt5eW1vLT37yE2bNmsXs2bO5//77Wbp0KT/60Y+YPXs2S5Ys4aGHHmLdunXs27dvQp59wu3cMbbjDAN2jXGy7km4/fbbeeONN6irq+PIkSNs2LCBT3ziE+n9TqeT2267LSOQ/d3vfkd5eTmXXnrpiNfdvn37kPfOZz7zmYxjVq5cyde+9jVqa2upra3lpptuQlEU/vrXv6aPefjhh9P5s1OR5KROdZUX9ueV6glzeN5bAqmEGdC9/kNwZsM5XzHri17wLwPOzTeH3/WEuXoVwOzrzXJPu8wyOLTtgupLYeln4JV/Awwz0I10QNchc3LTQHNuhFnXm8u0DhQYpo6jYoUFt5oz/l/7EeFwBIuiY7OcQML7gJ5jf8ROkBxKHc2UeIMcCRaQSsRp2fgCtStuGD4XVogJ5nbPoL397xmrQoE5Caq8/KOjnmu1+nrzVVVstjzi8bYRjkwRjTYRjfb/O/N4ZmOz5eF0VmCxuGlrewWXqwa3u/rUHmgKCLw0tNrBSPRQiNCGDfhWXTnu7VixYgX3339/xrbc3P6yYMuWLcvYV1JSwrXXXstDDz3E8uXLWbt2LdFolJtvvjnjuK9//ev8+7//O7FYDJvNxl133cXnPve5Ea/77rvvsm7dOjyeQfMGgIMHDzJz5swh2ye1VAqO1I39+MOH4LzzJ6Qp+fn5XHvttTz66KMYhsG1116bMSwP8NnPfpazzz6bhoYGysrKxhQ4zpo1K6P3Fcw0j4EG/5ztdjuf+MQneOihh7jlllvYunUr27Ztm1qT4AaRIHWq06ww85qh2y0Os66okYKI35yxrw2TlK8o/QFq+lw7zLjGXAq15Kz+4yx2c5KULQu0VvOab//cTAGYfb05MQvMoLlwvtnrGg9B97HMpVD7GAloeR8K59Fd+WF2vPoqmqpzVmkTVs0w0wn60gwU1cy5jQ/KH1NtoMdJ6bCrNR8DBSU3izJvDzn2CJ0JjZb6VgJP/Tfzb7wDVZPeU3F6WSxuCguvpKXlWQaOAqRSIQKBPXi9I8/YLSq6mry8C7BYfBw8+Ivj3stqzaWgYCVg4HJVpQPS1taX6el5n2DwANOmfW7Ua3wQ6METyzM90ePHyu12U1tbO+r+wT7zmc9w++23c++99/Lwww9z66234nK5Mo656667WL16NS6Xi5KSkiHBzuDr6rrOddddx49//OMh9yspKTmRR5ocksnMSjfHEx86GjGe1qxZk84Fve+++4bsX7JkCYsWLeKxxx5j1apVbN++nWeeeWbUa9pstlHfOzDy+2fx4sXU19fz0EMPcdlll1FVVXUCTzO5SJD6QVayxPyH7MwF29A386iqLhq6beZ15uz/mpWw5YgZpAKE2+C938D0K83Atn2Xud3qMSdXWR1mTqyiZU62Asg3lz9NHTOHtwzDrMGKPQuWfc4MYg88Z87YHxygOnIgfzbUv4WqgMcWJ5h04LFGAYPZec202Fwc6Mwl0BMjEQlj9wyzwIEQE8zrnYWmuYhEjhEM7iaR6AZ0WlqexWbLw24vGPY8RVGxWrPp6trIsMsNY878VxQDj2cO+fkXZyy52sftnk4wuB+PZ8Y4PtXkdaIz9rXs7IlpyEm45pprcLvd3H///Tz33HO8/vrrQ47Jz88/bgAz0NKlS/nzn/9MdXX1qDPEpwyrFVR17GWmHBM7inbVVVcR7w2EV61aNewxn/nMZ7j33ntpaGjg8ssvp6JilNUgT8GCBQtYtmwZDz74IL///e/5z//8zwm5z+nyAXi3ihEpKpQtO/5xYxHugKIFULrULGFVvQL2rSXjF+ehFzPPSQTNP/vqnBpJKFkKkU4zGs2tgYLZsPtJch0h5he1YtV0bJpurnzlzDbzZA+/aga5YFYJ6Ju4Fe0yJ4N5y1EC9SwsaUOvuRq17pV0amuBJ0xMt2NfcJ0EqOKMcrkqcLkqyM09h66uTXR1bUbTbFgsQ4dg++h6gpaWZ4lE6ke5skJl5SexWEZ+f7vd05k+/X+fQuunFu/VV9Pxm/8a07Gqz4f7ggsmpB2xWIzm5uaMbRaLZchw8ECaprF69Wq+8Y1vUFtby3nnnXfK7fjiF7/Igw8+yMc+9jHuuusu8vPzOXDgAE888QQPPvggmqYRDAY5cOBA+pzDhw+zdetWcnNzT7mc0rhTVZg2HQ4eOP6xANNHnnQ2HjRNY/fu3em/D+fjH/84X/va13jwwQd57LHHjnvNZDI55L2jKApFRUXHPfczn/kMd955Jy6Xiw9/+MNjeILJSyZOieNr2Axv3gObHzBn/m+8b2iAmjZg2EkdkEagqOaiAPNuhrM+awaada/B1kehYRP0HCNr/kpchVXmhK78GXDk77B3bX+ACsOvMhVoAIuTSNnl7Nmyn8YuG31vbVWByqwuirQmePkb5teRobUqhThdFEUjN/dcqqs/Q07Ouej6yEORzc1rCYUOousxLJZsrNbCAXvNHlPDiNLUNPrQ4T8a57x5OBcvHtOx2TfeiDpBPW3PP/88JSUlGV8XXnjhcc/79Kc/TTweH3HC1IkqLS1lw4YNpFIpVq1axfz58/nKV75CVlYWqmr+X7l582aWLFmSLp301a9+lSVLlvCtb31rXNow7ubPP/4xYAa0c+dNbFsAn8+Hz+cbdf9NN92Ex+PhhhtuOO71du7cOeS9M9Zh+4997GNYLBZuu+02HBPcizzRZFnUfxA97a08/q1/we508bHv343ddQLD/4dehUMvmZUDFn0CNv0aMwjUGRKoWj3gyoXuo/3bqi6BWMA8XrNBbi1sfzzz3OxpsPTT5qIEAM3vw47Hh29P7VVm0HvgxYz0gcORKhpbUygKnH/1+WYJrm2/NctrRTrMHtw+1SvMKgnD5ekKcRq0ta2ju3sLNlselZWfGrI/mQzS1PQ0sVgzFouP8vKP0dW1ke7uLQOOMv8dKoqVmpovnba2TwXxo0ep+/jHSbW1j3iMc9EiKh95GLW3EPtksWHDBi699FLq6+vH1HP2D2vzJnh388j7FQUuXTGhBf1PxBVXXMGcOXP4j//4jwm9z7Fjx6iurmbTpk0sXbp0Qu810WS4/x9E25E6gh3tBIHu1hYKq6eP/eTqS8zA01duLn967pfNHtVYwAwE9/y1N95M9g7x52WeryjQ/N6AxuymP0DtXbLUkWMu6VrcW8jcW2wGtKmBdV8LwOGB4iXmylXOfAj3D4cUao0EcmaQM202QWsZ6pG3cSUjYHVC9U3w3oP916pbZ+bpVk7MMJ8Qx2Ox+NJ/xuMdhMNH8Pnmoap2DMOgvv73JJNBsrIWkZt7Lo2Nf2Xo4JeOotixWrNoaPgzBQUrsNlyh9zrH5GtspLqx5+g5Uc/Irh+fUb+ouJykXX9P1F0112TKkCNxWIcO3aMb37zm9xyyy0SoB7PsrPB7YYt78HgRRlyc+Hsc6C6+ow0baDOzk5efPFFXn31VX75y19O2H0SiQRNTU3867/+K+eee+6UD1BBgtR/GNOXLOOi21Zjd7lOLEAFs3ezePGA7y2w5y/m3/NmmL2ZZWebvZ+pGHQPKFpuzzKL/XcfNWf6x3rMov8dB8wA13/IjFeb3zO/cqeDzWPWgZ35od6VrnoleiDaBm/9zAxeFRXyZpv1YBvewW1PsvCCSwjEbGz76x9RFFhaouGIBczrlp0DDe/0X69vsQMhzoCcnLPweGqxWDwcPfooiYSfRMJPQcHK3tnaZkBqsxXg928hFjM/kDmd1USjx9KrS2manXjcXBr46NFHyMu7iJycs8/IM002tvIyKn51H4nGRoKvvYYeCqHl5eO9/DI07+TLUX/88cf59Kc/zeLFi/ntb397ppszNcyZC7PnwLGj0NVldnoUFMIkqlqwdOlSurq6+PGPf8ysWRPXq7thwwZWrFjBzJkz+dOf/jRh9zmdZLhfnLhEBN75D/NPbyn4D5u9mpHeYTVPMaDAvFuh56gZaCoanPW/zAD0zZ9hzmxSoPRssxpAPGj2nJYsNZdu9RTBjA/Blt+Yk6ySkd7qAKneHtaEGSxf8DWw+8zJWakE2L0E21vZ9vSfUFWVpRcvwl4231wEAczgtm69mZZQOTF184Q4UU1NfyUUOkhBweXY7YVYLF4URSGR8ONwlBKNNlNf/wQAZWU3Y7cXEo02oigaFks2TU1PEo93YqbgKFitOZSXfwxNG36deCGEmAokSBUnR++tU3d0g7kcaSI8/HHeMnNiU5+sauiu6//e6jLLSEW7oetg5rl9QWmaBWovg6JFZlCq2cA1KLWgV9jfiapZcHhHTmQX4kyKxdoIhQ7h8y1A05zoepRw+AgtLc+iaS6qqz87YElU6Oh4h66uDYDK9OlfQO2dmJhMBqmr+/+GXL+i4vYRS1sJIcRUIMP94uSoFnNW/pHX+wvuZ1AAwyzAXzgf/EfN5VHDg1bMSYShqTdfVVF7CzT3fm7KCFBVKJpnrn41jFBHO7teXIs7N485V34IV7bk5YnJraXlWeLxDhIJP0VFq9A0J33L+xrG0EmJkcjh3r/pxGIdOJ3mcKbF4sHrnUsi0Y3Vmkci0YnTWTUkNzUY3Ieux/B650/ZJRKFEP9YJEgVJ6envj9f1OLor4UK5gSrmdeZK16VnmXWWO3Yb+5LDFgj2+rO/N4whyqHp0PeyEv3dTc3EA+HiIdDpOIxLHaz7Eb9tvc4tm0z1cvOpWTuwhN/TiEmiN1eTDzegcPRX1bN652LxeLDYvGhKJn/PefknEdT05MoihWbLSe9PR7vIBAwazQWFKwkHm+jpeV5gsFdFBVdjcNRTDzeSXPzWgA0zY3bfYJ56UIIcQZIkCpOjiMHnDmQiEFy0FB/tBuat5iz9V15sPN/Ms9LhM0JV9NWwoEXwF83oBZq7wpZhgFRPxm9SZaR670VzZxDPBTClZObDlABOo8eJseaR+pIF8YcQ3qQxKRRVLSKgoKVQ1aIcjrLhz3e7a6ipubLgEIwuJ+urnfIyTkbu72I/g93ColEDwCJRBf19X+guvrTaJoLi8WLrsexWnOGvb4QQkw2kpMqTk0qAbv/DLGgWYs06oeqS+HIenO/pwSCTebfsyrNXtVECBQLWGzD57JanGYKgS0L4t3mtmmXQc3lJ9y84NFGLDs7ALAuq0YrmHwzeoUA+P+zd97xUdTpH3/PztbsbrJJNr0QIKH3jiBdQRGxdxFBxV44Pc9+Zy/HgXpnPxB/dsWKZ0EQBJTeQg8hIb33zfaZ3x8TFpYkECChzvv1yivZmWe+853dze5nnu9Tiot/xuncR0zMREymBGpqtlFTs43IyGGYTAlBtvn5n+N05mE0xpOYeA0eTyUgo9dHIMs+ystXUVW1FlE0kpx8M6JoRJb9yLKMRqP6JlRUVE4P1E8rleND1EGPa5S//V4l079o84H9AYGaogjP/cv7sg+8voMGEpSapbIA5kjIW63UZ3VVIlsSqHLqMdc70B9NEwLAHBeDJ6sW2S+hsaiZziqnJrIsU1u7E/BTX5+FVmuhrOx3JMlJdfXGRiI1PHwogrAeq7Urfr8TvT4cSfJSVLQQWQaNxoDdPhqzuQMlJT8jyzKxsRfi8VRSVbUWq7UHZnMKfr8LhyOb6uoN2Gz9MZs7IAgifr8SvqPVhpyEZ0NFRUVFQRWpKq2HRoQtHx9YujeEKnVRoaF2qkwgoQqg6+WQ+bPysMNYKN0GiUPBZFPErs4Msb3J27iGnA1rMZp09L/2FiXBqoUIOhHDyM7IsrrUr3LqovTknoDTmUtYWF8KChYgSU5E0UxYWJ9G9iEhSfh8dRQX/whAWFhvTKZk6up2B2xqazXo9RE4HErVDJerkOrqTTgcmXg8Ffj9dZSULGqwlqmo+JOSkp/QakPxemsRBIiNnUxJyU9oNAaSkq4LVBRQUVFpHUaNGkWfPn2YM2fOyZ7KKUnLv+1VVI6EoFHiVPf/bY5WykVZ4jgQW9ogVPVWpbvUiMdg5GNKrdSKPZC1GPYuVryxGT8AoHFXAiD6HErC1rFM7QwWqI5qF8XZlUh+CY/LR3WpAzWK5/TDau1MdPQ4tFozOl0YADZbvyZjVGVZpqTkJ5RqABLV1RuRZT8WSydMpnZoNCYMhij0+nCs1m7odBH4fLVYrd3QaExotaGUlv6O8v8oYzDEYjQmIst+vN5KwIcs+6isXIPf78DrraCubu9p+74qS5fZ8rbEhtkS2+ZL1Oa1/XVMnToVQRAa/UyYMOG4xk1JSQmMFRISQo8ePXj77bePe74vvPACAwcOxGq1Eh0dzSWXXMKuXbuOe9wTgSS5qa7eTGnpEkpLf6OmZjuy7DvygcfB/tf3xRdfDNr+zTffHNX3zVdffcUzzzzTqnM7+P2m1WpJTk5m5syZuN3uIx98iqF6UlVal+ThsP1LJVO/Yo+yTXfwEr0ACYOg43lKndP9JJ2jNAdIHqbUR60thLj+ACT0G06oJwuTKbShUYCK5JcoK6ghNMJE+vIsJL9M5uZCNKKA5JexRVvoOiTpjBbnZxJVVRsoL19JRMQQwsMHEhs7CZ+vDp2u6Tq/yheQFln2AiJ6vQ2TKQGrVamAkZv7IW53MeXlK9Dpwqmt3U5JySLi469EkpzU1+8NGs/tLgp0tBIEXcO4NDQMMKLRaCkp+ZHq6o0YjbGEhvbCYGi6RvGpRMUumXUvS1QHXy7b58vEDoKBf9VgjGi7/5EJEyYwb968oG0GQ9NhR16vF51O1+S+Q3n66ae59dZbqaur4/333+f222/HZrNx9dVXN7L1eDzo9Uf2gC9btoy77rqLgQMH4vP5eOyxxzj//PPZvn07ZvPRhVmdKGRZpqJiJVVVGwPv2f2UlS0jImIoNlufNju/0WjkpZdeYsaMGYSHH1tCYkRE25RLnDdvHhMmTMDr9bJ582ZuvvlmzGZzqwvitkb1pKq0LrYUJYM/NBki0pSOVDE9FeEJgKy0Jj3UI2rvAoPuUtqvxvSE4X+FdsOVfXoz1hG3ox04PVjYnsXsXJvHng0FbPg1E8l/wCu0/++qkjqqSupO1vRUjhKHIwtZ9uJwKGpKEDTNCtT9JCZeS3j4UFJSbiE5+SYACgq+pbj4Z/R6pYi/TheJz1ePIGgxGKIpLPzhiHMJ/rL3I8suJMkDyLjdhVRXbyQ//9Njus4TScUumaX3NxaoAMhQtBp+u1fCXd12XlWDwUBsbGzQz34xIwgCb731FpMnT8ZsNvPss8+SmprKP//5z6Axtm7dikajITPzQLMTq9VKbGwsqampPPvss6SlpfHNN98AyvLx3XffzcyZM7Hb7Zx33nkAbN++nQsvvBCLxUJMTAw33ngjZWVlgTF/+uknpk6dSvfu3enduzfz5s0jJyeH9evXt9nzc7yUlPxCZeWaRgIVQJKclJUtoaJidRNHtg7jxo0jNjaWF154ocn95eXlXHvttSQmJhISEkLPnj355JNPgmxGjRrF/fffD8AjjzzCkCFDGo3Tq1cvnnrqqcDjefPm0bVrV4xGI126dOGNN95odIzNZiM2NpakpCQuuugiLr74YjZs2HAcV3tyUEWqSusSEqkIzEF3QL9pMPge6DIZxjyttEUNTVaEq7ceSre36VT8Xg97VvxG7qZ1bXqek4Hk8x92v96kIyS0+ZJdKicfn8+Bw7EXWZaw20cSFtaXqKgxAPj9TnJzPyQ398NAEtOhGAx2IiOHotUqXq7a2u3U12dSW7sNWZYxmdpRVbWOmppNyLIPt7sYWXYEjaHRmFo0V1n2HLJFaGg4cOqy9kWJZp66AHX5kP7uyQtheOqpp5g8eTLp6elMmzaNadOmNfK8zp07l3PPPZeOHTs2O47RaMTrPSDU5s+fj1arZeXKlbz99tsUFhYycuRI+vTpw7p16/jpp58oLi7mqquuanbM6mqlskpbefqOF4djL7W1245oV1GxsqFlcOsjiiLPP/88r7/+Onl5jUPRXC4X/fv3Z+HChWzdupXbbruNG2+8kdWrmxbO119/PatXrw66Idm2bRvp6elcf/31ALz77rs89thjPPfcc+zYsYPnn3+eJ554gvnz5zc7z927d/Pbb78xePDg47ziE48qUlXaDskPjlKl5qnkV0pQDboDul6m1E7d/H9QndNmpy/LyqR413Zy1q/GVVPdZuc5GaT2iz/s/h7D2mEwtWzpUOXkUFCwgMLCb6io+BODwU5U1OhAG1O3uxS3uwS3uwSPp+wIIymYzakIgvKaO525OJ37kKT9Ki34o14QjICIJDXVLe5QhEaNBSTJRVnZihbN62RQskmmJrtltjmLZTx1bSNUFy5ciMViCfo5eLn1uuuuY9q0aXTo0IF27dpx8803s2vXLtasWQMoIQAffvgh06ZNa3J8n8/H+++/T3p6OmPHjg1sT01N5eWXX6Zz58506dKFN998k379+vH888/TpUsX+vbty9y5c/ntt9/YvXt3o3FlWWbmzJkMHz6cHj16tPKz0jpUV28+stEx2B4tl156KX369AnydO4nISGBBx98kD59+tChQwfuuecexo8fzxdffNHkWD169KBXr158/PHHgW0fffQRAwcOpFMnJZTnmWeeYdasWVx22WW0b9+eyy67jAceeKBRXPK1116LxWLBaDTSuXNnunfvziOPPNKKV35iUGNSVdqGku1K5r6jBOL6QclWJTZ1yL2gNx8ozK9vqFvqc8OG98DvgX7TlcoAx4ktPhGzPQqD2YrBcmbVR60sOvxSvqPahdGshkacyuwXfkoMqExV1ToEQYvN1heTKYmIiKEo1TA0VFdvQqezodOFo9EYA2WpTKYknM4Cysp+w2LpRHz8lRQWfo3fXxt0Lo3GgCBoAA1hYX2oqFjJ/hasR0ZGlht77p3OXLzeKnQ623E8C21DwcqWi06/C4rXQdKo1p/H6NGjefPNN4O2HeyZHDBgQNC+uLg4Jk6cyNy5cxk0aBALFy7E5XJx5ZVXBtk9/PDDPP7447jdbvR6PQ899BAzZsxodtz169fz22+/YbFYGs0xMzMzIID2c/fdd7NlyxZWrDg1b0Rk2U99fXaL7R2OTKKiRrfZfF566SXGjBnDX/7yl6Dtfr+fF198kc8++4z8/Hzcbjdut/uwMb7XX389c+fO5YknnkCWZT755JNAOEBpaSm5ublMnz6dW2+9NXCMz+cjLCwsaJzZs2czbtw4/H4/e/bsYebMmdx44418+umpH6pzMKpIVWl9JD+kfwz7v9hq8hXx6feAowzCEmH4w8q+/WK1vuxAnGpNPkQdv0g1WKz0mdz8ctbpTGlezWH319e4MVndhFjV2rCnKgkJl+PxVGA0xuFwZFNevhwAozEOozGWiIih+P0esrLeYL+g1GgMmM2dqK1NbxhFaPiRcLtLaU54HuwxrahYfgyzDRZ9gqDF4ymmrGwZcXGTj2G8tuXQJnhHtt9fHq91MZvNpKamHnb/odxyyy3ceOONzJ49m3nz5nH11VcTEhJcr/ahhx5i6tSphISEEBcX1yhB8tBxJUli0qRJvPTSS43OFxcXF/T4nnvu4bvvvuP3338nMbHp7mcnG0nycuh78vD2h4artC4jRoxg/PjxPProo0ydOjWwfdasWcyePZs5c+bQs2dPzGYz999/Px5P8/O57rrr+Nvf/saGDRtwOp3k5uZyzTXXNFyH8v/97rvvNlq6F0Ux6PH+mGWAzp07U1tby7XXXhuIfT5dUEWqSuujESGyE5RnKEX7HcUH9lVlKSL10BanoQmQeoFSY9Xe+cTO9zQkpl04dZVNL9XqTVpyd5WSl1FG37EdMYaoHtVTEY3GgNGoCASDwY5WG9qQse/D661Fp7NSWbmag4WnIIiHCBKZA1/WjQWqwRCH213YzAz0REePoaxsBZJUhyja8PurWjR3szmNurpdmEzJLbI/0RhsR2t/6lTBuPDCCzGbzbz55pv8+OOP/P77741s7Hb7UQmNfv36sWDBAlJSUtBqm/7al2WZe+65h6+//pqlS5fSvn37Y76Gtkaj0SMIYpMe/qYQxZbFXh8PL774In369AnySi9fvpzJkydzww03AIrIzMjIoGvXrs2Ok5iYyIgRI/joo49wOp2MGzeOmJgYAGJiYkhISGDv3r2BGNWWsl/EOp0tCfE5dVBFqkrb0OViWPFy4+3Gw5TpSBnRdvM5w4hpZ6Mkp5LaigMfOHqTlrj24YRGmklfnt3gYzt1vnxVmkaSfEiSl5SUW6ivzyY//3M0GgPt2k1Hp1PKPGm1NuLiLkIUzQiCgCT58HjK8HhKDju2212IKFrw+5sKD/E01FoFUbTQ0mplOl00ISHJxMRMOGVLnCWPFdj5ccs8bfowiBlwZLtjwe12U1RUFLRNq9Vit9ubPUYURaZOncojjzxCamoqQ4cOPe553HXXXbz77rtce+21PPTQQ9jtdvbs2cOnn37Ku+++iyiK3HXXXXz88cd8++23WK3WwLzDwsIwmdpe5B0NgqDBYulEbe2OFtlbrV3aeEbQs2dPrr/+el5//fXAttTUVBYsWMAff/xBeHg4//rXvygqKjqsSAVlyf/vf/87Ho+H2bNnB+37+9//zr333ktoaCgXXHABbrebdevWUVlZycyZMwN2VVVVFBUVBYTx008/TadOnY547lMNNXFKpXWRJdi3Akp3gq6JlorpH0Nl1omf1xmI2Xbgi0OnF+l/XhoJaVFYI0LoPboDfcZ0xBCiJk+d6hQULCAnZx5VVZsCGfOS5ANk6up2AuDzVVFdvYW6uj1kZb2FLHtISrqehIRrA8lSStxp8Out0RiaEKiNhaXfX4fPV9Wi+Xq9JZSU/ExFxaqjus4TSVgHgag+LbPtcJGAqG8bsf3TTz8RFxcX9DN8+PAjHjd9+nQ8Hk+zCVNHS3x8PCtXrsTv9zN+/Hh69OjBfffdR1hYGBqNIgPefPNNqqurGTVqVNB8P/vss1aZQ2sTFta3RXaCIBIa2rONZ6PwzDPPBDW8eOKJJ+jXrx/jx49n1KhRxMbGcskllxxxnCuvvJLy8nLq6+sb2d9yyy289957vP/++/Ts2ZORI0fy/vvvN/J833zzzcTFxZGYmMi1115L9+7d+fHHH5v1pJ+qCPLp2kJE5dSkZCts+Uj5e8AdoNFCZSYUbAJHISBDj2sgtvfJnOUZQ2FmOWUFNcR3jCQy/vjjeFVOPNnZ/8Xnq8ZgiEcUjYFC+3b7KMrKltFc7J3V2p2YmPHs2zcfr7c8qAj/fjQaM5K0v+yUCCjLo/vDCo4HQdDTsePdxzVGW1JfKvPbPRL1xc3bRPeF4S9q2kykHisrV65k1KhR5OXlBZZ6VRpTWbmO8vLG4RAH0BAbeyEWS6fD2KicyqgiVaV1qS+DtW8pXtT4/rDnJ6WQv+xXukrZUiC6h5IkZQxrlSx+FZXTGa+3ioKCb/F6y4/qOKMxnsTEaygsXIjDsTtIpJrNXXE4dqB4TdvmI95kSiQh4dROTHSWy2x5WyZvqYx0kH7Xh0L7iQLdp7adF/VYcLvd5ObmcttttxEXF8dHH310sqd0ylNbu4vKylV4PMH/P0ZjPBER5xAScmrGTau0DFWkqrQ+sgyCoNRBLd2ueFMlP/S8DmJ6QNEW2PqJUpJq+MMgHmFJ2lUF2csgqquSkNXSafj8yNVOhAjzKRs7p3J4vPX15K9YSXS/voTY7Uh+PzX79hHWrh3CIdmspzN5eZ/hcuU3u99oTMLlyuNgwWk2p+F05iGKRgyGWDQaHTU1W47irMciYHVERY3GYkltSF45PSLG3NUyRWtkfE4whgvEDuaUEqf7ef/995k+fTp9+vThu+++IyEh4WRP6bTB6czH46lAEAQMhphAzWGV0xtVpKq0HXXFsG85JAwCYyiS3oK/ZCu6gnVQkQlaE5z7yOFFam0hbJwHnlrF63quUoy4trSE2pIiYjp3RdQ2fbxnzV6kcgdiih1d17gmbU5nPLW1yJKE4ZD6eGcS6+e8yr5fflEeaDQIgoDs9xMzcCBxgwYS3bcfJnskYgt6k7cFfq+XNS++hKemhsGPPYrRZjusvdNZgCS5MZvb4/VWU1eXhdO5F1nW4HQ27t8ZEtKe6OjzkCQ3OTkHd5TRAgcv17edx/RgTvUlfhUVlTOL0yuCVuX0oCobNs0HBPA5wWTDZwhlgeM36rQCkxw1RFtioa4I976lGDqc1+xQnr1/4HM4CdGhhAk0sO2n7/B73PjcLpL7DWr64P3f2WfgfZizrIxf77gTyedjzOuvYT1F6xkeK7LfT+6yZRT8+eeBjZIUeEmL166leO3awK6Oky+mx9SpiIYTWxe2Li+PwoY5lm1JJ3HEuc3a1tfnUFDwJQDR0RdSUvK/g/Y27ZGsr8/C73c1lKiy4vPVosSW7heo+8Vpa73HNRyuyL/FcvrUV1RRUTn9UUWqSutTmQU+F06NwD6rnnblu6A2n+r2NmRBYHOElVG1AhsiTWzSbKJvpZFB4Y2/3H0eDxu3VODzxNKtbwfCO18Q2GeOiKSmuJCQ8Ob7Suv6tUOqdqKJaL67x+mK1+HAW18PsoynugbOLI3K3v/9yOZDOvWIISH465uu0p757Xf4XW763XfvUZ/LWVGBr96JNfHol1ZDU1LodOWVeGpqiB00sPHYzgKqqzdiNMZRXr4ysF2jCf7oFUUTfr/j0MMBRdzKsh+TqQO1tZvZn/zUcCTBHtVjp/lSVQdQE1BUVFROJKpIVTl2ZAkyFym/U8fD/vi0pKGU+av4U1dGgVxFsl/ggmqZCWXwYxTsNWtwhIWh9eiAOso8Dem3NQVgtoOoD4y/v8OGFBH85djjgsn4vV60h/GcCToR0d64DeCZQGi7dpz7/HP4PV4iu3c72dNpVYrWrqNg1Z9B24zR0bhKDl8TVGduouTZEXDX1PDr7bfjrXcy4sUXsB+mT7nf78LrrcZoPJBtLQgCPW6e2qS911tLfv5nKKWkdgXtEwQNNtsAqqs3Ics+/H5Hg0h0cKhXtLz8D6C5DjXHL1Atlu44nfuaEajBYQQajbEJGxUVFZW2QRWpKsdO1T7IXqr8HZFGqcWCVRvG9rrNrNXuCXy3WcJSoc8YrCGhUKJkqxb7Srg66Ray63fT0dwV9i6Gvb9CeEfofwsAWoOR3pOvwlPvwBYTA3mrICwZrPEIGs1hBerZQFTvM6+MV11+PqueeQbJFyy+RN3hk+tSL72EHsdQU1L2+fB7vCBJ+Fyuw9rm5X2K11tBVNRYwsKCn3uPpwKNxoAoGhEEJaFLoxEbSj150eujEEUrLtc+AMrL/8RojCMkpAMOx26Aw3gxD9fSUQycx+8/1Mt8YOlep7Ph9VY1OUJ9ffZBZar2o0Xx2MqNbE2m+MPMR0VFRaX1UEWqyrFjjVNKSskSO8Qqfi/8GoNgxCO7GwwEJsVeTZwhEQQBs+RGiw4fSi0Yi2ihT1hD/2Ffg0DwBbdsC7GFE2ILh8xfIWuxUtpq5BOB/QVbN+OoKCNl0DnojCZqS4qoLS0mpnN3xNOsaHFLkSSZwsxyjGb9GVUbddv8D9j12WcYIyLw1NYqQrUhnljQHD4Tu2jdOnrdeutRn9MYEcHoObPx1NYS1bPpgt9+vxuNRh8o7yRJwbVI6+oyKSr6FtABXozGJBITr0QUQ2jXblpDPVKB8vIVhIcPpqpqMx5PMR5PMUbjoXEaLUuAslp7YLF0oq5uF4IgUlu7qwmrA7GlSixr0zQWqNCch9brrTji3FRUVFRaizPzW1zlxKA1woAZALir1yi/ZReRumgc/lp0go4lpT9wcew1hIoW9Nu/IdkgslfnRSfoqPPVYtM3xJSmjlcEb1jjmnYuv5M8ow9TnRlJF0mC5EejEfF53GStXgFASHgE8T36KAlVXi9+r5ekPm3U6/Akkp9RRnWZg6oSRVgMGN8JvfHM+Dd2lpYCEBITw8C/PsSaf87CXVYGQF1+wQFDQQCNBvwHYjPrcvNY8/Ir1Obk0P+B+7F17Nji84alpDS7r6ZmKyUlv2A2dyQx8Rrc7jJCQhR7hyOL0tKl+HyVDdaKeHW5cvF669DpLHg8pVRVrUcQdDgcexqN37jsVGOBKggGZNmNRmNCkpwN587A46nC7c5r0TW2tMf54YiIGHbCOveoqKiogCpSVVqJntb+bK/ZRK2/hu7WPkQZ4lhQqJTMKXEXEuoxQNEmqpJCAS0atHxW8F9iDQmkmbvRLbQPRHdvcuwlZT9QWp5Nank4IGHMyiSqYydEnZ7otC7UlZcSnpSCIAiERNipLSlqNqGqujCffetWEdO5GzGdTq8exo5qF/u2K3GZGo2AyWpAqz9zaoX2vuN2RJORip07Wf63R5o3lOUggbqfvKVLld+//46tY0fKd+xg36JFtBt3HnWFBcT064cxPLzZYSWfj1XPPIuzrIyhTz5JSEw0brcinF2uEhyOTETRgtdbRVHRD3i9Vchy00vx1dWbqa/PxOOpACREMaRBZLoJzp4/stdUpwtDp7MSFtaX2tqd1NZuRZLcAYGq0RjRaMz4fOUBQdvaaDQGbLa+aDQnp9SXiorK2YkqUlVaBVEjck3iLfxRsYTfK36hQ0gnzo08n4y6bSwuW4gj7Fx6x/ZmsL+eXaYIyr2luH1Oitz5FLnzaW9OwyQ2nYUfUqWhwzojskZApzdisUcDStJK2oixQbY9L7wEv8+Hdn/dzMItENkR9MrYhdvTqS0pwut2nXYi1WjWY40w4fNKdBuaRI1PZvr8taTYzTx5UbfTvmFBZUYGWT/8r+mdUvNlkQ5FHxZGzb59rHr2OdyVlRStXYurvAJ7r56MePFFQKkx+8ff/4Gg0XDO359CZzZTX1pKUUNZq+JNa0keNxa/34MgGPH76yktXQIQ5NFsjqqq1UGP98eLmkztcbuLkaSD40cFQIvBEInFkobRGEdZ2TJ8Pgd6fRSxsRMQRRMARmMckuTE4chhv+dWklyBfuGKQFXCDg43T602/CAPcPMIgh6dzkp8/OWnrUCVJYmsTevJXL8ar8tFSFgYXYePJqZD25bTmjp1KlVVVXzzzTdB25cuXcro0aOprKxk06ZNjB49GpvNRmFhIUbjgcS0NWvWMHiwEg61//Xdf+x+7HY7AwYM4MUXX6T3GRij3lI8znpKdu+gvqoSQRCw2KOISu1y4HvgJHDw53FISAjx8fEMGzaMe+65h/79+wfZyrLMu+++y3//+1+2bduGVqslNTWVG264gdtuu42QkKNPCj1TUEXqWcD6agd7nW4ujwlH04ZCRiNo8DXE7WXVZ5Bq7oa/YZkx35OHrl0XUkypJGst1Pqq2V27nfTadegFPZrDvBW7CF3IlAsREOh3+bXojMoXttdbg89XF5TIIWg0Bz6Yfv8nLHkGEgbAtZ+CJYr4Hr3xuV1En2YCFUDUauh5bntqXV7mrc6hst7Db7tKYVcpI9KiGN0l+mRP8bgwRUai0emQvN4jGx+MKAZ5VkOTk9n47//grqwEUSQspT2u8gpMkZEBm+ING6jYsQOAioxdRHRPJSf9S0J7RqMN0+FN3EF2dlaTntJDY1LBABzeeykIRmTZjdOZtX8LBxXyBbxYrV2w2fpRXb0Ft1upeOF0Oti37310Ois22yAkyUlk5Eiio02Uli7F7S5Glv2HCM794rVpgSoIIrLcdJKYIJgICUkgLGwgWq0Rvb55z/PpQO72dH5+cw7VJcVB29f/8C3xnboy8b6HCLWf/P8bq9XK119/zbXXXhvYNnfuXJKTk8nJyWlkv2vXLkJDQ8nJyeHee+9lwoQJ7Ny5k7AzuLFHU8iSxN5VKyjetQ35oBvZkoydZK9bRWKvfic17GvevHlMmDABl8vF7t27eeeddxg8eDBz585lypQpAbsbb7yRr776iscff5x///vfREVFsXnzZubMmUNKSgqXXHLJSbuGk83p0dNO5Zip8fm5bNMe7tmRw6dFbZP0sOXXn1j07r9JL1tLiasIEREZmZ21W/BIypd3jbeS5eWLWFr+EwBWbRipli64JRe1/hr2OTMA8Ms+dtVupbKhD/Oysp/4ybAEY//2dJswKSBQJclLbs588vM/bSZpBHDXKL/z18Pr/aG2mNCYOHpceAnRqZ3b5Lk4Eby2OIMXf9zJp2ty0YnKTceP6YUneVbHjzUpiT5336U80GgQDpPRrw8LU2JTAV1ICMaIA+EdFdt3HIhvjYqi29SbOO+dt+k/cyYAfo+HkvXrAdCaTJTV/cL2xf9i52s/U5NeginRisagCQjUQ8suWa2dsVr7Bh6bze1p7n7fYFBuoBRR2PzHrUZjJDRUKX/l8wVn6UuSE7e7hOLihZSWLiYnZy5ZWW9RV7cNn68SvT6mqSGbRZb92O2j0Wgae2dk2YnDsQePp+i0F6h527ey4PknGwnU/RTs3sGnTz5MbUXZCZ5ZY2666Sbmzp0beOx0Ovn000+56aabmrSPjo4mNjaWQYMGMWvWLIqKili1atWJmu4pgSzL7Fq6iKId6UECdT+S10vO+tVkr/2ziaNbB0mSeOmll0hNTcVgMJCcnMxzzz0X2G+z2YiNjSUlJYXzzz+fL7/8kuuvv567776bykrlxvLzzz/no48+4pNPPuHRRx9l4MCBpKSkMHnyZJYsWRLkOT8bUUXqGY5BIxBv0KEVINnYuksftd5qNpWvYtFHb7Hl15/YVPInlb6yQD/vHNdeqhs8PJ4G75NVPJCNbtGGkmzqQLg2kpz6vfyv+EvWVqxgafmPLCz+jHq/g5116bhlJ1sjsrDFH5QJnbEInNUA1JdWUFOsiLTCHVsp2LZFWR4b/TiMfhSQwVMH3qYLwZ9O1Ll9rMxUBHyNy8uV/RPpHh/KtYMbJ5ydbvjdbtLffQ8ArcGAIDYTbysIdJw0CU2DiPXW1uKqqEBoqOaQu3Qp9Q01VeuLilh6/wOIOj0aUaSqYBffXXkF+35dDBqB6P692f3SCjJfX4sh0oKg02CMO1BbVxRDSEm5lcjIkcTHX0NCwjXU1e3B6dyD1doLvT6S+vo9NJUNr9WGEhU1JvBYr7ej04Wj1doAGa3WilKMH2JiJgSW0w/2gAqCnqY/phXPsSz7qa/f2czTpEfx2CocLEoFQUtc3MUYDAloNKZGxx7abOB0Q5ZlfnnnNfxH8MrXlpey4uP5h7U5Edx4440sX7484DVdsGABKSkp9OvX74jHmkz7V5aOcgXiNKdiXxblWY2TEQ8lf8sGHBXlbTKHRx55hJdeeoknnniC7du38/HHHxMTc/ibxgceeIDa2loWLVoEwEcffUTnzp2ZPHlyI1tBEM467/ihnN6fRCpHxKDRsGRgFxx+Cbu+dV/uxWULKXYXEHHXOfgX59E7ZACr+TOw5H8wzoYyN3scOxkUPgKDaEQURC6IuZxKTzmfFyheBLPFCiieVpMmhERjCsXuAqL00RS58ohtKNmjcZSRvOgtakM7sd1gBHkDqeeOYe8fywAl298WnwgjH4akwaC3QkT7Vr3+w7JnMdRXQK8rW2W4T9fkEBNq5NftxWwvUDzEGuDjNbkYtBoSbI2FxumAz+Vi5RNP4qmtZfCjj+B1KLVCfU5nQITuR9DpkBvKUu346KNG7W5ln4/YwYOpzswEwBQTjbO4BNnvR9Ap7/2qwq3I3obQAEnGXVEDgoggSHS/5wo8IVlo9CJGqSMVuRuI7t6JuroMysuXodOFERk5Bll24/O5cTh2YzTG4Wnw+ptM7QAZpzMHQdARGzsJg8EemF9ISDvs9uE4HNlUVq7GZutLSEg7/H4XOt2BLyK7fQQOx158vurjSoI6NFRBiYNVwgyqqjY2VBYQiIgYRkXF7wE7UTSf9ln8+7ZspLKw4MiGwK5VKxh1062YrK1fzm3hwoVYLMENRfxNJP1FR0dzwQUX8P777/Pkk08yd+5cprWg7m95eTn/+Mc/sFqtDBrUTHvoM5TCHektti3akU7HYaNa9fy1tbW8+uqr/Pvf/w54vDt27Mjw4cMPe1yXLl0AyM7OBiAjI4POnU/flb22RhWpZwEmUYNLkvimuJJxkaFYtK2TEW7TRVLsLsBjF9Bc3Y7QqCik0qYSXA7E33lkN2sqf2dA+HBMYkjDOBH0sPbD4a9jSPhI+tvOwSSaEASBibFXsqpiKZtr1lJaUszk2OvId+WQ1GMSoaZwMkUP8h/FaGQRU5gNgzUUWZIwhR20VNlhFPh9ULhZqSAgHnjbyy4vksONGGlBlmRklxdNyBE8zpIEix4HSwykDIe4vkpJpP1UZMGHlyvXXLoLXFUw4kGwxh7L08y176ziz72KELqo14Ex/HJDyo1GQHOEOqKnKo6iIsq3bQOgfPt2kseOI3/FCjQ6Hd7a4Nqe8sGeokMEqiCKIAj43W6cZWWY7HZ63XILq59/gchu3TBFRCD5/dgie2IflkbZSiW8pHLPHs578w0kvx+tHcrLvVhCOvPHvf/CXVmJ7cGRGPspQlGSPJjNKURFjcPhyKS+PgunM5e4uEuVyhIhKfj9LqqrN2EyJQc6U9nto3G58rHZlMQWszkFszklMHeNJrgphSCIpKRMp6LiT9zuUkymeNzuMtzuUnQ6Gw5HxkHHHpocpaWxV1fkQCtV5XlzuQrYX2VAEajK/6jV2oOoqFFNvVSnFVkb17XY1u/1si99E13OGdHq8xg9ejRvHtLed/Xq1dxwww2NbKdNm8Z9993HDTfcwJ9//skXX3zB8uXLmxw3MVG5WXc4HKSlpfHFF18QHX3yY2tPFJLfT3VBy8qvAVTk7qPlRelaxo4dO3C73YwdO/bIxgexPwluf2KVLMunfdJrW6KK1LOEG7fsZV1NPeeGW/iiT+tktY6MHE+HkE78XPI1eo2RLTXrm7EMFhTb6zajEbQMixyD2+/C4a9lWOSBf3QDwTGA0YZ4NGgI1dr4rOC/AIRrI7ko7SrW572JdojAQNswQmPiGHDVjY1P73PDx1fD3t+g740w+d/KrGQZ9x97wO1D7haPVFKDVFaHtksc2vb2xuMArHwNls9ShGdgwmFgMCvi9cavITROEaSualj1BngdSqvXCc83Gq7G5cVq0B72Q2pN1oGlqt92BrcGvWpAIveO64TdYmDmZ5tYvLOEK/sn8vhFp0er1LCUFHpMn4anpobdX3yJo7CQ2MGDKFq9psVjCDodsteLKToaa2Iist9P8pjReGprCe/UiW5TlPfEhtlzyFmyhLTLL6M2vAx3ZSWSx0PB6tXs+/kXzL1DiD4/EaM+MVBNQJYkwsL6NJSBCqe0dDF+vwO7fSTV1aGYTO0aYlIVRNFIRMSQoPnZbH2BvhwtERFDA397PJXk5MzD4ynFYEjA7Vbqq0qSE50uHK93f+JUU0X4m6qReug25X/UYLCftln8B+N1H7572PHatxSz2UxqavDnbV5e0+LqwgsvZMaMGUyfPp1JkyYReVCi36EsX76c0NBQoqKiCA09cxp6tJRDO9Id2b71QyH2h1kcLTsaEjbbt1c+Nzp16hTYptIYNSb1LGBJeQ3rapR4zA3VrReXKQgCySEduC5xBpNirqLOryxD6wUD2iPc/8QZE0iv2cD/5b7BFwXvs6tua2BfgSsXp/9AF5wO5k5Mb/cAaZYDwsuqDcUkmulk7kaExU5KZBPZ+jv/B7N7wNwJikAFKFeWgiXJg9/vDCTfoBGQncoHmew8JKNb8sPepfDr3+G354MFKoC7GmoKoK4IVs4BUzjcswEmvQY6IxiskHZeo+nNW5lFr7//woNfbGn2eVqbXcG04e2xGBTvt8OjiKeYUAOPXdiVJyd1J8FmwuuX+HpjPtVOL++tyCKjuPkOQ6canS6/nB4334zY0ObWVVkFgDbEhDYkBGty0mGP14gioSkpOEtK2LtwIWXp6dQVFLLz40+o3LWLrP8pZa0qM5T2o2Vbt6E9qNTP7i8/pTY3l6IflP0GUySjX3+NES+/RLtx4xAEAbO5A7Lsp6ZmCw5HJm53MVFRY7FY2raM0X60WgsGQzSiaCYm5nw6dLiXiIhziIm5AL0+6hhGbHxTpNPZCQvrc9xzPRUw244u6eto7dsCURS58cYbWbp06RGX+tu3b0/Hjh3PSoEKSptkzVF0FNSZWr+EU1paGiaTicWLFx/VcXPmzCE0NJRx48YBcN1117F7926+/fbbRrayLFNdXd0q8z1dUT2pZwHFngN3kT5ZJtflIakhicovy4jHudRg1loocOVQ61P+mTyyG6sYSm2DaG1kL1pZVPpd0LZNVWvobOnB8rJf2F63GZMmBLs+hgRjO3qE9UUUtHSz9sGgMaIXDCSGKMX7R//4riIgLwuBXlcFn2jbV1CdC66D5pG3Bv/ad8ixy0iSi4QB16D3WdGEh6CJtCBV1CHG2YLH+e15WP7PA48tsRDXB/YsAtkPpkhwloNGq4QW/PE69J8KGz+A+nKlBFbH0VQ6PLz/RzbnptlZsaeMBRsUj8q2gsYfQisyyvhyfS7fbFLi6vRi8Gs0vnsst47oEHisEzU8cmFXXlucQYcoM4nhIbh9fgxakQ05lbSPNBNuPnU9ZDmLl2BNTMRZVkbV7t10mDSJ3N9+w1tXR/zQSeyt+AFv3YHe9hq9Hsmj3Ez4XS66TZnClnfeob6oCIDcZctwlpUhGgykNiQkaEOUWrmCVuTcl16kaPUa3CHZlG3eRukvDsI6tCcl5Ta0WiWGMMQe7E3X6yMIDe2J318f6Dp1otBodCQlBS8RR0QMwekswOHYfYitGaMxivr6bECpMOB2HxqfeWB1w2zuhM3WC4MhLpD0eLrT9dzRrPrqsxbZmm3htOt59J7utuCZZ57hoYceOqwXVUUpNWjvkEbJ7pZ5INuimovRaOThhx/mr3/9K3q9nmHDhlFaWsq2bduYPn06AFVVVRQVFeF2u9m9ezdvv/0233zzDR988AE2mw2Aq666KlB+7IknnuC8884jKiqK9PR0Zs+ezT333HNWl6BSRepZwER7GH8TcnHL4JZlnsss4K3uKVy6MYONNfV82KsDw8Otx3WO9iFp9LD2p8pbTp4rG4s2rAmRqsS9NZVYVeUrp9pbxfa6zQA4pXpyXVnkurLYUP0nySEdGGOfGORNBaB4GyAr8abdLwuKN2XEXxXh2O0SyFsLq98CTx3+rDVIob2QRT9+jQNNeAzIMhq5Bk3iQcJE8kP2cvA3eFbN0eAoUTymGT9BWCJc9q4Sg7rwfhh4Kyx6qsGmBIY/AIIGBt8BwGtLMpi3Mpv/W7WPCocy5vjusTw0vhMAW/Or0QgCpbUubpq3NugyPX4ZvSjw5KRu9E4Mp2di44zP20Z04LYG4froV+l8vCaH0Z2j+G1XKR3sZpY8OKrJ1y63oh6NRjipyVcbXnsNyesNeEdqc3OQGxJMCtesIbxLZ0rWHQgn0ZpMeBpEqj4sjJAoO8aICOqLihANBuKGDGbvd98j6vWENyQl9Jw+jczvF5J68cWE2O10mHghtbU7MbXX0eOmaYSH9+dwCIKG6OjGHvGTidEYQ2hoD3w+B/X1uYiinuTkKYiiicLCb3G5ChFFA8F1WRWioi7AYIgIxM6eSUTEJ5LSpz/Zm5oLQTpA7/MuRDwKr1xbotfrsdubCTVSCSK+Wy9KMnY2ik8/FI1OR0yntgl/euKJJ9BqtTz55JMUFBQQFxfH7bffHth/8803A4qgTUhIYPjw4axZsyaoaoMgCHz88ce88847zJ07l2effRatVktaWhpTpkxh/PjxbTL30wVBlo/wCquc9tT6/HRZnh6IQuts0vN+rw6MXLMLjyzzWIc47mnXel9UDl8dJk0InxX8lxpfFQICQ8JHsbryd6Qm4+MUromfzg/FXzTrge0bNgSv5GFQ+Ah0mobM78ItsPlTWPsehKfA7ctBa2jyeAo24t/wK15xArJBwNdPi9WmZFrK3z+KP68EsUsPhNH3K/a//h1WzIaEgXDePxRR+t/zwVEGsg+6XwpXvh98jg+vgD2/KuL1kMz+H7YUct+nGxnfPQaTXktWmYPXru1Lgs3E9oIaLnp9OYIg0D85nDXZwTVtRQH+dVUfJvdNaPb5O5h+Ty+iot6DUavB5ZNIDDfx1wld8EsSl/Y9UMprT0ktF766AkGAn+8fQYrdzM6iGuo9fvoln7gl0M1vvU3x+vV0uf46tr3/Ps6S0qD9ve+4nc1vvgWA1mLBd5BXFSC0XTui+vQhb+lSulx3HdV795L9889YkxLpdNVVhERFEdWr1wm7npOB0nJVg0YTXBUhK+st/P56TKZEBEGLLMtER5+PTnd8N6anOvXVVXz6979ReZgEm44DBnPxzEfRNFfuTOWUpmjnNjJXLm12v0YU6TL2AsKT2p24Sam0KqfG7aNKm2LViug0An5JuR/Z5fQwdt1uXu2axD6nh2mJrXvnbm5YLr0o9ioWlXxHqaeI9GaTqvYjsKj0e2r9NRg0RtySksgQrY/Dqg0jRLSwsVopVh3mlem6biFi54lKrGfRFvC7oXwPuOuaF6nxfUFOgS15CF6wmNMgdy3Sr7PwRD8MKVr8jmIOHL0/XlWElGHK3/esVxKxRJ0Sa3oo132uJEo1sW9irzgm9IhFbCITXycKiBoBAQGDrvGSq1+GF3/eyY6iGu4b2wmT/vBfqpP7xDPvj2x6J9m4d2waHp/Eze8r3tk/M8t56fJeCIKAyyvhlSQEYNH2Yt75fS/lDjeSDJ/PGMqg9hGHPQ/AQ19sZnlGGa9f15eBKcH27/yeydrsSv5+cffDemp73z5DuU6Phw2z5wS2m2Njie7Xj+TRowMiVW4iaaI2P5+affsY+NBDJI0exe4FCwBliX/9rH8hiCIXfDAfY/jJjz1sKw6tErCfmJiJ1NdnY7P1R6s9e9orhoTZuPbpl1n5+UfsWL4Ej/NAFQRLRCR9zp/IwMmXo9GoAvV0JbZLd/RmM3mb1lNbUhS0z5aYTHLfgVijj62qisqpgepJPUtYXFbN9elZgcdaYPXQbiS0coH/Q9lSvZY/K5cCIApa/LKPKF0MTskZSLRqClHQ0t3Sh95hAwnRWvDLfn4p+YYKTxl1vmqiygu5bOkP8OBupbzUhvchvD2kHr4ciCzLSMU1CCY9mjATfDkdf2E13tSnFAMBCDOhTYtBG2GGnD8htgcY276gclaZA40AUVYD32wsoN7j49kfGsdcDe0QSedYK49P7IpWbD6GcE9JLUkRIRi0IiU1Lsb963dqXEqoxYI7htK/XQQ+v8QX63NJjbLy1u+ZLN6hVA8QNQJv39ifrzfmM7RDJDcMad4Tkfbo//BKMred255HJx5YVvP6JdIe+xGA+8am8cB5nVr0PJSlbyV97lwqd+3CmpzMeW+9iSzL/DjlJlzlSqUDrcmEr0F02NLSqMpQyjJ1vPjigOB1VlRQX1TE8kcexRgZwbg33ghKmFI5e/A468ndno7H5SIkNIyk7j1VcXqG4agox1lVAYIGS6QdY+jZXQT/TEH1pJ4ljLWHcVN8JPMLlC95CTAfRuAAZDp2sbpyGT1D+9Mz9PDxes3RM3QAG6pX45acJJs6kFW/m1LvgTaFZk0oJo2BMl/w8q5WEBEEDW7JTQiWQOH/jVWrWVP1O9VhduSeVyq+TlELA29p0XwEQUCMbfjwWvoSbP0SjTUJjaECyWcHvwRVTnybctGO63bAg3oCaG83B/6+rqGDVP924fzl803sLVOqMhi0Gv7cW86fe8sZ3z2WoR2bT7BIjVa8uXtK6thX7uDn+8/llg/WoddqSItR9j313TY+Wp3DhT1jqaw7UNXg+sFJvPrrbtLza/gxvZBJveJ4euEO9pbVcXHveF5dnMGUoSnMPK8T3eJD2ZxXTX5VcBkfnahhxogOrM2uYFLvuBY/D/aePTjn70+R/csvxA4cCEDZli34XEqBf8nrDQjUsA4dSLvsUtLfeRdXVRUx/Q/EepkiIjBFRHDhh/+HaDAg6k/dxDGVtkVvCqFj/8EnexoqbYg5IhJzhJpwdqahitSziBc6JeL0SywoqaSDyYDDJ2Frvj06GXXbqPVVs7M2/ZhFqiAITI69lmJ3Pm7JTVZ9cCayQ6rBcUj9/wnRl5Nes57NNWsocudxSdz1+DweHFWVhJlt2PXR9LIPQkhrouzU0VCqeCllex8kokEvgLNhMl4/riU7EEQNuiEd0BgO80S1IX2Tw7mgZzz/+U1p/zf/5oH8+7dMBIFA8pTHJ/Hj1kJ6Jdpobzfj9PiZPn8tDreP16/pw8TXluP2Sbx0eU9+uPfcwNh+SQ50r3J5JYal2tmQWwXAB3/mBOwkGa54608ySuoC56uq9/JjeiEzz+tEQriJzXnVWAyNP04eufDYXiNDWBidrzwQ01uyeQs+R/2BkmEN9LzlFqL79CamXz88tbVY4uMbjaW3ntmxlyoqKipnKqpIPYvQCAKvdWvHlIRIJm/Yw6i1O/l9YBo2HJhMjeNSB9iGodPo6WzpcVznDddHEq6P5JPcdxu2NM40Ppg/K5YE/o4zKjUyP3/mUQp378T+zHm49B6y63eTZjlOkXrhPyFpCFL4WNjbRDFvtw8Z8GWWoO+WgOyT8O8rQwgLQbRbGtu3ETcMSWbxjmJiQo30axfBh7cEv1ZvLN3DnF8ziAsz8ucjY9lTUscfmYrHfNaiDNw+RXh7fMF3Aws25LExtwpREPjHxd1IijDTL9nGzfMPdOsJNWmpcfqoc/t4YFwan67NpbLew0W94pgyNAVQErpuGlpF32NItJIkmcU7S+gSayUpovl4ybRLL0HyeqkrKKDwzz8xRkTQ/aYpRPdRujjprVZVjKqoqKicYagi9SzEIyn9ZpySxNrtc9FWr6db2nWkJI0LsrMbYhgbdVGrnTfelExNXRUgI6LF30R3HBGRap/SPWdExHg6Wjrza+n31PbTYujVCY1bJsRoRpZlcur3khzSodEYLcZshyG3I/olZE0p/n1l4G3c1lUIDUH2+vFll+LfUwoaAc153RFOUCvSuDATP93ffMvGmFAlzjK64XePhFDuG5vGtvxqvt1c0GBjIKOkjiU7ixnTRank0DHKjF6rIdSo46uN+dw9Oo2hqQcE8OD2EXw4fRC/7iihR0IYIXqR2b8qsZ+3nRseSKoy6kQGdwheZnN6/GzMqaR/SjiGw7Thnbsyi2d/2IHdomftY+Oa7bylt1rpOX0asiRRtSeT0HbJgeL/KioqKipnJmdG5WaVo+KccAtf9unIwn6dCPUo5VmcrrI2P+9I+3gstYqQ8uOjc0jPoP0GjZFhEeMQJUXUrK5YxprK5WQ6dkLfSHQD46i3eqmXHGQ5M/i55Gu8Usvb3fmLa3CvzMCfXxm0XRA16NJiIKwJT54oIFj0uJfuxL+3DAQBjS3khAnUlnDtoGSWPTSKz25T2nFmljrIr3KSX6XEbQpAlxgrH/y5j3s+3hg4rn+7CD6fMYSyOjezF2WwaHuxIjhTFI/ouuwK6r1+LugZR1JECJEWA89c0oMpQ9txef/ERvM4mJmfb+K691bz2NdbD2sXZlLCKEKNuhb1rxY0GsI7pakCVUVFReUsQPWknqUMD7dS6fXxWshNRJpqGN+hd5ufU5L8lHy+FtOU7mgR2VWfHrS/S0hP4jfBtfVj+SV5DSUhVWyr3YhdH4NVDMMju6n3O6j0lgEC0YY4tELwWzhvx1aqiovoPmIMgib4Hsy/rxy5xoVvXzliQuOlaUO/dvhyyvBnlgY8qkKYCcEnw/6l8jAj+sHH4b1tI9pFHki6enVxBt9vLsBu0WPWiwxPtfPzdiVZbUSn4BaaqdFWusRaqar30j0+lHXZFazOVkS8X4Yr3/yTWVf1pn2UBYtBy42HyfI/mJbWDLlyQBK9k2zEhqlZ9yoqKioqwagi9Szmm+Iq/lfhAvRU7yjg393aode0nXNdoxEZ2P088j5MJ3H6GLZJ6QgIyA3xqdurN9DLMRYNGjpKHagTduLyO/H5fWR5dtPN0oeLYq+izF2MVWvDu3shBT+dS3afC+jU7RYs3hC+eOZxJL8SRtBjVHD4gtgxCgQQ2zWdASqIGnTto9G1j8a7uwh/ZilyRT1eXyFCeAhyZT2CoEGWZHy7i0AQ0HaKaZEH8EQysWcs67IruG5QMveMTWNddgWLdhQjA/sq6nF6/IE6qxaDNhBKoJS82h401u6SOib9eyXJESaWPjgaTQs9yLOv7sONOZUMSDlynGqnGDWWVEVFRUWlMapIPYtxSgfiL78rreamagfDjrM96pEYfs2NAHglL6ZqK7vqtlLrr0FAQBIhp4sTZ001xRF1SIuKqMvIQrpF8fLmODPJdCSxt2YX+r0edsXtRR45CTQaSsoXMcl+FaFRUdSUlmKLaVzAWYy0IEa2LOFJ2zEafBL+feVQ40KIDUM26RDjw5DKa/FnKeERYpQVIcJ8hNFOLBN6xDGhx4GSTwNSInjiom784/vtbC+ooajGFSh3JcsybyzNRJJkSutc/LytuMkxcyqcfL+lgMl9WtbxyqQXGZaqtndUUVFRUTl21GL+ZzG7HS4uXLeLOklGBDJG9CLkCLVTj5cNP37P3g1rGHzj9WzUbaLQnQvAZbFTMGlDsGit/FGxROlQJcmgEaDKi+TxYlhZA5cm48WDv9yJGGkCWUZAYED4cPrZhuLzePB63JgsrSO2fXtKkKrrkT0+5Cpn0D4hzIR+UHuEwyQGnSp4/RJv/JZJlNXAdYOTWbW3nN93l9In2cZtHyjdwMZ1jebXhmL+TRFlNdAl1sqsK3sHkrRUVE51ZJ+Ec2sZzh0VyB4/GrOOkD7RGFNtbXreqVOnUlVVxTfffBO0fenSpYwePZrKykpsNhtvv/02b7zxBnv27EGn09G+fXuuueYaHn744cAxNTU1vPTSSyxYsIDs7GxsNhs9evTgzjvv5NJLLz3lVnNONFK9B39uBbLDDYAmzISYGIHQRFm8E8WoUaNYtmwZAHq9HrvdTr9+/bj55pu57LLLGtn/9ttvvPLKK6xevRqn00lKSgoXXHABM2fOJCGhZc6BMxHVk3oW08ls5O0e7Zm2NYvREdY2F6gAKz79AH+0lnLfdyApH6wJxmTshujAB237kE7sqduBE6WAvQYNdbPWUg/Ej+uA1+pBE9EgkgQBGYE6Xy0AWr0ebSsWbdemRgPgL6/DuyYreF+7yNNCoIJSWP++cWkA7C6u5dp3ViED53WLpltcKH5J5vaRHViyswSpmdvW0lo3pbVuft5e3OLYVBWVk4lzVwWVX+xGqgtOsKxfV4wuNoSI67uiizp5rWL/+9//MnPmTF577TVGjhyJ2+1my5YtbN9+IOymqqqK4cOHU11dzbPPPsvAgQPRarUsW7aMv/71r4wZMwabzXbSruFkIvslvFvzkQqqgrZLxTX4MkoQ29tPakjWrbfeytNPP43X6yU/P5+vv/6aa665hqlTp/LOO+8E7N5++23uvPNObrrpJhYsWEBKSgo5OTl88MEHzJo1i3/9618nZf6nAqpIPcsZGxlK9oheJ+yfeNAlV7J+92LFQwroBB3nR1+CIAj4JB+V3jJiDQkMjRjD8vKf8ct+JBvYLu6Jb2UhA6znsEL4HQNGBFFDjb8KkNlRt5kulp5EG+PYVrORal8l/SxDqMjeR4ycj7j4CehzHZxzzzHNW4y0QL92+PMqkEoUQSybTk6B/+OlpMYdqFKbV+nk6zuHYdSJbCuoblagglIlYGzXaC7oofbCVjn1ce2upHz+dpp7U3uL6il9ewvRd/ZBG3FyVga+//57rrrqKqZPnx7Y1r179yCbRx99lOzsbHbv3k38Qc0qOnXqxLXXXovxLG31K8sy3o05SKW1zRng31sKPj+67q3viRw1ahQ9eig1xD/88ENEUeSOO+7gmWeeCXyfhoSEEBurfF4mJSUxZMgQunTpwrRp07jqqqsYN24ceXl53Hvvvdx7773Mnj07MH5KSgojRoygqqqq1ed+OqGWoFJBEARWVNby8K5cdlTk4nS2XTkqvcGAa0M+7v/tRcxxMzHmKvQapZzQ4tLv+arw/1hZsZglZT/glb2IDdn7vl6h9Jg4gbSUvkxNvpdYcyI1/io0B72Fvy76kB+LvmJFxa+k16zn+5/f4pMnHuKX/74NJdth9TtNzqmlaMJMyPUHWodKORXHNd7JYnianQcavKo7Cmt5+rvt/LytkCve/BOjTsPkPo27NgnA7aM68t5NA7Fb1PJPKqc2siRT+XVGswJ1P1Kdl+qfsg5r05bExsayatUq9u3b1+R+SZL49NNPuf7664ME6n4sFgta7dnpa5KKqpsXqAfhz6lAqnYe0e5YmD9/PlqtltWrV/Paa68xe/Zs3nvvvcMec9NNNxEeHs5XX30FwBdffIHH4+Gvf/1rk/Znq5d8P6pIVeGr4kqu2pTJ/IJy7t60gd/XPIHLXXnkA48Be3J7NKKIJUPixv73E2OM588vP+G9e6ZTXaOI48LcjEANoziD0nEKq5ate1YCiqhOs3TDpotggG04V8bfTIioJETluDIB0KFDW6Qs8XltHaHzRDj/6eOau7+4BrnOHXgsVdbj/jMT2d3yWq2nCjNGdqRHfCgAH6/N4fb/24DT68fllfhlW1Ej+yiLgYfO73yip6micky4dlXgr3Qf2RBwbivHX+s5suExsHDhQiwWS9DPBRdcENj/1FNPYbPZSElJoXPnzkydOpXPP/8cqSGptaysjMrKSrp06dIm8zud8e1ruZPAn1PeJnNISkpi9uzZdO7cmeuvv5577rknyBvaFBqNhk6dOpGdnQ1ARkYGoaGhxMXFHfa4sxVVpKrwQX4Z+/P8/Wjw+z243TVtcq7kHr24e95n3DzrTQxmRVimL/mF6pJijL9XEy8mUPj7Jurf2ohpRS0Zf/sEz5Ic9DudDB+gBJtvrl7DzyXf0MXSk762wUTo7QwLG4OGA/GhXrycd90MLvnrE0yY+RRc+zF0v/S45i7GhsH+uF2bCVxe5Kp6/GV1xzXuycCoE3loQpf9URdYDcpzJwDOJrpuldS5mfjacp77YTt7Sk6/61U5u3BnVLXc2C/jzjwK+6Ng9OjRbNq0KejnYE9bXFwcf/75J+np6dx77714vV5uuukmJkyYgCRJ7M9rPtsTow5F9kvIlY4W2/tb4HE9FoYMGRL02gwdOpSMjAz8fv9hj5NlOXDcwX+rNEYVqSo82D6WXhYTQ8LM3CT8AcgUFK9qs/PpDMagQvtjpt1OSp/+FMXLFPjzMVyciqbaT+n3G/A6nXh+zsKwoobEtG6s/X4Ba3Yvwi/7yKrZjbOuli+eeYwf7n2Cmod/w7dJKcQf7/Bi3vgOHXv1RG80tcq8BYMWw4hO6Pq1Q9fvQOKQ5Dz9PKkAIztFsfrRcSyeOYI+yUo9Uxm4ZXhKk/Y7imp5d3kWF762PNDNSkXlVERu4karNe1bitlsJjU1NeinqUztHj16cNddd/HRRx+xaNEiFi1axLJly4iKiiI8PJwdO3a0yfxOW44QxnHc9m2I3+8nIyOD9u3bA0pscXV1NYWFhSd5ZqcmqkhVYXi4lV8GduaL3u3oFNsOjWjC6Spn07Z38PrqA3Zbazbwv+IvqfIeeyzmys/+j5/feg2P64DISendj9wdW6lbuAZHtQsKfPiqgu+SCzN28r9/z2LFb1/gj9QhSzLRWS7ydmwlZ+tmPM56QMb7xW7sb21kUmEtgqsSWtkjLBh1iDGh4Dho2b+4Gv8h2aWnC1FWAx2jrWSWHXi+bzqnfSO7g2v4awQQ1Tt/lVMYTejRVfgQw06dOOtu3boB4HA40Gg0XH311Xz00UcUFBQ0snU4HPh8vhM9xZOPVnNgVasFCMa2SXJdtWpVo8dpaWmIYvNVX+bPn09lZSWXX345AFdccQV6vZ6XX365SfuzPXHq7Iy4VmlEVu4v/F6zEpfZQrcuEyna9iUA9ojuJMYNA2BV5TL8so8dtZsZGjH6iGNmb9lI5rpVDLz4CkLtUZTn5bLqq88ASO7eky7DRuKsrcFkDSUsKpryvFx2fZXFwGoRDrnx1RmMJHXvRdGf+xBkGUGS6OrYjmXIjfQYfT4+r4firD1U5uexL9/B8g1VDLjiekyWtslE9x2cNFXjwrs5F8JMiOZT58vuaHj9mj689NMurhyQSI0r2DOs1cAdo1IZ1TkKi0GHSSeqbUxVTmnM/aKpXZLT6HOkKcQwPYY2rpnaHHfccQfx8fGMGTOGxMRECgsLefbZZ4mKimLo0KEAPP/88yxdupTBgwfz3HPPMWDAAHQ6HcuXL+eFF15g7dq1Z11yjSAIiAk2/C1MXhUTbG0yj9zcXGbOnMmMGTPYsGEDr7/+OrNmzQrsr6+vp6ioCJ/PR35+Pl999RWzZ8/mjjvuYPRo5Tt0f1zr3XffTU1NDVOmTCElJYW8vDw++OADLBZL0JhnG6pIPYuRZIkc516i9LGUlm9DMih3prKoJT5mCC53FVERPQL2/cKGkOPcSydL9+aGDOLnN+dQV1GO3+fj/NvuwRYbR9qgc6irqiCpR28Wvvoyu1etYPRNt3LOlTewcPYLdNlZQi0QldKBlN79Kcnaw74tG/G6XWz86XtCTCGM3iVRtG09hkv/gk5vYPzt9wJKDdYNP36H1+Vig+Bhh7CK2MJcLo67ttWfOzHSgq+wWgnibPgi9KbnIQ7p2OrnOhH0axfBZzOUL0W3z4/VqKXW5UMAOkZZeH3JHhZtL+Kn+0ee3ImqqLQAbaQJY9dIXNuPnDBjOSceoYXtflubcePGMXfuXN58803Ky8ux2+0MHTqUxYsXExmptG8ODw9n1apVvPjiizz77LPs27eP8PBwevbsySuvvEJYWNhJmfvJRkyOxJ9bGUiybRatBjExok3mMGXKFJxOJ4MGDUIURe655x5uu+22wP53332Xd999F71eT2RkJP379+ezzz7j0kuD8yPuvPNOOnXqxD//+U8uvfTSQDH/iy66iJkzZ7bJ3E8X1I5TZyE5+csoLtuAN64r6fXphGnDSSoqoqJuH9b4gQxPnYZWo8Xrrcfnd2EyHts/+JL332bH8qWMv/0+UgcOwefx8McXH2GNtNN3wiTmP3gXZbn7MJot6EwmastKlQByQSC+c1ec1dVUFOQBEJnYjvK84DIt3UeOpcs5I0AQaNezD69NvRKf203ipedQNURZ3pF9ElPj78QY0rrtXuvdfn7bXMaw8hIO9inqzumIGHbyioO3Fptzq7jq7T9x+w7E6mk1Aj8/MIKOUS1rLauicjKR6r2UvpuOt7D5BBtTnygirup80kSqyvHhL6jCuyW3eY+5qEHfvx2aFrbDPhpGjRpFnz59mDNnTquPrXIANSb1LGRX5peUlqdTW5MNgF5jQABEyU+MxoZWo8Xnc/L76sdY+udfKa/cdUznGTN1Bnf99xNSBw5RzvvnctZ+t4Al896mqriISTMfpc/4ibgcddSWlTLyhmnc+PLr3PDiq+Tv2BYQqAazhXG33IEhxBw0vik0jAUvPMWC55+kZF8Wsamd0IhaEnr2BED2+nH93zYcFa1fTsvtlfBIjZcivH9k4l6bhew+vePEeifZmDd1IFf2Twxs80kyf1uw5STOSkWl5WhCdETd3gvrqEQ05uCYRG2UCdvFHYm4WhWopzNivA39oA5o7IeIUEFAExuGfmjHNhGoKicOdbn/DMflrkTUGPH66tmT/R01tTmYTNHI9YVEONyEGSOwa6JJ7n0pVTV7sYcrS/mS5MPrcyDLEh5PdavMJaFLd2yxcVgjo7BERKLV6RgzdQYhYTY0opb+Fyk9qDcv+l/QcSm9+/H1S08jHBSMPuK6m0kbMozNi35UjtFtpnpKJBeEP02XsF7YK5PZsOInevWfSGRicqvM/2DCLTqGdrahWV8O3uByI3JZHe5VmQgaAU1YCBVSGcXr1tH5qqswhIa2+lzainNS7ZyTamfhloJAWSq1kL/K6YTGoCVsQntCx7XDk1OD5JEQzTp0iRa17M8ZgibCjD6iPVK9B9nhBgE0ViOC4fTsCKgSjLrcfwazfsvrFJdtRNQY8Uuuw9omxA6nZ5cpaDQH7luqa7JxuSuJierbqvMq2L2TpR+8S+ehI+g/cTIANWWlyJJEWHQMhRm7+PwffyXU4KfLiPOI6j6Eb195BgB7UjuqS0s479a7yNm6mdQBQ4jv0o2vqz6h1ldNV0tvRtjP54vd71KhryLWG8PktCmtOv+Dkeo9SBV1+NLzg3doBJBk/D4vP7/9FEgSCcOHMfjRR9tsLm1FUbWLbzbmIckw5ZwULAb13lZFRUVFpe1Rv23OEPySl517PkfU6Onc8XLSd86nuGxjw77GAtVktON0HWh/ml+0gurabM4d9I+AhyEsNIUwUpBlmZ0rlxFqjyahSze83no2bnsLUTTQp9ttiOLR3bGmL/mFwoxdVJcU03/iZKpLinj/L3chSX6ue3YWsR3TuKN7Onp3OYR3YIezV+DY1EHncM4V1/LNP59l7/o1FO/dw5SXX2ecbhI59XuRkfgyay7FGZnoukdSmbEP0o7lGW0ZmhA9sqeJTPeGunxSqD4Q2G+whbfdRNqQ2DAjt49KPdnTUFFRUVE5y1BF6hlCWcU29uUtBpSyUWUVWxvZxEYNJNrei4TYoeQVriR957yg/XWOPCqrM4iwdQravmH5x6xe8CXuUpkZb82n1r03MH5tXQ62sKPLaO874SJqy0sJi45l5ecfER4Xj8+j1B396NEHMJgt+Jw9uLZ/PdH9puDPOBBTunfjGpK69SR70wY0Wi1dhinZ5tGGOKINcby3bzZ+wYdc6cQxay1dhlzQ5BxaE8nRvJdaVy8z9PLbqfdU0+G21q8yoKKioqKicqaiitQzhPCwVAQ0yEiUlG2hT7fbSN/1f3h99Xg8VQD4fE4c9cW4PXU46osIs7YnMqIbWTk/I8s+TMYoZMmP1+tAp1OSlMord1LsW0y7STZKfhXQGQzYzd1JjBuOKBoIC21c+L0pFs55iaLM3Uya+Sgx7Tsy6YFH+PfNVwMyot5I/8jzMYkW1pT9D1etUoB/cVVf/P/5kIvu/xtagxGf20VkQhL5O7ch+X1oRJG+Eybh93lx1dVRmr2XRF8Uee59eFcXoqnwMvCi42uF2hLkivrD7g+Pa0ek3RLUZUtFRUVFRUXl8Kgi9RTD8ccflP93LhE3TcEyYkSj/bVLl+Lakk7EtGmIlv1Ccgebtr2DwWDD7akiwpZGRHhnRg55ljWbZlFWUYUgaKmuzaKsciv1rlIKi1cDEBXZC5AxGe2kJI1j9aZXMIfEMnLI8wDodRY0Gi0aUcfEmbfjk+owiXZ6dZ3W4mvyul3sWrUCZJl9WzYS074jeqOR8Lh4KgvzCZHNpIYqca/dbSPYuPsXwqJjKdiltALc9cfvhEZFUZGXy44Vy4hKTmHIpVezddli3r59ClZ7FGW5+zBc1wVtBxv+hVkI5R6GX3cTIWG243g1WoZwpBjN+DDErvFtPg8VFRUVFZUzCVWkniLIkoTs9VL4j6fx7tuHY8MGBFkmZMgQkt96U7Hxesm7517wehH0euy3zwCgpGwzbk81omhgzDn/wu2porBkHbFR/TGbYihj2/6zAALhoR3xuKupqtlLeeU2ZNmPy12Bw6H0Dvb5Xaza8DJ6nYU+3W9j9NBXqK7LYd3mOYiinpFDXsBosLX42nQGI+Nn3EvR3j30HDseAEGjYcy02/nqhaeo81Wwp2YjFmM4HS4bRsWiMnxeN9UlRRgtVlZ8+sFBT5RMVXEh3UaOZdXXSvcqqUQCZLTd7QiiBinBgLTDR2RC0vG8JC2/vk6x+MvqoLqZfvYF1XiLaqBHAmLC6RmXqqKioqKicqJRs/tPAXwVFWRdcQVSdTV+Zz1+qx5ttSewv0v6FgSdjtrffqNk1r/w5OeT/PZbmAcNAsDlriIj61tcrgpKK9IRBBFZ9mMyRuN0lTQ6nz2iOxFhndid9TUAsVH9KSpdr+wL705oaDv27lPKQIVZU+jU4TI0Gh2rN76ERqNl5JAXj7nAP0B5fi6iqMUWG8fOlb/zw2v7exYLxKekUpCdgajT4fd6CYuJo7q4MOh4S6SdbiPGYLKEohE1JHTtwRdPP4ovxYiYEkrINi89hoxm0OQrTtgSu3tzDnLB4Ut1CVYjhuFtmMWloqKioqJyBqF6Uk8BvPn5+AoUIVZ8Vxec3cOJ/HQvob8XI1ituPftA0ki7867QJaxXngB7t0ZAZEKUFq+BbenFoD99x37BaqoMRBijsXpLMXnq0ej0ZMUP4K6+kJMhkiy8n4JjFNWuQ23pxqdNgSvr57q2mz2ZH/H0P6Pck7/xxC1xuMSqMVZmXz06ANoRJGb//VmUCHtMcMvp2tqf9Zs+pW1mxZjjbQTl9aZ6uJC9KYQojukUrpvL3XlZaz5+nNErZZpc94hNCqa6JQO5G7bQpeovox49MQs8x+MvnMc7nIHNFfE36RDrvfg21uKtkPUCZ2bioqKiorK6YiayXESkBwOnFu24MzIYPc5w9h38zQEq9IVwxtlDPot19aSddEksq+8CsFiwdkplG3nlrA162P+WPgg/1s0lfVf/A2XuxJZ3i+QJCLDuwMCWtGEwWCjtnYfPp+S4FNdk43BEEaf7reh1YUgSV4ARNEEQGR4F+JiBgNgMNholzgOAFtYR6zmhOO6dlmSkGUZWZKRJIlOg4fRY8z5pA48hyibMrYt1A5AbXkZu1YuCxx79ZPP07HfAWEuy+By1AFw+aNPM+OtD5hwx30nXKACCEYdxjFdEVOjQSeCSQdmvbJTq0FjMYJfwl/UOo0RVFRUWobP5yInfylrN89h1YaX2LD1DYpKNyDL0pEPPg6mTp2KIAjcfvvtjfbdeeedCILA1KlTg2wP/ZkwYULgmJSUlMD2kJAQevTowdtvv92m13C6UJ2VxaY33mT5I4+y4tHH2DpvHo7i4pM9rWNi1KhRgddZo9EQExPDlVdeyb59+4588BmIutx/Aqj56WdKZs3C2Ls3ushInFu34ly/Ho3djlRWFmTrjTbiHd6JuNghVL/xDr5QHWKNFwFAFCm9rh11Q2OUOpwNXkiNw0e3ftPYuusjILj7kYBIbHR/CkvWBLaFhbbH53MiCCK20A7kFS5HuV9RPrTPGfAkMhIbtvwHt6cSQdYiao0kJ45AI4gUl24kPnYIHdtdeEzPR/HePYhaLfbkFAB8Xi9VP+7Fs74Ud2gdP2yei9vjBFkmJMxGfU01Y6bOoO+Ei9iXvokFzz2BLMvYYuOpKirAnpxCco/ehITZGHzJlcc0p7bCX12PEGIAlxf/vnLExHA0tpCTPS0VlbOCwpK1pO94H5+/cby4yWinf8+7CbW2fkc6UITnkiVLqKmpobCwEJNJcQK4XC7i4uIIDQ1l9OjRvP/++0ydOpXi4mLmzQsuC2gwGAgPV+LYU1JSmD59Orfeeit1dXW8//77PPfcc3z66adcffXVbXINpzo+p5O1/5xF4Z9/Nt6p0dD+ggn0vv12NAd1KzxZeDwe9Hr9Ee1GjRpFp06dePrpp5FlmX379nH//fej0+lYvnz5CZjpqYW63N8GVH72OaVvv40+NhZ/TTX+ujr8RcV4c3MB0MbGAiCVlweOCRk+HNFqxbl5M7qvtqC5rju+9+4k17ORqMIwOgoDKXnln+gKG2pyHrRMbrLEkJwwisLitZRX7QiaS7ukccRE9g6IVFE0EB3Zh4yGeNTUlEmUV+7E6SpFELQNonUFOflLAmPIgg+fvy4QpwqQnftrkEitd5ZSUr6FhJghgfJVzRHT4UBh+Kpfsshc9CcGnwGbIRqxxMioqKv5Nf9Dhl57HQMvuhSvx40hRBmzXc8+XP/CHDLXrSZr0zoAynKyKcvJBqBDv4FENYjfUwExrEGQ6kQ0PY7PC62iotJyikrXs3HrWygJo41xuspYvfEVhvZ/FIs5rk3m0K9fP/bu3ctXX33F9ddfD8BXX31FUlISHTp0CLI1GAzENnw3NIfVag3YPPvss3z++ed88803Z6VIlXw+/vzH05Ru2dKMgUTWD//D7/YwYOYDrX7+UaNG0aNHDwA+/PBDRFHkjjvu4JlnnkEQBFJSUrjlllvYs2cPX3/9NZdccgnz58/njz/+4G9/+xtr167Fbrdz6aWX8sILL2A2H/jeDAkJCbzOcXFx3HXXXU165M8G1OX+VkL2+6lasICM0WMoeuop/AUFODdswLMnE9kRXEfTMm5sw0EHPjyl2lrin38OQ6pSGF/Q6XGHNnQt6pZAxHXX0eGbr+l5y8t0SB5PiCmGEFMM0ZG96dVPyfIf1PdBRgx+jsS4/aWrBDomTyDclkpi3LkkxJ7DuOGvkZpyEZ07XE6X1KuIjxlEaspEAGKj+jG0/98CYQFWcxKi/+BuShqQQSPpSY4fHXRNG7e+yfbdH7E949MWP2e+ajd1S/KIEZOwGaLxyh40gohNF02INpTNv/yARqsNCFQAb0k9oS4b51x5HefPuAeD2YzYcHeqM5qwxbbNl42KisrpgyT52b77Y5oTqPvx+hzs2rugTedy8803B3lI586dy7RpLS/hdziMRiNer7dVxjrdyFv2e/MC9SByfv2V8h07jmh3LMyfPx+tVsvq1at57bXXmD17Nu+9915g/yuvvEKPHj1Yv349TzzxBOnp6YwfP57LLruMLVu28Nlnn7FixQruvvvuZs9RUVHBF198weDBg9vkGk51VE/qceKvc+DN2Uf+Qw/hydzbaL+ha1fcu3YBIBiNCEYjxtRUIm6Zjjt7H/XLlyO73bg2b8aduZfEOXNw7dyJqXdvbL46CsO7ERPVDwB9u3bo27WjC/3pktr4zlkQBCzmOHp1nUpy/LkYDBEYDGEA9Op6c5BtxwZhCpAUP4K46EGIogGA7p1vJDqyF/aI7uzLX0JG1reEhXagumYvCCAJHvbm/EioNRGHs5iUxHGYQ2Kprs3GEnJ4T8DBiFY9YqQRf7kLGRmtxQAOGbfspNZbTlJsryB7yeOn5D+bkN1+bFekUVi5C7fDEdjfafA56PSGFp9fRUXlzKSkbBMud+WRDQ+yNRrapjzcjTfeyCOPPEJ2djaCILBy5Uo+/fRTli5dGmS3cOFCLBZL0LaHH36YJ554otGYPp+PDz/8kPT0dO644442mfepzt4ffmi57cIfiOzatdXnkJSUxOzZsxEEgc6dO5Oens7s2bO59dZbARgzZgwPPvhgwH7KlClcd9113H///QCkpaXx2muvMXLkSN58802MRsUp9MYbb/Dee+8hyzL19fV06tSJn3/+udXnfzqgitTjZO/kyfjy8xttt4wfT9hll2LuP4Dyd96h7o8/sN92K/kPPkTR3/9B/CsvE/Pgg/jdbkpnzUJjNmPs3k0Jiu+niFKDGEZK0rhjmtfRtirVag94THVaE/GxQwBIaz+ZhNihuFxVrNr4kuL9FcBkimR9+r8BGZ02hN7dbqFL6lWHrZ9a8dkunFvLiLimC/okC7W/5+OvPVBqK92xnPbRvVm96RsARk25Neh4QSOgMYr43X6qvskk0hRGdHwHLHF2zrv1bsw2tQapiooKlFftbLGtLEtUVO4KfOa1Nna7nYkTJzJ//nxkWWbixInY7fZGdqNHj+bNN98M2hYREVxJ5eGHH+bxxx/H7Xaj1+t56KGHmDFjRpvM+1TG7/FQsbPlr3FLPK7HwpAhQxCEA6F3Q4cOZdasWfj9Sm7IgAEDguzXr1/Pnj17+OijjwLbZFlJIs7KyqJrg5C+/vrreeyxxwAoLi7m+eef5/zzz2f9+vVYrdY2uZZTFVWkHgey34+voKDRdjE2lri/P0X2FVfiq6gg5aMP8eTmkH/vfWisViS/H22UUoZINBiIffTREz31oyLEFE2IKZpx576KqNHh9dWjFY2s2vASDmcxoZZkBEGD0WBD9kvIPhmNoXGgumt3BbJXonpRNr7i+v29BUAjkOPfxY7slZT48ih3K89pSXYm4bFxrPr6M6KSU+gybCQx9/Wj6NX1SNVe5FqJqx96Hn28pdG5VFRUzl72VyxpKf6jtD9apk2bFljS/c9//tOkjdlsJjU1tcl9+3nooYeYOnUqISEhxMXFBQmkswnpKEMcjta+tTg4zhRAkiRmzJjBvffe28g2OflAAl9YWFjgvZCamsp///tf4uLi+Oyzz7jlllvadtKnGKpIPQb81dU4t2zBW1ISFFcKgCAQdecd+MrK8BYWgiThyckNhAJYx40l6r770B0hQP5URK9TxOD+sIDhg/6OJPnRaBRBKnslil/dgK/KTdRtPTEkhwYdb7ssjeqFew8IVFB+yzLxaV1oF9qXQaMuZcufv+L1OUkbOJQti39mzTdfgCDQvu8ADCFmdPFW3NUVmHpGqgJVRUWlESZD5NHZG4/O/miZMGECHo+yajR+/PhjHsdutx9RyJ4NaE0mdBYz3jrHkY0BU1Tb1KZetWpVo8dpaWmIzVQT6NevH9u2bTvq13D/eE5nM10Nz2BUkXoM5N11N/Xr1mE4NMZFFNF37kzRk09hHj6cpLffxldSgnX8+Ri7dKZuxUrCLp6EGBra9MCnIfsFKoDk9uGrcIIEvlJnQKTKkkz9+mJ8lS78le4mx9HnaRjiGw/fO+hvHw2yRPm/tpBwcWdMoWFEtWuP3qiUcLHf0A1/tRtthLHJsVRUVM5uEuLOISP72xbVQjUZ7USGd2nT+YiiyI6G5J3mBIzb7aaoqChom1arbTI04GxH0GhIHjuOzG+/bZF9yvnntck8cnNzmTlzJjNmzGDDhg28/vrrzJo1q1n7hx9+mCFDhnDXXXdx6623Yjab2bFjB4sWLeL1118P2NXX1wfeC8XFxTz77LMYjUbOP//8NrmOUxlVpB4Dgl4HgHvHDkwDBuDJzMRfWQl+P57MTGXfvn3ULV+OxmzGV1iIPiWFiJSUkzjrtke06LHf3ANfhYuQvtGB7bVLc6n5ZR8IoIs34y1ouPsVBfArLlXZdaC+q1TnQ3YpjQkizDHc+e6B+B0AQRRUgaqiotIsJmMksVEDgupDN0f7pPMRhLYvdBN6BOfETz/9RFxccHWSzp07s/MoYi/PJjpOuoisH39E8ngOa2cICyN57Ng2mcOUKVNwOp0MGjQIURS55557uO2225q179WrF8uWLeOxxx7j3HPPRZZlOnbs2KiE2Lvvvsu7774LQHh4OL169eJ///sfnTt3bpPrOJVRi/m3EFmSyL/vfly7dpHwysvk3n4H/spKxMhIBKOxyeSp/VjGjSXp3/8+gbM9dZCcPornbMBf7QZRIKRvNM7NpcheSRGshQ6QQZ9sxV/jwdg9EuvQeNy5tcheP5ZBakkpFRWVo8fnc7Fm0yyqajKbtUmKH0nPLjedwFmptCYFf/7JmhdfajbmVGexMOyZp4loA3E3atQo+vTpw5w5c1p9bJUDqHVSW4hUW0vtokV4c3JwbtpE6KRJAPjLy/Hl56M5OAtTpws6NqR/cIbf2YRjTaEiUAF9qo36dcXIXmUJzlugCFRDlwis57Uj/MpO+EoclH+6E9mjClQVFZVjR6s1MrjvQ3TqcHmjmNNQazt6dZ2uCtTTnPihQxn5z1dIGD4cQXtgYVg0GEgZP57Rc2a3iUBVOXGoy/0tRAwLI/bpf+DetZuwyy5DtFox9uhO4eNPgMeDLiYGd0WFYtxwV6fv3JkOC75E0Gpxbt2GPqUdouXsSvQxdLQhhurRJVmDumQF0IB7TyXunRVBm6sKM7EMVkWqiorKsSOKelJTJtKx3QXU1OXi97vR66xt1mFK5cQTnpbG4EcfwVVVhaOgAEGjwZqUhM58+M6HKqcH6nL/4di5E3btgIGDIT4eAFdmJuVvvIl59CgKH3wIgLiXXsSTm0f5IUv6poEDaPfBB1TMe5+Sl1/G2L077Rd8eaKv4pSh6ucs6n7LA0CMNiJVe9F3DsOzuzooJhXAfE484RcfXa1XFRUVFRUVlTMHdbn/cGzaAEVFsC09sCln6s3U/PADhY8cqG1qSEvDdtmljQ53rl1H/v33I9UrbUb3/z5bOTizX3L6kd1+3FsqGglUgJA+bVMyREVFRUVFReX0QF3uPxz9Biie1B4H2nPqYmPxl5YiaLXIXi+CyUTeHXcSMmhgk0PULl2Gc+s2oh9+mNCJF56omZ9ySG4folV/YMNh/PfGXvZGNVZVVFRUVFRUzi7U5f4W4qusRGM2I+h01Pz0MxUffIBr48YDBuYQcAR7SrWJifjylOVty5gxJL3RdKeRs4HiOevxFtWjCTcg1XgCpaeaRITE5849cZNTUVFRUVFROeVQl/tbQN3y5WQMG87eiyaB30/VJ5/g2rgRTVjYASNHPZbx54MooktOxnb11diuvALRHokQEkLEtJtP3gWcAvgcSjKZVOkOEqiGzuGIEYZg48ar/yoqKioqKipnGapIBWRZpuDxx8m++ho8DZ7Pg6n8/AuQJLw5OUgeDyEDB4IgYD73XDiod3Ldz78QMngw7T//jKoFCyibPQfZ40Wur8e7bx/O9K04N206gVd26mAZ1HQbWPeuSvwVwV2oTH3VeFQVFRUVFZWzHVWkAv6KCqq/XIBz82bqlvzWaL9lxAjlD0Gg6OlniLrnbtp98gm1P/zQyLZ+9WqEkBD0SUmg1SK5XAB4CwrJvvpqsq+9jtply9r0ek5FTN3tCAZR6TLVdFdA0AuIEUbCJrQ/oXNTUVFRUVFROfU4O0VqcRGkbwGf0npTGxlJ1H33EjpxIqGTLmpkbh4yWPlDlqn55hvK/vtfBL0eRBFEEcv48YROmoRgNmPo1AnZ46HDd9+ijY0Fj4eQc4Zi6NwJZBlkmfwHZuKrqGh0njMZfbwFBJSl/uaW8z0y/goX7r3VJ3JqKioqKioqKqcgZ7ZIlWXIzwOHA/78A9avU7b9sBD+WAlbNgdM7XfcQcKsf6INDweg5pdfyLr6anLvuhtZkrFOGI/GagWg9JV/UvfbEmIefRR8Pup+/pmIG65Hdrtx79hB0dNPI+h0hF00ETEigvCrrqb6hx+UcwP4/cgNAvlsIuzC9hjSbIiRxqDtgknxsBp7RGIeHIupe2QzI6ioqKgcHRVeH//JKeGSDRmMW7uLazdn8kVRBW5JatPzTp06FUEQuP322xvtu/POOxEEgalTp7bpHM4WasrryVifz+ale9mybC+Zmwtx1LhO9rROOVJSUhAEAUEQEEWR+Ph4pk+fTmVl5cmeWrOcudn9fj8s/Q32ZIDFAnV1yvYJF8DiX5WuUOePh/Yd8BaXUPnZp+jj4wm96CI0RiN7xp2HtyE+1XbN1ZgHD6Z+3ToqP/pYGUcQMHTtinvXLvD7MXTrhnv79sDp23/7LcbOnQKPs2+4Aee69egSE0l6600Mqakn7Kk41fDk1lLyn00AaMw67Lf1RB+jdgdRUVFpXT4uLOfR3Xm4pMZfc9F6LXN7tGdAWNt89kydOpUlS5ZQU1NDYWEhJpMJAJfLRVxcHKGhoYwePZr333+/Tc5/NuDz+Nm1NpfqsqZrkNsTw0jtE4dGPPn+OI/Hg16vP7LhMeD1etEd0o69KVJSUpg+fTq33norfr+f3bt3c9tttzFkyBD+7//+r03mdryc/FeuLfB4YOVyRaAC6PVgt0N8AlRUBNqWyot/xfn5ZxS/+CLlb7xJ4eNPkDP1Zhx//EH49dcjGBWPn2vHDvIfmEnlx5+gT0sDnQ5kWRGlfmXt2pufHzSFsjfeoOKjj8i68krK35+PLikJAI3Nxt5LL6Pk1VdP0JNx6qFPshL78EDiHh9M/BNDVIGqoqLS6iwoqmDmztwmBSpAicfHNZsz2V7nbLM59OvXj+TkZL766qvAtq+++oqkpCT69u0b2CbLMi+//DIdOnTAZDLRu3dvvvwyuDvhd999R1paGiaTidGjRzN//nwEQaCqqgqA8vJyrr32WhITEwkJCaFnz5588sknbXZtJxvJL7F9VU6zAhWgLK+ajA35ze4/HkaNGsXdd9/N3Xffjc1mIzIykscff5z9fr+UlBSeffZZpk6dSlhYGLfeeisAf/zxByNGjMBkMpGUlMS9996Lw+EIjJuSksIzzzzDddddh8ViIT4+ntdffz3o3IIg8NZbbzF58mTMZjPPPvssAG+++SYdO3ZEr9fTuXPnJoWn1WolNjaWhIQERo8ezZQpU9iwYUObPEetwZknUjN2wftzlZamADYb2MJhwECYdDF06QIdOiKbzQh+P/4NGwKxqQDOTZvImX4Lpp49sJyr1OqU650BYerJyCDq3nuIe+lFTH37En7jDejbt0eqrkYIO1CA3rltK8XPPIsrfSslL75I7a+LAfAVFIDXi2PlHyfsKTkV0YYbES1tc1epoqJyduOVZP6RWXBEuzq/xAt7C9t0LjfffDPz5s0LPJ47dy7Tpk0Lsnn88ceZN28eb775Jtu2beOBBx7ghhtuYFlDkm12djZXXHEFl1xyCZs2bWLGjBk89thjQWO4XC769+/PwoUL2bp1K7fddhs33ngjq1evbtPrO1mU5lVTV3nkG4zyglqqyxxHtDsW5s+fj1arZfXq1bz22mvMnj2b9957L7D/lVdeoUePHqxfv54nnniC9PR0xo8fz2WXXcaWLVv47LPPWLFiBXfffXfQuK+88gq9evViw4YNPPLIIzzwwAMsWrQoyOapp55i8uTJpKenM23aNL7++mvuu+8+/vKXv7B161ZmzJjBzTffzG+/NU4G309+fj4LFy5k8ODBrfvEtCJn1nL/+nWwbm3wNqsVamvBaIIbp4BG0eVySTHV//wn5Sv/IOrJpwg5Zyi1P/1M0ZNPKrGjWi2Rt0xH0OoIGTIYfbt2FD3+BL7ychL//Tq62AMllSo++D/K580DZHyFRQDoUlKQ6urwl5UFTcfQuTPurCyM3bqhi47GtXUrif9+HWO3bm361KioqKicLXxTXMnt2/e1yFYDrBrSlWST4Yi2R8PUqVOpqqrivffeIzExkZ07dyIIAl26dCE3N5dbbrkFm83Gf/7zH+x2O0uWLGHo0KGB42+55Rbq6+v5+OOP+dvf/sYPP/xAevqBFt2PP/44zz33HJWVldhstibnMHHiRLp27co///nPVr22U4HNS/fiqG5Z3GlkQiidByS26vlHjRpFSUkJ27ZtQ2goRfm3v/2N7777ju3bt5OSkkLfvn35+uuvA8dMmTIFk8nE22+/Hdi2YsUKRo4cicPhwGg0kpKSQteuXfnxxx8DNtdccw01NTX873//AxRP6v3338/s2bMDNsOGDaN79+688847gW1XXXUVDoeDHxoqEaWkpFBYWIhOp8Pv9+NyuRg8eDA//fRTs++hk82Z5Umtb+JuqbZWEaYup5I81YAQHUPoU38n6bPPCR1/PlqrlfArryBiyhTFwOfDsWIldb/9Rs6NU6j95RfcmZkgSYhWK2VvvU3h3/+OVF+PdewYDKkdEUwhgfG92dnIzsZ3ed6SEvB4cG3aRO0vv+AtKMDxx9ntVVVRUVFpTVZXt9xzJgFrj8L+aLHb7UycOJH58+czb948Jk6ciN1uD+zfvn07LpeL8847D4vFEvj54IMPyMzMBGDXrl0MHBjcenvQoEFBj/1+P8899xy9evUiMjISi8XCL7/8Qk5OTptd28nC75daLFABag4TEnA8DBkyJCBQAYYOHUpGRgb+hjDAAQMGBNmvX7+e999/P+h1Hj9+PJIkkZWVFTTOwQwdOpQdO3YEbTt07B07djBs2LCgbcOGDWt03EMPPcSmTZvYsmULixcrK7wTJ04MzPlUQ3uyJ9Cq9OoNByUvAYon1RoKBflwSDanxmxGbw6Oh4x55G+YR42k5n8/EnbxJHJvvwNkGeemzXjz8vACjrXrKJ0zBwDBYKDqk0+RPR4AQq+5Gsfvv+MvKAzUSA0gCMgOhyKaJQmNzUbYRRdhu+qq1nwWVFRUVM5qvM3EoTZr38YLitOmTQss6f7nP8HtsaWG76UffviBhISEoH0Gg+LdlWU5SAzt33Yws2bNYvbs2cyZM4eePXtiNpu5//778TR8N51JyEf5+p6sBWPzIfpCkiRmzJjBvffe28g2OTn5sGMd+vofOnZTNk29b+x2O6kNidtpaWnMmTOHoUOH8ttvvzFu3LjDzuFkcGaJVFEbEICA8ve482HJrxAWpsSlejxKItVhMA8aRMV/55J3193IDgfGXr2Ie/of6NunIIaFYRl2DpaxY/EVFuIrLAoIVICaL76EhizO/UlViCLG3r1wbdio2Da8aUJ69yb28ccOPb2KioqKynHQznR08e7JxtZd6j+UCRMmBMTi+PHjg/Z169YNg8FATk4OI0eObPL4Ll26BJZ697Nu3bqgx8uXL2fy5MnccMMNgCKIMjIy6Nq1a2tdximDqNWg1Yv4PC3z/hlDjpz5fiysWrWq0eO0tDREsemONf369WPbtm0BkXg043bp0uWwx3Tt2pUVK1YwZf9qMEqS1pFe//1zdTax8nsqcGaI1PTNUFwCnTtDv/6g1cKa1YpYrK2B6obi8L8vg+ws/N17UPzjT1jOHUHohPHUr1uHLiEBXVwcAFWff45jxYrA8NqoKDQmE1F33hnYFv2Xv1Dz4/8IGTQIjcmENj4OjcFA6ZxXwe1Gl5iINy8PQ5cuuHfuxLM748B8G+7qQi+7tO2fGxUVFZWzjKtiI3gpqxBfCxxoHU0GhtratsKIKIqBZddDBYzVauXBBx/kgQceQJIkhg8fTk1NDX/88QcWi4WbbrqJGTNm8K9//YuHH36Y6dOns2nTpkDpqv2estTUVBYsWMAff/xBeHg4//rXvygqKjojRaogCEQn2yjYU94i++h24W0yj9zcXGbOnMmMGTPYsGEDr7/+OrNmzWrW/uGHH2bIkCHcdddd3HrrrZjNZnbs2MGiRYuCMvhXrlzJyy+/zCWXXMKiRYv44osvAnGlzfHQQw9x1VVX0a9fP8aOHcv333/PV199xa+//hpkV1tbS1FREbIsk5uby1//+lfsdjvnnHPO8T0ZbcTpL1KLimB/TGfmHuV3uxS47gYlI1+rhf4DlL/3KPs9GzZQveAran/6GcnppPCRRxAjIkhb+huCXq/UMBUEkGXE2FgErZbq778nbNIkfJWVlL3xJnW//4533z60cXFETLkRy4gRGDp2xNSrF5qwMPLuvidomk0V73es/IOwQ+6qVVRUVFSOjxiDjitiIvi06Mid/e5Mjm60JNoWhIaGNrvvmWeeITo6mhdeeIG9e/dis9no168fjz76KADt27fnyy+/5C9/+QuvvvoqQ4cO5bHHHuOOO+4IhAQ88cQTZGVlMX78eEJCQrjtttu45JJLqK4+Mzv4xbYPpyirEsl/+KYMeqOWqMSwNpnDlClTcDqdDBo0CFEUueeee7jtttuate/VqxfLli3jscce49xzz0WWZTp27MjVV18dZPeXv/yF9evX849//AOr1cqsWbMaeeAP5ZJLLuHVV1/llVde4d5776V9+/bMmzePUaNGBdk9+eSTPPnkkwBERUUxcOBAFi1aRGTkqdlE5/TP7nc44JOPwX+QCOzRE4YNb2y7cwds3IjLFk7uK69gHTsOU88eFPz1YcQoO2lLllD8wotUffMN5iGDqVvyG5rQUKSaGgSzmdRffiZn6s24MzICInY/mpAQUn//HdGi3JF7cnNxbd2KsWdPan7+BUGrpXzuXPzFxYFj2n/3LcZOnRpNU0VFRUXl+HD6Jaak72V5ZV2zNnckRfFUakKz+09lnnvuOd566y1yc3NP9lROGlUldexck4vkb1rG6Awi3Ya2wxxmbHL/8TBq1Cj69OnDnIb8lNYiJSWF+++/n/vvv79Vxz1dOf09qWYz3Hyz4lGtrYXYWAizNW27Zw/UVGM0GEhbsiSwWZ+SgjY2FkGno27pUuT6esSICBL/8x/y7roLAK3dTvU33yoCFbCOG4t5xEhKXnkFqaYGjdmMO2M3JS+9jGX0aELHn4/k87H3oknok5LwFBYi19Wha9cO7759GLp1UwWqioqKShthEjV83KsjHxaW835+GbscBxJZzw23MD0higlRbeNhawveeOMNBg4cSGRkJCtXruSVV15pVF/zbMMWbaHXyA4UZJZTllcdEKtanUh0chhxHSMxmNomHlXlxHB6i1RZht27wGCE6iooK4XkdoHEpIPxFhZSt207oXExiL16Be0zHfQ4/qUXqV28hIibp1L3++9KuIDPFyg1VfPjjxi7diXumacBCL/yCtx7sxDDbZS98SbOTZtw7dxJ2X/+g+z1Kp2p9u5V6qPu3En0zJnoEhMwtG/fpk+NioqKytmOTiNwc4KdmxPs7HO6qfNL2HVaYgynn3DJyMjg2WefpaKiguTkZP7yl7/wyCOPnOxpnXRCrAZS+8TTvnsMLqcXQRAwhuhOiVaoKsfP6b3cn50NP/8YvG3wEOjTt5Fp4RNPUvXFF2hjYkhbtrTJ4So/+5zSV1/FdvXVmPr2Je+g2BJjz56kfPoJQjNZewBFzz1H5f99iLFPH1ybNgFgu+46rKNHYR4yBG9JCfqE03NpSUVFRUVFRUXlRHL63mosWwp/rFA8nWYzdOoMUVGQktKkuWXkCDRhYVjPP7/ZIcveegt/RQXlb7+Nc8P6wPboJ58AYFf/AdQfUvbDW1SkeEwB985dAMged2C/e+dO9CkpVH35JZljx1HwyKPHcrUqKioqKioqKmcVp6dIlSQlCaq2VkmSuvZ6GD2GspIysu+4C9euXY0OsY4bR+fVq4h9rHmRGH7NNSAIGDqlYeyqtCkVo6OxTZ6Ma8cOZJcLZ/rWgH3FRx+xZ9Rocu+4A29REbF/f4rIW28lYdYsYp97FgDnhg3k3jaDys8+Vx6nb2nNZ0JFRUVFRUVF5Yzk9F3u37kDCguV5f0QpR3pzl69kT0eIqZOJeZvDx/2cNeOHXiLi7EeUp5Bcrmo/Oxz3BkZ1P70E1JdHbFP/wNdbCzO9HQsI0di6tEDgOIXXqRi/vxApn/4jTcGieCS2XOo/OwzpKoqAESbjeQP5qsJUyoqKioqKioqR+D09KQCdOkKo8cEBCpA9IN/wXzuuUdsM+qrrCT7mmvJu/0OKv7vQ+rXH1jalxwOcl/+mF0/J+MUla4QGpMJ8/Dh1K9dR/YVV1L88isARN13L+ZRowKlqOo3bAiM4y0uQXa7FYGqbchPEwSlBquKioqKioqKisphOf08qeXlsDcTunYDi+WYhvDXOcicMAF/eTmCwYDscpEw+1+EXnABsiTx6yXbqartSnj7evqe+yVlb7yJGBEOCHj37QMg8tZbsN91F/l/eZC6xYsBCBk6hIgbbqB28RKqv/oKQ9euuHfswDRoEJFTb8KQmor+CP15VVRUVFRUVFRUTkeRuuALKCvDHx2NPGoM2vBja3fmr6qidncZa/+SgbF+M3b3RyTMmkXlRx9RqrmC7PwL6TA4E9OXkwPHRN5zD+UNrcsEgwFj1644G7L4ASxjxwYEKwAaDVEPP0zE1VehMbZ+MWEVFRUVFRUVlTOV02e53+eDrekQFoYMlH76OVkXT8bvcJBzy61kXnQRnry8Fg8n2mwUZXagSns+RaEP4fcbqF20iPo1azCv+isXzyskdVgmAILRiHnsmIBABZDdbrwlJcp+k4l2n36Cr7gYNBo0NpvShlWSqP7yS1WgqqioqKioqKgcJaePSN2aDitXwN69lFZUYe2Uhr1fH6oX/oBjxQo8ezKpX7uu0WGevHyyb7iBwn/8g0OdxgnDBMI7QdLAEuIfe4CYvz1M6IUXEnnrregTEwm94ALaffIxHX/8H9rwiEZj+woKQKej3f99oBTx37oVJInk//sAbYTi4ZVdLiSXq9GxKioqKiptT73bz/bcOhZvKeeXjWUs3VpBZlE9Xt/he74fL1OnTkUQBG6//fZG++68804EQWDq1KltOoezAUmSWbKzmFvmr2P0P5cyZtZS7vt0I2uzK0721I6JUaNGIQgCgiCg0WiIiYnhyiuvZF9DqOHZxumz3L92NTQkJjnLyjHZIwGo2JtF8bffg0ZDp7VrkGprEfR6tBGKqKyYP5/iF14EIO2PlYHt+/Hk5uJYsYLQiy5CtFqbPb3s81EyZw6yz49n716cu3YhFReDIBB+ww3/3959x1dZHvwf/9z3WdmLJBBGiLJBQPZGEAEVtxa3BbRaq1UffbSO2p91PLX62Gpbta0TZ6lCfaRiWUIxoDJFRkBWIKwEyJ5n3PfvjzskhI1Nco74fb9eMedc97pOwku+XJPyzxcRyN1O9IABeDMzKZk+ve7alg89SMqPf9yoPw4RETm+zXsqWLu9nKP9JedxGQzsnEh6oq9Jnj1p0iQ+++wzSktL2bNnD9HR0QBUV1eTkZFBQkICo0eP5s0332yS5/8Q7Cur4Zapy1i9s+Sox8d0TeeP1/Uhxhv+zTX9fj9er/eE540aNYrOnTvz+OOPY9s227dv55577sHj8fD55583Q00jy/enJbVFat1LMzGRYGUlANGJibhSUmj50EMEhVHHxQAAJVdJREFUduxg89hxbD5vLNuuvIrCt94iYcIE4sePJ/WOOxoEVNu2KZo5ly0XXMjeXz9OwTPPULVmDRsHDGTblVcRLGn4h7509mzKZs+h6O23qVi0iFb33Uvy9deDbVP09tsEcp1/5cQO6I8rvn5ClxEdTVTPhtuwiohI09qWX8maYwRUgEDI5suNxRSVB5qsDn379iUzM5MZM2bUlc2YMYN27drRp0/9zog1NTXcddddpKenExUVxfDhw1m2bFnd8YULF2IYBvPnz6d///7ExMQwdOhQNh62JvjLL79Mhw4d8Hq9dOnShbfffrvJPlu4VflD3PT60mMGVID5Gwq4492VR/SiNoZRo0Zx5513cuedd5KUlESLFi345S9/WfesrKwsnnzySSZNmkRiYiI/+clPAJg+fTo9evTA5/ORlZXFc889d8S9Y2JiaNWqFRkZGQwePJg77riDlYesHvRD8v0JqWlpznqkgO/qq+HiS/DHxZM/fwGhwkICe/cQKiklqVtXkrp0pnrdOvb/9RXcqam0feF5km6+g+rC+j+oi/7bYt7vzqHQcwnl3sF4s7Ko+no1VlkZ1evWkfujhstYlUyfQSAvz9kW1ePB07o1SVdegTs9HVwucLlImTKFrZ+34otFd7N3+L/J/HguXVYsJ6bvkdu0iohI0whZNuvzyk/iPE7qvP/E5MmTeeONN+rev/7660yZMqXBOQ888ADTp09n6tSprFy5ko4dOzJ+/HgKCxt2WT/yyCM899xzLF++HLfb3eA+//jHP7j77ru57777WLt2LbfddhuTJ09mwYIFTfr5wmX6yp3k7Ck94XkLNu5j8eYDTVKHqVOn4na7+eqrr/jDH/7A73//e1599dW6488++yxnnXUWK1as4NFHH2XFihVMnDiRa665hjVr1vDYY4/x6KOPHrc1vbCwkA8++IBBgwY1yWeIdN+f7v6iIvhgmrMm6eVXQno6AMUffkjR36bhz8sj7dqrSYl2Jinl52zEN3w4SVddRaDS5tOryvBX+Rj+aDHx3dP5100WVgBMswbL8tGx5wLic54hVFKCVVqKq0ULOmV/jlEbjCtXraLwzakkXn4Z0b16YVdXYyYk4oqLJZCfj3/7dnb/4kHWB1/B784CoO9/GXS45Pvz7wARkdPBjn1VrNhy4gBz0NizWxAX1bhdwpMmTaK4uJhXX32Vtm3bsmHDBgzDoGvXruTl5XHLLbeQlJTEiy++SHJyMm+++SbXXXcdAIFAgKysLO655x7uv/9+Fi5cyOjRo5k3bx5jxowBYNasWUyYMIGqqiqioqIYNmwYPXr04K9//WtdHSZOnEhFRQWffPJJo362SHD+84vYsLfspM4d36Mlf7mxf6M+f9SoURQUFLBu3bq6nPDggw/y8ccfs379erKysujTpw//+Mc/6q65/vrr2bdvH3PmzKkre+CBB/jkk09Yt25d3X2XLFmC1+vFtm0qKyvp3Lkzs2fPJusY276fziI3Qdk2bNsKRYWwrwC258KEi+HSy+sCKkDSVVfhz83FKilh//vTICEB4uJo+dRTJF11FQDB8iD+Si82bg7MXsGyZ5yAGp0OvmQPAPnLYM+Bc7BKS0mZPImsv71f9wcPIKZPH9q+8Dzxo0ZRvW6dM6Tg0kux/H48LVtSOvOfBPfsIaP8GbwxVbijoWij3STdDCIicmyFp9iF35Rd/qmpqUyYMIGpU6fyxhtvMGHCBFJT64evbdmyhUAgwLBhw+rKPB4PAwcOJCcnp8G9evWqHzqWkZEBQEHtKjM5OTkN7gEwbNiwI+5xOqjyh046oAKs2lHcJPUYPHhwg5wwZMgQNm3aRCgUAqB//4bB+Fi/o0OvASfMfv3116xevZrs7Gw6duzIuHHjKCs7+c98ugj/aOKjCQZh40bIXgSm6SznVFMDPXvB0GFHnB599tlUZGcTP24sXHv9kcfTPfQePoOy9fl0uPViKqfDPqCqAIY+bpH72BvsjrqZsqjRpLTLJ/X223ElJBy7egX7wLIIFhZCIABeL/Hjx1OW/QWlaQ9ghqLxF8K2WdDxckjSJlMiIs3mVNsGrKad6M+UKVO48847AXjxxRcbHDvYkHFo2DlYfniZx+Ope33wmHVI5U/mHqeD4Cn+woJWeBqLYmNjG7w/2u/jaA1ZiYmJdKzdnbJjx4689tprZGRkMG3aNG655Zamq3AEiryQmpsLc2dDYqLz3rLAxhmPmpaGHQyy98knqdm6lYSxY0m+/nravfJXp4v+4DVH0enJq+tedx6/h+2fJmLjxs5dSe+pkyj5hYVZtovo+CqsysrjhtTEKy7HjI3F2z6TmsJqVj1lE91qCB1f+4jVNzvDDeLagicWDJcNnH7/kxARiVRxUa5TOz/61M4/Veeffz5+vx+A8ePHNzjWsWNHvF4v2dnZDbr7ly9fzj333HPSz+jWrRvZ2dncdNNNdWVLliyhW7du//kHiDBxPjepcT72l9ec1PlnpMae+KTv4MsvvzzifadOnXC5jv7nqXv37mRnZzcoW7JkCZ07dz7mNUDdsaqqqv+wxt8/kRdS13zjBNPSUkhLh7JSGDAQOnUGj4eSjz+m+G/TAKhaugx3aipxo0dTtXYtMf36ndTC+f6V/8YT7EfITCHvub+zeVlvuk+spuoXF1ADlC9YQPK11x7zesMwSDh/PMGiIlZM+F92+R4HbKKXPk1GiRuz13jihw5gw/uw6H6biz9spJ+NiIicUGZaNOvzyjmZBrT4aBct4k+8NNB/wuVy1XW7Hx5GYmNjuf3227n//vtJSUkhMzOTZ555hsrKSm6++eaTfsb999/PxIkT6du3L2PGjGHmzJnMmDGDefPmNepniQSGYXDNgHb8acHmkzr/mgHtmqQeeXl53Hvvvdx2222sXLmSP/7xj0edrX/Qfffdx4ABA3jiiSe4+uqr+eKLL/jTn/7ESy+91OC8yspK9u7dC0B+fj5PPvkkUVFRjBs3rkk+RySLnJC6YzssWQwHl34KhZyxqPEJkJxCcM035P7iQQJ5tbtKmSaG10vgwAG2XXkl/i1bMaKj8bZtS/t33zluS6hn0Pn433LWRM1Pvp/AGh9VRT56T76TLct64LL7E1NiU7QRWvYDw3X0llDb7ye2fBFxdjYJg7rgK95Di8rFuFu2Zf2sAbX1bLSfkIiInASfx6R9ejTb8k/c8tS5ddO0sh0u4Th/Jz399NNYlsWNN95IWVkZ/fv3Z/bs2SSfwrbfl112GS+88ALPPvssd911F2eccQZvvPEGo0aNaoTaR54bBrfnrS9yKa0OHve8dinRXNy7dZPU4aabbqKqqoqBAwficrn4+c9/zq233nrM8/v27cvf//53fvWrX/HEE0+QkZHB448/fsSmDq+88gqvvPIKAMnJyfTq1YtZs2bRpUuXJvkckSxyZvf/61NnctRxFK9bz545zr8Ko/r3x79pE1bJkWuktXvtVeKGHTl29aDCHJvtcy2q9lvs+rw+Rab2hP1rnNcJZ0DpNuh2g8FZN9efEyorw/T5KN7mwXBBlD+HUEkJ0f0HU7q1Es+B1cz9zUAC5U6wTe4C5/25abuSRESkIcuyWbqphD1Fx+4S7tY2lq5t4455XCLb8txCpry57JhBtU1SNG/fPJAz0xr/dzxq1CjOPvtsnn/++Ua/t9SLnJbUs/tAdZWz1FTt2J3DBauqICoKX4czSb/nbnbdfU/9QbcbO2hhGTG409KOuLY6J4cN87Io3OjlwAYgBGdMaNjMWVMKce0gIRPKdjll5bucDO8vtalYtYb8O28gkDGCDcEXMAyDEb/tytLnbIKVFsGqKM76yWAC5fW53/TAtlkWZ1yoJlURkeZimgaDOieSt7+abflVdTP+DaBVso8OrWJIS2zabn5pWv2zUvjkrhFMXZLLByt2UlLl/I5bJURx7cBMbhzSnpRY/Y6/zyInpLZq5YxBzc+vL3O7ITYOXC5q/H5S+/fDm96SXdNnsOfBhxrsJBLVvTvrd91PpbcXyQUeYk2b9W9ZtB9nEpP3NzY8/RU7U/7XObk2LxZvgvT+ULHbxhtv0++/XCR3MQmU2yz5fxZluVC0Car228yebBGq6kqW3YHgfhsjBQwTdiywqT5kneDq/dDvXoM9y2x8SbBtJhxYa9N2pI0nThOoRESai2EYZKZFk5kWTU3AIhiy8XoMPC41Gpwu2qXE8MuLuvPA+V3ZX16DyzRIjfPhMvX37ekg/CHVsiD7c6iogP4DnLGppbWLMAeDUFIM192A92/vARDduhX4fAR276bGaEtuxnSikkP06v8h1fmdAReluTZ5n8Guz6F4i8Wg0RUE3OkcnGXv9kGwCoq+hR431lCw3KQCDyufq+Tcl2OZe5tFxW6IToP242HjBxaBcgAXO9q8S9DvI6n0AzIvSOCbWc5MzawJkN7boM0Ig5piWP+OjcvrrMWa0gXczTPsSUREjsLnMfF5TnyefD953Satk6Kb7XkLFy5stmf9kIU/pJaUQM5653V5WX1APVTutrqF7PZ9vpiuK5ZT/MEHrJrWkUBJKoEiSLjh54w4p4ySXQZnTjDYtxpKttq0OxfiLpmMd8NuWAEx6WD6oDzPufW6t33OonoGFG/1Ylk2/toqdL3OIH+Fze5DVowI+n0A+N1ZlG/diifOxsag23Umca2dEJy/2abKWV+Zca+ZJJ6pf9GJiIiInIrwh9SkJHC5nNn8RUUNjxmGEyA9HipbtebAn/9MwHSBy0XytdfiWR2CryCuDcS1NghWxbPhWYu9X9kM/x+ToU+YzLnZYsN74Et2ZvdV1obHlO5QkguhytrnAL5UF4EygzEvGpTugNbDYPuc+kEFhgd88RAot4hLtdm601l7dcjjBrHO5h9YAZs9X1kYLnDHwIEcm7i24PIqqIqIiIicrMiY3Z+9CNatg4wMCFlQkO8E18FDICERMjMBCOzZgyspCTPaadKv2Guz6gWLkm3Q8ycGW/7Prpudf/F0g71LYdlvnY/X7QbYMR8q9jR8tMsHoRrwxkOg2hkQMOABg8QOBosftghUg7/4yCp7osqhvATLlUCb8fHsmAsdLnOGGFTvb3hu+7EGAx/WGCgRERGRkxX+ltTCQvj2W+e1YcKoc+DTWc4i/tu3w4SL6k711O5VfFBsK4PqYqjMh9x/2VQf0hC7dzkkdqh/7y+H9mNh/VsNHx+qXZ3EHQf+Mmdzq6VP2/SYbFPhrKWLLwVSzwLTDcFqsC3IHB1DQsl8DvgHsuq1eLBh1yLqJ1EZcHBml6EVqEREREROSXhDamUlzPjQ6eoHiI+H5GQ44wz4ZnX91qjH0esnJltm2nT+kQGGzZJHbUI18PWLNp5DxlBv+egEVTmkhTU6HVoNgLWvOu/TesKQx1zYIZt5P7UoyYXOV7kp91zGqscPGQ5Q+9PscAn0udukLM+isgDSz1ZXv4iIiMipCH9L6sGA6ouCESOd10OGQq9eEHPiKfHpfQ3S+x4MgQYDfmHz7QcW+csgcJQ5WIczY8E0ILE9HFjnlFXugbI8g5TuNpX50GOK01UfCkDpdrCDULq9foIVQI9JsGmG8zo6zeDLJy12LgBXFIx/0ya2pYKqiIiIyMkKb0iNiXHWQg0GoabaGYd6UOx32yFixXMWlfmQ3BUSg3PJ3TwS8HJw+SmnD/6QwOgHMxr63AUb/gb5y5zlqWJaGox5seE4UneUwchnTUq22px5kUFNiU2wChKyDM64wKTNSJuijTaZYwymj3daWEPV8M1fbIb86jt9HBEREZEfpPDO5qmurptZD8DOvBNeEsjP58Brr+Pfueuox9uNNvAlQ+a5IbZvGgn4aBBKD76ujedWAPyl8NVTsHMB+JLhwvcNUs86estnWm+Djpeb1JTA7Mk2mz+Cbz+wWfm8ReIZBlnnm1QdAF/SIdf0UiuqiIiIyKkIX0gNBuGDv0MgUF+24DNnyanj2PvYryl49ln2PPzwUY/3us1kxNMmVtCFbfjqyg230/V+UItuDa+rqp3wVJ4H2z8NsfN3b7Lo1u1sm2Ud9TmBCufL8kPpNtjyfzbBKqfuy35rUVMEnngY8Sx0vEwz+0VERERORfi6+20bAn7ndUqKM8u/shLy90KrjGNeFtWtG+ULFhDVrdsRxwIVNhvec76Mwz5ZbCsIVjrd71HpYB6280iwAieyW5DzTpCQfyIYPvKftfAmQpthDYNmQnuDc35nUrXPZvcSSOkG7minxdRTO5Q2UAbeOAVUERERkVMVvnVSc9Y726G6XGCaEB0DXi9cOAF8vuNeGiopwXWUmf/ZD4fY84Xz2vA4E5w4/NOZYLrA9IIdckKr6XVaRN2xtWEVnHWmDCdgJneD+LYGbUcatBl+4q773YstFv/SxnTDhGkGUSkKqiIiIiKnIjzpKRCAFcudrU4DAaipgUGD4PIrThhQgaMGVHC63w9KP5sjAyqA5YxDDVZA33udRfwtP7QeGiIhq/60DpebRKU6ryt2wo65NitfsMj9l/N1PLZVG2QNMEyNRxURERE5VeHp7t+ZBxWHJMq0dGif9R/fdshjBkuftknpCum9TYq+tfCXHPv8b6dBVvel7Fpkk5IzjW0xvwOg7SjofJVJpytg80c2cW1g04c2qb3rd7CKa2OT2rM+gO7Ktsl5xyLtbIOib23cseCOouGcLRERERE5Kc3b3V9QAPPmQHpLpwX1wH4nrGZkQPcesHUrDBjoLOjfCGzLZvXLNmU7bNqNhWVPNTwe1QL8RQEsy4NBFXii6XARbJnpHB/7FwN3HESnGJhug6oDNvNutbBtGPsXk+i0+gT67/tCFKx0JmeFquufMeYlk5RuSqoiIiIip6J5W1J3bIeyMudr2HDnfWoadOwI/17ozPj3eeGc0Sd1u+oim5piSDzj6CHQMA3OvsOgfJfN6pcO6aKvnSBVfQBMjxsssImCgDNOFcuZ17XoIZvqAvDE2Zx1C2z6EHr9FNqda2K6nGdunGaR+6lN5jiDYLWNNx4KVjpDCjIGoYAqIiIi8h0075jUHj2cBfwBcnKc7/4ayM52AqrXC5ntT+pWwWqbOTdbzJlisXvJ8RuDV//ZYvcSZx5UbGvody913fBxbep3qwLY9w2Mednk3JcMqgucI4Fy2PYJlO+Eta85qwQctPkjm9LtsPF9m8L1sHeZE1AB/OUn9VFERERE5DDNG1LdHieIAsREw6DBTtf/wREHgQDMnwcHDpz4XjaEalewClYfP6S26m9geqDL1TDiaYOiDQaZ54E3AUpzG55bvguSOxu06GriiXfKTA90vAIwoDIfFv6XRdlO55kZg8Gb6ARZAMMF8VmQMQT63atZ/SIiIiLfRfN29y9bCsXFzuudOyEhAUqK64/bNoRCUFV1wlu5ow3O+7NJVQGk9z1+l3qHS006XAoVe2wW3mNRWQCxGdDlGoM1f7UxXE43v2HCgF849wr5baJSnLVOrQCs+F/qVgso2QI5b9ucebHNlo/qnxOdBj0mwRkXug6vgoiIiIicguZt6ouPb/h+wwanJRUgKgpGjIDxF0Dbtid3u7bGCQPqobZ8bFNZ24Uf3w66Xmvi8tWOQ61lY7PmFYu5t1qUbT+k/OA5Brijoe0oA39p/XHDBVX7IOfdk66OiIiIiBxD87akBoMN31sWbN8O114P0dHg8Rz9ukbS7lyD3Nk2NUWwdyn8a3KobshAfHtoPRSWPgmHL7DqiXdaWf0lziHbgtZDDGwbev8MrJAzJtVfWr/blIiIiIh8d83bklpQcGRZeZkzZ6mJAypAcieDEU+bzqL9BpTlQushkNbbmem/8f3DLqjttQ+U0WC9VW+C890wDKwAhKqg05UQ1w563qJxqCIiIiL/qeZtSR02HNLSYP8+2LbNKTNN8Pqc9VKtEMQnNNnjQzU2gXI47y8m335oU7zJpvdPTT5/yKqb+OSJq58ExSHDAAx37TarOC2pAIU5NmtecVpdhz9t0v0mLTclIiIi0hiaN6TGxUHffvDGa4fUwA2rv4Y13zjd/5df4ayd2gSW/dYmb4FNYico2eRMnrJCcPbPTDZOs4jPhLYjYd9q2PpPZ4wpgOmDhEwo3uS0op7zOyeMxmdCclcI1UBypyapsoiIiMgPUni2RXW7wV87GNTvh1Ur648Fgke/phEcXOmqptD5XrEH5kyxaDMS8pdD/grYPAMwIK5N/XVWDbQbDcEq6HaDQfG3sOqFEGdNMTnvZc3kFxEREWlszbst6kE1NfC396D6kP1D+/ajICWO/9mxmBFte3Blp6GN/thQjc3+teCOsVh4D1h+nHGnoeNfF9cOLnirPox+cnWIygJwxzirBJTlQacrDc6aovGoIiIiIo0hPKnK53O+DoqPh+QU/rh3Jf/KXckj2W8zYcavOVBVeux7fAcun0HLfgYturm4YpbJeX816Hjxsc9vcZYTRLPGNyyPqV01K1gJRRud77mfNn/WFxERETldhaclFWDhAti4wen6r12a6osWPhIOFLHSrOQ3rt28ev5dDG3drcmrEqiw2TrTpmgTZF1gs3+NMwZ1/TvOCgAAbUZATEuD7jca5Lxn8+20+h+bJw4GPmzQeohaUkVEREQaQ/hCqm07M/q3bIIvvzzi8MxuLZkw4jJMo3mCX8HXNr5E+Oopi5ItkNzFaSU9XPcfG3S60iDnHbtuI4AuVxt4EzSzX0RERKSxhGfiFIBhOLP9e/eBjDZOy2pRISFsSnxuLh54obOCfjNYN9Vi/Zs2hlnflV+++9C6Qst+ULwFWvYz8MYbtDsXopIhJl3hVERERKSxRUb/dHo6XHIpf2wN/aM2sPa8oc42qc2kZKvTmGxbEAo4ZYGy+uNtz4GRz7q4ZIaL1J4GuxfbzP+pxZxbLAKVGosqIiIi0tjC15J6uKgofn7x7dxp2xhG87ZO9r3bxB1lkb8CqvfXFhpg1v50KvOdcaueWKdedQMklE9FREREmkRktKQeorkDKsDSpy12LnJm6R+UeS4MetTZ9rQwB/7vMouCr52tptoMN+h9B1hB+PwXFraltCoiIiLSmCIupDY3K2hTsApC1dBqIBi1y6HuWgwb3rVp0dN5bwdhx7z6MLp9jnPNgbUQqDzKjUVERETkO/vBh1TTbTDscZNuNxqk9XZm64MTQIs2OuG0jg1V+52g2makUxTTErxxmjwlIiIi0pgiZ0xqGGUMMcgYYlBdZLPnK4vYVga+JMh5x6YwB3wp4C+GbbPAX2Ex9DEXB9Y611bmw4H1Ni26K6iKiIiINJbwrZMawfavsdn8sUXefMCG1F6w/xvnmOGGqBSoPuC0urpjYcL7Jt54hVQRERGRxhKx3f0L89Yw8N17eXTxO83+7BW/t8ibB9jgiYWsC2DAgwamz+n+ryqoHxYw9hVDAVVERESkkUVsSF28K4dSfxVzt3/dbM/clW2z6g9W3WQpgEAFLP8tVObbWP6G5/uSNR5VREREpClE7JjUW3uNxzQMhrft0Wj3rNxns/Q3FvHtDPreY9Qtd+Uvs9k+1+abvzhBtMs1Br1/ZpPzDvhLnWv3fQMJmVCRD52uNMgcDbGtDdzRCqkiIiIijS1iQ2paTCKGYfBo9js8NfxGhrXp9p3uU7XPpmIvzk5R2Tb7VsG+VTbJneDMi5yA+c2fbbbNsvHEOwv4F260ObAWrED9fQpWQO87oOOlJqZHwVRERESkKUVsdz/AR5u+ZG9lEfN2fM3W4r34Q4Hjnm+FbA6dB2aHbOb91GLBXRbbZlm0HWXgS3SObfuk/ryE9s73VgOcpaf2rQLjKD+Zyr0ooIqIiIg0g4htSQVoHZ9C8YEKPtr8Je9vWMSw1t14bfxdRz1332qb+f/tZ3fKDnreAf6XziQ6rf64YUJUssGAh0w2vGvR6ar6FNp5okn7cTbeBPAl2hSstOl4OXz9Mrh80HooxGU454mIiIhI04vokGrgtFpWBZ0ZS/urSo957uIlO3AF25KxP5PczzeTlO+sYdp+HHS4xKRFD+deGYMMMga5jrjel+Qc73NXfUvpmZfYYdmmVUREROSHLqKbBl8673Yu7TCI9vFpnJ12Bv1bdqTn1Dt5a/1nDc7bXlrAL93PMbfPx2y4eh6X/iSrrhU1vp1RF1BPlQKqiIiISHhEdEhtGZPElLPGsr1sH1/v28bi3TkErBCLd+U0OK9FdAJtUpJZ3S+bH1/dj/T0WC54x2Tc6ybdbojojygiIiIiRxHRO079a9sKfrtsOnGeaBJ80dzV52IW7VzHxC7DaZ+Q3uBc27YJ2RZu88iufBERERH5fonoMan/3LqMPRVFdEqK4t0L/xuAvi078kj2W5TUVPLMyEkk+mIBp2vebSigioiIiJwOIjqk/rzPRcR4fFzWcXBd2aai3Xy8ZSkAr62dy739LgtT7URERESkqUR0d/9BC/LWUBmoJtrtJdEXyxtr57Mwbw1BO8T/DL+JKzoNCXcVRURERKQRRXRLKsCqgq3cPu+lBmUGzmSp/VWlJHpjwlMxEREREWkyER9SV+7dfESZy3Dx8pjbifVGcWZiqzDUSkRERESaUsSH1NyygiPKHh92HT3Tspq/MiIiIiLSLCJ+EdFHBk1sMGvfwGB46+5U+KspqCwOX8VEREREpMlEfEiNcnsZnNG57r2NzcbCXQx47z5GTnuIt9cvCGPtRERERKQpRHxILa6pYHdFUd37W3uO5/998R6WbQHwwsqZ4aqaiIiIiDSRiA+pT305ja0lewFnVv/Q1t3YXVFYd9xlRPxHEBEREZFTFNEJ7995a5m5dVndexuYPPt5DAwAYtw+Hh78ozDVTkRERESaSkTP7k+NTsBtuAjaoboyu/a/r427i2FtuoWraiIiIiLShCK6JbVHaia/HnotMW4fUS4P0S5v3bEv9mwIY81EREREpClFdEj9cs9GHln8DjWhAL8fdQsBK1h37NU1c1i+d1MYayciIiIiTSWiu/tj3D5MwyBkW9w+/2UAkn1xVIf8JHpjaZ+QHuYaioiIiEhTMGzbtsNdieN5Zul0Xl83r0HZJ5f/ig5JGWGqkYiIiIg0tYju7ge4u+8lPDzwRw2Wmnp66YdhrJGIiIiINLWI7u7fV1nCtpJ8/vT1PwnVLt4PUFxdEcZaiYiIiEhTi9iQ+tmO1dz52V9I9sVR6q9qcGzgIdukioiIiMjpJ2K7+/PK9mPZNlVBP2Mz+zQ41r1F2zDVSkRERESaQ8ROnApaIWZuWcqW4r28unZOg2Np0YksmPgUbtMVptqJiIiISFOK2JZUt+ni8k5DeDvnsyOOFVWXEbKso1wlIiIiIqeDiA2pB2XEphxR9rPeF+Jze8JQGxERERFpDhEdUneW7aciUH1kefmBMNRGRERERJpLRIfUO+b/mX1VpQ3KDODHPc4NT4VEREREpFlEbEjdW1HEwTldhy7kH+eJpnNyG2qCgXBVTURERESaWMTO7p8y+wWW7N5AjNuH3woQrJ0oFeXy0Cm5NRuLdvHK2J8zSGumioiIiJx2IrYl1W04y0tVBmvqAqrLMJnUYwwbCnfhDwXZUJgXziqKiIiISBOJ2B2not0+AEwMLJzG3pBtsTx/M78bdTM7y/ZzbdeR4ayiiIiIiDSRiGtJ9YcC3LfwNf69cw0AYzJ70SGhVd3x5fmbWVWwlclnnYfXpWWoRERERE5HERdSJ878LZ9sW051yJkY1SI6gfcvur/B5KnX187l3fULw1RDEREREWlqERdSD10DNdYTxZjM3lSF/Fh2wx2mPs1d0dxVExEREZFmElEh9a11n1EeqCba5eXcdr14acztjGjbg42FO7m4w8AG5/ZKzQpPJUVERESkyUXUxKl1B3YAkB6bxGd53/BZ3jc8MvBHPLX0A0zDwGd6qLECuE0XXVLahLm2IiIiItJUImqd1MLqMmZs+oK2cS24Z+GrdeUGBl7TTY3VcAH/mZc9Sqfk1s1dTRERERFpYhHV3Z8SFc8tPceR5IttUG5j0zI2qUGZgTOBSkREREROPxEVUg/KLS1o8D7eE02M29ugzAb+sflLthTvacaaiYiIiEhziMiQWlxd0bDAgD+ceyuxtQv8H1JMxIxVEBEREZFGE5Eh9fPd6+peuwyT3wy/iTfXfUaiL6bBedd1PYeOSRnNXT0RERERaWIRGVJ/1nsCXrN+4YG/b8zmvQ3/ZndFEeAE17NatOfWXueHq4oiIiIi0oQianb/oaoDfv709SdMXTefgB3CxMDCJjM+jR1l+/CYLr656Y8YhhHuqoqIiIhII4vIllSAKI+Xu/pejGk6VbRqR5/uKNuH23Rx8ZkDFFBFRERETlMRG1IBKgLVeEzXEeVBK8QdfS4KQ41EREREpDlE1I5Th1uev5nyQHWDsjhPFL8cfDVt4lqEqVYiIiIi0tQiOqSObNODa7qMoLC6nNX7tpFfWcwlHQZxWcfB4a6aiIiIiDShiJ0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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Verify that branch annotations match expected celltypes\n", + "plt.rcParams[\"figure.figsize\"] = (5, 5)\n", + "sc.pl.scatter(rna_ad, basis='umap', color='celltype', frameon = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load gene-peak correlations" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with open (gene_peak_scores_file, \"rb\") as handle:\n", + " gene_peak_scores =pickle.load(handle)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Prepare differential gene expression using scanpy.tl.rank_genes_groups" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\n" + ] + } + ], + "source": [ + "sc.tl.rank_genes_groups(rna_ad, groupby = 'celltype', rankby_abs = True, groups = [target_lineage], reference = base_fate, use_raw = False)\n", + "ct_genes = rna_ad.uns['rank_genes_groups']\n", + "diff_gene_exp = dict()\n", + "for celltype in [target_lineage]:\n", + " diff_gene_exp[celltype] = da.process_gene_ranks(ct_genes, celltype)\n", + " diff_gene_exp[celltype].rename({\"logfoldchanges\": \"logFC\"}, inplace = True, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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pvalspvals_adjlogFClog10p
FCRL10.000000e+000.000000e+0037.329556200.000000
MS4A15.637793e-1932.427916e-18910.717134188.614766
NKAIN24.626907e-782.213975e-75-14.54000774.654827
PAX59.955049e-1111.008739e-10710.172239106.996221
INPP4B1.566179e-746.580245e-72-13.62189971.181758
...............
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AC007376.21.000000e+001.000000e+000.000000-0.000000
TNFRSF91.000000e+001.000000e+000.000000-0.000000
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17226 rows × 4 columns

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" + ], + "text/plain": [ + " pvals pvals_adj logFC log10p\n", + "FCRL1 0.000000e+00 0.000000e+00 37.329556 200.000000\n", + "MS4A1 5.637793e-193 2.427916e-189 10.717134 188.614766\n", + "NKAIN2 4.626907e-78 2.213975e-75 -14.540007 74.654827\n", + "PAX5 9.955049e-111 1.008739e-107 10.172239 106.996221\n", + "INPP4B 1.566179e-74 6.580245e-72 -13.621899 71.181758\n", + "... ... ... ... ...\n", + "AL162464.2 1.000000e+00 1.000000e+00 0.000000 -0.000000\n", + "AL162464.1 1.000000e+00 1.000000e+00 0.000000 -0.000000\n", + "AC008534.1 1.000000e+00 1.000000e+00 0.000000 -0.000000\n", + "AC007376.2 1.000000e+00 1.000000e+00 0.000000 -0.000000\n", + "TNFRSF9 1.000000e+00 1.000000e+00 0.000000 -0.000000\n", + "\n", + "[17226 rows x 4 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gene_info_df = diff_gene_exp[target_lineage]\n", + "gene_info_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Structure of enhancer plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plots produced in this notebook display both differential gene expression and the differential accessibility of the peaks significantly correlated to each gene's expression. Genes are plotted in order of most negative to most positive logFC RNA expression along the X axis. The stacked dots above each gene represent the number of peaks correlated to the expression of the gene and are ordered by most negative to most positive logFC accessibility, with the Y axis position of the first dot matching the logFC RNA expression for that gene. Grouping can be used to add large spaces to separate the stack of peaks into discrete categories - in the examples provided here, the peaks are grouped into downregulated, unchanged, and upregulated peaks. \n", + "\n", + "The example shown uses continuous colors to represent the logFC accessibility, but a discrete color scale can also be used to color the cells by groups. This can be performed by preparing color annotations using `discretize_colors`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](images/plot_example.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Enhancer plot examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare peak deltas (logFC accessibility values) for correlated peaks" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bdf3dbe8211e4926b4965f4a50b18faa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/17226 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, _ = enhancer_plot(\n", + " gene_info_df, cont_colors, n_genes=n_genes, min_peaks=min_peaks\n", + ")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate in top {n_genes} genes with at least {min_peaks} peaks\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Create an enhancer plot with discrete colors" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Here, each gene is represented with a color based on its peak group assignment.\n", + "fig, ax, _ = enhancer_plot(\n", + " gene_info_df, disc_colors, n_genes=n_genes, min_peaks=min_peaks, cmap=cmap\n", + ")\n", + "ax.legend(handles=legend_elements, loc=\"upper left\")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate in top {n_genes} genes with at least {min_peaks} peaks\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a grouped enhancer plot with continuous colors" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, _ = enhancer_plot(\n", + " gene_info_df,\n", + " cont_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + ")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate in top {n_genes} genes with at least {min_peaks} peaks\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a grouped enhancer plot with discrete colors" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax, _ = enhancer_plot(\n", + " gene_info_df,\n", + " disc_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + " cmap=cmap,\n", + ")\n", + "ax.legend(handles=legend_elements, loc=\"upper left\")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate in top {n_genes} genes with at least {min_peaks} peaks\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Show a custom subset of genes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Genes with no associated peaks may be shown - set min_peaks to 1 to exclude these peaks)\n", + "show_mask = (gene_info_df[\"logFC\"]>5) & (gene_info_df[\"logFC\"]<20) # an arbitraty condition\n", + "sorted_gene_df = pd.concat(\n", + " [gene_info_df.loc[show_mask, :], gene_info_df.loc[~show_mask, :]]\n", + ")\n", + "n_selected_genes = int(np.sum(show_mask))\n", + "fig, ax, _ = enhancer_plot(\n", + " sorted_gene_df,\n", + " disc_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_selected_genes,\n", + " min_peaks=0,\n", + " cmap=cmap,\n", + ")\n", + "ax.legend(handles=legend_elements, loc=\"upper left\")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate of {n_selected_genes} selected genes\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use alternative markers" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap, legend_elements = setup_cmap_and_legend(cmapdict, marker=\"D\")\n", + "fig, ax, _ = enhancer_plot(\n", + " sorted_gene_df,\n", + " disc_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_selected_genes,\n", + " min_peaks=0,\n", + " marker=\"D\",\n", + " cmap=cmap,\n", + ")\n", + "ax.legend(handles=legend_elements, loc=\"upper left\")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate of {n_selected_genes} selected genes\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More space between markers" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap, legend_elements = setup_cmap_and_legend(cmapdict, marker=\"D\")\n", + "fig, ax, _ = enhancer_plot(\n", + " sorted_gene_df,\n", + " disc_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_selected_genes,\n", + " min_peaks=0,\n", + " marker=\"D\",\n", + " point_distance=2e-2,\n", + " cmap=cmap,\n", + ")\n", + "ax.legend(handles=legend_elements, loc=\"upper left\")\n", + "ax.set_title(\n", + " f\"DORC: Differential expression for the {target_lineage} fate of {n_selected_genes} selected genes\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Multi subplots example" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(20, 10))\n", + "\n", + "# Plot using a discrete color scheme\n", + "enhancer_plot(\n", + " gene_info_df,\n", + " disc_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + " cmap=cmap,\n", + " ax=axs[0, 0],\n", + ")\n", + "axs[0, 0].legend(handles=legend_elements, loc=\"upper left\")\n", + "\n", + "# Plot using a continuous color scheme\n", + "enhancer_plot(\n", + " gene_info_df,\n", + " cont_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + " ax=axs[1, 0],\n", + " color_bar_bounds=None, # Disable colorbar\n", + " vmin=-0.5,\n", + " vmax=0.5, # Normalize colors to range [-0.5, 0.5]\n", + ")\n", + "\n", + "# Plot using a discrete color scheme and grouping the points\n", + "enhancer_plot(\n", + " gene_info_df,\n", + " disc_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + " cmap=cmap,\n", + " ax=axs[0, 1],\n", + ")\n", + "\n", + "# Plot using a continuous color scheme and grouping the points\n", + "fig, ax, points = enhancer_plot(\n", + " gene_info_df,\n", + " cont_colors,\n", + " peak_groups=disc_colors,\n", + " n_genes=n_genes,\n", + " min_peaks=min_peaks,\n", + " ax=axs[1, 1],\n", + " color_bar_bounds=None,\n", + " vmin=-0.5,\n", + " vmax=0.5,\n", + ")\n", + "\n", + "# Add a colorbar in the bottom right corner\n", + "cbar_axes = fig.add_axes([0.85, 0.15, 0.03, 0.01])\n", + "cbar = fig.colorbar(points, cax=cbar_axes, orientation=\"horizontal\")\n", + "cbar.set_label(\"peak logFC\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "density-analysis", + "language": "python", + "name": "density-analysis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.15" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/images/plot_example.png b/notebooks/images/plot_example.png new file mode 100644 index 0000000..aca7724 Binary files /dev/null and b/notebooks/images/plot_example.png differ diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..2121da8 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1,2 @@ +import .pl as pl +import .util as util diff --git a/src/pl/__init__.py b/src/pl/__init__.py new file mode 100644 index 0000000..8a3eca1 --- /dev/null +++ b/src/pl/__init__.py @@ -0,0 +1,6 @@ +from .enhancer_plot import ( + setup_cmap_and_legend, + discretize_colors, + enhancer_plot, + get_peak_deltas +) diff --git a/src/pl/enhancer_plot.py b/src/pl/enhancer_plot.py new file mode 100644 index 0000000..a27de1d --- /dev/null +++ b/src/pl/enhancer_plot.py @@ -0,0 +1,546 @@ +from typing import Union, Tuple, List, Optional +import numpy as np +import pandas as pd +import matplotlib as mpl +import matplotlib.pyplot as plt +from matplotlib.colors import Normalize, Colormap +from tqdm.auto import tqdm + +def preprocess_data(df, peak_colors, min_peaks, n_genes): + """ + Prepares the data for plotting by filtering and sorting. + + Args: + df (pd.DataFrame): Input dataframe with gene information. + peak_colors (dict): Dictionary with peak colors. + min_peaks (int): Minimum number of peaks for a gene to be included in the plot. + n_genes (int): Number of top genes to be included in the plot. + + Returns: + pd.DataFrame, pd.DataFrame: Two dataframes, first contains the top genes, second the original dataframe after filtering. + """ + df = df.copy() + df["n_peaks"] = [len(peak_colors[g]) for g in df.index] + df = df.loc[df["n_peaks"] >= min_peaks, :] + top_df = df.head(n_genes).sort_values("logFC") + top_df["index"] = np.arange(top_df.shape[0]) + 1 + return top_df, df + +def get_peak_deltas(ad, all_deltas, gene_peak_scores, corr_cutoff = 0, invert_deltas = False): + """ + Creates a dictionary with genes as keys and DataFrames of logFC differential accessibility of the correlated peaks as values. + + Args: + ad (anndata.AnnData): AnnData object containing scRNA-seq data used to generate differential gene expression results. + all_deltas (pd.DataFrame): pd.DataFrame containing differential accessibility results from atac-metacell-utilities output. + gene_peak_scores (dict): dictionary with genes as keys and data frames of gene-peak correlations as values, from atac-metacell-utilities output. + corr_cutoff (int): Minimum correlation coefficient or peaks to pass filtering. Defaults to 0 to retain all positively correlated peaks. + invert_deltas(bool): Boolean value indicating if sign of differential accessibility values should be inverted. Defaults to False. + + Returns: + dict: a dictionary, with string keys corresponding to genes and pd.DataFrames of differential accessibility results for correlated peaks as values. + + """ + dorc_peak_deltas = dict() + for gene in tqdm(ad.var_names, total=len(ad.var_names)): + dorc_peak_deltas[gene] = pd.Series(dtype='float64') + peak_df = gene_peak_scores.get(gene) + if peak_df is None or isinstance(peak_df, int): + continue + selected_peaks = peak_df[peak_df.index.isin(all_deltas.index)] + peak_df = peak_df[peak_df['cor'] >corr_cutoff] + if len(selected_peaks) == 0: + continue + dorc_peak_deltas[gene] = all_deltas[selected_peaks.index].sort_values() + if invert_deltas == True: + dorc_peak_deltas[gene] = -dorc_peak_deltas[gene] + dorc_peak_deltas[gene] = dorc_peak_deltas[gene].sort_values() + return dorc_peak_deltas + +def set_plot_axes(ax, n_genes): + """ + Configures the plot axes. + + Args: + ax (plt.Axes): The Axes object to be configured. + n_genes (int): Number of top genes to be included in the plot. + """ + ax.spines["bottom"].set_position("center") + ax.spines["bottom"].set_color("none") + ax.spines["right"].set_color("none") + ax.spines["top"].set_color("none") + ax.xaxis.set_ticks_position("none") + ax.yaxis.set_ticks_position("none") + ax.hlines(0, 0, n_genes, colors="black", linestyles="solid", lw=0.5, zorder=-1) + ax.set(xlim=(0, n_genes + 6)) + ax.set_ylabel("logFC") + + +def gene_scatter_plot(ax, top_df, s=1): + """ + Draws a scatter plot. + + Args: + ax (plt.Axes): The Axes object to draw on. + top_df (pd.DataFrame): Dataframe with top genes information. + s (float): Size of the scatter points. Defaults to 1. + """ + ax.scatter(top_df["index"], top_df["logFC"], c="black", s=s) + + +def box_plot(ax, gene_info, n_genes): + """ + Draws a box plot. + + Args: + ax (plt.Axes): The Axes object to draw on. + gene_info (pd.DataFrame): Dataframe with differential expression information. + n_genes (int): Number of top genes to be included in the plot. + + Returns: + boxplot: boxplot object of the gene log-fold change. + """ + bp = ax.boxplot( + gene_info["logFC"], + widths=3, + positions=[n_genes + 3], + showfliers=False, + labels=[""], + ) + return bp + + +def plot_text_and_hlines(ax, bp, n_genes, text, fontsize, gene_info): + """ + Adds text and horizontal lines to the plot. + + Args: + ax (plt.Axes): The Axes object to draw on. + bp (boxplot): Boxplot object of the gene log-fold change. + n_genes (int): Number of top genes to be included in the plot. + text (str): Text to be plotted. + fontsize: Union(str, float): Size of the text. + gene_info (pd.DataFrame): Dataframe with differential expression information. + """ + text_y = bp["medians"][0].get_data()[1][0] + ax.text( + n_genes + 1, + text_y, + text, + {"ha": "center", "va": "center", "size": fontsize}, + rotation=90, + ) + ax.hlines(0, 0, n_genes, colors="black", linestyles="solid", lw=0.5, zorder=-1) + ax.set(xlim=(0, n_genes + 6)) + ax.set_ylabel("logFC") + + +def calculate_ypaddings(data, ypaddings, groups, range_size, point_distance, group_gap): + """ + Calculates the y position of the peaks on the plot. + + Args: + data (pd.Series): Row from the dataframe with gene information. + ypaddings (np.array): Array of y position values. + groups (np.array): Array of group values for peaks. + range_size (float): The range of log-fold change values. + point_distance (float): Point distance as fraction of the data range. + group_gap (int): Gap between groups. + + Returns: + np.array: Array of calculated y positions for peaks. + """ + n_peaks = int(data["n_peaks"]) + y = data["logFC"] + ypaddings[:n_peaks] + if groups is not None: + y = adjust_ypaddings_for_groups( + data, ypaddings, groups, range_size, point_distance, group_gap + ) + return y + + +def adjust_ypaddings_for_groups( + data, ypaddings, groups, range_size, point_distance, group_gap +): + """ + Adjusts the y position of the peaks based on groups. + + Args: + data (pd.Series): Row from the dataframe with gene information. + ypaddings (np.array): Array of y position values. + groups (np.array): Array of group values for peaks. + range_size (float): The range of log-fold change values. + point_distance (float): Point distance as fraction of the data range. + group_gap (int): Gap between groups. + + Returns: + np.array: Array of adjusted y positions for peaks. + """ + n_peaks = int(data["n_peaks"]) + y = np.zeros(n_peaks) + extra_buff = 0 + last_idx = 0 + for g in np.sort(np.unique(groups))[::-1]: + idx = groups == g + new_last = last_idx + np.sum(idx) + y[idx] = data["logFC"] + ypaddings[last_idx:new_last] + extra_buff + last_idx = new_last + extra_buff += group_gap * range_size * point_distance + return y + + +def peak_scatter_plot( + ax: plt.Axes, + xpos: List, + ypos: List, + colors: List, + cmap: Union[str, Colormap], + vmin: Optional[float] = None, + vmax: Optional[float] = None, + color_bar_bounds: Optional[List] = None, + **kwargs, +): + """ + Plots colored scatter points on the plot. + + Args: + ax (plt.Axes): The Axes object to draw on. + xpos (list): List of x positions of the scatter points. + ypos (list): List of y positions of the scatter points. + colors (list): List of colors for the scatter points. + cmap (Union[str, matplotlib.colors.Colormap]): The colormap or name of a colormap to use for color-coding the data points. + vmin (Optional[float]): The minimum value for colormap normalization. Defaults to None. + vmax (Optional[float]): The maximum value for colormap normalization. Defaults to None. + color_bar_bounds (Optional[list]): The bounds for the color bar. Defaults to None. + **kwargs: Additional keyword arguments passed to the scatter function. + + Raises: + ValueError: If `xpos`, `ypos` or `colors` lists have different lengths. + """ + if not (len(xpos) == len(ypos) == len(colors)): + raise ValueError("`xpos`, `ypos`, and `colors` must have the same length.") + + colors = np.concatenate(colors) + + if not isinstance(cmap, str): + points = ax.scatter( + np.concatenate(xpos), np.concatenate(ypos), c=colors, cmap=cmap, **kwargs + ) + return points + + if vmin is None: + vmin = -max(np.abs(colors)) + if vmax is None: + vmax = max(np.abs(colors)) + + norm = Normalize(vmin=vmin, vmax=vmax) + points = ax.scatter( + np.concatenate(xpos), + np.concatenate(ypos), + c=colors, + cmap=cmap, + norm=norm, + **kwargs, + ) + + if color_bar_bounds is not None: + cax = ax.inset_axes(color_bar_bounds) + cb = plt.colorbar(points, cax=cax) + cb.set_label("peak logFC") + + return points + + +def _validate_inputs( + gene_info: pd.DataFrame, + peak_colors: dict, + peak_groups: Optional[dict], + group_gap: int, + n_genes: int, + min_peaks: int, +): + """ + Validate inputs for the `enhancer_plot` function. Raises ValueError if an input is not of the correct type or contains an invalid value. + + Args: + gene_info (pd.DataFrame): Input dataframe containing gene information. + peak_colors (dict): Dictionary mapping genes to their associated peak colors. + peak_groups (dict, optional): Dictionary mapping genes to their group labels. + group_gap (int): The spacing between groups in number of peak units. + n_genes (int): The number of top genes to be included in the plot. + min_peaks (int): The minimum number of peaks a gene must have to be included in the plot. + + Raises: + ValueError: If an input is not of the correct type or contains an invalid value. + """ + if not isinstance(gene_info, pd.DataFrame): + raise ValueError("gene_info must be a pandas DataFrame.") + if "logFC" not in gene_info.columns: + raise ValueError("gene_info must contain a 'logFC' column.") + if not isinstance(peak_colors, dict): + raise ValueError("peak_colors must be a dictionary.") + if peak_groups is not None and not isinstance(peak_groups, dict): + raise ValueError("peak_groups must be either None or a dictionary.") + if not isinstance(group_gap, int) or group_gap < 0: + raise ValueError("group_gap must be a non-negative integer.") + if not isinstance(n_genes, int) or n_genes < 0: + raise ValueError("n_genes must be a non-negative integer.") + if not isinstance(min_peaks, int) or min_peaks < 0: + raise ValueError("min_peaks must be a non-negative integer.") + + +def _calculate_fontsize(ax: plt.Axes, n_genes: int) -> float: + """ + Calculate the optimal font size based on the size of the axes and the number of genes. + + Args: + ax (plt.Axes): The Axes object for the plot. + n_genes (int): The number of genes in the plot. + + Returns: + float: The calculated font size. + """ + bbox = ax.get_window_extent().transformed(ax.figure.dpi_scale_trans.inverted()) + ax_width, _ = bbox.width, bbox.height + fontsize = 50 * ax_width / (n_genes + 4) + return min(fontsize, 10) + + +def prepare_scatter_points( + top_df: pd.DataFrame, + peak_colors: dict, + peak_groups: dict, + ypaddings: np.ndarray, + range_size: float, + point_distance: float, + group_gap: int, + ax: plt.Axes, + text_properties: dict, + text_offset: float, + text_rotation: int, +) -> Tuple[List[float], List[float], List[str]]: + """ + Prepare the positions and colors for the scatter points and write gene names. + + Args: + [rest of arguments omitted for brevity] + + Returns: + Tuple[List[float], List[float], List[str]]: The x and y positions and colors for the scatter points. + """ + ypos = [] + xpos = [] + colors = [] + for gene, data in top_df.iterrows(): + ax.text( + data["index"], + data["logFC"] - text_offset, + gene, + text_properties, + rotation=text_rotation, + ) + cs = peak_colors[gene] + colors.append(cs) + groups = None + if peak_groups is not None: + groups = peak_groups.get(gene) + y = calculate_ypaddings( + data, ypaddings, groups, range_size, point_distance, group_gap + ) + ypos.append(y) + xpos.append(np.repeat(data["index"], len(cs))) + return xpos, ypos, colors + + +def enhancer_plot( + gene_info: Union[pd.DataFrame, pd.Series], + peak_colors: dict, + peak_groups: dict = None, + point_distance: float = 1e-2, + group_gap: int = 2, + n_genes: int = 100, + min_peaks: int = 0, + cmap: Union[str, Colormap] = "coolwarm", + color_bar_bounds: list = [1, 0.4, 0.01, 0.2], + vmin: Optional[float] = None, + vmax: Optional[float] = None, + gene_text_offset: float = 0.1, + gene_text_rotation: int = 90, + ax: Optional[mpl.axes.Axes] = None, + **kwargs, +) -> Tuple[plt.Figure, plt.Axes]: + """ + Creates a scatterplot of log-fold changes of genes with an optional boxplot of the same data. + The data points are color-coded based on associated peak colors and can be grouped by peak groups. + It shows that scatter for the first `n_genes` in the passed `gene_info` dataframe and a boxplot + for all genes in the dataframe in the far right. + + Args: + gene_info (Union[pd.DataFrame, pd.Series]): + Input pandas dataframe or series containing gene information. It should have the column "logFC" + and the index must correspond to the gene names. If it's a series, it's casted to a dataframe with + the column logFC. + + peak_colors (dict): + Dictionary mapping genes to their associated peak colors. + + peak_groups (dict, optional): + Dictionary mapping genes to their group labels. Defaults to None. + + point_distance (float, optional): + The spacing between the peak scatter points as a fraction of the data range. + Defaults to 1e-2. + + group_gap (int, optional): + The spacing between groups in the number of peak units. Defaults to 2. + + n_genes (int, optional): + The number of top genes in `gene_info` to be included in the scatter plot. + Defaults to 100. + + min_peaks (int, optional): + The minimum number of peaks a gene must have to be included in the plot. + Defaults to 0. + + cmap (Union[str, matplotlib.colors.Colormap], optional): + The colormap or name of a colormap to use for color-coding the data points. + Defaults to "coolwarm". + + color_bar_bounds (list, optional): + The bounds for the color bar. Defaults to [1, 0.4, 0.01, 0.2]. + + vmin (float, optional): + Minimum data value that the colormap covers. Defaults to None. + + vmax (float, optional): + Maximum data value that the colormap covers. Defaults to None. + + gene_text_offset (float, optional): + The vertical offset to apply when placing the gene labels. + A larger value will place the text lower below the data points. Defaults to 0.1. + + gene_text_rotation (int, optional): + The angle of rotation for the gene labels, in degrees. Defaults to 90. + + ax (Optional[mpl.axes.Axes], optional): + An instance of Axes to which to draw the plot. If None, a new figure and axes object + will be created. Defaults to None. + + **kwargs: + Additional keyword arguments passed to the scatter function. + + Returns: + Tuple[plt.Figure, plt.Axes, matplotlib.collections.PathCollection]: + The resulting Figure and Axes objects of the plot, and the PathCollection object + returned by ax.scatter, representing the peaks on the plot. + """ + + # If the input is a series, convert it to a dataframe with the column name as 'logFC' + if isinstance(gene_info, pd.Series): + gene_info = gene_info.to_frame(name="logFC") + + # Validate inputs and preprocess data + _validate_inputs(gene_info, peak_colors, peak_groups, group_gap, n_genes, min_peaks) + top_df, gene_info = preprocess_data(gene_info, peak_colors, min_peaks, n_genes) + + # Set up the figure and plot elements + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + set_plot_axes(ax, n_genes) + gene_scatter_plot(ax, top_df) + bp = box_plot(ax, gene_info, n_genes) + + # Calculate font and scatter plot point size for later use + max_len = top_df["n_peaks"].max() + + # Use numpy's PTP (Peak-To-Peak) function to calculate range_size + range_size = np.ptp(np.append(top_df["logFC"].values, 0)) + + ypaddings = np.linspace(0, max_len * range_size * point_distance, num=max_len) + + + fontsize = _calculate_fontsize(ax, n_genes) + kwargs["s"] = kwargs.get("s", fontsize) # Set the size for scatter points + + # Add text, lines, and scatter points to the plot + plot_text_and_hlines( + ax, bp, n_genes, f"{gene_info.shape[0]:,} genes", fontsize, gene_info + ) + + # Prepare scatter plot and write gene names + text_properties = {"ha": "center", "va": "top", "size": fontsize} + xpos, ypos, colors = prepare_scatter_points( + top_df, + peak_colors, + peak_groups, + ypaddings, + range_size, + point_distance, + group_gap, + ax, + text_properties, + gene_text_offset, + gene_text_rotation, + ) + + points = peak_scatter_plot( + ax, xpos, ypos, colors, cmap, vmin, vmax, color_bar_bounds, **kwargs + ) + + return fig, ax, points + + +def setup_cmap_and_legend( + cmapdict: dict, marker: str = "o" +) -> Tuple[Colormap, List[mpl.lines.Line2D]]: + """ + Creates a colormap and the corresponding legend elements from a given dictionary mapping labels to colors. + + Args: + cmapdict (dict): + A dictionary where keys are labels and values are colors. Each key-value pair corresponds to + a different class/label in your data. For example, it can be + {'up-regulated': '#D13927', 'unchanged': (0.7, 0.7, 0.7, 1.0), 'down-regulated': '#4A7CB5'}. + + marker (str, optional): + The marker style for the legend elements. Default is 'o', which corresponds to a circle. + Other matplotlib marker styles can be used (https://matplotlib.org/stable/api/markers_api.html). + + Returns: + tuple: + A 2-tuple containing: + - The colormap generated from the color values of cmapdict. This colormap can be used for color-coding + the data points in a plot. + - A list of Line2D objects that can be used to create a legend for the plot. Each Line2D object + corresponds to a label and its associated color in cmapdict. + """ + cmap = mpl.colors.LinearSegmentedColormap.from_list( + "Custom cmap", list(cmapdict.values()), len(cmapdict) + ) + legend_elements = [ + mpl.lines.Line2D( + [0], [0], marker=marker, color="w", label=label, markerfacecolor=c + ) + for label, c in cmapdict.items() + ] + return cmap, legend_elements + + +def discretize_colors(data, threshold): + """ + Discretize colors based on a threshold. + + Args: + data (dict): Dictionary mapping genes to their associated peak colors. + threshold (tuple): Tuple containing lower and upper bounds for discretization. + + Returns: + dict: Discretized colors. + """ + return { + gene: (v.values[:, None] < np.array(threshold)[None, :]).sum(axis=1).astype(int) + for gene, v in data.items() + } diff --git a/src/util/__init__.py b/src/util/__init__.py new file mode 100644 index 0000000..496ad54 --- /dev/null +++ b/src/util/__init__.py @@ -0,0 +1 @@ +from .lmod import load_module diff --git a/src/util/lmod.py b/src/util/lmod.py new file mode 100644 index 0000000..e7b40f4 --- /dev/null +++ b/src/util/lmod.py @@ -0,0 +1,27 @@ +import os +import subprocess +import warnings + + +class LmodError(Exception): + pass + + +def load_module(module_name): + lmod = os.environ.get("LMOD_CMD") + if lmod is None: + raise LmodError('Environment variable "LMOD_CMD" not set. Is lmod available?') + + cmd = [lmod, "python", "load", module_name] + process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + stdout, stderr = process.communicate() + + if process.returncode: + raise LmodError(stderr.decode("utf-8")) + if stderr: + warnings.warn(stderr.decode("utf-8")) + exec(stdout) diff --git a/test-logs/fimo.out b/test-logs/fimo.out new file mode 100644 index 0000000..0cab8f0 --- /dev/null +++ b/test-logs/fimo.out @@ -0,0 +1,35464 @@ +Using motif +M02753_2.00 of width 12. +Using motif -M02753_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868 +# Estimated pi_0=0.882535 +Using motif +M02754_2.00 of width 11. +Using motif -M02754_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893739 +# Estimated pi_0=0.900153 +Using motif +M02755_2.00 of width 13. +Using motif -M02755_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870588 +# Estimated pi_0=0.876111 +Using motif +M04046_2.00 of width 11. +Using motif -M04046_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.834757 +# Estimated pi_0=0.848594 +Using motif +M04047_2.00 of width 11. +Using motif -M04047_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8758 +# Estimated pi_0=0.880909 +Using motif +M08701_2.00 of width 10. +Using motif -M08701_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8862 +# Estimated pi_0=0.902745 +Using motif +M00111_2.00 of width 10. +Using motif -M00111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892642 +# Estimated pi_0=0.900183 +Using motif +M02756_2.00 of width 12. +Using motif -M02756_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86198 +# Estimated pi_0=0.878065 +Using motif +M02757_2.00 of width 11. +Using motif -M02757_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877115 +# Estimated pi_0=0.89056 +Using motif +M02758_2.00 of width 13. +Using motif -M02758_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870833 +# Estimated pi_0=0.88227 +Using motif +M02759_2.00 of width 12. +Using motif -M02759_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8624 +# Estimated pi_0=0.870806 +Using motif +M02760_2.00 of width 13. +Using motif -M02760_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.858938 +# Estimated pi_0=0.870606 +Using motif +M02761_2.00 of width 11. +Using motif -M02761_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856697 +# Estimated pi_0=0.859099 +Using motif +M04048_2.00 of width 11. +Using motif -M04048_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898113 +# Estimated pi_0=0.909933 +Using motif +M04049_2.00 of width 11. +Using motif -M04049_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8928 +# Estimated pi_0=0.9025 +Using motif +M07783_2.00 of width 15. +Using motif -M07783_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87068 +# Estimated pi_0=0.87392 +Using motif +M08702_2.00 of width 14. +Using motif -M08702_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8912 +# Estimated pi_0=0.906667 +Using motif +M09451_2.00 of width 12. +Using motif -M09451_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898407 +# Estimated pi_0=0.905303 +Using motif +M09751_2.00 of width 9. +Using motif -M09751_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856991 +# Estimated pi_0=0.863932 +Using motif +M04050_2.00 of width 11. +Using motif -M04050_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8382 +# Estimated pi_0=0.852034 +Using motif +M04051_2.00 of width 12. +Using motif -M04051_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860784 +# Estimated pi_0=0.873088 +Using motif +M04052_2.00 of width 11. +Using motif -M04052_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868673 +# Estimated pi_0=0.872913 +Using motif +M04053_2.00 of width 12. +Using motif -M04053_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8892 +# Estimated pi_0=0.893023 +Using motif +M02762_2.00 of width 12. +Using motif -M02762_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.850099 +# Estimated pi_0=0.852477 +Using motif +M02763_2.00 of width 11. +Using motif -M02763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864118 +# Estimated pi_0=0.872833 +Using motif +M02764_2.00 of width 13. +Using motif -M02764_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.845098 +# Estimated pi_0=0.856364 +Using motif +M02765_2.00 of width 11. +Using motif -M02765_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8778 +# Estimated pi_0=0.882295 +Using motif +M02766_2.00 of width 12. +Using motif -M02766_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870185 +# Estimated pi_0=0.88087 +Using motif +M02767_2.00 of width 13. +Using motif -M02767_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.844752 +# Estimated pi_0=0.850991 +Using motif +M04054_2.00 of width 11. +Using motif -M04054_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.849109 +# Estimated pi_0=0.856667 +Using motif +M04055_2.00 of width 11. +Using motif -M04055_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8924 +# Estimated pi_0=0.89968 +Using motif +M07784_2.00 of width 15. +Using motif -M07784_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874476 +# Estimated pi_0=0.893243 +Using motif +M08703_2.00 of width 15. +Using motif -M08703_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922157 +# Estimated pi_0=0.933741 +Using motif +M09755_2.00 of width 9. +Using motif -M09755_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868571 +# Estimated pi_0=0.87028 +Using motif +M08707_2.00 of width 13. +Using motif -M08707_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9944 +# Estimated pi_0=1 +Using motif +M09767_2.00 of width 14. +Using motif -M09767_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M01659_2.00 of width 11. +Using motif -M01659_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00116_2.00 of width 11. +Using motif -M00116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M00115_2.00 of width 9. +Using motif -M00115_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M02771_2.00 of width 13. +Using motif -M02771_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9946 +# Estimated pi_0=1 +Using motif +M04056_2.00 of width 12. +Using motif -M04056_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983725 +# Estimated pi_0=1 +Using motif +M04057_2.00 of width 12. +Using motif -M04057_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978235 +# Estimated pi_0=1 +Using motif +M02772_2.00 of width 10. +Using motif -M02772_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968627 +# Estimated pi_0=0.981119 +Using motif +M08754_2.00 of width 15. +Using motif -M08754_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8412 +# Estimated pi_0=0.853504 +Using motif +M02773_2.00 of width 10. +Using motif -M02773_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936 +# Estimated pi_0=0.944706 +Using motif +M04058_2.00 of width 11. +Using motif -M04058_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9116 +# Estimated pi_0=0.919818 +Using motif +M04059_2.00 of width 11. +Using motif -M04059_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956078 +# Estimated pi_0=0.964151 +Using motif +M08709_2.00 of width 10. +Using motif -M08709_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96 +# Estimated pi_0=0.973544 +Using motif +M04060_2.00 of width 10. +Using motif -M04060_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874653 +# Estimated pi_0=0.882991 +Using motif +M04061_2.00 of width 10. +Using motif -M04061_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9134 +# Estimated pi_0=0.917905 +Using motif +M02774_2.00 of width 10. +Using motif -M02774_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.959315 +Using motif +M02775_2.00 of width 10. +Using motif -M02775_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928491 +# Estimated pi_0=0.930394 +Using motif +M08710_2.00 of width 11. +Using motif -M08710_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893529 +# Estimated pi_0=0.903 +Using motif +M09794_2.00 of width 15. +Using motif -M09794_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89098 +# Estimated pi_0=0.90806 +Using motif +M09795_2.00 of width 16. +Using motif -M09795_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959304 +# Estimated pi_0=0.967179 +Using motif +M04062_2.00 of width 10. +Using motif -M04062_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.888 +# Estimated pi_0=0.896147 +Using motif +M04063_2.00 of width 10. +Using motif -M04063_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8778 +# Estimated pi_0=0.884854 +Using motif +M04064_2.00 of width 10. +Using motif -M04064_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93505 +# Estimated pi_0=0.946667 +Using motif +M04065_2.00 of width 10. +Using motif -M04065_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964038 +# Estimated pi_0=0.975659 +Using motif +M08048_2.00 of width 10. +Using motif -M08048_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920594 +# Estimated pi_0=0.926286 +Using motif +M08711_2.00 of width 11. +Using motif -M08711_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926306 +# Estimated pi_0=0.934545 +Using motif +M09797_2.00 of width 11. +Using motif -M09797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9676 +# Estimated pi_0=0.987536 +Using motif +M09798_2.00 of width 11. +Using motif -M09798_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940536 +# Estimated pi_0=0.964024 +Using motif +M02776_2.00 of width 10. +Using motif -M02776_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987816 +# Estimated pi_0=0.992169 +Using motif +M02777_2.00 of width 10. +Using motif -M02777_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9912 +# Estimated pi_0=0.994416 +Using motif +M04066_2.00 of width 10. +Using motif -M04066_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981887 +# Estimated pi_0=0.988092 +Using motif +M04067_2.00 of width 10. +Using motif -M04067_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9792 +# Estimated pi_0=0.985989 +Using motif +M04068_2.00 of width 10. +Using motif -M04068_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997766 +# Estimated pi_0=0.998693 +Using motif +M04069_2.00 of width 10. +Using motif -M04069_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986374 +# Estimated pi_0=0.993807 +Using motif +M04070_2.00 of width 10. +Using motif -M04070_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996114 +# Estimated pi_0=0.999095 +Using motif +M04071_2.00 of width 10. +Using motif -M04071_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966423 +# Estimated pi_0=0.970994 +Using motif +M04072_2.00 of width 10. +Using motif -M04072_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994483 +# Estimated pi_0=0.999497 +Using motif +M04073_2.00 of width 10. +Using motif -M04073_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980468 +# Estimated pi_0=0.987568 +Using motif +M08712_2.00 of width 10. +Using motif -M08712_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963212 +# Estimated pi_0=0.966871 +Using motif +M09453_2.00 of width 10. +Using motif -M09453_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962655 +# Estimated pi_0=0.969944 +Using motif +M09802_2.00 of width 18. +Using motif -M09802_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931881 +# Estimated pi_0=0.93932 +Using motif +M09803_2.00 of width 10. +Using motif -M09803_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944328 +# Estimated pi_0=0.94987 +Using motif +M09806_2.00 of width 10. +Using motif -M09806_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9208 +# Estimated pi_0=0.936974 +Using motif +M08049_2.00 of width 10. +Using motif -M08049_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9098 +# Estimated pi_0=0.917885 +Using motif +M08713_2.00 of width 8. +Using motif -M08713_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940588 +# Estimated pi_0=0.940588 +Using motif +M09454_2.00 of width 8. +Using motif -M09454_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9312 +# Estimated pi_0=0.9312 +Using motif +M04074_2.00 of width 10. +Using motif -M04074_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8774 +# Estimated pi_0=0.886538 +Using motif +M04075_2.00 of width 10. +Using motif -M04075_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877864 +# Estimated pi_0=0.882202 +Using motif +M08714_2.00 of width 14. +Using motif -M08714_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930294 +# Estimated pi_0=0.940462 +Using motif +M04076_2.00 of width 11. +Using motif -M04076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93009 +# Estimated pi_0=0.944211 +Using motif +M04077_2.00 of width 11. +Using motif -M04077_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9784 +# Estimated pi_0=1 +Using motif +M07785_2.00 of width 15. +Using motif -M07785_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8798 +# Estimated pi_0=0.884 +Using motif +M07786_2.00 of width 16. +Using motif -M07786_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903137 +# Estimated pi_0=0.911215 +Using motif +M07787_2.00 of width 11. +Using motif -M07787_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955 +# Estimated pi_0=0.956952 +Using motif +M07788_2.00 of width 13. +Using motif -M07788_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924906 +# Estimated pi_0=0.938898 +Using motif +M07789_2.00 of width 13. +Using motif -M07789_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9274 +# Estimated pi_0=0.941176 +Using motif +M08050_2.00 of width 16. +Using motif -M08050_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925 +# Estimated pi_0=0.936613 +Using motif +M08715_2.00 of width 19. +Using motif -M08715_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.863366 +# Estimated pi_0=0.863689 +Using motif +M02778_2.00 of width 10. +Using motif -M02778_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93505 +# Estimated pi_0=0.94913 +Using motif +M04078_2.00 of width 11. +Using motif -M04078_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9488 +# Estimated pi_0=0.966846 +Using motif +M04079_2.00 of width 11. +Using motif -M04079_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9932 +# Estimated pi_0=0.999598 +Using motif +M08716_2.00 of width 9. +Using motif -M08716_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9092 +# Estimated pi_0=0.912308 +Using motif +M09816_2.00 of width 18. +Using motif -M09816_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870196 +# Estimated pi_0=0.870196 +Using motif +M02779_2.00 of width 10. +Using motif -M02779_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.999899 +Using motif +M04080_2.00 of width 8. +Using motif -M04080_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945797 +# Estimated pi_0=0.953289 +Using motif +M04081_2.00 of width 8. +Using motif -M04081_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973276 +# Estimated pi_0=0.983088 +Using motif +M04082_2.00 of width 11. +Using motif -M04082_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9358 +# Estimated pi_0=0.9358 +Using motif +M04083_2.00 of width 11. +Using motif -M04083_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9598 +# Estimated pi_0=0.9598 +Using motif +M02780_2.00 of width 10. +Using motif -M02780_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987941 +# Estimated pi_0=0.993862 +Using motif +M04084_2.00 of width 12. +Using motif -M04084_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936571 +# Estimated pi_0=0.948143 +Using motif +M04085_2.00 of width 12. +Using motif -M04085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942941 +# Estimated pi_0=0.95913 +Using motif +M04086_2.00 of width 12. +Using motif -M04086_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965641 +# Estimated pi_0=0.980676 +Using motif +M04087_2.00 of width 12. +Using motif -M04087_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95873 +# Estimated pi_0=0.97 +Using motif +M05831_2.00 of width 11. +Using motif -M05831_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992294 +# Estimated pi_0=0.996954 +Using motif +M08717_2.00 of width 7. +Using motif -M08717_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933611 +# Estimated pi_0=0.945153 +Using motif +M02727_2.00 of width 10. +Using motif -M02727_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981 +# Estimated pi_0=1 +Using motif +M02781_2.00 of width 10. +Using motif -M02781_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953204 +# Estimated pi_0=0.958772 +Using motif +M04088_2.00 of width 11. +Using motif -M04088_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950099 +# Estimated pi_0=0.951053 +Using motif +M04089_2.00 of width 11. +Using motif -M04089_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9816 +# Estimated pi_0=0.999795 +Using motif +M08718_2.00 of width 9. +Using motif -M08718_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955478 +# Estimated pi_0=0.960889 +Using motif +M08769_2.00 of width 17. +Using motif -M08769_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953205 +# Estimated pi_0=0.956548 +Using motif +M04090_2.00 of width 10. +Using motif -M04090_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8802 +# Estimated pi_0=0.8802 +Using motif +M04091_2.00 of width 10. +Using motif -M04091_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936481 +# Estimated pi_0=0.940536 +Using motif +M08719_2.00 of width 9. +Using motif -M08719_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94434 +# Estimated pi_0=0.945321 +Using motif +M09455_2.00 of width 10. +Using motif -M09455_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932673 +# Estimated pi_0=0.938857 +Using motif +M04092_2.00 of width 12. +Using motif -M04092_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944483 +# Estimated pi_0=0.952658 +Using motif +M04093_2.00 of width 12. +Using motif -M04093_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9602 +# Estimated pi_0=0.963301 +Using motif +M04094_2.00 of width 12. +Using motif -M04094_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9755 +# Estimated pi_0=0.981921 +Using motif +M04095_2.00 of width 12. +Using motif -M04095_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982913 +# Estimated pi_0=1 +Using motif +M07790_2.00 of width 11. +Using motif -M07790_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941176 +# Estimated pi_0=0.948951 +Using motif +M08051_2.00 of width 13. +Using motif -M08051_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8716 +# Estimated pi_0=0.877255 +Using motif +M08181_2.00 of width 15. +Using motif -M08181_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9388 +# Estimated pi_0=0.955238 +Using motif +M08182_2.00 of width 8. +Using motif -M08182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.839802 +# Estimated pi_0=0.844571 +Using motif +M08720_2.00 of width 15. +Using motif -M08720_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.830392 +# Estimated pi_0=0.834206 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923962 +# Estimated pi_0=0.932 +Using motif +M04096_2.00 of width 10. +Using motif -M04096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9394 +# Estimated pi_0=0.948095 +Using motif +M04097_2.00 of width 10. +Using motif -M04097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04098_2.00 of width 12. +Using motif -M04098_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956078 +# Estimated pi_0=0.967852 +Using motif +M04099_2.00 of width 12. +Using motif -M04099_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981786 +# Estimated pi_0=0.985683 +Using motif +M04100_2.00 of width 12. +Using motif -M04100_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969216 +# Estimated pi_0=0.980874 +Using motif +M04101_2.00 of width 12. +Using motif -M04101_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987862 +# Estimated pi_0=0.99567 +Using motif +M04102_2.00 of width 12. +Using motif -M04102_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969714 +# Estimated pi_0=0.980914 +Using motif +M04103_2.00 of width 12. +Using motif -M04103_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9785 +# Estimated pi_0=0.986378 +Using motif +M04104_2.00 of width 12. +Using motif -M04104_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984114 +# Estimated pi_0=0.99043 +Using motif +M04105_2.00 of width 12. +Using motif -M04105_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986437 +# Estimated pi_0=0.991204 +Using motif +M08721_2.00 of width 13. +Using motif -M08721_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920312 +# Estimated pi_0=0.92596 +Using motif +M08052_2.00 of width 13. +Using motif -M08052_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974323 +# Estimated pi_0=0.985185 +Using motif +M08722_2.00 of width 17. +Using motif -M08722_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996684 +# Estimated pi_0=0.998693 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M02782_2.00 of width 10. +Using motif -M02782_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9346 +# Estimated pi_0=0.951429 +Using motif +M02783_2.00 of width 10. +Using motif -M02783_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9346 +# Estimated pi_0=0.9346 +Using motif +M04106_2.00 of width 10. +Using motif -M04106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9448 +# Estimated pi_0=0.96014 +Using motif +M04107_2.00 of width 10. +Using motif -M04107_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935229 +# Estimated pi_0=0.940777 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M02784_2.00 of width 10. +Using motif -M02784_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9948 +# Estimated pi_0=1 +Using motif +M04108_2.00 of width 10. +Using motif -M04108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969907 +# Estimated pi_0=0.973514 +Using motif +M04109_2.00 of width 10. +Using motif -M04109_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996786 +# Estimated pi_0=1 +Using motif +M04110_2.00 of width 10. +Using motif -M04110_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9502 +# Estimated pi_0=0.96595 +Using motif +M04111_2.00 of width 10. +Using motif -M04111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963922 +# Estimated pi_0=0.974286 +Using motif +M00938_2.00 of width 8. +Using motif -M00938_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9424 +# Estimated pi_0=0.94991 +Using motif +M01497_2.00 of width 9. +Using motif -M01497_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973905 +# Estimated pi_0=0.990345 +Using motif +M01498_2.00 of width 8. +Using motif -M01498_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9696 +# Estimated pi_0=0.97763 +Using motif +M02785_2.00 of width 17. +Using motif -M02785_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.962381 +Using motif +M02786_2.00 of width 10. +Using motif -M02786_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9466 +# Estimated pi_0=0.965772 +Using motif +M04112_2.00 of width 19. +Using motif -M04112_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909804 +# Estimated pi_0=0.910693 +Using motif +M04113_2.00 of width 19. +Using motif -M04113_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935221 +# Estimated pi_0=0.941983 +Using motif +M04114_2.00 of width 10. +Using motif -M04114_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9332 +# Estimated pi_0=0.949043 +Using motif +M04115_2.00 of width 10. +Using motif -M04115_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955439 +# Estimated pi_0=0.960813 +Using motif +M05832_2.00 of width 6. +Using motif -M05832_2.00 of width 6. +Computing q-values. +Using motif +M07791_2.00 of width 13. +Using motif -M07791_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934 +# Estimated pi_0=0.942645 +Using motif +M07792_2.00 of width 11. +Using motif -M07792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9328 +# Estimated pi_0=0.944706 +Using motif +M07793_2.00 of width 11. +Using motif -M07793_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935294 +# Estimated pi_0=0.947069 +Using motif +M07794_2.00 of width 13. +Using motif -M07794_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8956 +# Estimated pi_0=0.91 +Using motif +M07795_2.00 of width 10. +Using motif -M07795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935534 +# Estimated pi_0=0.941197 +Using motif +M07796_2.00 of width 10. +Using motif -M07796_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920594 +# Estimated pi_0=0.920594 +Using motif +M07797_2.00 of width 11. +Using motif -M07797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9324 +# Estimated pi_0=0.943594 +Using motif +M08723_2.00 of width 10. +Using motif -M08723_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906931 +# Estimated pi_0=0.921587 +Using motif +M09835_2.00 of width 14. +Using motif -M09835_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966733 +# Estimated pi_0=0.979407 +Using motif +M04116_2.00 of width 12. +Using motif -M04116_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965588 +# Estimated pi_0=0.969306 +Using motif +M04117_2.00 of width 12. +Using motif -M04117_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9064 +# Estimated pi_0=0.913455 +Using motif +M04118_2.00 of width 12. +Using motif -M04118_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973214 +# Estimated pi_0=0.986818 +Using motif +M04119_2.00 of width 12. +Using motif -M04119_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8876 +# Estimated pi_0=0.895926 +Using motif +M04120_2.00 of width 12. +Using motif -M04120_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970476 +# Estimated pi_0=0.981778 +Using motif +M04121_2.00 of width 12. +Using motif -M04121_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928036 +# Estimated pi_0=0.946621 +Using motif +M04122_2.00 of width 12. +Using motif -M04122_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959667 +# Estimated pi_0=0.964762 +Using motif +M04123_2.00 of width 12. +Using motif -M04123_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901176 +# Estimated pi_0=0.909821 +Using motif +M08724_2.00 of width 15. +Using motif -M08724_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913519 +# Estimated pi_0=0.917872 +Using motif +M09838_2.00 of width 12. +Using motif -M09838_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955 +# Estimated pi_0=0.965629 +Using motif +M09840_2.00 of width 10. +Using motif -M09840_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954333 +# Estimated pi_0=0.966531 +Using motif +M02787_2.00 of width 10. +Using motif -M02787_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952941 +# Estimated pi_0=0.975105 +Using motif +M04124_2.00 of width 10. +Using motif -M04124_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936832 +# Estimated pi_0=0.946325 +Using motif +M04125_2.00 of width 10. +Using motif -M04125_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975 +# Estimated pi_0=0.987654 +Using motif +M08725_2.00 of width 11. +Using motif -M08725_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943333 +# Estimated pi_0=0.962973 +Using motif +M02788_2.00 of width 10. +Using motif -M02788_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945 +# Estimated pi_0=0.956269 +Using motif +M04126_2.00 of width 10. +Using motif -M04126_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9324 +# Estimated pi_0=0.934211 +Using motif +M04127_2.00 of width 10. +Using motif -M04127_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919 +# Estimated pi_0=0.9275 +Using motif +M07798_2.00 of width 10. +Using motif -M07798_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937426 +# Estimated pi_0=0.950339 +Using motif +M08726_2.00 of width 10. +Using motif -M08726_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944078 +# Estimated pi_0=0.946812 +Using motif +M09456_2.00 of width 10. +Using motif -M09456_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96736 +# Estimated pi_0=0.982484 +Using motif +M04128_2.00 of width 10. +Using motif -M04128_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8778 +# Estimated pi_0=0.881188 +Using motif +M08053_2.00 of width 12. +Using motif -M08053_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9008 +# Estimated pi_0=0.912 +Using motif +M08727_2.00 of width 12. +Using motif -M08727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928119 +# Estimated pi_0=0.934312 +Using motif +M09846_2.00 of width 12. +Using motif -M09846_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929 +# Estimated pi_0=0.929109 +Using motif +M02789_2.00 of width 10. +Using motif -M02789_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983119 +# Estimated pi_0=1 +Using motif +M04129_2.00 of width 10. +Using motif -M04129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935 +# Estimated pi_0=0.941186 +Using motif +M04130_2.00 of width 10. +Using motif -M04130_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9788 +# Estimated pi_0=0.998995 +Using motif +M08728_2.00 of width 14. +Using motif -M08728_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943048 +# Estimated pi_0=0.95437 +Using motif +M02790_2.00 of width 10. +Using motif -M02790_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8748 +# Estimated pi_0=0.882679 +Using motif +M02791_2.00 of width 10. +Using motif -M02791_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8834 +# Estimated pi_0=0.890385 +Using motif +M04131_2.00 of width 10. +Using motif -M04131_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8882 +# Estimated pi_0=0.896731 +Using motif +M04132_2.00 of width 10. +Using motif -M04132_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8638 +# Estimated pi_0=0.864554 +Using motif +M04133_2.00 of width 10. +Using motif -M04133_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8714 +# Estimated pi_0=0.871683 +Using motif +M04134_2.00 of width 10. +Using motif -M04134_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8532 +# Estimated pi_0=0.8532 +Using motif +M07799_2.00 of width 10. +Using motif -M07799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9296 +# Estimated pi_0=0.935596 +Using motif +M07800_2.00 of width 11. +Using motif -M07800_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9062 +# Estimated pi_0=0.909231 +Using motif +M07801_2.00 of width 11. +Using motif -M07801_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9282 +# Estimated pi_0=0.941709 +Using motif +M07802_2.00 of width 11. +Using motif -M07802_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918218 +# Estimated pi_0=0.927818 +Using motif +M07803_2.00 of width 8. +Using motif -M07803_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.942149 +Using motif +M07804_2.00 of width 11. +Using motif -M07804_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9034 +# Estimated pi_0=0.91037 +Using motif +M08054_2.00 of width 12. +Using motif -M08054_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894455 +# Estimated pi_0=0.898727 +Using motif +M08729_2.00 of width 11. +Using motif -M08729_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9238 +# Estimated pi_0=0.928952 +Using motif +M09457_2.00 of width 8. +Using motif -M09457_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8996 +# Estimated pi_0=0.901359 +Using motif +M04135_2.00 of width 10. +Using motif -M04135_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93669 +# Estimated pi_0=0.945432 +Using motif +M04136_2.00 of width 10. +Using motif -M04136_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925645 +# Estimated pi_0=0.933333 +Using motif +M08055_2.00 of width 13. +Using motif -M08055_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895652 +# Estimated pi_0=0.902158 +Using motif +M08730_2.00 of width 14. +Using motif -M08730_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902752 +# Estimated pi_0=0.9136 +Using motif +M04137_2.00 of width 8. +Using motif -M04137_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955752 +# Estimated pi_0=0.966707 +Using motif +M04138_2.00 of width 8. +Using motif -M04138_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961867 +# Estimated pi_0=0.967841 +Using motif +M07805_2.00 of width 11. +Using motif -M07805_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915043 +# Estimated pi_0=0.925306 +Using motif +M07806_2.00 of width 8. +Using motif -M07806_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936124 +# Estimated pi_0=0.945294 +Using motif +M08731_2.00 of width 10. +Using motif -M08731_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946154 +# Estimated pi_0=0.953291 +Using motif +M09458_2.00 of width 10. +Using motif -M09458_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929057 +# Estimated pi_0=0.942516 +Using motif +M08732_2.00 of width 9. +Using motif -M08732_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9192 +# Estimated pi_0=0.9192 +Using motif +M09459_2.00 of width 8. +Using motif -M09459_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964356 +# Estimated pi_0=0.970179 +Using motif +M09863_2.00 of width 16. +Using motif -M09863_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9302 +# Estimated pi_0=0.941538 +Using motif +M09864_2.00 of width 20. +Using motif -M09864_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9372 +# Estimated pi_0=0.945 +Using motif +M04139_2.00 of width 10. +Using motif -M04139_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943 +# Estimated pi_0=0.958833 +Using motif +M04140_2.00 of width 14. +Using motif -M04140_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935593 +# Estimated pi_0=0.943556 +Using motif +M04141_2.00 of width 14. +Using motif -M04141_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9438 +# Estimated pi_0=0.946239 +Using motif +M04142_2.00 of width 14. +Using motif -M04142_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948627 +# Estimated pi_0=0.961918 +Using motif +M04143_2.00 of width 14. +Using motif -M04143_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932871 +# Estimated pi_0=0.935534 +Using motif +M04144_2.00 of width 12. +Using motif -M04144_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983762 +# Estimated pi_0=0.999799 +Using motif +M04145_2.00 of width 12. +Using motif -M04145_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991881 +# Estimated pi_0=0.998392 +Using motif +M02792_2.00 of width 10. +Using motif -M02792_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967308 +# Estimated pi_0=0.985342 +Using motif +M04146_2.00 of width 11. +Using motif -M04146_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933509 +# Estimated pi_0=0.93952 +Using motif +M04147_2.00 of width 11. +Using motif -M04147_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999791 +# Estimated pi_0=1 +Using motif +M07807_2.00 of width 15. +Using motif -M07807_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8822 +# Estimated pi_0=0.89181 +Using motif +M07808_2.00 of width 15. +Using motif -M07808_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877822 +# Estimated pi_0=0.882342 +Using motif +M07809_2.00 of width 15. +Using motif -M07809_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8834 +# Estimated pi_0=0.893578 +Using motif +M07810_2.00 of width 11. +Using motif -M07810_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9116 +# Estimated pi_0=0.923478 +Using motif +M07811_2.00 of width 14. +Using motif -M07811_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8884 +# Estimated pi_0=0.898559 +Using motif +M08056_2.00 of width 11. +Using motif -M08056_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975966 +# Estimated pi_0=0.985422 +Using motif +M08733_2.00 of width 12. +Using motif -M08733_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938614 +# Estimated pi_0=0.944486 +Using motif +M09460_2.00 of width 10. +Using motif -M09460_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9334 +# Estimated pi_0=0.94629 +Using motif +M09867_2.00 of width 14. +Using motif -M09867_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92932 +# Estimated pi_0=0.94065 +Using motif +M09868_2.00 of width 14. +Using motif -M09868_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952661 +# Estimated pi_0=0.962416 +Using motif +M09869_2.00 of width 8. +Using motif -M09869_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940968 +# Estimated pi_0=0.950286 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972571 +# Estimated pi_0=0.983007 +Using motif +M04148_2.00 of width 19. +Using motif -M04148_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04149_2.00 of width 19. +Using motif -M04149_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08057_2.00 of width 13. +Using motif -M08057_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95024 +# Estimated pi_0=0.954745 +Using motif +M08735_2.00 of width 10. +Using motif -M08735_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964262 +# Estimated pi_0=0.97435 +Using motif +M01722_2.00 of width 9. +Using motif -M01722_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9164 +# Estimated pi_0=0.919604 +Using motif +M04150_2.00 of width 10. +Using motif -M04150_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964032 +# Estimated pi_0=0.967619 +Using motif +M04151_2.00 of width 10. +Using motif -M04151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94 +# Estimated pi_0=0.951852 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9576 +# Estimated pi_0=0.965586 +Using motif +M02793_2.00 of width 10. +Using motif -M02793_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891456 +# Estimated pi_0=0.893846 +Using motif +M04152_2.00 of width 10. +Using motif -M04152_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890485 +# Estimated pi_0=0.9 +Using motif +M04153_2.00 of width 10. +Using motif -M04153_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9094 +# Estimated pi_0=0.920909 +Using motif +M04154_2.00 of width 10. +Using motif -M04154_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866 +# Estimated pi_0=0.869808 +Using motif +M04155_2.00 of width 10. +Using motif -M04155_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8614 +# Estimated pi_0=0.864231 +Using motif +M02794_2.00 of width 10. +Using motif -M02794_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903168 +# Estimated pi_0=0.919848 +Using motif +M04156_2.00 of width 12. +Using motif -M04156_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9504 +# Estimated pi_0=0.959837 +Using motif +M04157_2.00 of width 12. +Using motif -M04157_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968618 +# Estimated pi_0=0.979888 +Using motif +M08736_2.00 of width 18. +Using motif -M08736_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904211 +# Estimated pi_0=0.906269 +Using motif +M04158_2.00 of width 10. +Using motif -M04158_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9504 +# Estimated pi_0=0.960719 +Using motif +M04159_2.00 of width 10. +Using motif -M04159_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955728 +# Estimated pi_0=0.964144 +Using motif +M02795_2.00 of width 10. +Using motif -M02795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9594 +# Estimated pi_0=0.970847 +Using motif +M04160_2.00 of width 10. +Using motif -M04160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967407 +# Estimated pi_0=0.977727 +Using motif +M04161_2.00 of width 10. +Using motif -M04161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980777 +# Estimated pi_0=1 +Using motif +M08737_2.00 of width 9. +Using motif -M08737_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944531 +# Estimated pi_0=0.957365 +Using motif +M02796_2.00 of width 10. +Using motif -M02796_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901731 +# Estimated pi_0=0.910244 +Using motif +M02797_2.00 of width 10. +Using motif -M02797_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938425 +# Estimated pi_0=0.945679 +Using motif +M04162_2.00 of width 18. +Using motif -M04162_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9004 +# Estimated pi_0=0.905793 +Using motif +M04163_2.00 of width 18. +Using motif -M04163_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9256 +# Estimated pi_0=0.936291 +Using motif +M04164_2.00 of width 18. +Using motif -M04164_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913462 +# Estimated pi_0=0.925806 +Using motif +M04165_2.00 of width 18. +Using motif -M04165_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91549 +# Estimated pi_0=0.927903 +Using motif +M09875_2.00 of width 22. +Using motif -M09875_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916038 +# Estimated pi_0=0.925513 +Using motif +M09876_2.00 of width 22. +Using motif -M09876_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894 +# Estimated pi_0=0.900699 +Using motif +M04166_2.00 of width 10. +Using motif -M04166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970769 +# Estimated pi_0=0.976818 +Using motif +M04167_2.00 of width 10. +Using motif -M04167_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9666 +# Estimated pi_0=0.976581 +Using motif +M04168_2.00 of width 10. +Using motif -M04168_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9732 +# Estimated pi_0=0.977062 +Using motif +M04169_2.00 of width 10. +Using motif -M04169_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9848 +# Estimated pi_0=0.997879 +Using motif +M08738_2.00 of width 9. +Using motif -M08738_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944237 +# Estimated pi_0=0.950122 +Using motif +M04170_2.00 of width 10. +Using motif -M04170_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96069 +# Estimated pi_0=0.976914 +Using motif +M01743_2.00 of width 8. +Using motif -M01743_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927525 +# Estimated pi_0=0.927525 +Using motif +M02798_2.00 of width 10. +Using motif -M02798_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9928 +# Estimated pi_0=1 +Using motif +M04171_2.00 of width 10. +Using motif -M04171_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04172_2.00 of width 10. +Using motif -M04172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988507 +# Estimated pi_0=0.996122 +Using motif +M04173_2.00 of width 10. +Using motif -M04173_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04174_2.00 of width 10. +Using motif -M04174_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974074 +# Estimated pi_0=0.990241 +Using motif +M04175_2.00 of width 10. +Using motif -M04175_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04176_2.00 of width 10. +Using motif -M04176_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04177_2.00 of width 18. +Using motif -M04177_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903725 +# Estimated pi_0=0.918723 +Using motif +M04178_2.00 of width 18. +Using motif -M04178_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913578 +# Estimated pi_0=0.928199 +Using motif +M02799_2.00 of width 10. +Using motif -M02799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M02800_2.00 of width 10. +Using motif -M02800_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996639 +# Estimated pi_0=1 +Using motif +M04179_2.00 of width 10. +Using motif -M04179_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995433 +# Estimated pi_0=0.999598 +Using motif +M04180_2.00 of width 10. +Using motif -M04180_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994118 +# Estimated pi_0=0.999596 +Using motif +M04181_2.00 of width 10. +Using motif -M04181_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8998 +# Estimated pi_0=0.903137 +Using motif +M04182_2.00 of width 10. +Using motif -M04182_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907 +# Estimated pi_0=0.923214 +Using motif +M02801_2.00 of width 10. +Using motif -M02801_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965641 +# Estimated pi_0=0.971733 +Using motif +M04183_2.00 of width 12. +Using motif -M04183_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975766 +# Estimated pi_0=0.985333 +Using motif +M04184_2.00 of width 12. +Using motif -M04184_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941143 +# Estimated pi_0=0.953385 +Using motif +M04185_2.00 of width 12. +Using motif -M04185_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931111 +# Estimated pi_0=0.940444 +Using motif +M04186_2.00 of width 12. +Using motif -M04186_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04187_2.00 of width 12. +Using motif -M04187_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945243 +# Estimated pi_0=0.954568 +Using motif +M04188_2.00 of width 12. +Using motif -M04188_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925 +# Estimated pi_0=0.942222 +Using motif +M04189_2.00 of width 12. +Using motif -M04189_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94871 +# Estimated pi_0=0.960127 +Using motif +M04190_2.00 of width 12. +Using motif -M04190_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M02802_2.00 of width 12. +Using motif -M02802_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9126 +# Estimated pi_0=0.920667 +Using motif +M04191_2.00 of width 10. +Using motif -M04191_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8546 +# Estimated pi_0=0.859412 +Using motif +M04192_2.00 of width 10. +Using motif -M04192_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8962 +# Estimated pi_0=0.896634 +Using motif +M04193_2.00 of width 10. +Using motif -M04193_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975126 +# Estimated pi_0=0.986554 +Using motif +M04194_2.00 of width 10. +Using motif -M04194_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969 +# Estimated pi_0=0.990458 +Using motif +M02803_2.00 of width 10. +Using motif -M02803_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04195_2.00 of width 10. +Using motif -M04195_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962282 +# Estimated pi_0=0.968621 +Using motif +M04196_2.00 of width 10. +Using motif -M04196_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953158 +# Estimated pi_0=0.960292 +Using motif +M04197_2.00 of width 10. +Using motif -M04197_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965909 +# Estimated pi_0=0.970465 +Using motif +M04198_2.00 of width 10. +Using motif -M04198_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98198 +# Estimated pi_0=0.999697 +Using motif +M08739_2.00 of width 11. +Using motif -M08739_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915146 +# Estimated pi_0=0.92512 +Using motif +M02804_2.00 of width 12. +Using motif -M02804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04199_2.00 of width 10. +Using motif -M04199_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991262 +# Estimated pi_0=1 +Using motif +M04200_2.00 of width 10. +Using motif -M04200_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04201_2.00 of width 10. +Using motif -M04201_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9872 +# Estimated pi_0=0.99899 +Using motif +M04202_2.00 of width 10. +Using motif -M04202_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M04203_2.00 of width 10. +Using motif -M04203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989804 +# Estimated pi_0=0.999899 +Using motif +M04204_2.00 of width 10. +Using motif -M04204_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996585 +# Estimated pi_0=1 +Using motif +M02805_2.00 of width 10. +Using motif -M02805_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959469 +# Estimated pi_0=0.969419 +Using motif +M04205_2.00 of width 9. +Using motif -M04205_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948819 +# Estimated pi_0=0.950256 +Using motif +M04206_2.00 of width 9. +Using motif -M04206_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952248 +# Estimated pi_0=0.970349 +Using motif +M04207_2.00 of width 10. +Using motif -M04207_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923333 +# Estimated pi_0=0.92922 +Using motif +M04208_2.00 of width 10. +Using motif -M04208_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942222 +# Estimated pi_0=0.956265 +Using motif +M04209_2.00 of width 10. +Using motif -M04209_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91802 +# Estimated pi_0=0.93 +Using motif +M04210_2.00 of width 10. +Using motif -M04210_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923448 +# Estimated pi_0=0.934359 +Using motif +M04211_2.00 of width 10. +Using motif -M04211_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932537 +# Estimated pi_0=0.934265 +Using motif +M02806_2.00 of width 10. +Using motif -M02806_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08734_2.00 of width 19. +Using motif -M08734_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964516 +# Estimated pi_0=0.973846 +Using motif +M08058_2.00 of width 18. +Using motif -M08058_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966783 +# Estimated pi_0=0.969787 +Using motif +M08740_2.00 of width 10. +Using motif -M08740_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964528 +# Estimated pi_0=0.977333 +Using motif +M04212_2.00 of width 12. +Using motif -M04212_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04213_2.00 of width 12. +Using motif -M04213_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9942 +# Estimated pi_0=1 +Using motif +M01726_2.00 of width 10. +Using motif -M01726_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898812 +# Estimated pi_0=0.908571 +Using motif +M02728_2.00 of width 7. +Using motif -M02728_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934314 +# Estimated pi_0=0.948447 +Using motif +M02807_2.00 of width 10. +Using motif -M02807_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937167 +# Estimated pi_0=0.948214 +Using motif +M02808_2.00 of width 10. +Using motif -M02808_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941923 +# Estimated pi_0=0.95012 +Using motif +M04214_2.00 of width 8. +Using motif -M04214_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9366 +# Estimated pi_0=0.952911 +Using motif +M04215_2.00 of width 8. +Using motif -M04215_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961791 +# Estimated pi_0=0.97486 +Using motif +M09461_2.00 of width 12. +Using motif -M09461_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991264 +# Estimated pi_0=0.997071 +Using motif +M02809_2.00 of width 12. +Using motif -M02809_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9194 +# Estimated pi_0=0.929298 +Using motif +M02810_2.00 of width 12. +Using motif -M02810_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9164 +# Estimated pi_0=0.923333 +Using motif +M04216_2.00 of width 10. +Using motif -M04216_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9046 +# Estimated pi_0=0.90875 +Using motif +M04217_2.00 of width 10. +Using motif -M04217_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8826 +# Estimated pi_0=0.895752 +Using motif +M04218_2.00 of width 8. +Using motif -M04218_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9238 +# Estimated pi_0=0.948872 +Using motif +M02811_2.00 of width 10. +Using motif -M02811_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9684 +# Estimated pi_0=0.981832 +Using motif +M03591_2.00 of width 12. +Using motif -M03591_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9434 +# Estimated pi_0=0.959531 +Using motif +M04219_2.00 of width 10. +Using motif -M04219_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9394 +# Estimated pi_0=0.947395 +Using motif +M04220_2.00 of width 10. +Using motif -M04220_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931089 +# Estimated pi_0=0.940183 +Using motif +M04221_2.00 of width 10. +Using motif -M04221_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9462 +# Estimated pi_0=0.947963 +Using motif +M04222_2.00 of width 10. +Using motif -M04222_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983301 +# Estimated pi_0=0.998021 +Using motif +M08059_2.00 of width 10. +Using motif -M08059_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9598 +# Estimated pi_0=0.973986 +Using motif +M08741_2.00 of width 13. +Using motif -M08741_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.906119 +Using motif +M09462_2.00 of width 12. +Using motif -M09462_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934462 +# Estimated pi_0=0.944 +Using motif +M02812_2.00 of width 10. +Using motif -M02812_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M02813_2.00 of width 10. +Using motif -M02813_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04223_2.00 of width 10. +Using motif -M04223_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9726 +# Estimated pi_0=0.99 +Using motif +M04224_2.00 of width 10. +Using motif -M04224_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04225_2.00 of width 10. +Using motif -M04225_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986389 +# Estimated pi_0=0.993617 +Using motif +M04226_2.00 of width 10. +Using motif -M04226_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M08742_2.00 of width 18. +Using motif -M08742_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951367 +# Estimated pi_0=0.960714 +Using motif +M01718_2.00 of width 10. +Using motif -M01718_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M01113_2.00 of width 9. +Using motif -M01113_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936636 +# Estimated pi_0=0.949231 +Using motif +M02821_2.00 of width 8. +Using motif -M02821_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9705 +# Estimated pi_0=0.97871 +Using motif +M02822_2.00 of width 18. +Using motif -M02822_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992427 +# Estimated pi_0=1 +Using motif +M02823_2.00 of width 18. +Using motif -M02823_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976226 +# Estimated pi_0=0.994815 +Using motif +M01131_2.00 of width 8. +Using motif -M01131_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97625 +# Estimated pi_0=0.984593 +Using motif +M05834_2.00 of width 10. +Using motif -M05834_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993267 +# Estimated pi_0=0.999497 +Using motif +M05835_2.00 of width 8. +Using motif -M05835_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M07812_2.00 of width 14. +Using motif -M07812_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972871 +# Estimated pi_0=0.987719 +Using motif +M08064_2.00 of width 11. +Using motif -M08064_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984667 +# Estimated pi_0=0.991915 +Using motif +M08781_2.00 of width 12. +Using motif -M08781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993818 +# Estimated pi_0=0.99809 +Using motif +M04008_2.00 of width 9. +Using motif -M04008_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M08782_2.00 of width 7. +Using motif -M08782_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M09926_2.00 of width 13. +Using motif -M09926_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M02729_2.00 of width 10. +Using motif -M02729_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M02824_2.00 of width 10. +Using motif -M02824_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04009_2.00 of width 12. +Using motif -M04009_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993704 +# Estimated pi_0=1 +Using motif +M04227_2.00 of width 12. +Using motif -M04227_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996667 +# Estimated pi_0=0.999899 +Using motif +M04228_2.00 of width 12. +Using motif -M04228_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M08783_2.00 of width 12. +Using motif -M08783_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M04229_2.00 of width 12. +Using motif -M04229_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9246 +# Estimated pi_0=0.930286 +Using motif +M04230_2.00 of width 12. +Using motif -M04230_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959 +# Estimated pi_0=0.971681 +Using motif +M08784_2.00 of width 11. +Using motif -M08784_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952453 +# Estimated pi_0=0.958018 +Using motif +M02730_2.00 of width 11. +Using motif -M02730_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958938 +# Estimated pi_0=0.965333 +Using motif +M02825_2.00 of width 12. +Using motif -M02825_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9564 +# Estimated pi_0=0.97374 +Using motif +M02826_2.00 of width 14. +Using motif -M02826_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974653 +# Estimated pi_0=0.992941 +Using motif +M04010_2.00 of width 12. +Using motif -M04010_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934038 +# Estimated pi_0=0.938966 +Using motif +M04011_2.00 of width 12. +Using motif -M04011_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944 +# Estimated pi_0=0.947647 +Using motif +M04012_2.00 of width 13. +Using motif -M04012_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960594 +# Estimated pi_0=0.965688 +Using motif +M04231_2.00 of width 12. +Using motif -M04231_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915686 +# Estimated pi_0=0.915686 +Using motif +M04232_2.00 of width 12. +Using motif -M04232_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9644 +# Estimated pi_0=0.97395 +Using motif +M04233_2.00 of width 12. +Using motif -M04233_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9308 +# Estimated pi_0=0.933529 +Using motif +M04234_2.00 of width 12. +Using motif -M04234_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925 +# Estimated pi_0=0.929515 +Using motif +M09932_2.00 of width 24. +Using motif -M09932_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M09933_2.00 of width 17. +Using motif -M09933_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M02827_2.00 of width 12. +Using motif -M02827_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M02828_2.00 of width 12. +Using motif -M02828_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04013_2.00 of width 10. +Using motif -M04013_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04235_2.00 of width 12. +Using motif -M04235_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04236_2.00 of width 12. +Using motif -M04236_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04237_2.00 of width 12. +Using motif -M04237_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04238_2.00 of width 12. +Using motif -M04238_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08785_2.00 of width 11. +Using motif -M08785_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M02829_2.00 of width 14. +Using motif -M02829_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950476 +# Estimated pi_0=0.964688 +Using motif +M02830_2.00 of width 14. +Using motif -M02830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9456 +# Estimated pi_0=0.952182 +Using motif +M04014_2.00 of width 12. +Using motif -M04014_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898627 +# Estimated pi_0=0.913 +Using motif +M04015_2.00 of width 12. +Using motif -M04015_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931683 +# Estimated pi_0=0.943471 +Using motif +M04239_2.00 of width 12. +Using motif -M04239_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9384 +# Estimated pi_0=0.945472 +Using motif +M04240_2.00 of width 12. +Using motif -M04240_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9192 +# Estimated pi_0=0.919231 +Using motif +M04241_2.00 of width 12. +Using motif -M04241_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9674 +# Estimated pi_0=0.973451 +Using motif +M04242_2.00 of width 12. +Using motif -M04242_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8862 +# Estimated pi_0=0.891321 +Using motif +M04243_2.00 of width 12. +Using motif -M04243_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962 +# Estimated pi_0=0.965577 +Using motif +M04244_2.00 of width 12. +Using motif -M04244_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981905 +# Estimated pi_0=0.998454 +Using motif +M02831_2.00 of width 12. +Using motif -M02831_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04016_2.00 of width 10. +Using motif -M04016_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04245_2.00 of width 12. +Using motif -M04245_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9626 +# Estimated pi_0=0.968621 +Using motif +M04246_2.00 of width 12. +Using motif -M04246_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965545 +# Estimated pi_0=0.968544 +Using motif +M08786_2.00 of width 13. +Using motif -M08786_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M09937_2.00 of width 10. +Using motif -M09937_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03649_2.00 of width 10. +Using motif -M03649_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04247_2.00 of width 19. +Using motif -M04247_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00097 +# Estimated pi_0=1 +Using motif +M04248_2.00 of width 20. +Using motif -M04248_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04249_2.00 of width 19. +Using motif -M04249_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M08065_2.00 of width 14. +Using motif -M08065_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985161 +# Estimated pi_0=0.990842 +Using motif +M08787_2.00 of width 11. +Using motif -M08787_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9756 +# Estimated pi_0=0.985914 +Using motif +M09484_2.00 of width 10. +Using motif -M09484_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986415 +# Estimated pi_0=0.997143 +Using motif +M09938_2.00 of width 11. +Using motif -M09938_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9516 +# Estimated pi_0=0.972319 +Using motif +M04017_2.00 of width 10. +Using motif -M04017_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9932 +# Estimated pi_0=1 +Using motif +M04250_2.00 of width 12. +Using motif -M04250_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973 +# Estimated pi_0=1 +Using motif +M04251_2.00 of width 12. +Using motif -M04251_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99901 +# Estimated pi_0=0.999497 +Using motif +M04252_2.00 of width 12. +Using motif -M04252_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9288 +# Estimated pi_0=0.938716 +Using motif +M04253_2.00 of width 12. +Using motif -M04253_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9734 +# Estimated pi_0=0.999899 +Using motif +M08788_2.00 of width 11. +Using motif -M08788_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99 +# Estimated pi_0=0.99433 +Using motif +M09941_2.00 of width 8. +Using motif -M09941_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M09942_2.00 of width 12. +Using motif -M09942_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9368 +# Estimated pi_0=0.953906 +Using motif +M08789_2.00 of width 14. +Using motif -M08789_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999689 +# Estimated pi_0=1 +Using motif +M04018_2.00 of width 12. +Using motif -M04018_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9344 +# Estimated pi_0=0.942034 +Using motif +M04019_2.00 of width 11. +Using motif -M04019_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9478 +# Estimated pi_0=0.958814 +Using motif +M04020_2.00 of width 14. +Using motif -M04020_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9352 +# Estimated pi_0=0.942692 +Using motif +M04021_2.00 of width 11. +Using motif -M04021_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9868 +# Estimated pi_0=1 +Using motif +M04254_2.00 of width 14. +Using motif -M04254_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96 +# Estimated pi_0=0.97 +Using motif +M04255_2.00 of width 12. +Using motif -M04255_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970667 +# Estimated pi_0=0.972 +Using motif +M04256_2.00 of width 14. +Using motif -M04256_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937451 +# Estimated pi_0=0.941043 +Using motif +M04257_2.00 of width 12. +Using motif -M04257_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980196 +# Estimated pi_0=0.998769 +Using motif +M08790_2.00 of width 12. +Using motif -M08790_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877308 +# Estimated pi_0=0.886071 +Using motif +M09947_2.00 of width 8. +Using motif -M09947_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93505 +# Estimated pi_0=0.94672 +Using motif +M04022_2.00 of width 12. +Using motif -M04022_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976636 +# Estimated pi_0=0.995669 +Using motif +M04023_2.00 of width 10. +Using motif -M04023_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95 +# Estimated pi_0=0.95537 +Using motif +M04258_2.00 of width 12. +Using motif -M04258_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9304 +# Estimated pi_0=0.939099 +Using motif +M04259_2.00 of width 9. +Using motif -M04259_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9654 +# Estimated pi_0=0.988516 +Using motif +M04260_2.00 of width 12. +Using motif -M04260_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960777 +# Estimated pi_0=0.979301 +Using motif +M04261_2.00 of width 11. +Using motif -M04261_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987 +# Estimated pi_0=1 +Using motif +M04262_2.00 of width 12. +Using motif -M04262_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982745 +# Estimated pi_0=1 +Using motif +M04263_2.00 of width 9. +Using motif -M04263_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981 +# Estimated pi_0=1 +Using motif +M04264_2.00 of width 12. +Using motif -M04264_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983564 +# Estimated pi_0=1 +Using motif +M04265_2.00 of width 12. +Using motif -M04265_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993861 +# Estimated pi_0=0.999799 +Using motif +M08066_2.00 of width 12. +Using motif -M08066_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970693 +# Estimated pi_0=0.992632 +Using motif +M08791_2.00 of width 11. +Using motif -M08791_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977426 +# Estimated pi_0=0.998964 +Using motif +M09948_2.00 of width 8. +Using motif -M09948_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9802 +# Estimated pi_0=1 +Using motif +M09949_2.00 of width 12. +Using motif -M09949_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9442 +# Estimated pi_0=0.9442 +Using motif +M09952_2.00 of width 12. +Using motif -M09952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960566 +# Estimated pi_0=0.975658 +Using motif +M09953_2.00 of width 12. +Using motif -M09953_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946471 +# Estimated pi_0=0.962295 +Using motif +M09954_2.00 of width 15. +Using motif -M09954_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9096 +# Estimated pi_0=0.922167 +Using motif +M09955_2.00 of width 15. +Using motif -M09955_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947563 +# Estimated pi_0=0.947563 +Using motif +M08792_2.00 of width 11. +Using motif -M08792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982243 +# Estimated pi_0=0.989385 +Using motif +M09485_2.00 of width 10. +Using motif -M09485_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99984 +# Estimated pi_0=1 +Using motif +M02832_2.00 of width 11. +Using motif -M02832_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04024_2.00 of width 9. +Using motif -M04024_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998693 +# Estimated pi_0=0.998693 +Using motif +M04266_2.00 of width 11. +Using motif -M04266_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99469 +# Estimated pi_0=1 +Using motif +M04267_2.00 of width 11. +Using motif -M04267_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M07813_2.00 of width 17. +Using motif -M07813_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997457 +# Estimated pi_0=0.999296 +Using motif +M08793_2.00 of width 13. +Using motif -M08793_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964752 +# Estimated pi_0=0.973418 +Using motif +M09486_2.00 of width 12. +Using motif -M09486_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995319 +# Estimated pi_0=0.997487 +Using motif +M09959_2.00 of width 11. +Using motif -M09959_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982075 +# Estimated pi_0=0.992953 +Using motif +M02833_2.00 of width 14. +Using motif -M02833_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998 +# Estimated pi_0=1 +Using motif +M04025_2.00 of width 12. +Using motif -M04025_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965545 +# Estimated pi_0=0.977122 +Using motif +M04026_2.00 of width 11. +Using motif -M04026_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943396 +# Estimated pi_0=0.957463 +Using motif +M04268_2.00 of width 12. +Using motif -M04268_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9556 +# Estimated pi_0=0.972308 +Using motif +M04269_2.00 of width 12. +Using motif -M04269_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993465 +# Estimated pi_0=0.999196 +Using motif +M04270_2.00 of width 12. +Using motif -M04270_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973725 +# Estimated pi_0=0.999397 +Using motif +M04271_2.00 of width 12. +Using motif -M04271_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986355 +# Estimated pi_0=0.999497 +Using motif +M08794_2.00 of width 17. +Using motif -M08794_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996853 +# Estimated pi_0=0.998693 +Using motif +M04272_2.00 of width 12. +Using motif -M04272_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978 +# Estimated pi_0=1 +Using motif +M04273_2.00 of width 12. +Using motif -M04273_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997407 +# Estimated pi_0=1 +Using motif +M08795_2.00 of width 9. +Using motif -M08795_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.998593 +Using motif +M02834_2.00 of width 13. +Using motif -M02834_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04027_2.00 of width 14. +Using motif -M04027_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9566 +# Estimated pi_0=0.974069 +Using motif +M04274_2.00 of width 14. +Using motif -M04274_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960198 +# Estimated pi_0=0.9744 +Using motif +M04275_2.00 of width 14. +Using motif -M04275_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999082 +# Estimated pi_0=1 +Using motif +M04276_2.00 of width 14. +Using motif -M04276_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974364 +# Estimated pi_0=0.990238 +Using motif +M08796_2.00 of width 12. +Using motif -M08796_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M02835_2.00 of width 11. +Using motif -M02835_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995638 +# Estimated pi_0=0.998693 +Using motif +M04277_2.00 of width 13. +Using motif -M04277_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04278_2.00 of width 13. +Using motif -M04278_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9828 +# Estimated pi_0=0.991951 +Using motif +M04279_2.00 of width 13. +Using motif -M04279_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04280_2.00 of width 13. +Using motif -M04280_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04281_2.00 of width 11. +Using motif -M04281_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9512 +# Estimated pi_0=0.95534 +Using motif +M04282_2.00 of width 12. +Using motif -M04282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97 +# Estimated pi_0=0.999799 +Using motif +M04283_2.00 of width 11. +Using motif -M04283_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987921 +# Estimated pi_0=1 +Using motif +M04284_2.00 of width 11. +Using motif -M04284_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9696 +# Estimated pi_0=0.987059 +Using motif +M05836_2.00 of width 10. +Using motif -M05836_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05837_2.00 of width 11. +Using motif -M05837_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M07814_2.00 of width 19. +Using motif -M07814_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M07815_2.00 of width 11. +Using motif -M07815_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9762 +# Estimated pi_0=0.994891 +Using motif +M07816_2.00 of width 15. +Using motif -M07816_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9922 +# Estimated pi_0=0.997513 +Using motif +M07817_2.00 of width 13. +Using motif -M07817_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9508 +# Estimated pi_0=0.965 +Using motif +M07818_2.00 of width 15. +Using motif -M07818_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991176 +# Estimated pi_0=0.996907 +Using motif +M07819_2.00 of width 14. +Using motif -M07819_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973663 +# Estimated pi_0=0.998602 +Using motif +M07820_2.00 of width 15. +Using motif -M07820_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96198 +# Estimated pi_0=0.977692 +Using motif +M08067_2.00 of width 11. +Using motif -M08067_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9952 +# Estimated pi_0=0.999598 +Using motif +M08068_2.00 of width 15. +Using motif -M08068_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08797_2.00 of width 11. +Using motif -M08797_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989143 +# Estimated pi_0=0.997778 +Using motif +M09487_2.00 of width 12. +Using motif -M09487_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M01813_2.00 of width 8. +Using motif -M01813_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M02836_2.00 of width 9. +Using motif -M02836_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995039 +# Estimated pi_0=0.999394 +Using motif +M02837_2.00 of width 12. +Using motif -M02837_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986667 +# Estimated pi_0=1 +Using motif +M02838_2.00 of width 9. +Using motif -M02838_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997688 +# Estimated pi_0=0.997688 +Using motif +M02839_2.00 of width 12. +Using motif -M02839_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9908 +# Estimated pi_0=1 +Using motif +M04285_2.00 of width 11. +Using motif -M04285_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997561 +# Estimated pi_0=0.999196 +Using motif +M04286_2.00 of width 12. +Using motif -M04286_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04287_2.00 of width 11. +Using motif -M04287_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987167 +# Estimated pi_0=0.999497 +Using motif +M04288_2.00 of width 12. +Using motif -M04288_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M04289_2.00 of width 11. +Using motif -M04289_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940792 +# Estimated pi_0=0.9412 +Using motif +M04290_2.00 of width 12. +Using motif -M04290_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9912 +# Estimated pi_0=1 +Using motif +M04291_2.00 of width 11. +Using motif -M04291_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.994503 +Using motif +M04292_2.00 of width 12. +Using motif -M04292_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9658 +# Estimated pi_0=0.983193 +Using motif +M04293_2.00 of width 12. +Using motif -M04293_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911683 +# Estimated pi_0=0.916762 +Using motif +M04294_2.00 of width 12. +Using motif -M04294_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9138 +# Estimated pi_0=0.921553 +Using motif +M04295_2.00 of width 12. +Using motif -M04295_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8958 +# Estimated pi_0=0.901101 +Using motif +M04296_2.00 of width 12. +Using motif -M04296_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9434 +# Estimated pi_0=0.946019 +Using motif +M04297_2.00 of width 12. +Using motif -M04297_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91549 +# Estimated pi_0=0.928448 +Using motif +M04298_2.00 of width 12. +Using motif -M04298_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968738 +# Estimated pi_0=0.981783 +Using motif +M04299_2.00 of width 12. +Using motif -M04299_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937228 +# Estimated pi_0=0.948571 +Using motif +M04300_2.00 of width 12. +Using motif -M04300_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994455 +# Estimated pi_0=1 +Using motif +M04301_2.00 of width 12. +Using motif -M04301_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04302_2.00 of width 12. +Using motif -M04302_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02840_2.00 of width 10. +Using motif -M02840_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994 +# Estimated pi_0=1 +Using motif +M02841_2.00 of width 10. +Using motif -M02841_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04028_2.00 of width 12. +Using motif -M04028_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982095 +# Estimated pi_0=0.999196 +Using motif +M04303_2.00 of width 12. +Using motif -M04303_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99799 +# Estimated pi_0=0.99799 +Using motif +M04304_2.00 of width 12. +Using motif -M04304_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M04305_2.00 of width 12. +Using motif -M04305_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971048 +# Estimated pi_0=0.975315 +Using motif +M04306_2.00 of width 12. +Using motif -M04306_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996889 +# Estimated pi_0=0.999497 +Using motif +M08798_2.00 of width 12. +Using motif -M08798_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04307_2.00 of width 10. +Using motif -M04307_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8962 +# Estimated pi_0=0.898846 +Using motif +M04308_2.00 of width 10. +Using motif -M04308_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.901553 +Using motif +M07821_2.00 of width 15. +Using motif -M07821_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=0.999899 +Using motif +M08799_2.00 of width 18. +Using motif -M08799_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M08184_2.00 of width 8. +Using motif -M08184_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954019 +# Estimated pi_0=0.960625 +Using motif +M08185_2.00 of width 10. +Using motif -M08185_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97245 +# Estimated pi_0=0.9832 +Using motif +M08186_2.00 of width 9. +Using motif -M08186_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998889 +# Estimated pi_0=0.999799 +Using motif +M08800_2.00 of width 13. +Using motif -M08800_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8784 +# Estimated pi_0=0.898655 +Using motif +M09488_2.00 of width 15. +Using motif -M09488_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997219 +# Estimated pi_0=1 +Using motif +M09975_2.00 of width 15. +Using motif -M09975_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996415 +# Estimated pi_0=1 +Using motif +M02842_2.00 of width 14. +Using motif -M02842_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962835 +# Estimated pi_0=0.973506 +Using motif +M02843_2.00 of width 12. +Using motif -M02843_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933592 +# Estimated pi_0=0.942623 +Using motif +M02844_2.00 of width 12. +Using motif -M02844_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9382 +# Estimated pi_0=0.9382 +Using motif +M02845_2.00 of width 14. +Using motif -M02845_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931 +# Estimated pi_0=0.944262 +Using motif +M02846_2.00 of width 12. +Using motif -M02846_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930693 +# Estimated pi_0=0.93434 +Using motif +M02847_2.00 of width 14. +Using motif -M02847_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9368 +# Estimated pi_0=0.949635 +Using motif +M04029_2.00 of width 13. +Using motif -M04029_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9442 +# Estimated pi_0=0.949926 +Using motif +M04030_2.00 of width 13. +Using motif -M04030_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9356 +# Estimated pi_0=0.942286 +Using motif +M04031_2.00 of width 12. +Using motif -M04031_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938846 +# Estimated pi_0=0.944364 +Using motif +M04309_2.00 of width 14. +Using motif -M04309_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9008 +# Estimated pi_0=0.908269 +Using motif +M04310_2.00 of width 12. +Using motif -M04310_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916436 +# Estimated pi_0=0.921346 +Using motif +M04311_2.00 of width 12. +Using motif -M04311_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9136 +# Estimated pi_0=0.932713 +Using motif +M04312_2.00 of width 14. +Using motif -M04312_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909905 +# Estimated pi_0=0.924 +Using motif +M04313_2.00 of width 12. +Using motif -M04313_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913465 +# Estimated pi_0=0.924737 +Using motif +M04314_2.00 of width 12. +Using motif -M04314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9336 +# Estimated pi_0=0.943065 +Using motif +M04315_2.00 of width 12. +Using motif -M04315_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9134 +# Estimated pi_0=0.918148 +Using motif +M04316_2.00 of width 12. +Using motif -M04316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904158 +# Estimated pi_0=0.906078 +Using motif +M04317_2.00 of width 13. +Using motif -M04317_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9066 +# Estimated pi_0=0.913211 +Using motif +M04318_2.00 of width 12. +Using motif -M04318_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9208 +# Estimated pi_0=0.927899 +Using motif +M04319_2.00 of width 12. +Using motif -M04319_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9536 +# Estimated pi_0=0.963212 +Using motif +M04320_2.00 of width 13. +Using motif -M04320_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9264 +# Estimated pi_0=0.935833 +Using motif +M04032_2.00 of width 10. +Using motif -M04032_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998218 +# Estimated pi_0=1 +Using motif +M04033_2.00 of width 9. +Using motif -M04033_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995625 +# Estimated pi_0=1 +Using motif +M04321_2.00 of width 12. +Using motif -M04321_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04322_2.00 of width 12. +Using motif -M04322_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04323_2.00 of width 12. +Using motif -M04323_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9854 +# Estimated pi_0=1 +Using motif +M04324_2.00 of width 12. +Using motif -M04324_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9724 +# Estimated pi_0=0.999698 +Using motif +M07822_2.00 of width 11. +Using motif -M07822_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901 +# Estimated pi_0=0.905686 +Using motif +M07823_2.00 of width 11. +Using motif -M07823_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9214 +# Estimated pi_0=0.929643 +Using motif +M07824_2.00 of width 11. +Using motif -M07824_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922 +# Estimated pi_0=0.932432 +Using motif +M07825_2.00 of width 10. +Using motif -M07825_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942178 +# Estimated pi_0=0.955763 +Using motif +M08187_2.00 of width 8. +Using motif -M08187_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88902 +# Estimated pi_0=0.89037 +Using motif +M08188_2.00 of width 10. +Using motif -M08188_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986789 +# Estimated pi_0=0.999286 +Using motif +M08801_2.00 of width 11. +Using motif -M08801_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923883 +# Estimated pi_0=0.931964 +Using motif +M09489_2.00 of width 12. +Using motif -M09489_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992581 +# Estimated pi_0=0.996244 +Using motif +M02658_2.00 of width 11. +Using motif -M02658_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M02848_2.00 of width 12. +Using motif -M02848_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04034_2.00 of width 10. +Using motif -M04034_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09982_2.00 of width 12. +Using motif -M09982_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M02849_2.00 of width 12. +Using motif -M02849_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M02850_2.00 of width 12. +Using motif -M02850_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04325_2.00 of width 12. +Using motif -M04325_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04326_2.00 of width 12. +Using motif -M04326_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09986_2.00 of width 10. +Using motif -M09986_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04327_2.00 of width 12. +Using motif -M04327_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964854 +# Estimated pi_0=0.966355 +Using motif +M04328_2.00 of width 12. +Using motif -M04328_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9822 +# Estimated pi_0=1 +Using motif +M07826_2.00 of width 13. +Using motif -M07826_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9738 +# Estimated pi_0=0.99797 +Using motif +M07827_2.00 of width 11. +Using motif -M07827_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993299 +# Estimated pi_0=0.995102 +Using motif +M07828_2.00 of width 15. +Using motif -M07828_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964038 +# Estimated pi_0=0.980142 +Using motif +M08069_2.00 of width 11. +Using motif -M08069_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997551 +# Estimated pi_0=0.999698 +Using motif +M08802_2.00 of width 9. +Using motif -M08802_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989474 +# Estimated pi_0=0.993646 +Using motif +M04329_2.00 of width 12. +Using motif -M04329_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9842 +# Estimated pi_0=1 +Using motif +M04330_2.00 of width 11. +Using motif -M04330_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985047 +# Estimated pi_0=0.999898 +Using motif +M05838_2.00 of width 8. +Using motif -M05838_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99899 +# Estimated pi_0=1 +Using motif +M05839_2.00 of width 10. +Using motif -M05839_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M07829_2.00 of width 15. +Using motif -M07829_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981636 +# Estimated pi_0=0.996495 +Using motif +M08070_2.00 of width 11. +Using motif -M08070_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996458 +# Estimated pi_0=0.997085 +Using motif +M08803_2.00 of width 11. +Using motif -M08803_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993548 +# Estimated pi_0=0.997576 +Using motif +M02851_2.00 of width 10. +Using motif -M02851_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M02852_2.00 of width 10. +Using motif -M02852_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04331_2.00 of width 12. +Using motif -M04331_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04332_2.00 of width 12. +Using motif -M04332_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05840_2.00 of width 11. +Using motif -M05840_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M07830_2.00 of width 14. +Using motif -M07830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998201 +# Estimated pi_0=0.998894 +Using motif +M07831_2.00 of width 11. +Using motif -M07831_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M07832_2.00 of width 14. +Using motif -M07832_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998462 +# Estimated pi_0=0.999296 +Using motif +M08804_2.00 of width 12. +Using motif -M08804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999192 +# Estimated pi_0=0.999799 +Using motif +M09993_2.00 of width 14. +Using motif -M09993_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995026 +# Estimated pi_0=0.997462 +Using motif +M09994_2.00 of width 14. +Using motif -M09994_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04035_2.00 of width 10. +Using motif -M04035_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991262 +# Estimated pi_0=1 +Using motif +M04036_2.00 of width 12. +Using motif -M04036_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961333 +# Estimated pi_0=0.971007 +Using motif +M04333_2.00 of width 11. +Using motif -M04333_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=1 +Using motif +M04334_2.00 of width 12. +Using motif -M04334_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9568 +# Estimated pi_0=0.970442 +Using motif +M04335_2.00 of width 9. +Using motif -M04335_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969307 +# Estimated pi_0=0.976923 +Using motif +M04336_2.00 of width 11. +Using motif -M04336_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9778 +# Estimated pi_0=0.999596 +Using motif +M04337_2.00 of width 12. +Using motif -M04337_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9462 +# Estimated pi_0=0.946733 +Using motif +M04338_2.00 of width 9. +Using motif -M04338_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976863 +# Estimated pi_0=1 +Using motif +M07833_2.00 of width 11. +Using motif -M07833_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984118 +# Estimated pi_0=0.995544 +Using motif +M08071_2.00 of width 11. +Using motif -M08071_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984186 +# Estimated pi_0=0.996508 +Using motif +M08805_2.00 of width 12. +Using motif -M08805_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=0.99899 +Using motif +M09490_2.00 of width 12. +Using motif -M09490_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M04037_2.00 of width 9. +Using motif -M04037_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974038 +# Estimated pi_0=0.986765 +Using motif +M04038_2.00 of width 10. +Using motif -M04038_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994059 +# Estimated pi_0=1 +Using motif +M04339_2.00 of width 12. +Using motif -M04339_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9914 +# Estimated pi_0=0.999899 +Using motif +M04340_2.00 of width 11. +Using motif -M04340_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992523 +# Estimated pi_0=0.999394 +Using motif +M04341_2.00 of width 11. +Using motif -M04341_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99604 +# Estimated pi_0=1 +Using motif +M04342_2.00 of width 11. +Using motif -M04342_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05841_2.00 of width 11. +Using motif -M05841_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05842_2.00 of width 17. +Using motif -M05842_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M07834_2.00 of width 20. +Using motif -M07834_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966555 +# Estimated pi_0=0.969431 +Using motif +M07835_2.00 of width 11. +Using motif -M07835_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998144 +# Estimated pi_0=0.998894 +Using motif +M07836_2.00 of width 15. +Using motif -M07836_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993585 +# Estimated pi_0=0.999598 +Using motif +M07837_2.00 of width 15. +Using motif -M07837_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998392 +# Estimated pi_0=0.998392 +Using motif +M07838_2.00 of width 11. +Using motif -M07838_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982453 +# Estimated pi_0=0.994043 +Using motif +M08072_2.00 of width 13. +Using motif -M08072_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999795 +# Estimated pi_0=0.999899 +Using motif +M08073_2.00 of width 14. +Using motif -M08073_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992865 +# Estimated pi_0=0.998593 +Using motif +M08806_2.00 of width 11. +Using motif -M08806_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995263 +# Estimated pi_0=0.999296 +Using motif +M04343_2.00 of width 9. +Using motif -M04343_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983571 +# Estimated pi_0=0.988152 +Using motif +M04344_2.00 of width 9. +Using motif -M04344_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994219 +# Estimated pi_0=0.999698 +Using motif +M04345_2.00 of width 14. +Using motif -M04345_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992324 +# Estimated pi_0=0.996821 +Using motif +M04346_2.00 of width 15. +Using motif -M04346_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989524 +# Estimated pi_0=0.994898 +Using motif +M04347_2.00 of width 16. +Using motif -M04347_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9698 +# Estimated pi_0=0.988261 +Using motif +M04348_2.00 of width 14. +Using motif -M04348_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967731 +# Estimated pi_0=0.98337 +Using motif +M04349_2.00 of width 15. +Using motif -M04349_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969931 +# Estimated pi_0=0.973896 +Using motif +M04350_2.00 of width 16. +Using motif -M04350_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985954 +# Estimated pi_0=0.995897 +Using motif +M08807_2.00 of width 19. +Using motif -M08807_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986842 +# Estimated pi_0=0.991371 +Using motif +M01812_2.00 of width 9. +Using motif -M01812_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9484 +# Estimated pi_0=0.960354 +Using motif +M04351_2.00 of width 13. +Using motif -M04351_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998265 +# Estimated pi_0=0.999497 +Using motif +M04352_2.00 of width 15. +Using motif -M04352_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963717 +# Estimated pi_0=0.977024 +Using motif +M04353_2.00 of width 16. +Using motif -M04353_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943762 +# Estimated pi_0=0.958295 +Using motif +M04354_2.00 of width 15. +Using motif -M04354_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940847 +# Estimated pi_0=0.950986 +Using motif +M04355_2.00 of width 16. +Using motif -M04355_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944274 +# Estimated pi_0=0.951579 +Using motif +M04356_2.00 of width 13. +Using motif -M04356_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998646 +# Estimated pi_0=1 +Using motif +M02853_2.00 of width 15. +Using motif -M02853_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986854 +# Estimated pi_0=0.993128 +Using motif +M04357_2.00 of width 15. +Using motif -M04357_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968955 +# Estimated pi_0=0.971976 +Using motif +M04358_2.00 of width 16. +Using motif -M04358_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966733 +# Estimated pi_0=0.982333 +Using motif +M04359_2.00 of width 15. +Using motif -M04359_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97072 +# Estimated pi_0=0.980351 +Using motif +M04360_2.00 of width 16. +Using motif -M04360_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9744 +# Estimated pi_0=0.983256 +Using motif +M04361_2.00 of width 15. +Using motif -M04361_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972059 +# Estimated pi_0=0.982842 +Using motif +M04362_2.00 of width 16. +Using motif -M04362_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965405 +# Estimated pi_0=0.973425 +Using motif +M04363_2.00 of width 15. +Using motif -M04363_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985739 +# Estimated pi_0=0.997677 +Using motif +M04364_2.00 of width 16. +Using motif -M04364_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97451 +# Estimated pi_0=0.986629 +Using motif +M07839_2.00 of width 15. +Using motif -M07839_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996062 +# Estimated pi_0=0.999095 +Using motif +M08074_2.00 of width 21. +Using motif -M08074_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997629 +# Estimated pi_0=0.998291 +Using motif +M08808_2.00 of width 18. +Using motif -M08808_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978929 +# Estimated pi_0=0.983529 +Using motif +M02854_2.00 of width 21. +Using motif -M02854_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995344 +# Estimated pi_0=0.998191 +Using motif +M04039_2.00 of width 17. +Using motif -M04039_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983443 +# Estimated pi_0=0.993404 +Using motif +M04365_2.00 of width 13. +Using motif -M04365_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M04366_2.00 of width 15. +Using motif -M04366_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967206 +# Estimated pi_0=0.977667 +Using motif +M04367_2.00 of width 16. +Using motif -M04367_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962115 +# Estimated pi_0=0.970833 +Using motif +M04368_2.00 of width 13. +Using motif -M04368_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04369_2.00 of width 15. +Using motif -M04369_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976842 +# Estimated pi_0=0.982485 +Using motif +M04370_2.00 of width 16. +Using motif -M04370_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974074 +# Estimated pi_0=0.994286 +Using motif +M04371_2.00 of width 10. +Using motif -M04371_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999378 +# Estimated pi_0=0.999598 +Using motif +M04372_2.00 of width 13. +Using motif -M04372_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974109 +# Estimated pi_0=0.982237 +Using motif +M04373_2.00 of width 10. +Using motif -M04373_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998673 +# Estimated pi_0=0.999296 +Using motif +M04374_2.00 of width 13. +Using motif -M04374_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970407 +# Estimated pi_0=0.982346 +Using motif +M08809_2.00 of width 18. +Using motif -M08809_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99 +# Estimated pi_0=0.996923 +Using motif +M02855_2.00 of width 12. +Using motif -M02855_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997173 +# Estimated pi_0=0.999397 +Using motif +M02856_2.00 of width 21. +Using motif -M02856_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996393 +# Estimated pi_0=0.998593 +Using motif +M02857_2.00 of width 12. +Using motif -M02857_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998051 +# Estimated pi_0=0.998191 +Using motif +M02858_2.00 of width 15. +Using motif -M02858_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99071 +# Estimated pi_0=0.996784 +Using motif +M07840_2.00 of width 13. +Using motif -M07840_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977517 +# Estimated pi_0=0.983516 +Using motif +M07841_2.00 of width 15. +Using motif -M07841_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994197 +# Estimated pi_0=0.997387 +Using motif +M08075_2.00 of width 19. +Using motif -M08075_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992324 +# Estimated pi_0=0.995152 +Using motif +M08810_2.00 of width 18. +Using motif -M08810_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995385 +# Estimated pi_0=0.997487 +Using motif +M04040_2.00 of width 18. +Using motif -M04040_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994737 +# Estimated pi_0=0.999495 +Using motif +M08811_2.00 of width 11. +Using motif -M08811_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984809 +# Estimated pi_0=0.98715 +Using motif +M04041_2.00 of width 12. +Using motif -M04041_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935294 +# Estimated pi_0=0.945714 +Using motif +M04042_2.00 of width 12. +Using motif -M04042_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.927963 +Using motif +M04043_2.00 of width 11. +Using motif -M04043_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9536 +# Estimated pi_0=0.968871 +Using motif +M04044_2.00 of width 10. +Using motif -M04044_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9794 +# Estimated pi_0=0.999899 +Using motif +M04375_2.00 of width 12. +Using motif -M04375_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968889 +# Estimated pi_0=0.969725 +Using motif +M04376_2.00 of width 12. +Using motif -M04376_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9296 +# Estimated pi_0=0.933077 +Using motif +M04377_2.00 of width 12. +Using motif -M04377_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943 +# Estimated pi_0=0.956557 +Using motif +M04378_2.00 of width 12. +Using motif -M04378_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9258 +# Estimated pi_0=0.93181 +Using motif +M04379_2.00 of width 12. +Using motif -M04379_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9632 +# Estimated pi_0=0.9752 +Using motif +M02859_2.00 of width 10. +Using motif -M02859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04380_2.00 of width 12. +Using motif -M04380_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04381_2.00 of width 12. +Using motif -M04381_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M08812_2.00 of width 11. +Using motif -M08812_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988989 +# Estimated pi_0=0.991753 +Using motif +M04045_2.00 of width 12. +Using motif -M04045_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991961 +# Estimated pi_0=0.999296 +Using motif +M08076_2.00 of width 11. +Using motif -M08076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M08813_2.00 of width 12. +Using motif -M08813_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998985 +# Estimated pi_0=0.999296 +Using motif +M10023_2.00 of width 13. +Using motif -M10023_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998687 +# Estimated pi_0=0.999397 +Using motif +M10024_2.00 of width 14. +Using motif -M10024_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M10028_2.00 of width 18. +Using motif -M10028_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9786 +# Estimated pi_0=1 +Using motif +M10029_2.00 of width 14. +Using motif -M10029_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04382_2.00 of width 12. +Using motif -M04382_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04383_2.00 of width 12. +Using motif -M04383_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02872_2.00 of width 14. +Using motif -M02872_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M01849_2.00 of width 9. +Using motif -M01849_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M07842_2.00 of width 8. +Using motif -M07842_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954017 +# Estimated pi_0=0.961241 +Using motif +M08080_2.00 of width 11. +Using motif -M08080_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9124 +# Estimated pi_0=0.923684 +Using motif +M08190_2.00 of width 10. +Using motif -M08190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9692 +# Estimated pi_0=0.980638 +Using motif +M08848_2.00 of width 10. +Using motif -M08848_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965166 +# Estimated pi_0=0.971529 +Using motif +M10089_2.00 of width 13. +Using motif -M10089_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980851 +# Estimated pi_0=0.981899 +Using motif +M10090_2.00 of width 12. +Using motif -M10090_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977029 +# Estimated pi_0=0.981087 +Using motif +M10091_2.00 of width 12. +Using motif -M10091_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9376 +# Estimated pi_0=0.956875 +Using motif +M10092_2.00 of width 9. +Using motif -M10092_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984874 +# Estimated pi_0=0.996718 +Using motif +M10093_2.00 of width 11. +Using motif -M10093_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987299 +# Estimated pi_0=0.995795 +Using motif +M01165_2.00 of width 10. +Using motif -M01165_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M04384_2.00 of width 20. +Using motif -M04384_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9378 +# Estimated pi_0=0.944037 +Using motif +M04385_2.00 of width 20. +Using motif -M04385_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9646 +# Estimated pi_0=0.972389 +Using motif +M08271_2.00 of width 12. +Using motif -M08271_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9604 +# Estimated pi_0=0.961346 +Using motif +M07562_2.00 of width 9. +Using motif -M07562_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987933 +# Estimated pi_0=0.992959 +Using motif +M08850_2.00 of width 10. +Using motif -M08850_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892381 +# Estimated pi_0=0.892381 +Using motif +M04386_2.00 of width 10. +Using motif -M04386_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937481 +# Estimated pi_0=0.944937 +Using motif +M04387_2.00 of width 10. +Using motif -M04387_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936129 +# Estimated pi_0=0.945521 +Using motif +M07563_2.00 of width 9. +Using motif -M07563_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925862 +# Estimated pi_0=0.9335 +Using motif +M07843_2.00 of width 21. +Using motif -M07843_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880584 +# Estimated pi_0=0.887545 +Using motif +M07844_2.00 of width 15. +Using motif -M07844_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89295 +# Estimated pi_0=0.900122 +Using motif +M08082_2.00 of width 21. +Using motif -M08082_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908862 +# Estimated pi_0=0.912184 +Using motif +M08272_2.00 of width 21. +Using motif -M08272_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885299 +# Estimated pi_0=0.890935 +Using motif +M08851_2.00 of width 20. +Using motif -M08851_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859365 +# Estimated pi_0=0.861507 +Using motif +M00217_2.00 of width 11. +Using motif -M00217_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M00218_2.00 of width 10. +Using motif -M00218_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970656 +# Estimated pi_0=0.978736 +Using motif +M00219_2.00 of width 10. +Using motif -M00219_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978095 +# Estimated pi_0=0.984865 +Using motif +M08232_2.00 of width 6. +Using motif -M08232_2.00 of width 6. +Computing q-values. +Using motif +M08273_2.00 of width 12. +Using motif -M08273_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92099 +# Estimated pi_0=0.925546 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00018 +# Estimated pi_0=1 +Using motif +M04389_2.00 of width 11. +Using motif -M04389_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9788 +# Estimated pi_0=1 +Using motif +M04390_2.00 of width 19. +Using motif -M04390_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04391_2.00 of width 19. +Using motif -M04391_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M07564_2.00 of width 28. +Using motif -M07564_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899065 +# Estimated pi_0=0.911833 +Using motif +M08274_2.00 of width 27. +Using motif -M08274_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918 +# Estimated pi_0=0.935195 +Using motif +M08852_2.00 of width 24. +Using motif -M08852_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974127 +# Estimated pi_0=0.98581 +Using motif +M00220_2.00 of width 8. +Using motif -M00220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971858 +# Estimated pi_0=0.98044 +Using motif +M00221_2.00 of width 7. +Using motif -M00221_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992216 +# Estimated pi_0=0.996 +Using motif +M00222_2.00 of width 10. +Using motif -M00222_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997347 +# Estimated pi_0=0.999196 +Using motif +M02873_2.00 of width 9. +Using motif -M02873_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973373 +# Estimated pi_0=0.977127 +Using motif +M04392_2.00 of width 10. +Using motif -M04392_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963 +# Estimated pi_0=0.974872 +Using motif +M04393_2.00 of width 10. +Using motif -M04393_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950365 +# Estimated pi_0=0.950519 +Using motif +M04394_2.00 of width 10. +Using motif -M04394_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940274 +# Estimated pi_0=0.945235 +Using motif +M04395_2.00 of width 10. +Using motif -M04395_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953774 +# Estimated pi_0=0.959387 +Using motif +M08853_2.00 of width 10. +Using motif -M08853_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944902 +# Estimated pi_0=0.952216 +Using motif +M08275_2.00 of width 15. +Using motif -M08275_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8238 +# Estimated pi_0=0.826542 +Using motif +M08233_2.00 of width 11. +Using motif -M08233_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889216 +# Estimated pi_0=0.899855 +Using motif +M10107_2.00 of width 9. +Using motif -M10107_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9244 +# Estimated pi_0=0.932157 +Using motif +M02731_2.00 of width 12. +Using motif -M02731_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995635 +# Estimated pi_0=0.998191 +Using motif +M02874_2.00 of width 15. +Using motif -M02874_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987807 +# Estimated pi_0=0.992923 +Using motif +M04396_2.00 of width 12. +Using motif -M04396_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971875 +# Estimated pi_0=0.974913 +Using motif +M04397_2.00 of width 12. +Using motif -M04397_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970769 +# Estimated pi_0=0.977572 +Using motif +M07845_2.00 of width 15. +Using motif -M07845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980328 +# Estimated pi_0=0.982842 +Using motif +M08083_2.00 of width 10. +Using motif -M08083_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994898 +# Estimated pi_0=0.995657 +Using motif +M08191_2.00 of width 8. +Using motif -M08191_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984417 +# Estimated pi_0=0.989424 +Using motif +M08192_2.00 of width 9. +Using motif -M08192_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999239 +# Estimated pi_0=1 +Using motif +M08276_2.00 of width 15. +Using motif -M08276_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950778 +# Estimated pi_0=0.953256 +Using motif +M08854_2.00 of width 14. +Using motif -M08854_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96593 +# Estimated pi_0=0.972258 +Using motif +M09500_2.00 of width 12. +Using motif -M09500_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984186 +# Estimated pi_0=0.989744 +Using motif +M08277_2.00 of width 21. +Using motif -M08277_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97016 +# Estimated pi_0=0.973437 +Using motif +M08855_2.00 of width 13. +Using motif -M08855_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977765 +# Estimated pi_0=0.985155 +Using motif +M04398_2.00 of width 21. +Using motif -M04398_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944724 +# Estimated pi_0=0.954765 +Using motif +M04399_2.00 of width 21. +Using motif -M04399_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993077 +# Estimated pi_0=0.997172 +Using motif +M08278_2.00 of width 21. +Using motif -M08278_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906789 +# Estimated pi_0=0.909204 +Using motif +M08856_2.00 of width 22. +Using motif -M08856_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915429 +# Estimated pi_0=0.925882 +Using motif +M04400_2.00 of width 11. +Using motif -M04400_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910095 +# Estimated pi_0=0.910095 +Using motif +M04401_2.00 of width 11. +Using motif -M04401_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8888 +# Estimated pi_0=0.892203 +Using motif +M08857_2.00 of width 19. +Using motif -M08857_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879516 +# Estimated pi_0=0.891141 +Using motif +M08100_2.00 of width 14. +Using motif -M08100_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.862157 +# Estimated pi_0=0.867963 +Using motif +M02641_2.00 of width 11. +Using motif -M02641_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930545 +# Estimated pi_0=0.945324 +Using motif +M02732_2.00 of width 10. +Using motif -M02732_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922 +# Estimated pi_0=0.928099 +Using motif +M02875_2.00 of width 14. +Using motif -M02875_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9148 +# Estimated pi_0=0.923548 +Using motif +M02876_2.00 of width 12. +Using motif -M02876_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914563 +# Estimated pi_0=0.920354 +Using motif +M04402_2.00 of width 15. +Using motif -M04402_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8992 +# Estimated pi_0=0.914159 +Using motif +M04403_2.00 of width 15. +Using motif -M04403_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93375 +# Estimated pi_0=0.936825 +Using motif +M08279_2.00 of width 9. +Using motif -M08279_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92649 +# Estimated pi_0=0.934405 +Using motif +M07565_2.00 of width 18. +Using motif -M07565_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8948 +# Estimated pi_0=0.903697 +Using motif +M07566_2.00 of width 15. +Using motif -M07566_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963939 +# Estimated pi_0=0.966203 +Using motif +M08280_2.00 of width 15. +Using motif -M08280_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997124 +# Estimated pi_0=1 +Using motif +M08858_2.00 of width 23. +Using motif -M08858_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910727 +# Estimated pi_0=0.919452 +Using motif +M07567_2.00 of width 12. +Using motif -M07567_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967451 +# Estimated pi_0=0.977407 +Using motif +M08234_2.00 of width 18. +Using motif -M08234_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967925 +# Estimated pi_0=0.984211 +Using motif +M08281_2.00 of width 12. +Using motif -M08281_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92918 +# Estimated pi_0=0.941227 +Using motif +M08859_2.00 of width 20. +Using motif -M08859_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978161 +# Estimated pi_0=0.984921 +Using motif +M07568_2.00 of width 30. +Using motif -M07568_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8816 +# Estimated pi_0=0.894087 +Using motif +M08235_2.00 of width 15. +Using motif -M08235_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9902 +# Estimated pi_0=0.999082 +Using motif +M08282_2.00 of width 12. +Using motif -M08282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972079 +# Estimated pi_0=0.989024 +Using motif +M08860_2.00 of width 24. +Using motif -M08860_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982393 +# Estimated pi_0=0.99162 +Using motif +M07846_2.00 of width 14. +Using motif -M07846_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940168 +# Estimated pi_0=0.949155 +Using motif +M07847_2.00 of width 15. +Using motif -M07847_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930189 +# Estimated pi_0=0.953506 +Using motif +M07848_2.00 of width 16. +Using motif -M07848_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94096 +# Estimated pi_0=0.948 +Using motif +M07849_2.00 of width 15. +Using motif -M07849_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9216 +# Estimated pi_0=0.935902 +Using motif +M07850_2.00 of width 15. +Using motif -M07850_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930297 +# Estimated pi_0=0.937966 +Using motif +M07851_2.00 of width 16. +Using motif -M07851_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911273 +# Estimated pi_0=0.918626 +Using motif +M07852_2.00 of width 21. +Using motif -M07852_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919823 +# Estimated pi_0=0.929077 +Using motif +M07853_2.00 of width 15. +Using motif -M07853_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935478 +# Estimated pi_0=0.950581 +Using motif +M07854_2.00 of width 15. +Using motif -M07854_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928596 +# Estimated pi_0=0.939697 +Using motif +M08084_2.00 of width 21. +Using motif -M08084_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913137 +# Estimated pi_0=0.923667 +Using motif +M08861_2.00 of width 22. +Using motif -M08861_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930579 +# Estimated pi_0=0.943333 +Using motif +M09501_2.00 of width 20. +Using motif -M09501_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9196 +# Estimated pi_0=0.93303 +Using motif +M10117_2.00 of width 21. +Using motif -M10117_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920769 +# Estimated pi_0=0.928596 +Using motif +M08283_2.00 of width 12. +Using motif -M08283_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.998693 +Using motif +M08862_2.00 of width 16. +Using motif -M08862_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997487 +# Estimated pi_0=0.997487 +Using motif +M10125_2.00 of width 16. +Using motif -M10125_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M10126_2.00 of width 11. +Using motif -M10126_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M10127_2.00 of width 11. +Using motif -M10127_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00123 +# Estimated pi_0=1 +Using motif +M10128_2.00 of width 15. +Using motif -M10128_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M10129_2.00 of width 11. +Using motif -M10129_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M10130_2.00 of width 9. +Using motif -M10130_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M08284_2.00 of width 15. +Using motif -M08284_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891238 +# Estimated pi_0=0.901094 +Using motif +M04404_2.00 of width 17. +Using motif -M04404_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912035 +# Estimated pi_0=0.92208 +Using motif +M04405_2.00 of width 17. +Using motif -M04405_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9274 +# Estimated pi_0=0.936207 +Using motif +M07569_2.00 of width 9. +Using motif -M07569_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926 +# Estimated pi_0=0.931562 +Using motif +M07570_2.00 of width 18. +Using motif -M07570_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982051 +# Estimated pi_0=0.989787 +Using motif +M07571_2.00 of width 27. +Using motif -M07571_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973988 +# Estimated pi_0=0.983646 +Using motif +M08285_2.00 of width 9. +Using motif -M08285_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.984086 +Using motif +M02663_2.00 of width 6. +Using motif -M02663_2.00 of width 6. +Computing q-values. +Using motif +M02664_2.00 of width 10. +Using motif -M02664_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961295 +# Estimated pi_0=0.967765 +Using motif +M08236_2.00 of width 7. +Using motif -M08236_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999795 +# Estimated pi_0=1 +Using motif +M08286_2.00 of width 15. +Using motif -M08286_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9512 +# Estimated pi_0=0.963307 +Using motif +M08863_2.00 of width 13. +Using motif -M08863_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948553 +# Estimated pi_0=0.960686 +Using motif +M10133_2.00 of width 8. +Using motif -M10133_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93457 +# Estimated pi_0=0.946118 +Using motif +M10134_2.00 of width 13. +Using motif -M10134_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953165 +# Estimated pi_0=0.957542 +Using motif +M08287_2.00 of width 8. +Using motif -M08287_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912586 +# Estimated pi_0=0.920915 +Using motif +M08864_2.00 of width 22. +Using motif -M08864_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86 +# Estimated pi_0=0.865091 +Using motif +M10137_2.00 of width 13. +Using motif -M10137_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893729 +# Estimated pi_0=0.904706 +Using motif +M02877_2.00 of width 11. +Using motif -M02877_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977143 +# Estimated pi_0=0.989586 +Using motif +M04406_2.00 of width 23. +Using motif -M04406_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992673 +# Estimated pi_0=0.999695 +Using motif +M04407_2.00 of width 14. +Using motif -M04407_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979802 +# Estimated pi_0=0.997647 +Using motif +M04408_2.00 of width 23. +Using motif -M04408_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998211 +# Estimated pi_0=1 +Using motif +M04409_2.00 of width 14. +Using motif -M04409_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996496 +# Estimated pi_0=1 +Using motif +M05845_2.00 of width 13. +Using motif -M05845_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986535 +# Estimated pi_0=0.998985 +Using motif +M07855_2.00 of width 15. +Using motif -M07855_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916078 +# Estimated pi_0=0.921273 +Using motif +M07856_2.00 of width 11. +Using motif -M07856_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947129 +# Estimated pi_0=0.965325 +Using motif +M07857_2.00 of width 15. +Using motif -M07857_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8962 +# Estimated pi_0=0.902545 +Using motif +M07858_2.00 of width 15. +Using motif -M07858_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9218 +# Estimated pi_0=0.933739 +Using motif +M07859_2.00 of width 15. +Using motif -M07859_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900396 +# Estimated pi_0=0.906364 +Using motif +M07860_2.00 of width 15. +Using motif -M07860_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930291 +# Estimated pi_0=0.934679 +Using motif +M07861_2.00 of width 13. +Using motif -M07861_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9346 +# Estimated pi_0=0.944685 +Using motif +M08085_2.00 of width 12. +Using motif -M08085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984737 +# Estimated pi_0=0.993936 +Using motif +M08237_2.00 of width 15. +Using motif -M08237_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931 +# Estimated pi_0=0.942256 +Using motif +M08288_2.00 of width 12. +Using motif -M08288_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9128 +# Estimated pi_0=0.915185 +Using motif +M08865_2.00 of width 12. +Using motif -M08865_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965538 +# Estimated pi_0=0.973562 +Using motif +M10138_2.00 of width 17. +Using motif -M10138_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993978 +# Estimated pi_0=0.993978 +Using motif +M10139_2.00 of width 20. +Using motif -M10139_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925882 +# Estimated pi_0=0.935714 +Using motif +M08866_2.00 of width 10. +Using motif -M08866_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918797 +# Estimated pi_0=0.928121 +Using motif +M08867_2.00 of width 9. +Using motif -M08867_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933725 +# Estimated pi_0=0.945676 +Using motif +M04410_2.00 of width 10. +Using motif -M04410_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923676 +# Estimated pi_0=0.934545 +Using motif +M04411_2.00 of width 10. +Using motif -M04411_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92717 +# Estimated pi_0=0.939841 +Using motif +M08086_2.00 of width 10. +Using motif -M08086_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925755 +# Estimated pi_0=0.931579 +Using motif +M08868_2.00 of width 14. +Using motif -M08868_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917031 +# Estimated pi_0=0.924571 +Using motif +M09502_2.00 of width 10. +Using motif -M09502_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935588 +# Estimated pi_0=0.942169 +Using motif +M10151_2.00 of width 14. +Using motif -M10151_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935446 +# Estimated pi_0=0.940438 +Using motif +M02878_2.00 of width 17. +Using motif -M02878_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9166 +# Estimated pi_0=0.928095 +Using motif +M05846_2.00 of width 17. +Using motif -M05846_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883725 +# Estimated pi_0=0.888031 +Using motif +M07550_2.00 of width 15. +Using motif -M07550_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920877 +# Estimated pi_0=0.930798 +Using motif +M07551_2.00 of width 11. +Using motif -M07551_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991675 +# Estimated pi_0=0.992424 +Using motif +M07552_2.00 of width 19. +Using motif -M07552_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993068 +# Estimated pi_0=0.997128 +Using motif +M07862_2.00 of width 21. +Using motif -M07862_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8836 +# Estimated pi_0=0.89648 +Using motif +M07863_2.00 of width 16. +Using motif -M07863_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.862353 +# Estimated pi_0=0.870827 +Using motif +M07864_2.00 of width 15. +Using motif -M07864_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916822 +# Estimated pi_0=0.93037 +Using motif +M07865_2.00 of width 16. +Using motif -M07865_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875842 +# Estimated pi_0=0.8764 +Using motif +M07866_2.00 of width 15. +Using motif -M07866_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911818 +# Estimated pi_0=0.920267 +Using motif +M07867_2.00 of width 13. +Using motif -M07867_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945781 +# Estimated pi_0=0.951053 +Using motif +M07868_2.00 of width 15. +Using motif -M07868_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911597 +# Estimated pi_0=0.921233 +Using motif +M07869_2.00 of width 15. +Using motif -M07869_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875882 +# Estimated pi_0=0.881429 +Using motif +M07870_2.00 of width 15. +Using motif -M07870_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897025 +# Estimated pi_0=0.903676 +Using motif +M07871_2.00 of width 15. +Using motif -M07871_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880354 +# Estimated pi_0=0.88992 +Using motif +M07872_2.00 of width 15. +Using motif -M07872_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891 +# Estimated pi_0=0.891731 +Using motif +M07873_2.00 of width 21. +Using motif -M07873_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.882342 +# Estimated pi_0=0.894825 +Using motif +M07874_2.00 of width 15. +Using motif -M07874_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884505 +# Estimated pi_0=0.895915 +Using motif +M07875_2.00 of width 15. +Using motif -M07875_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8608 +# Estimated pi_0=0.867727 +Using motif +M07876_2.00 of width 15. +Using motif -M07876_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.887523 +# Estimated pi_0=0.898014 +Using motif +M07877_2.00 of width 15. +Using motif -M07877_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8926 +# Estimated pi_0=0.904414 +Using motif +M07878_2.00 of width 15. +Using motif -M07878_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.86 +# Estimated pi_0=0.865556 +Using motif +M07879_2.00 of width 15. +Using motif -M07879_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886606 +# Estimated pi_0=0.892923 +Using motif +M07880_2.00 of width 15. +Using motif -M07880_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893271 +# Estimated pi_0=0.903846 +Using motif +M07881_2.00 of width 15. +Using motif -M07881_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885636 +# Estimated pi_0=0.895852 +Using motif +M07882_2.00 of width 12. +Using motif -M07882_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923429 +# Estimated pi_0=0.935195 +Using motif +M07883_2.00 of width 17. +Using motif -M07883_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886 +# Estimated pi_0=0.89697 +Using motif +M07884_2.00 of width 15. +Using motif -M07884_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89717 +# Estimated pi_0=0.904336 +Using motif +M07885_2.00 of width 15. +Using motif -M07885_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906346 +# Estimated pi_0=0.918333 +Using motif +M07886_2.00 of width 15. +Using motif -M07886_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.887843 +# Estimated pi_0=0.895704 +Using motif +M07887_2.00 of width 18. +Using motif -M07887_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9056 +# Estimated pi_0=0.914795 +Using motif +M07888_2.00 of width 15. +Using motif -M07888_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9208 +# Estimated pi_0=0.93092 +Using motif +M07889_2.00 of width 14. +Using motif -M07889_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922013 +# Estimated pi_0=0.927342 +Using motif +M07890_2.00 of width 13. +Using motif -M07890_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9218 +# Estimated pi_0=0.936232 +Using motif +M07891_2.00 of width 18. +Using motif -M07891_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877414 +# Estimated pi_0=0.88563 +Using motif +M07892_2.00 of width 15. +Using motif -M07892_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9064 +# Estimated pi_0=0.917778 +Using motif +M07893_2.00 of width 15. +Using motif -M07893_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921314 +# Estimated pi_0=0.923537 +Using motif +M07894_2.00 of width 17. +Using motif -M07894_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860727 +# Estimated pi_0=0.86661 +Using motif +M07895_2.00 of width 18. +Using motif -M07895_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8888 +# Estimated pi_0=0.896378 +Using motif +M07896_2.00 of width 15. +Using motif -M07896_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904158 +# Estimated pi_0=0.919865 +Using motif +M07897_2.00 of width 15. +Using motif -M07897_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948333 +# Estimated pi_0=0.959882 +Using motif +M07898_2.00 of width 14. +Using motif -M07898_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900381 +# Estimated pi_0=0.914406 +Using motif +M07899_2.00 of width 20. +Using motif -M07899_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913514 +# Estimated pi_0=0.917042 +Using motif +M07900_2.00 of width 17. +Using motif -M07900_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860784 +# Estimated pi_0=0.873893 +Using motif +M07901_2.00 of width 11. +Using motif -M07901_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944275 +# Estimated pi_0=0.949753 +Using motif +M07902_2.00 of width 15. +Using motif -M07902_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916909 +# Estimated pi_0=0.928082 +Using motif +M07903_2.00 of width 13. +Using motif -M07903_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928571 +# Estimated pi_0=0.94 +Using motif +M07904_2.00 of width 15. +Using motif -M07904_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889109 +# Estimated pi_0=0.897937 +Using motif +M07905_2.00 of width 12. +Using motif -M07905_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94965 +# Estimated pi_0=0.955682 +Using motif +M07906_2.00 of width 18. +Using motif -M07906_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926275 +# Estimated pi_0=0.933475 +Using motif +M07907_2.00 of width 18. +Using motif -M07907_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908113 +# Estimated pi_0=0.910909 +Using motif +M07908_2.00 of width 21. +Using motif -M07908_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857 +# Estimated pi_0=0.872782 +Using motif +M07909_2.00 of width 15. +Using motif -M07909_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959206 +# Estimated pi_0=0.968477 +Using motif +M07910_2.00 of width 20. +Using motif -M07910_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.848824 +# Estimated pi_0=0.861495 +Using motif +M07911_2.00 of width 18. +Using motif -M07911_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8762 +# Estimated pi_0=0.890455 +Using motif +M07912_2.00 of width 15. +Using motif -M07912_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905192 +# Estimated pi_0=0.911189 +Using motif +M07913_2.00 of width 15. +Using motif -M07913_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897037 +# Estimated pi_0=0.913595 +Using motif +M07914_2.00 of width 15. +Using motif -M07914_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906916 +# Estimated pi_0=0.916667 +Using motif +M07915_2.00 of width 21. +Using motif -M07915_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927967 +# Estimated pi_0=0.942013 +Using motif +M07916_2.00 of width 17. +Using motif -M07916_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9186 +# Estimated pi_0=0.925772 +Using motif +M07917_2.00 of width 15. +Using motif -M07917_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920678 +# Estimated pi_0=0.930267 +Using motif +M07918_2.00 of width 14. +Using motif -M07918_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.859804 +# Estimated pi_0=0.868943 +Using motif +M07919_2.00 of width 15. +Using motif -M07919_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.876505 +# Estimated pi_0=0.887727 +Using motif +M07920_2.00 of width 18. +Using motif -M07920_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895926 +# Estimated pi_0=0.909618 +Using motif +M07921_2.00 of width 15. +Using motif -M07921_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902178 +# Estimated pi_0=0.910147 +Using motif +M07922_2.00 of width 13. +Using motif -M07922_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8922 +# Estimated pi_0=0.913194 +Using motif +M07923_2.00 of width 15. +Using motif -M07923_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9056 +# Estimated pi_0=0.924314 +Using motif +M07924_2.00 of width 15. +Using motif -M07924_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891712 +# Estimated pi_0=0.904857 +Using motif +M07925_2.00 of width 15. +Using motif -M07925_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895888 +# Estimated pi_0=0.908986 +Using motif +M08087_2.00 of width 19. +Using motif -M08087_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917165 +# Estimated pi_0=0.927083 +Using motif +M08238_2.00 of width 15. +Using motif -M08238_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866796 +# Estimated pi_0=0.872456 +Using motif +M08289_2.00 of width 15. +Using motif -M08289_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.871163 +# Estimated pi_0=0.874559 +Using motif +M08869_2.00 of width 19. +Using motif -M08869_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8752 +# Estimated pi_0=0.879531 +Using motif +M09503_2.00 of width 20. +Using motif -M09503_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917037 +# Estimated pi_0=0.93 +Using motif +M09504_2.00 of width 20. +Using motif -M09504_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974757 +# Estimated pi_0=0.985109 +Using motif +M04412_2.00 of width 15. +Using motif -M04412_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975248 +# Estimated pi_0=0.989222 +Using motif +M04413_2.00 of width 15. +Using motif -M04413_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972424 +# Estimated pi_0=0.983908 +Using motif +M04414_2.00 of width 16. +Using motif -M04414_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917282 +# Estimated pi_0=0.92 +Using motif +M04415_2.00 of width 16. +Using motif -M04415_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92699 +# Estimated pi_0=0.930275 +Using motif +M08290_2.00 of width 9. +Using motif -M08290_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89875 +# Estimated pi_0=0.904196 +Using motif +M08870_2.00 of width 22. +Using motif -M08870_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867125 +# Estimated pi_0=0.871529 +Using motif +M09505_2.00 of width 8. +Using motif -M09505_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879398 +# Estimated pi_0=0.884841 +Using motif +M07572_2.00 of width 21. +Using motif -M07572_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95969 +# Estimated pi_0=0.969302 +Using motif +M08291_2.00 of width 21. +Using motif -M08291_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975086 +# Estimated pi_0=0.978723 +Using motif +M08292_2.00 of width 21. +Using motif -M08292_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998342 +# Estimated pi_0=0.999296 +Using motif +M00223_2.00 of width 9. +Using motif -M00223_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937962 +# Estimated pi_0=0.948111 +Using motif +M00224_2.00 of width 8. +Using motif -M00224_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896481 +# Estimated pi_0=0.900492 +Using motif +M00225_2.00 of width 12. +Using motif -M00225_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00226_2.00 of width 8. +Using motif -M00226_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9174 +# Estimated pi_0=0.932625 +Using motif +M08293_2.00 of width 15. +Using motif -M08293_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880541 +# Estimated pi_0=0.892168 +Using motif +M08871_2.00 of width 14. +Using motif -M08871_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899836 +# Estimated pi_0=0.91071 +Using motif +M08239_2.00 of width 20. +Using motif -M08239_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8876 +# Estimated pi_0=0.893871 +Using motif +M08294_2.00 of width 12. +Using motif -M08294_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925149 +# Estimated pi_0=0.943529 +Using motif +M07573_2.00 of width 18. +Using motif -M07573_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967826 +# Estimated pi_0=0.976471 +Using motif +M08295_2.00 of width 15. +Using motif -M08295_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976832 +# Estimated pi_0=0.98791 +Using motif +M08872_2.00 of width 20. +Using motif -M08872_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996244 +# Estimated pi_0=0.998392 +Using motif +M02879_2.00 of width 17. +Using motif -M02879_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925 +# Estimated pi_0=0.929767 +Using motif +M08296_2.00 of width 15. +Using motif -M08296_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905692 +# Estimated pi_0=0.911429 +Using motif +M08873_2.00 of width 20. +Using motif -M08873_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889747 +# Estimated pi_0=0.894233 +Using motif +M05847_2.00 of width 13. +Using motif -M05847_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985905 +# Estimated pi_0=0.99663 +Using motif +M08874_2.00 of width 19. +Using motif -M08874_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9392 +# Estimated pi_0=0.951549 +Using motif +M02642_2.00 of width 11. +Using motif -M02642_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937557 +# Estimated pi_0=0.945752 +Using motif +M04416_2.00 of width 15. +Using motif -M04416_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897009 +# Estimated pi_0=0.90448 +Using motif +M04417_2.00 of width 15. +Using motif -M04417_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926471 +# Estimated pi_0=0.938374 +Using motif +M08875_2.00 of width 11. +Using motif -M08875_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9492 +# Estimated pi_0=0.967619 +Using motif +M02880_2.00 of width 14. +Using motif -M02880_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932157 +# Estimated pi_0=0.94189 +Using motif +M04418_2.00 of width 11. +Using motif -M04418_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906847 +# Estimated pi_0=0.911587 +Using motif +M04419_2.00 of width 11. +Using motif -M04419_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.881818 +# Estimated pi_0=0.883529 +Using motif +M04420_2.00 of width 12. +Using motif -M04420_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889307 +# Estimated pi_0=0.908652 +Using motif +M08876_2.00 of width 19. +Using motif -M08876_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.871074 +# Estimated pi_0=0.880909 +Using motif +M02881_2.00 of width 13. +Using motif -M02881_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963729 +# Estimated pi_0=0.975031 +Using motif +M04421_2.00 of width 20. +Using motif -M04421_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941 +# Estimated pi_0=0.952571 +Using motif +M04422_2.00 of width 20. +Using motif -M04422_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947525 +# Estimated pi_0=0.95386 +Using motif +M04423_2.00 of width 20. +Using motif -M04423_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947706 +# Estimated pi_0=0.958779 +Using motif +M02643_2.00 of width 11. +Using motif -M02643_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933137 +# Estimated pi_0=0.945547 +Using motif +M00227_2.00 of width 10. +Using motif -M00227_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00228_2.00 of width 10. +Using motif -M00228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04424_2.00 of width 16. +Using motif -M04424_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04425_2.00 of width 16. +Using motif -M04425_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04426_2.00 of width 16. +Using motif -M04426_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=0.999899 +Using motif +M04427_2.00 of width 16. +Using motif -M04427_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M08877_2.00 of width 13. +Using motif -M08877_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995213 +# Estimated pi_0=0.998593 +Using motif +M08878_2.00 of width 19. +Using motif -M08878_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901375 +# Estimated pi_0=0.908506 +Using motif +M04428_2.00 of width 18. +Using motif -M04428_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963761 +# Estimated pi_0=0.973261 +Using motif +M04429_2.00 of width 18. +Using motif -M04429_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96592 +# Estimated pi_0=0.97978 +Using motif +M07574_2.00 of width 9. +Using motif -M07574_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944872 +# Estimated pi_0=0.947654 +Using motif +M08240_2.00 of width 14. +Using motif -M08240_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961926 +# Estimated pi_0=0.970455 +Using motif +M08297_2.00 of width 15. +Using motif -M08297_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948689 +# Estimated pi_0=0.955185 +Using motif +M08298_2.00 of width 11. +Using motif -M08298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89 +# Estimated pi_0=0.903165 +Using motif +M00130_2.00 of width 10. +Using motif -M00130_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898049 +# Estimated pi_0=0.905306 +Using motif +M07575_2.00 of width 18. +Using motif -M07575_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91814 +# Estimated pi_0=0.922647 +Using motif +M04430_2.00 of width 11. +Using motif -M04430_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904528 +# Estimated pi_0=0.912388 +Using motif +M04431_2.00 of width 12. +Using motif -M04431_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8772 +# Estimated pi_0=0.888793 +Using motif +M04432_2.00 of width 11. +Using motif -M04432_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915619 +# Estimated pi_0=0.92597 +Using motif +M08299_2.00 of width 15. +Using motif -M08299_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869412 +# Estimated pi_0=0.880976 +Using motif +M08879_2.00 of width 11. +Using motif -M08879_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889423 +# Estimated pi_0=0.894697 +Using motif +M10186_2.00 of width 7. +Using motif -M10186_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968103 +# Estimated pi_0=0.978125 +Using motif +M08088_2.00 of width 13. +Using motif -M08088_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910364 +# Estimated pi_0=0.919687 +Using motif +M08880_2.00 of width 15. +Using motif -M08880_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886275 +# Estimated pi_0=0.898873 +Using motif +M04433_2.00 of width 11. +Using motif -M04433_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989558 +# Estimated pi_0=1 +Using motif +M04434_2.00 of width 11. +Using motif -M04434_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9882 +# Estimated pi_0=1 +Using motif +M02882_2.00 of width 17. +Using motif -M02882_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04435_2.00 of width 14. +Using motif -M04435_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04436_2.00 of width 14. +Using motif -M04436_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00983_2.00 of width 10. +Using motif -M00983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9474 +# Estimated pi_0=0.958611 +Using motif +M00984_2.00 of width 8. +Using motif -M00984_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9288 +# Estimated pi_0=0.932745 +Using motif +M00985_2.00 of width 10. +Using motif -M00985_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8402 +# Estimated pi_0=0.842376 +Using motif +M07926_2.00 of width 15. +Using motif -M07926_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971461 +# Estimated pi_0=0.977474 +Using motif +M08881_2.00 of width 17. +Using motif -M08881_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940361 +# Estimated pi_0=0.94989 +Using motif +M02883_2.00 of width 14. +Using motif -M02883_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9002 +# Estimated pi_0=0.91127 +Using motif +M02884_2.00 of width 14. +Using motif -M02884_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911 +# Estimated pi_0=0.913981 +Using motif +M04437_2.00 of width 13. +Using motif -M04437_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899273 +# Estimated pi_0=0.911181 +Using motif +M04438_2.00 of width 13. +Using motif -M04438_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91635 +# Estimated pi_0=0.922603 +Using motif +M07927_2.00 of width 20. +Using motif -M07927_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8568 +# Estimated pi_0=0.860938 +Using motif +M07928_2.00 of width 15. +Using motif -M07928_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883077 +# Estimated pi_0=0.898248 +Using motif +M07929_2.00 of width 20. +Using motif -M07929_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8632 +# Estimated pi_0=0.875846 +Using motif +M08882_2.00 of width 17. +Using motif -M08882_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909718 +# Estimated pi_0=0.910411 +Using motif +M09506_2.00 of width 10. +Using motif -M09506_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945667 +# Estimated pi_0=0.954802 +Using motif +M10188_2.00 of width 12. +Using motif -M10188_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925631 +# Estimated pi_0=0.935652 +Using motif +M07576_2.00 of width 15. +Using motif -M07576_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966226 +# Estimated pi_0=0.980787 +Using motif +M08300_2.00 of width 13. +Using motif -M08300_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976489 +# Estimated pi_0=0.984859 +Using motif +M08883_2.00 of width 20. +Using motif -M08883_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972222 +# Estimated pi_0=0.98046 +Using motif +M07577_2.00 of width 9. +Using motif -M07577_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M08301_2.00 of width 9. +Using motif -M08301_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954634 +# Estimated pi_0=0.958953 +Using motif +M07578_2.00 of width 9. +Using motif -M07578_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946111 +# Estimated pi_0=0.956806 +Using motif +M00229_2.00 of width 9. +Using motif -M00229_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920541 +# Estimated pi_0=0.929343 +Using motif +M00230_2.00 of width 10. +Using motif -M00230_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927324 +# Estimated pi_0=0.931647 +Using motif +M00231_2.00 of width 8. +Using motif -M00231_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924493 +# Estimated pi_0=0.93035 +Using motif +M00232_2.00 of width 10. +Using motif -M00232_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945421 +# Estimated pi_0=0.955663 +Using motif +M00233_2.00 of width 10. +Using motif -M00233_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927921 +# Estimated pi_0=0.937403 +Using motif +M02885_2.00 of width 11. +Using motif -M02885_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8926 +# Estimated pi_0=0.89964 +Using motif +M02886_2.00 of width 15. +Using motif -M02886_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8962 +# Estimated pi_0=0.90896 +Using motif +M04439_2.00 of width 13. +Using motif -M04439_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928548 +# Estimated pi_0=0.938323 +Using motif +M04440_2.00 of width 13. +Using motif -M04440_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9016 +# Estimated pi_0=0.919508 +Using motif +M08302_2.00 of width 9. +Using motif -M08302_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924627 +# Estimated pi_0=0.935185 +Using motif +M08884_2.00 of width 18. +Using motif -M08884_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906839 +# Estimated pi_0=0.911124 +Using motif +M10197_2.00 of width 12. +Using motif -M10197_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945941 +# Estimated pi_0=0.955397 +Using motif +M07930_2.00 of width 15. +Using motif -M07930_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8428 +# Estimated pi_0=0.846789 +Using motif +M08089_2.00 of width 14. +Using motif -M08089_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.855 +# Estimated pi_0=0.869774 +Using motif +M08885_2.00 of width 17. +Using motif -M08885_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.863495 +# Estimated pi_0=0.871971 +Using motif +M09507_2.00 of width 20. +Using motif -M09507_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874194 +# Estimated pi_0=0.881045 +Using motif +M04441_2.00 of width 10. +Using motif -M04441_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963137 +# Estimated pi_0=0.972426 +Using motif +M04442_2.00 of width 10. +Using motif -M04442_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949076 +# Estimated pi_0=0.955256 +Using motif +M04443_2.00 of width 10. +Using motif -M04443_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967785 +# Estimated pi_0=0.977363 +Using motif +M04444_2.00 of width 10. +Using motif -M04444_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935676 +# Estimated pi_0=0.949697 +Using motif +M08303_2.00 of width 15. +Using motif -M08303_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913861 +# Estimated pi_0=0.925135 +Using motif +M08886_2.00 of width 8. +Using motif -M08886_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939595 +# Estimated pi_0=0.945732 +Using motif +M07579_2.00 of width 11. +Using motif -M07579_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966434 +# Estimated pi_0=0.971638 +Using motif +M08241_2.00 of width 15. +Using motif -M08241_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998492 +# Estimated pi_0=0.998492 +Using motif +M08304_2.00 of width 9. +Using motif -M08304_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989524 +# Estimated pi_0=0.990103 +Using motif +M02665_2.00 of width 20. +Using motif -M02665_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920779 +# Estimated pi_0=0.924304 +Using motif +M10203_2.00 of width 14. +Using motif -M10203_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937826 +# Estimated pi_0=0.940479 +Using motif +M07580_2.00 of width 30. +Using motif -M07580_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991469 +# Estimated pi_0=0.997778 +Using motif +M00234_2.00 of width 10. +Using motif -M00234_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00235_2.00 of width 10. +Using motif -M00235_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9856 +# Estimated pi_0=1 +Using motif +M04445_2.00 of width 13. +Using motif -M04445_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961 +# Estimated pi_0=0.97 +Using motif +M04446_2.00 of width 13. +Using motif -M04446_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9752 +# Estimated pi_0=1 +Using motif +M04447_2.00 of width 13. +Using motif -M04447_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9834 +# Estimated pi_0=1 +Using motif +M04448_2.00 of width 13. +Using motif -M04448_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993905 +# Estimated pi_0=1 +Using motif +M08305_2.00 of width 15. +Using motif -M08305_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937248 +# Estimated pi_0=0.939231 +Using motif +M08887_2.00 of width 24. +Using motif -M08887_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915818 +# Estimated pi_0=0.925532 +Using motif +M01171_2.00 of width 10. +Using motif -M01171_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.85981 +# Estimated pi_0=0.867231 +Using motif +M02887_2.00 of width 14. +Using motif -M02887_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905556 +# Estimated pi_0=0.915489 +Using motif +M04449_2.00 of width 14. +Using motif -M04449_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913204 +# Estimated pi_0=0.928816 +Using motif +M04450_2.00 of width 14. +Using motif -M04450_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92177 +# Estimated pi_0=0.93482 +Using motif +M04451_2.00 of width 22. +Using motif -M04451_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990282 +# Estimated pi_0=0.996429 +Using motif +M04452_2.00 of width 22. +Using motif -M04452_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996205 +# Estimated pi_0=0.998894 +Using motif +M08090_2.00 of width 12. +Using motif -M08090_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992474 +# Estimated pi_0=0.995176 +Using motif +M08306_2.00 of width 9. +Using motif -M08306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989215 +# Estimated pi_0=0.992929 +Using motif +M08888_2.00 of width 12. +Using motif -M08888_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990538 +# Estimated pi_0=0.993711 +Using motif +M00775_2.00 of width 10. +Using motif -M00775_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9098 +# Estimated pi_0=0.910291 +Using motif +M07581_2.00 of width 21. +Using motif -M07581_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884648 +# Estimated pi_0=0.892201 +Using motif +M00986_2.00 of width 9. +Using motif -M00986_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954953 +# Estimated pi_0=0.970299 +Using motif +M00987_2.00 of width 10. +Using motif -M00987_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923564 +# Estimated pi_0=0.927573 +Using motif +M00988_2.00 of width 9. +Using motif -M00988_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9294 +# Estimated pi_0=0.936762 +Using motif +M04453_2.00 of width 19. +Using motif -M04453_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9816 +# Estimated pi_0=0.999385 +Using motif +M04454_2.00 of width 19. +Using motif -M04454_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04455_2.00 of width 11. +Using motif -M04455_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898957 +# Estimated pi_0=0.907945 +Using motif +M04456_2.00 of width 11. +Using motif -M04456_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8568 +# Estimated pi_0=0.867931 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974591 +# Estimated pi_0=0.980947 +Using motif +M08889_2.00 of width 12. +Using motif -M08889_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979509 +# Estimated pi_0=0.983437 +Using motif +M02888_2.00 of width 11. +Using motif -M02888_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899661 +# Estimated pi_0=0.910074 +Using motif +M04457_2.00 of width 10. +Using motif -M04457_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903307 +# Estimated pi_0=0.9092 +Using motif +M04458_2.00 of width 10. +Using motif -M04458_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925565 +# Estimated pi_0=0.931948 +Using motif +M07582_2.00 of width 6. +Using motif -M07582_2.00 of width 6. +Computing q-values. +Using motif +M07583_2.00 of width 15. +Using motif -M07583_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M07584_2.00 of width 24. +Using motif -M07584_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M08242_2.00 of width 9. +Using motif -M08242_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08308_2.00 of width 12. +Using motif -M08308_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988902 +# Estimated pi_0=0.994592 +Using motif +M08890_2.00 of width 20. +Using motif -M08890_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998667 +# Estimated pi_0=1 +Using motif +M07585_2.00 of width 12. +Using motif -M07585_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965419 +# Estimated pi_0=0.973118 +Using motif +M08309_2.00 of width 27. +Using motif -M08309_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928855 +# Estimated pi_0=0.94024 +Using motif +M08891_2.00 of width 20. +Using motif -M08891_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940667 +# Estimated pi_0=0.949939 +Using motif +M08310_2.00 of width 21. +Using motif -M08310_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.828264 +# Estimated pi_0=0.832 +Using motif +M08892_2.00 of width 22. +Using motif -M08892_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857931 +# Estimated pi_0=0.860533 +Using motif +M07586_2.00 of width 24. +Using motif -M07586_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.84699 +# Estimated pi_0=0.853274 +Using motif +M07587_2.00 of width 24. +Using motif -M07587_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.833271 +# Estimated pi_0=0.844496 +Using motif +M04459_2.00 of width 18. +Using motif -M04459_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927222 +# Estimated pi_0=0.941259 +Using motif +M04460_2.00 of width 18. +Using motif -M04460_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975497 +# Estimated pi_0=0.98033 +Using motif +M07588_2.00 of width 15. +Using motif -M07588_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921074 +# Estimated pi_0=0.927701 +Using motif +M02889_2.00 of width 16. +Using motif -M02889_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877476 +# Estimated pi_0=0.886949 +Using motif +M02890_2.00 of width 16. +Using motif -M02890_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920935 +# Estimated pi_0=0.932941 +Using motif +M04461_2.00 of width 13. +Using motif -M04461_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889703 +# Estimated pi_0=0.896316 +Using motif +M04462_2.00 of width 13. +Using motif -M04462_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90396 +# Estimated pi_0=0.909431 +Using motif +M10216_2.00 of width 12. +Using motif -M10216_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929 +# Estimated pi_0=0.943231 +Using motif +M01172_2.00 of width 10. +Using motif -M01172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905139 +# Estimated pi_0=0.916125 +Using motif +M08893_2.00 of width 22. +Using motif -M08893_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864684 +# Estimated pi_0=0.870539 +Using motif +M04463_2.00 of width 11. +Using motif -M04463_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8918 +# Estimated pi_0=0.908345 +Using motif +M04464_2.00 of width 11. +Using motif -M04464_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911346 +# Estimated pi_0=0.919333 +Using motif +M08091_2.00 of width 11. +Using motif -M08091_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94878 +# Estimated pi_0=0.96 +Using motif +M08243_2.00 of width 10. +Using motif -M08243_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911765 +# Estimated pi_0=0.92705 +Using motif +M08894_2.00 of width 10. +Using motif -M08894_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908077 +# Estimated pi_0=0.916552 +Using motif +M07589_2.00 of width 12. +Using motif -M07589_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922034 +# Estimated pi_0=0.928593 +Using motif +M08244_2.00 of width 7. +Using motif -M08244_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994375 +# Estimated pi_0=0.997273 +Using motif +M08311_2.00 of width 18. +Using motif -M08311_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925957 +# Estimated pi_0=0.935488 +Using motif +M04465_2.00 of width 20. +Using motif -M04465_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.998593 +Using motif +M04466_2.00 of width 20. +Using motif -M04466_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999495 +# Estimated pi_0=1 +Using motif +M08986_2.00 of width 15. +Using motif -M08986_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917364 +# Estimated pi_0=0.926795 +Using motif +M02891_2.00 of width 10. +Using motif -M02891_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903378 +# Estimated pi_0=0.907305 +Using motif +M02892_2.00 of width 10. +Using motif -M02892_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898582 +# Estimated pi_0=0.908521 +Using motif +M04467_2.00 of width 13. +Using motif -M04467_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897922 +# Estimated pi_0=0.901333 +Using motif +M04468_2.00 of width 13. +Using motif -M04468_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865625 +# Estimated pi_0=0.880976 +Using motif +M04469_2.00 of width 13. +Using motif -M04469_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902222 +# Estimated pi_0=0.906211 +Using motif +M04470_2.00 of width 13. +Using motif -M04470_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884526 +# Estimated pi_0=0.890458 +Using motif +M04471_2.00 of width 16. +Using motif -M04471_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895385 +# Estimated pi_0=0.902353 +Using motif +M04472_2.00 of width 16. +Using motif -M04472_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905926 +# Estimated pi_0=0.918394 +Using motif +M07590_2.00 of width 33. +Using motif -M07590_2.00 of width 33. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97164 +# Estimated pi_0=0.974819 +Using motif +M07591_2.00 of width 12. +Using motif -M07591_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993298 +# Estimated pi_0=0.994619 +Using motif +M08312_2.00 of width 18. +Using motif -M08312_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991099 +# Estimated pi_0=0.992143 +Using motif +M08895_2.00 of width 19. +Using motif -M08895_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985684 +# Estimated pi_0=0.989128 +Using motif +M05848_2.00 of width 17. +Using motif -M05848_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948527 +# Estimated pi_0=0.960127 +Using motif +M07592_2.00 of width 15. +Using motif -M07592_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927874 +# Estimated pi_0=0.935329 +Using motif +M04473_2.00 of width 20. +Using motif -M04473_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9404 +# Estimated pi_0=0.96 +Using motif +M04474_2.00 of width 15. +Using motif -M04474_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951966 +# Estimated pi_0=0.961857 +Using motif +M04475_2.00 of width 20. +Using motif -M04475_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9224 +# Estimated pi_0=0.935 +Using motif +M04476_2.00 of width 15. +Using motif -M04476_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995635 +# Estimated pi_0=0.996583 +Using motif +M07593_2.00 of width 21. +Using motif -M07593_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958931 +# Estimated pi_0=0.965802 +Using motif +M04477_2.00 of width 10. +Using motif -M04477_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96297 +# Estimated pi_0=0.980952 +Using motif +M04478_2.00 of width 10. +Using motif -M04478_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969275 +# Estimated pi_0=0.97359 +Using motif +M04479_2.00 of width 18. +Using motif -M04479_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987308 +# Estimated pi_0=0.997653 +Using motif +M04480_2.00 of width 18. +Using motif -M04480_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975068 +# Estimated pi_0=0.986631 +Using motif +M07594_2.00 of width 29. +Using motif -M07594_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911825 +# Estimated pi_0=0.920859 +Using motif +M07595_2.00 of width 15. +Using motif -M07595_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904444 +# Estimated pi_0=0.910458 +Using motif +M07596_2.00 of width 24. +Using motif -M07596_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9434 +# Estimated pi_0=0.953706 +Using motif +M07597_2.00 of width 12. +Using motif -M07597_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988542 +# Estimated pi_0=0.991168 +Using motif +M08245_2.00 of width 15. +Using motif -M08245_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956178 +# Estimated pi_0=0.964767 +Using motif +M08313_2.00 of width 33. +Using motif -M08313_2.00 of width 33. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93245 +# Estimated pi_0=0.93869 +Using motif +M08896_2.00 of width 24. +Using motif -M08896_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943636 +# Estimated pi_0=0.951176 +Using motif +M09508_2.00 of width 8. +Using motif -M09508_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901373 +# Estimated pi_0=0.905273 +Using motif +M08897_2.00 of width 14. +Using motif -M08897_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9728 +# Estimated pi_0=0.982148 +Using motif +M09509_2.00 of width 12. +Using motif -M09509_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974 +# Estimated pi_0=0.995165 +Using motif +M07598_2.00 of width 21. +Using motif -M07598_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995889 +# Estimated pi_0=0.998191 +Using motif +M07599_2.00 of width 20. +Using motif -M07599_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944 +# Estimated pi_0=0.946549 +Using motif +M08314_2.00 of width 24. +Using motif -M08314_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M08898_2.00 of width 22. +Using motif -M08898_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993115 +# Estimated pi_0=0.997157 +Using motif +M04481_2.00 of width 12. +Using motif -M04481_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9032 +# Estimated pi_0=0.908544 +Using motif +M04482_2.00 of width 12. +Using motif -M04482_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873 +# Estimated pi_0=0.880909 +Using motif +M07600_2.00 of width 8. +Using motif -M07600_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984295 +# Estimated pi_0=0.993895 +Using motif +M08315_2.00 of width 15. +Using motif -M08315_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964174 +# Estimated pi_0=0.982439 +Using motif +M08899_2.00 of width 20. +Using motif -M08899_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958797 +# Estimated pi_0=0.965828 +Using motif +M07601_2.00 of width 21. +Using motif -M07601_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.852353 +# Estimated pi_0=0.8635 +Using motif +M04483_2.00 of width 17. +Using motif -M04483_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98479 +# Estimated pi_0=0.985978 +Using motif +M04484_2.00 of width 17. +Using motif -M04484_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975373 +# Estimated pi_0=0.981111 +Using motif +M02893_2.00 of width 14. +Using motif -M02893_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89802 +# Estimated pi_0=0.911316 +Using motif +M04485_2.00 of width 15. +Using motif -M04485_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903871 +# Estimated pi_0=0.904286 +Using motif +M04486_2.00 of width 15. +Using motif -M04486_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917949 +# Estimated pi_0=0.923359 +Using motif +M04487_2.00 of width 15. +Using motif -M04487_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902261 +# Estimated pi_0=0.90863 +Using motif +M04488_2.00 of width 15. +Using motif -M04488_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941818 +# Estimated pi_0=0.94517 +Using motif +M08900_2.00 of width 9. +Using motif -M08900_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88835 +# Estimated pi_0=0.895304 +Using motif +M10226_2.00 of width 9. +Using motif -M10226_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886471 +# Estimated pi_0=0.894364 +Using motif +M08307_2.00 of width 15. +Using motif -M08307_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975385 +# Estimated pi_0=0.977204 +Using motif +M07602_2.00 of width 15. +Using motif -M07602_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9434 +# Estimated pi_0=0.952138 +Using motif +M08316_2.00 of width 12. +Using motif -M08316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961135 +# Estimated pi_0=0.970919 +Using motif +M08901_2.00 of width 12. +Using motif -M08901_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965752 +# Estimated pi_0=0.978162 +Using motif +M04489_2.00 of width 22. +Using motif -M04489_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902414 +# Estimated pi_0=0.908112 +Using motif +M04490_2.00 of width 22. +Using motif -M04490_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906713 +# Estimated pi_0=0.91027 +Using motif +M08246_2.00 of width 10. +Using motif -M08246_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915048 +# Estimated pi_0=0.928442 +Using motif +M08317_2.00 of width 15. +Using motif -M08317_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.876893 +# Estimated pi_0=0.886259 +Using motif +M02894_2.00 of width 15. +Using motif -M02894_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903762 +# Estimated pi_0=0.91302 +Using motif +M04491_2.00 of width 16. +Using motif -M04491_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891845 +# Estimated pi_0=0.90383 +Using motif +M04492_2.00 of width 16. +Using motif -M04492_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899048 +# Estimated pi_0=0.912817 +Using motif +M04493_2.00 of width 15. +Using motif -M04493_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889423 +# Estimated pi_0=0.893565 +Using motif +M04494_2.00 of width 15. +Using motif -M04494_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912696 +# Estimated pi_0=0.924085 +Using motif +M08902_2.00 of width 15. +Using motif -M08902_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932903 +# Estimated pi_0=0.941728 +Using motif +M10229_2.00 of width 9. +Using motif -M10229_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917436 +# Estimated pi_0=0.922326 +Using motif +M07603_2.00 of width 18. +Using motif -M07603_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938912 +# Estimated pi_0=0.943949 +Using motif +M04495_2.00 of width 25. +Using motif -M04495_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04496_2.00 of width 26. +Using motif -M04496_2.00 of width 26. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M07604_2.00 of width 9. +Using motif -M07604_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9452 +# Estimated pi_0=0.953617 +Using motif +M07605_2.00 of width 30. +Using motif -M07605_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978211 +# Estimated pi_0=0.983147 +Using motif +M07606_2.00 of width 12. +Using motif -M07606_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936535 +# Estimated pi_0=0.941912 +Using motif +M02895_2.00 of width 12. +Using motif -M02895_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903429 +# Estimated pi_0=0.91328 +Using motif +M04497_2.00 of width 10. +Using motif -M04497_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894 +# Estimated pi_0=0.901143 +Using motif +M04498_2.00 of width 10. +Using motif -M04498_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907925 +# Estimated pi_0=0.919855 +Using motif +M08318_2.00 of width 20. +Using motif -M08318_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905556 +# Estimated pi_0=0.915733 +Using motif +M08903_2.00 of width 20. +Using motif -M08903_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985055 +# Estimated pi_0=0.988247 +Using motif +M07607_2.00 of width 9. +Using motif -M07607_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905545 +# Estimated pi_0=0.9145 +Using motif +M07608_2.00 of width 21. +Using motif -M07608_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977209 +# Estimated pi_0=0.986979 +Using motif +M08247_2.00 of width 15. +Using motif -M08247_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997968 +# Estimated pi_0=0.999095 +Using motif +M08319_2.00 of width 24. +Using motif -M08319_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96777 +# Estimated pi_0=0.979205 +Using motif +M08904_2.00 of width 22. +Using motif -M08904_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=0.997041 +Using motif +M08320_2.00 of width 9. +Using motif -M08320_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944035 +# Estimated pi_0=0.95 +Using motif +M08248_2.00 of width 24. +Using motif -M08248_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M02896_2.00 of width 17. +Using motif -M02896_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04499_2.00 of width 16. +Using motif -M04499_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00091 +# Estimated pi_0=1 +Using motif +M04500_2.00 of width 16. +Using motif -M04500_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04501_2.00 of width 16. +Using motif -M04501_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04502_2.00 of width 16. +Using motif -M04502_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02897_2.00 of width 12. +Using motif -M02897_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976452 +# Estimated pi_0=0.989005 +Using motif +M04503_2.00 of width 14. +Using motif -M04503_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9835 +# Estimated pi_0=0.988586 +Using motif +M04504_2.00 of width 14. +Using motif -M04504_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95807 +# Estimated pi_0=0.964 +Using motif +M04505_2.00 of width 14. +Using motif -M04505_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968947 +# Estimated pi_0=0.978202 +Using motif +M04506_2.00 of width 14. +Using motif -M04506_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983571 +# Estimated pi_0=0.992086 +Using motif +M00236_2.00 of width 9. +Using motif -M00236_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00237_2.00 of width 8. +Using motif -M00237_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M00238_2.00 of width 9. +Using motif -M00238_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00162 +# Estimated pi_0=1 +Using motif +M04507_2.00 of width 21. +Using motif -M04507_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995959 +# Estimated pi_0=0.999698 +Using motif +M04508_2.00 of width 21. +Using motif -M04508_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992356 +# Estimated pi_0=0.994667 +Using motif +M08905_2.00 of width 10. +Using motif -M08905_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M10235_2.00 of width 24. +Using motif -M10235_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998376 +# Estimated pi_0=1 +Using motif +M04509_2.00 of width 12. +Using motif -M04509_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906713 +# Estimated pi_0=0.91125 +Using motif +M04510_2.00 of width 12. +Using motif -M04510_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900792 +# Estimated pi_0=0.913662 +Using motif +M08321_2.00 of width 12. +Using motif -M08321_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.900629 +Using motif +M08906_2.00 of width 15. +Using motif -M08906_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899699 +# Estimated pi_0=0.902209 +Using motif +M07609_2.00 of width 30. +Using motif -M07609_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970926 +# Estimated pi_0=0.987594 +Using motif +M08322_2.00 of width 15. +Using motif -M08322_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979236 +# Estimated pi_0=0.983333 +Using motif +M04511_2.00 of width 12. +Using motif -M04511_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.885588 +# Estimated pi_0=0.889262 +Using motif +M04512_2.00 of width 12. +Using motif -M04512_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899375 +# Estimated pi_0=0.906316 +Using motif +M04513_2.00 of width 12. +Using motif -M04513_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91363 +# Estimated pi_0=0.923614 +Using motif +M04514_2.00 of width 12. +Using motif -M04514_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934558 +# Estimated pi_0=0.936107 +Using motif +M08323_2.00 of width 15. +Using motif -M08323_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896056 +# Estimated pi_0=0.899342 +Using motif +M08907_2.00 of width 19. +Using motif -M08907_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880148 +# Estimated pi_0=0.885488 +Using motif +M00142_2.00 of width 8. +Using motif -M00142_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963366 +# Estimated pi_0=0.97131 +Using motif +M07610_2.00 of width 24. +Using motif -M07610_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869612 +# Estimated pi_0=0.877484 +Using motif +M04515_2.00 of width 21. +Using motif -M04515_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98297 +# Estimated pi_0=0.998144 +Using motif +M04516_2.00 of width 21. +Using motif -M04516_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9552 +# Estimated pi_0=0.967727 +Using motif +M07611_2.00 of width 18. +Using motif -M07611_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860177 +# Estimated pi_0=0.863566 +Using motif +M02898_2.00 of width 12. +Using motif -M02898_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925041 +# Estimated pi_0=0.932611 +Using motif +M04517_2.00 of width 12. +Using motif -M04517_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910297 +# Estimated pi_0=0.920134 +Using motif +M04518_2.00 of width 12. +Using motif -M04518_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917778 +# Estimated pi_0=0.93195 +Using motif +M04519_2.00 of width 12. +Using motif -M04519_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.826535 +# Estimated pi_0=0.826535 +Using motif +M04520_2.00 of width 12. +Using motif -M04520_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8388 +# Estimated pi_0=0.844324 +Using motif +M04521_2.00 of width 10. +Using motif -M04521_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956381 +# Estimated pi_0=0.964785 +Using motif +M04522_2.00 of width 10. +Using motif -M04522_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977343 +# Estimated pi_0=0.985714 +Using motif +M08324_2.00 of width 11. +Using motif -M08324_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932109 +# Estimated pi_0=0.937 +Using motif +M08908_2.00 of width 16. +Using motif -M08908_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927778 +# Estimated pi_0=0.939535 +Using motif +M08325_2.00 of width 9. +Using motif -M08325_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980656 +# Estimated pi_0=0.984718 +Using motif +M00239_2.00 of width 11. +Using motif -M00239_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997026 +# Estimated pi_0=0.999698 +Using motif +M00240_2.00 of width 11. +Using motif -M00240_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M04523_2.00 of width 21. +Using motif -M04523_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9925 +# Estimated pi_0=0.994256 +Using motif +M04524_2.00 of width 21. +Using motif -M04524_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993261 +# Estimated pi_0=0.997588 +Using motif +M08909_2.00 of width 10. +Using motif -M08909_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997576 +# Estimated pi_0=0.999598 +Using motif +M07612_2.00 of width 8. +Using motif -M07612_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906731 +# Estimated pi_0=0.923425 +Using motif +M02899_2.00 of width 16. +Using motif -M02899_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991881 +# Estimated pi_0=0.997766 +Using motif +M07931_2.00 of width 15. +Using motif -M07931_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970078 +# Estimated pi_0=0.981091 +Using motif +M08910_2.00 of width 22. +Using motif -M08910_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94396 +# Estimated pi_0=0.951935 +Using motif +M09510_2.00 of width 15. +Using motif -M09510_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95037 +# Estimated pi_0=0.958372 +Using motif +M10247_2.00 of width 22. +Using motif -M10247_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955315 +# Estimated pi_0=0.972099 +Using motif +M10248_2.00 of width 21. +Using motif -M10248_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943301 +# Estimated pi_0=0.960897 +Using motif +M07613_2.00 of width 15. +Using motif -M07613_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998191 +# Estimated pi_0=0.998191 +Using motif +M04525_2.00 of width 17. +Using motif -M04525_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934834 +# Estimated pi_0=0.938896 +Using motif +M04526_2.00 of width 17. +Using motif -M04526_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933143 +# Estimated pi_0=0.939878 +Using motif +M07932_2.00 of width 15. +Using motif -M07932_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.91313 +Using motif +M08092_2.00 of width 15. +Using motif -M08092_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87646 +# Estimated pi_0=0.888387 +Using motif +M08326_2.00 of width 9. +Using motif -M08326_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.872277 +# Estimated pi_0=0.886713 +Using motif +M08911_2.00 of width 22. +Using motif -M08911_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.807899 +# Estimated pi_0=0.811587 +Using motif +M04527_2.00 of width 19. +Using motif -M04527_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.878846 +# Estimated pi_0=0.888527 +Using motif +M04528_2.00 of width 19. +Using motif -M04528_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874175 +# Estimated pi_0=0.887007 +Using motif +M07614_2.00 of width 21. +Using motif -M07614_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8132 +# Estimated pi_0=0.822483 +Using motif +M07615_2.00 of width 9. +Using motif -M07615_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898289 +# Estimated pi_0=0.90183 +Using motif +M07616_2.00 of width 9. +Using motif -M07616_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89686 +# Estimated pi_0=0.902143 +Using motif +M08327_2.00 of width 9. +Using motif -M08327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890973 +# Estimated pi_0=0.896923 +Using motif +M07617_2.00 of width 15. +Using motif -M07617_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963038 +# Estimated pi_0=0.971685 +Using motif +M08249_2.00 of width 14. +Using motif -M08249_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965373 +# Estimated pi_0=0.97858 +Using motif +M08328_2.00 of width 12. +Using motif -M08328_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994111 +# Estimated pi_0=0.997487 +Using motif +M08912_2.00 of width 20. +Using motif -M08912_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952963 +# Estimated pi_0=0.968846 +Using motif +M07618_2.00 of width 12. +Using motif -M07618_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941746 +# Estimated pi_0=0.948354 +Using motif +M08329_2.00 of width 15. +Using motif -M08329_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990741 +# Estimated pi_0=0.999899 +Using motif +M08913_2.00 of width 24. +Using motif -M08913_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99886 +# Estimated pi_0=0.999899 +Using motif +M07619_2.00 of width 24. +Using motif -M07619_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.818929 +# Estimated pi_0=0.82979 +Using motif +M04529_2.00 of width 16. +Using motif -M04529_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.895641 +# Estimated pi_0=0.9 +Using motif +M04530_2.00 of width 16. +Using motif -M04530_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889718 +# Estimated pi_0=0.892892 +Using motif +M07620_2.00 of width 27. +Using motif -M07620_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996 +# Estimated pi_0=1 +Using motif +M02900_2.00 of width 19. +Using motif -M02900_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04531_2.00 of width 14. +Using motif -M04531_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9796 +# Estimated pi_0=0.997368 +Using motif +M04532_2.00 of width 14. +Using motif -M04532_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970556 +# Estimated pi_0=0.981277 +Using motif +M07621_2.00 of width 15. +Using motif -M07621_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8408 +# Estimated pi_0=0.8408 +Using motif +M08330_2.00 of width 21. +Using motif -M08330_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8778 +# Estimated pi_0=0.890229 +Using motif +M02901_2.00 of width 17. +Using motif -M02901_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930891 +# Estimated pi_0=0.936911 +Using motif +M07622_2.00 of width 15. +Using motif -M07622_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08331_2.00 of width 15. +Using motif -M08331_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M08914_2.00 of width 24. +Using motif -M08914_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992812 +# Estimated pi_0=0.996583 +Using motif +M04533_2.00 of width 20. +Using motif -M04533_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954257 +# Estimated pi_0=0.961918 +Using motif +M04534_2.00 of width 20. +Using motif -M04534_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966916 +# Estimated pi_0=0.972966 +Using motif +M08332_2.00 of width 24. +Using motif -M08332_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998305 +# Estimated pi_0=0.999196 +Using motif +M02902_2.00 of width 9. +Using motif -M02902_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94699 +# Estimated pi_0=0.956732 +Using motif +M04535_2.00 of width 15. +Using motif -M04535_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9866 +# Estimated pi_0=1 +Using motif +M04536_2.00 of width 15. +Using motif -M04536_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00128 +# Estimated pi_0=1 +Using motif +M02903_2.00 of width 18. +Using motif -M02903_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953153 +# Estimated pi_0=0.963 +Using motif +M04537_2.00 of width 13. +Using motif -M04537_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918387 +# Estimated pi_0=0.930828 +Using motif +M04538_2.00 of width 13. +Using motif -M04538_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898448 +# Estimated pi_0=0.913154 +Using motif +M04539_2.00 of width 13. +Using motif -M04539_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908065 +# Estimated pi_0=0.914583 +Using motif +M04540_2.00 of width 13. +Using motif -M04540_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8956 +# Estimated pi_0=0.907752 +Using motif +M07623_2.00 of width 21. +Using motif -M07623_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980671 +# Estimated pi_0=0.987234 +Using motif +M08333_2.00 of width 15. +Using motif -M08333_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972921 +# Estimated pi_0=0.974889 +Using motif +M08915_2.00 of width 20. +Using motif -M08915_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937624 +# Estimated pi_0=0.951361 +Using motif +M08250_2.00 of width 21. +Using motif -M08250_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00091 +# Estimated pi_0=1 +Using motif +M08334_2.00 of width 30. +Using motif -M08334_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986974 +# Estimated pi_0=0.989137 +Using motif +M07624_2.00 of width 15. +Using motif -M07624_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931881 +# Estimated pi_0=0.947299 +Using motif +M08335_2.00 of width 12. +Using motif -M08335_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962187 +# Estimated pi_0=0.972625 +Using motif +M07625_2.00 of width 30. +Using motif -M07625_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938795 +# Estimated pi_0=0.945 +Using motif +M02904_2.00 of width 17. +Using motif -M02904_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986974 +# Estimated pi_0=0.993265 +Using motif +M04541_2.00 of width 14. +Using motif -M04541_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937671 +# Estimated pi_0=0.943851 +Using motif +M04542_2.00 of width 14. +Using motif -M04542_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937727 +# Estimated pi_0=0.946071 +Using motif +M05849_2.00 of width 11. +Using motif -M05849_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927083 +# Estimated pi_0=0.934479 +Using motif +M07626_2.00 of width 6. +Using motif -M07626_2.00 of width 6. +Computing q-values. +Using motif +M08251_2.00 of width 15. +Using motif -M08251_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948552 +# Estimated pi_0=0.959775 +Using motif +M08336_2.00 of width 18. +Using motif -M08336_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968548 +# Estimated pi_0=0.970909 +Using motif +M04543_2.00 of width 11. +Using motif -M04543_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94 +# Estimated pi_0=0.954016 +Using motif +M04544_2.00 of width 11. +Using motif -M04544_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925962 +# Estimated pi_0=0.936471 +Using motif +M05850_2.00 of width 16. +Using motif -M05850_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965686 +# Estimated pi_0=0.974074 +Using motif +M08252_2.00 of width 15. +Using motif -M08252_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926078 +# Estimated pi_0=0.932192 +Using motif +M08337_2.00 of width 15. +Using motif -M08337_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918835 +# Estimated pi_0=0.924571 +Using motif +M08253_2.00 of width 13. +Using motif -M08253_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857822 +# Estimated pi_0=0.867706 +Using motif +M08338_2.00 of width 13. +Using motif -M08338_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8598 +# Estimated pi_0=0.868644 +Using motif +M07627_2.00 of width 14. +Using motif -M07627_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922524 +# Estimated pi_0=0.940156 +Using motif +M07628_2.00 of width 15. +Using motif -M07628_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986557 +# Estimated pi_0=0.990938 +Using motif +M02905_2.00 of width 12. +Using motif -M02905_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926916 +# Estimated pi_0=0.936522 +Using motif +M02906_2.00 of width 14. +Using motif -M02906_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9288 +# Estimated pi_0=0.939225 +Using motif +M04545_2.00 of width 12. +Using motif -M04545_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8894 +# Estimated pi_0=0.9 +Using motif +M04546_2.00 of width 12. +Using motif -M04546_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8806 +# Estimated pi_0=0.892844 +Using motif +M08339_2.00 of width 15. +Using motif -M08339_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9074 +# Estimated pi_0=0.921912 +Using motif +M04547_2.00 of width 12. +Using motif -M04547_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.999598 +Using motif +M04548_2.00 of width 12. +Using motif -M04548_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99868 +# Estimated pi_0=1 +Using motif +M08340_2.00 of width 21. +Using motif -M08340_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.816 +# Estimated pi_0=0.829483 +Using motif +M07629_2.00 of width 17. +Using motif -M07629_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998291 +# Estimated pi_0=0.998291 +Using motif +M07630_2.00 of width 7. +Using motif -M07630_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982642 +# Estimated pi_0=0.987876 +Using motif +M07631_2.00 of width 21. +Using motif -M07631_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04549_2.00 of width 16. +Using motif -M04549_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98177 +# Estimated pi_0=0.998154 +Using motif +M04550_2.00 of width 16. +Using motif -M04550_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987143 +# Estimated pi_0=0.992787 +Using motif +M07632_2.00 of width 15. +Using motif -M07632_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999293 +# Estimated pi_0=0.999497 +Using motif +M07933_2.00 of width 17. +Using motif -M07933_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988415 +# Estimated pi_0=0.992539 +Using motif +M07934_2.00 of width 15. +Using motif -M07934_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9775 +# Estimated pi_0=0.986105 +Using motif +M07935_2.00 of width 15. +Using motif -M07935_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M07936_2.00 of width 15. +Using motif -M07936_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990595 +# Estimated pi_0=0.996884 +Using motif +M08916_2.00 of width 20. +Using motif -M08916_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990289 +# Estimated pi_0=0.995714 +Using motif +M07633_2.00 of width 21. +Using motif -M07633_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97525 +# Estimated pi_0=0.983854 +Using motif +M07634_2.00 of width 11. +Using motif -M07634_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M07635_2.00 of width 21. +Using motif -M07635_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998384 +# Estimated pi_0=0.998392 +Using motif +M04551_2.00 of width 15. +Using motif -M04551_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923721 +# Estimated pi_0=0.926939 +Using motif +M04552_2.00 of width 12. +Using motif -M04552_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939333 +# Estimated pi_0=0.946391 +Using motif +M04553_2.00 of width 15. +Using motif -M04553_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932319 +# Estimated pi_0=0.938947 +Using motif +M08341_2.00 of width 21. +Using motif -M08341_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913592 +# Estimated pi_0=0.925985 +Using motif +M08917_2.00 of width 20. +Using motif -M08917_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936271 +# Estimated pi_0=0.943247 +Using motif +M00241_2.00 of width 8. +Using motif -M00241_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884706 +# Estimated pi_0=0.900552 +Using motif +M00242_2.00 of width 8. +Using motif -M00242_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898378 +# Estimated pi_0=0.909313 +Using motif +M04554_2.00 of width 22. +Using motif -M04554_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.892773 +# Estimated pi_0=0.901366 +Using motif +M04555_2.00 of width 22. +Using motif -M04555_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899852 +# Estimated pi_0=0.907722 +Using motif +M02907_2.00 of width 12. +Using motif -M02907_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8348 +# Estimated pi_0=0.838431 +Using motif +M02908_2.00 of width 20. +Using motif -M02908_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8252 +# Estimated pi_0=0.837345 +Using motif +M02909_2.00 of width 19. +Using motif -M02909_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8104 +# Estimated pi_0=0.822124 +Using motif +M08918_2.00 of width 16. +Using motif -M08918_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8844 +# Estimated pi_0=0.896 +Using motif +M08093_2.00 of width 13. +Using motif -M08093_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999797 +# Estimated pi_0=0.999899 +Using motif +M08342_2.00 of width 15. +Using motif -M08342_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877638 +# Estimated pi_0=0.883212 +Using motif +M04556_2.00 of width 14. +Using motif -M04556_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04557_2.00 of width 14. +Using motif -M04557_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M08919_2.00 of width 9. +Using motif -M08919_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M02910_2.00 of width 11. +Using motif -M02910_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920156 +# Estimated pi_0=0.924815 +Using motif +M04558_2.00 of width 13. +Using motif -M04558_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928154 +# Estimated pi_0=0.935571 +Using motif +M04559_2.00 of width 13. +Using motif -M04559_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912973 +# Estimated pi_0=0.924516 +Using motif +M08920_2.00 of width 20. +Using motif -M08920_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.833504 +# Estimated pi_0=0.836643 +Using motif +M07636_2.00 of width 15. +Using motif -M07636_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965038 +# Estimated pi_0=0.975455 +Using motif +M08254_2.00 of width 15. +Using motif -M08254_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942957 +# Estimated pi_0=0.949639 +Using motif +M07637_2.00 of width 21. +Using motif -M07637_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974725 +# Estimated pi_0=0.979259 +Using motif +M08343_2.00 of width 21. +Using motif -M08343_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993261 +# Estimated pi_0=0.996041 +Using motif +M08921_2.00 of width 20. +Using motif -M08921_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986077 +# Estimated pi_0=0.989948 +Using motif +M07638_2.00 of width 15. +Using motif -M07638_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930192 +# Estimated pi_0=0.940479 +Using motif +M04560_2.00 of width 15. +Using motif -M04560_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919216 +# Estimated pi_0=0.937333 +Using motif +M04561_2.00 of width 10. +Using motif -M04561_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8822 +# Estimated pi_0=0.887407 +Using motif +M04562_2.00 of width 10. +Using motif -M04562_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898654 +# Estimated pi_0=0.901579 +Using motif +M04563_2.00 of width 15. +Using motif -M04563_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8984 +# Estimated pi_0=0.900943 +Using motif +M08344_2.00 of width 21. +Using motif -M08344_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896731 +# Estimated pi_0=0.904878 +Using motif +M08922_2.00 of width 11. +Using motif -M08922_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9308 +# Estimated pi_0=0.941705 +Using motif +M08445_2.00 of width 12. +Using motif -M08445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949277 +# Estimated pi_0=0.958212 +Using motif +M08923_2.00 of width 12. +Using motif -M08923_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921111 +# Estimated pi_0=0.93974 +Using motif +M07639_2.00 of width 21. +Using motif -M07639_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896436 +# Estimated pi_0=0.901905 +Using motif +M07640_2.00 of width 24. +Using motif -M07640_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947 +# Estimated pi_0=0.957531 +Using motif +M02911_2.00 of width 16. +Using motif -M02911_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9214 +# Estimated pi_0=0.938235 +Using motif +M04564_2.00 of width 15. +Using motif -M04564_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890495 +# Estimated pi_0=0.900986 +Using motif +M04565_2.00 of width 15. +Using motif -M04565_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897692 +# Estimated pi_0=0.914685 +Using motif +M08345_2.00 of width 14. +Using motif -M08345_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.89703 +# Estimated pi_0=0.907832 +Using motif +M07641_2.00 of width 15. +Using motif -M07641_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966667 +# Estimated pi_0=0.977126 +Using motif +M02912_2.00 of width 15. +Using motif -M02912_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.882963 +# Estimated pi_0=0.89622 +Using motif +M04566_2.00 of width 15. +Using motif -M04566_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911683 +# Estimated pi_0=0.920448 +Using motif +M04567_2.00 of width 15. +Using motif -M04567_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929739 +# Estimated pi_0=0.938194 +Using motif +M07642_2.00 of width 15. +Using motif -M07642_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.842281 +# Estimated pi_0=0.844628 +Using motif +M08255_2.00 of width 10. +Using motif -M08255_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939833 +# Estimated pi_0=0.952895 +Using motif +M07643_2.00 of width 29. +Using motif -M07643_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998384 +# Estimated pi_0=0.998894 +Using motif +M07644_2.00 of width 7. +Using motif -M07644_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975163 +# Estimated pi_0=0.979512 +Using motif +M07645_2.00 of width 6. +Using motif -M07645_2.00 of width 6. +Computing q-values. +Using motif +M07646_2.00 of width 9. +Using motif -M07646_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992347 +# Estimated pi_0=0.994949 +Using motif +M05851_2.00 of width 15. +Using motif -M05851_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98642 +# Estimated pi_0=0.993862 +Using motif +M07647_2.00 of width 9. +Using motif -M07647_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960374 +# Estimated pi_0=0.975116 +Using motif +M04568_2.00 of width 10. +Using motif -M04568_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940268 +# Estimated pi_0=0.943742 +Using motif +M04569_2.00 of width 10. +Using motif -M04569_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921468 +# Estimated pi_0=0.932537 +Using motif +M08924_2.00 of width 9. +Using motif -M08924_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923366 +# Estimated pi_0=0.932951 +Using motif +M04570_2.00 of width 20. +Using motif -M04570_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M08094_2.00 of width 15. +Using motif -M08094_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8924 +# Estimated pi_0=0.903276 +Using motif +M08925_2.00 of width 15. +Using motif -M08925_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.87604 +# Estimated pi_0=0.87604 +Using motif +M09511_2.00 of width 15. +Using motif -M09511_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873267 +# Estimated pi_0=0.884071 +Using motif +M07648_2.00 of width 29. +Using motif -M07648_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M07649_2.00 of width 23. +Using motif -M07649_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864571 +# Estimated pi_0=0.880155 +Using motif +M02666_2.00 of width 6. +Using motif -M02666_2.00 of width 6. +Computing q-values. +Using motif +M07650_2.00 of width 28. +Using motif -M07650_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.854476 +# Estimated pi_0=0.854476 +Using motif +M04571_2.00 of width 15. +Using motif -M04571_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.896436 +# Estimated pi_0=0.914571 +Using motif +M04572_2.00 of width 14. +Using motif -M04572_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856436 +# Estimated pi_0=0.868393 +Using motif +M04573_2.00 of width 15. +Using motif -M04573_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8864 +# Estimated pi_0=0.893273 +Using motif +M04574_2.00 of width 14. +Using motif -M04574_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889 +# Estimated pi_0=0.894259 +Using motif +M07651_2.00 of width 29. +Using motif -M07651_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8596 +# Estimated pi_0=0.866783 +Using motif +M08256_2.00 of width 7. +Using motif -M08256_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978411 +# Estimated pi_0=0.993846 +Using motif +M08346_2.00 of width 27. +Using motif -M08346_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930244 +# Estimated pi_0=0.936 +Using motif +M07652_2.00 of width 12. +Using motif -M07652_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870693 +# Estimated pi_0=0.873761 +Using motif +M08347_2.00 of width 12. +Using motif -M08347_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.858252 +# Estimated pi_0=0.868682 +Using motif +M07653_2.00 of width 12. +Using motif -M07653_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M07654_2.00 of width 14. +Using motif -M07654_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938333 +# Estimated pi_0=0.938987 +Using motif +M02913_2.00 of width 17. +Using motif -M02913_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995676 +# Estimated pi_0=0.998392 +Using motif +M04575_2.00 of width 19. +Using motif -M04575_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989255 +# Estimated pi_0=0.993198 +Using motif +M04576_2.00 of width 19. +Using motif -M04576_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967573 +# Estimated pi_0=0.978693 +Using motif +M04577_2.00 of width 19. +Using motif -M04577_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931456 +# Estimated pi_0=0.944522 +Using motif +M04578_2.00 of width 19. +Using motif -M04578_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987979 +# Estimated pi_0=0.989388 +Using motif +M07655_2.00 of width 11. +Using motif -M07655_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9092 +# Estimated pi_0=0.9092 +Using motif +M02914_2.00 of width 12. +Using motif -M02914_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.886733 +# Estimated pi_0=0.896429 +Using motif +M04579_2.00 of width 11. +Using motif -M04579_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934 +# Estimated pi_0=0.94624 +Using motif +M04580_2.00 of width 11. +Using motif -M04580_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.934697 +Using motif +M07937_2.00 of width 15. +Using motif -M07937_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8676 +# Estimated pi_0=0.875944 +Using motif +M08095_2.00 of width 13. +Using motif -M08095_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8534 +# Estimated pi_0=0.86 +Using motif +M08926_2.00 of width 9. +Using motif -M08926_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893929 +# Estimated pi_0=0.901301 +Using motif +M04581_2.00 of width 15. +Using motif -M04581_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957 +# Estimated pi_0=0.967068 +Using motif +M04582_2.00 of width 15. +Using motif -M04582_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9872 +# Estimated pi_0=0.999397 +Using motif +M08348_2.00 of width 12. +Using motif -M08348_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9678 +# Estimated pi_0=0.992381 +Using motif +M08927_2.00 of width 12. +Using motif -M08927_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967544 +# Estimated pi_0=0.978647 +Using motif +M02915_2.00 of width 15. +Using motif -M02915_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.878235 +# Estimated pi_0=0.884202 +Using motif +M04583_2.00 of width 13. +Using motif -M04583_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9078 +# Estimated pi_0=0.92155 +Using motif +M04584_2.00 of width 13. +Using motif -M04584_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905818 +# Estimated pi_0=0.917167 +Using motif +M08349_2.00 of width 9. +Using motif -M08349_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925649 +# Estimated pi_0=0.939209 +Using motif +M10279_2.00 of width 12. +Using motif -M10279_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934622 +# Estimated pi_0=0.945342 +Using motif +M02916_2.00 of width 13. +Using motif -M02916_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984925 +# Estimated pi_0=0.993298 +Using motif +M02917_2.00 of width 13. +Using motif -M02917_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984512 +# Estimated pi_0=0.991832 +Using motif +M04585_2.00 of width 13. +Using motif -M04585_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969116 +# Estimated pi_0=0.976608 +Using motif +M04586_2.00 of width 13. +Using motif -M04586_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964557 +# Estimated pi_0=0.972584 +Using motif +M08257_2.00 of width 12. +Using motif -M08257_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932923 +# Estimated pi_0=0.941139 +Using motif +M08350_2.00 of width 9. +Using motif -M08350_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95708 +# Estimated pi_0=0.965096 +Using motif +M08928_2.00 of width 11. +Using motif -M08928_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959646 +# Estimated pi_0=0.968537 +Using motif +M10282_2.00 of width 12. +Using motif -M10282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989643 +# Estimated pi_0=0.996 +Using motif +M08351_2.00 of width 9. +Using motif -M08351_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9258 +# Estimated pi_0=0.932903 +Using motif +M07656_2.00 of width 15. +Using motif -M07656_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M02918_2.00 of width 10. +Using motif -M02918_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955 +# Estimated pi_0=0.961007 +Using motif +M04587_2.00 of width 11. +Using motif -M04587_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973394 +# Estimated pi_0=0.982695 +Using motif +M04588_2.00 of width 11. +Using motif -M04588_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972252 +# Estimated pi_0=0.981224 +Using motif +M08352_2.00 of width 9. +Using motif -M08352_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970364 +# Estimated pi_0=0.978243 +Using motif +M04589_2.00 of width 15. +Using motif -M04589_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989231 +# Estimated pi_0=1 +Using motif +M04590_2.00 of width 15. +Using motif -M04590_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995 +# Estimated pi_0=1 +Using motif +M04591_2.00 of width 15. +Using motif -M04591_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966602 +# Estimated pi_0=0.976912 +Using motif +M04592_2.00 of width 15. +Using motif -M04592_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96 +# Estimated pi_0=0.973469 +Using motif +M07657_2.00 of width 21. +Using motif -M07657_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908189 +# Estimated pi_0=0.922264 +Using motif +M08353_2.00 of width 18. +Using motif -M08353_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926034 +# Estimated pi_0=0.934694 +Using motif +M08929_2.00 of width 21. +Using motif -M08929_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942687 +# Estimated pi_0=0.95 +Using motif +M07658_2.00 of width 15. +Using motif -M07658_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.79802 +# Estimated pi_0=0.80748 +Using motif +M02919_2.00 of width 15. +Using motif -M02919_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988161 +# Estimated pi_0=0.993472 +Using motif +M04593_2.00 of width 17. +Using motif -M04593_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962373 +# Estimated pi_0=0.972125 +Using motif +M04594_2.00 of width 17. +Using motif -M04594_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984431 +# Estimated pi_0=0.992887 +Using motif +M05852_2.00 of width 13. +Using motif -M05852_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975088 +# Estimated pi_0=0.978883 +Using motif +M08354_2.00 of width 15. +Using motif -M08354_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917308 +# Estimated pi_0=0.924255 +Using motif +M08258_2.00 of width 15. +Using motif -M08258_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991951 +# Estimated pi_0=0.997778 +Using motif +M08355_2.00 of width 18. +Using motif -M08355_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979359 +# Estimated pi_0=0.985189 +Using motif +M07659_2.00 of width 21. +Using motif -M07659_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964538 +# Estimated pi_0=0.968947 +Using motif +M07660_2.00 of width 30. +Using motif -M07660_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.7816 +# Estimated pi_0=0.801818 +Using motif +M05853_2.00 of width 18. +Using motif -M05853_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968831 +# Estimated pi_0=0.977486 +Using motif +M08356_2.00 of width 21. +Using motif -M08356_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996142 +# Estimated pi_0=0.996583 +Using motif +M08930_2.00 of width 24. +Using motif -M08930_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970409 +# Estimated pi_0=0.976335 +Using motif +M07661_2.00 of width 15. +Using motif -M07661_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938323 +# Estimated pi_0=0.939636 +Using motif +M08259_2.00 of width 17. +Using motif -M08259_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940388 +# Estimated pi_0=0.954815 +Using motif +M08357_2.00 of width 21. +Using motif -M08357_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883883 +# Estimated pi_0=0.894825 +Using motif +M08931_2.00 of width 20. +Using motif -M08931_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902115 +# Estimated pi_0=0.91875 +Using motif +M08260_2.00 of width 21. +Using motif -M08260_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874788 +# Estimated pi_0=0.878844 +Using motif +M08358_2.00 of width 12. +Using motif -M08358_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.891429 +# Estimated pi_0=0.895974 +Using motif +M08932_2.00 of width 22. +Using motif -M08932_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.871096 +# Estimated pi_0=0.875556 +Using motif +M04595_2.00 of width 12. +Using motif -M04595_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9854 +# Estimated pi_0=1 +Using motif +M04596_2.00 of width 12. +Using motif -M04596_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953465 +# Estimated pi_0=0.963537 +Using motif +M04598_2.00 of width 15. +Using motif -M04598_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9676 +# Estimated pi_0=0.97761 +Using motif +M07662_2.00 of width 21. +Using motif -M07662_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.876721 +# Estimated pi_0=0.880593 +Using motif +M06465_2.00 of width 14. +Using motif -M06465_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904483 +# Estimated pi_0=0.911043 +Using motif +M04599_2.00 of width 12. +Using motif -M04599_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04600_2.00 of width 12. +Using motif -M04600_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08359_2.00 of width 21. +Using motif -M08359_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893333 +# Estimated pi_0=0.909726 +Using motif +M08933_2.00 of width 20. +Using motif -M08933_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961475 +# Estimated pi_0=0.969643 +Using motif +M07663_2.00 of width 15. +Using motif -M07663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939324 +# Estimated pi_0=0.941457 +Using motif +M07664_2.00 of width 15. +Using motif -M07664_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994769 +# Estimated pi_0=0.997186 +Using motif +M07665_2.00 of width 12. +Using motif -M07665_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9258 +# Estimated pi_0=0.937778 +Using motif +M08360_2.00 of width 21. +Using motif -M08360_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959406 +# Estimated pi_0=0.965212 +Using motif +M08934_2.00 of width 20. +Using motif -M08934_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965031 +# Estimated pi_0=0.970112 +Using motif +M08261_2.00 of width 15. +Using motif -M08261_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933289 +# Estimated pi_0=0.935528 +Using motif +M08361_2.00 of width 18. +Using motif -M08361_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890654 +# Estimated pi_0=0.8936 +Using motif +M08935_2.00 of width 16. +Using motif -M08935_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912727 +# Estimated pi_0=0.915337 +Using motif +M07666_2.00 of width 18. +Using motif -M07666_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914844 +# Estimated pi_0=0.928125 +Using motif +M07667_2.00 of width 21. +Using motif -M07667_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889381 +# Estimated pi_0=0.895333 +Using motif +M08362_2.00 of width 24. +Using motif -M08362_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874436 +# Estimated pi_0=0.874436 +Using motif +M08936_2.00 of width 20. +Using motif -M08936_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901584 +# Estimated pi_0=0.91 +Using motif +M07668_2.00 of width 18. +Using motif -M07668_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894648 +# Estimated pi_0=0.903791 +Using motif +M07669_2.00 of width 9. +Using motif -M07669_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943016 +# Estimated pi_0=0.948627 +Using motif +M04601_2.00 of width 15. +Using motif -M04601_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998519 +# Estimated pi_0=0.999799 +Using motif +M04602_2.00 of width 15. +Using motif -M04602_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M05854_2.00 of width 15. +Using motif -M05854_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985062 +# Estimated pi_0=0.993125 +Using motif +M05855_2.00 of width 15. +Using motif -M05855_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998392 +# Estimated pi_0=0.998392 +Using motif +M07670_2.00 of width 28. +Using motif -M07670_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M07671_2.00 of width 39. +Using motif -M07671_2.00 of width 39. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.867706 +# Estimated pi_0=0.875447 +Using motif +M02920_2.00 of width 12. +Using motif -M02920_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910693 +# Estimated pi_0=0.929787 +Using motif +M00243_2.00 of width 7. +Using motif -M00243_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879 +# Estimated pi_0=0.890084 +Using motif +M00244_2.00 of width 12. +Using motif -M00244_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00245_2.00 of width 7. +Using motif -M00245_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8964 +# Estimated pi_0=0.906777 +Using motif +M00246_2.00 of width 7. +Using motif -M00246_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875534 +# Estimated pi_0=0.885487 +Using motif +M00247_2.00 of width 11. +Using motif -M00247_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00248_2.00 of width 13. +Using motif -M00248_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00249_2.00 of width 11. +Using motif -M00249_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.883214 +# Estimated pi_0=0.887914 +Using motif +M08937_2.00 of width 20. +Using motif -M08937_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.83888 +# Estimated pi_0=0.841504 +Using motif +M07672_2.00 of width 12. +Using motif -M07672_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99082 +# Estimated pi_0=0.997157 +Using motif +M07673_2.00 of width 18. +Using motif -M07673_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92 +# Estimated pi_0=0.936934 +Using motif +M07674_2.00 of width 29. +Using motif -M07674_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M07675_2.00 of width 21. +Using motif -M07675_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04603_2.00 of width 17. +Using motif -M04603_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9202 +# Estimated pi_0=0.9202 +Using motif +M04604_2.00 of width 17. +Using motif -M04604_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8554 +# Estimated pi_0=0.860577 +Using motif +M02921_2.00 of width 11. +Using motif -M02921_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928702 +# Estimated pi_0=0.939018 +Using motif +M04605_2.00 of width 15. +Using motif -M04605_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931652 +# Estimated pi_0=0.942051 +Using motif +M04606_2.00 of width 15. +Using motif -M04606_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920734 +# Estimated pi_0=0.927571 +Using motif +M08096_2.00 of width 11. +Using motif -M08096_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908794 +# Estimated pi_0=0.916049 +Using motif +M08363_2.00 of width 15. +Using motif -M08363_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869545 +# Estimated pi_0=0.879742 +Using motif +M08938_2.00 of width 22. +Using motif -M08938_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.831845 +# Estimated pi_0=0.842105 +Using motif +M10294_2.00 of width 10. +Using motif -M10294_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916075 +# Estimated pi_0=0.925103 +Using motif +M10300_2.00 of width 13. +Using motif -M10300_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864673 +# Estimated pi_0=0.881111 +Using motif +M04607_2.00 of width 10. +Using motif -M04607_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965241 +# Estimated pi_0=0.978696 +Using motif +M04608_2.00 of width 10. +Using motif -M04608_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9434 +# Estimated pi_0=0.951126 +Using motif +M00152_2.00 of width 9. +Using motif -M00152_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970991 +# Estimated pi_0=0.977717 +Using motif +M08939_2.00 of width 8. +Using motif -M08939_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961324 +# Estimated pi_0=0.969162 +Using motif +M10301_2.00 of width 13. +Using motif -M10301_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986903 +# Estimated pi_0=0.99545 +Using motif +M10302_2.00 of width 12. +Using motif -M10302_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9728 +# Estimated pi_0=0.989576 +Using motif +M10303_2.00 of width 13. +Using motif -M10303_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=1 +Using motif +M10306_2.00 of width 9. +Using motif -M10306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965679 +# Estimated pi_0=0.965714 +Using motif +M07676_2.00 of width 21. +Using motif -M07676_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.822353 +# Estimated pi_0=0.829167 +Using motif +M07677_2.00 of width 21. +Using motif -M07677_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.84729 +# Estimated pi_0=0.85197 +Using motif +M07678_2.00 of width 27. +Using motif -M07678_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.838974 +# Estimated pi_0=0.84254 +Using motif +M07679_2.00 of width 9. +Using motif -M07679_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00198 +# Estimated pi_0=1 +Using motif +M08364_2.00 of width 15. +Using motif -M08364_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912623 +# Estimated pi_0=0.914307 +Using motif +M08940_2.00 of width 13. +Using motif -M08940_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944658 +# Estimated pi_0=0.954699 +Using motif +M10307_2.00 of width 13. +Using motif -M10307_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957778 +# Estimated pi_0=0.978778 +Using motif +M07680_2.00 of width 9. +Using motif -M07680_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95339 +# Estimated pi_0=0.95962 +Using motif +M07681_2.00 of width 30. +Using motif -M07681_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893398 +# Estimated pi_0=0.899552 +Using motif +M07682_2.00 of width 30. +Using motif -M07682_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884554 +# Estimated pi_0=0.902676 +Using motif +M07683_2.00 of width 12. +Using motif -M07683_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971678 +# Estimated pi_0=0.981413 +Using motif +M04609_2.00 of width 15. +Using motif -M04609_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8502 +# Estimated pi_0=0.856822 +Using motif +M04610_2.00 of width 15. +Using motif -M04610_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.874653 +# Estimated pi_0=0.877255 +Using motif +M07684_2.00 of width 15. +Using motif -M07684_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917308 +# Estimated pi_0=0.923614 +Using motif +M04611_2.00 of width 11. +Using motif -M04611_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04612_2.00 of width 11. +Using motif -M04612_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M08365_2.00 of width 15. +Using motif -M08365_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.845333 +# Estimated pi_0=0.85482 +Using motif +M07685_2.00 of width 12. +Using motif -M07685_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930704 +# Estimated pi_0=0.935062 +Using motif +M07686_2.00 of width 18. +Using motif -M07686_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985275 +# Estimated pi_0=0.986947 +Using motif +M04613_2.00 of width 19. +Using motif -M04613_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04614_2.00 of width 19. +Using motif -M04614_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M08366_2.00 of width 15. +Using motif -M08366_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968765 +# Estimated pi_0=0.973222 +Using motif +M08941_2.00 of width 24. +Using motif -M08941_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994839 +# Estimated pi_0=0.998384 +Using motif +M07687_2.00 of width 27. +Using motif -M07687_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944444 +# Estimated pi_0=0.953412 +Using motif +M07688_2.00 of width 12. +Using motif -M07688_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924587 +# Estimated pi_0=0.931452 +Using motif +M07689_2.00 of width 27. +Using motif -M07689_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.842698 +# Estimated pi_0=0.842787 +Using motif +M07690_2.00 of width 15. +Using motif -M07690_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899208 +# Estimated pi_0=0.906364 +Using motif +M08262_2.00 of width 7. +Using motif -M08262_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953137 +# Estimated pi_0=0.958333 +Using motif +M08367_2.00 of width 27. +Using motif -M08367_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907327 +# Estimated pi_0=0.914407 +Using motif +M04615_2.00 of width 23. +Using motif -M04615_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970297 +# Estimated pi_0=0.981935 +Using motif +M04616_2.00 of width 23. +Using motif -M04616_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98717 +# Estimated pi_0=1 +Using motif +M07691_2.00 of width 21. +Using motif -M07691_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.815472 +# Estimated pi_0=0.822149 +Using motif +M02922_2.00 of width 14. +Using motif -M02922_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959434 +# Estimated pi_0=0.97039 +Using motif +M04617_2.00 of width 17. +Using motif -M04617_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9786 +# Estimated pi_0=0.99435 +Using motif +M04618_2.00 of width 17. +Using motif -M04618_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946729 +# Estimated pi_0=0.958507 +Using motif +M08942_2.00 of width 17. +Using motif -M08942_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929 +# Estimated pi_0=0.935263 +Using motif +M08368_2.00 of width 18. +Using motif -M08368_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915676 +# Estimated pi_0=0.916438 +Using motif +M08943_2.00 of width 21. +Using motif -M08943_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905079 +# Estimated pi_0=0.911275 +Using motif +M07692_2.00 of width 30. +Using motif -M07692_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989744 +# Estimated pi_0=0.993198 +Using motif +M03682_2.00 of width 14. +Using motif -M03682_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912871 +# Estimated pi_0=0.92797 +Using motif +M07693_2.00 of width 21. +Using motif -M07693_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922143 +# Estimated pi_0=0.933605 +Using motif +M07694_2.00 of width 21. +Using motif -M07694_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911028 +# Estimated pi_0=0.91817 +Using motif +M07695_2.00 of width 6. +Using motif -M07695_2.00 of width 6. +Computing q-values. +Using motif +M08263_2.00 of width 22. +Using motif -M08263_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9046 +# Estimated pi_0=0.916036 +Using motif +M08369_2.00 of width 9. +Using motif -M08369_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963273 +# Estimated pi_0=0.972349 +Using motif +M02923_2.00 of width 14. +Using motif -M02923_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940583 +# Estimated pi_0=0.952203 +Using motif +M07696_2.00 of width 9. +Using motif -M07696_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930698 +# Estimated pi_0=0.932727 +Using motif +M04619_2.00 of width 10. +Using motif -M04619_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899231 +# Estimated pi_0=0.901481 +Using motif +M04620_2.00 of width 10. +Using motif -M04620_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959406 +# Estimated pi_0=0.97961 +Using motif +M08264_2.00 of width 15. +Using motif -M08264_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999897 +# Estimated pi_0=1 +Using motif +M08370_2.00 of width 9. +Using motif -M08370_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910099 +# Estimated pi_0=0.914 +Using motif +M08944_2.00 of width 20. +Using motif -M08944_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M07697_2.00 of width 18. +Using motif -M07697_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99665 +# Estimated pi_0=0.999095 +Using motif +M07698_2.00 of width 12. +Using motif -M07698_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936434 +# Estimated pi_0=0.94 +Using motif +M07699_2.00 of width 7. +Using motif -M07699_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997672 +# Estimated pi_0=1 +Using motif +M01173_2.00 of width 11. +Using motif -M01173_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987545 +# Estimated pi_0=0.998071 +Using motif +M05856_2.00 of width 17. +Using motif -M05856_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996892 +# Estimated pi_0=0.999296 +Using motif +M07700_2.00 of width 18. +Using motif -M07700_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981457 +# Estimated pi_0=0.989944 +Using motif +M08371_2.00 of width 15. +Using motif -M08371_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970769 +# Estimated pi_0=0.972093 +Using motif +M04621_2.00 of width 14. +Using motif -M04621_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989844 +# Estimated pi_0=0.998687 +Using motif +M04622_2.00 of width 14. +Using motif -M04622_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9616 +# Estimated pi_0=0.975683 +Using motif +M08372_2.00 of width 24. +Using motif -M08372_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93632 +# Estimated pi_0=0.942308 +Using motif +M08945_2.00 of width 24. +Using motif -M08945_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983057 +# Estimated pi_0=0.990053 +Using motif +M08373_2.00 of width 18. +Using motif -M08373_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909487 +# Estimated pi_0=0.915906 +Using motif +M07701_2.00 of width 18. +Using motif -M07701_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857593 +# Estimated pi_0=0.866614 +Using motif +M07702_2.00 of width 12. +Using motif -M07702_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946548 +# Estimated pi_0=0.950636 +Using motif +M05857_2.00 of width 19. +Using motif -M05857_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9442 +# Estimated pi_0=0.945 +Using motif +M07703_2.00 of width 7. +Using motif -M07703_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865545 +# Estimated pi_0=0.865545 +Using motif +M07704_2.00 of width 21. +Using motif -M07704_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935504 +# Estimated pi_0=0.941519 +Using motif +M07705_2.00 of width 27. +Using motif -M07705_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986786 +# Estimated pi_0=0.991789 +Using motif +M07706_2.00 of width 30. +Using motif -M07706_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986889 +# Estimated pi_0=0.990737 +Using motif +M07707_2.00 of width 6. +Using motif -M07707_2.00 of width 6. +Computing q-values. +Using motif +M07708_2.00 of width 27. +Using motif -M07708_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M08265_2.00 of width 21. +Using motif -M08265_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M08374_2.00 of width 18. +Using motif -M08374_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M08946_2.00 of width 24. +Using motif -M08946_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M07709_2.00 of width 9. +Using motif -M07709_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963714 +# Estimated pi_0=0.969274 +Using motif +M08375_2.00 of width 27. +Using motif -M08375_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960465 +# Estimated pi_0=0.973216 +Using motif +M08947_2.00 of width 20. +Using motif -M08947_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999681 +# Estimated pi_0=0.999698 +Using motif +M07710_2.00 of width 21. +Using motif -M07710_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996739 +# Estimated pi_0=0.999899 +Using motif +M07711_2.00 of width 15. +Using motif -M07711_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92381 +# Estimated pi_0=0.936599 +Using motif +M07712_2.00 of width 30. +Using motif -M07712_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M08376_2.00 of width 15. +Using motif -M08376_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974591 +# Estimated pi_0=0.981838 +Using motif +M08948_2.00 of width 17. +Using motif -M08948_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979875 +# Estimated pi_0=0.985938 +Using motif +M02924_2.00 of width 18. +Using motif -M02924_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998611 +# Estimated pi_0=0.999799 +Using motif +M04623_2.00 of width 19. +Using motif -M04623_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995892 +# Estimated pi_0=0.998384 +Using motif +M04624_2.00 of width 19. +Using motif -M04624_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996071 +# Estimated pi_0=0.99809 +Using motif +M04625_2.00 of width 19. +Using motif -M04625_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992402 +# Estimated pi_0=0.998291 +Using motif +M04626_2.00 of width 19. +Using motif -M04626_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988209 +# Estimated pi_0=0.994479 +Using motif +M08377_2.00 of width 24. +Using motif -M08377_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M08378_2.00 of width 33. +Using motif -M08378_2.00 of width 33. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979072 +# Estimated pi_0=0.981231 +Using motif +M08949_2.00 of width 20. +Using motif -M08949_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987083 +# Estimated pi_0=0.990051 +Using motif +M07713_2.00 of width 9. +Using motif -M07713_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997098 +# Estimated pi_0=0.998492 +Using motif +M07714_2.00 of width 21. +Using motif -M07714_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915273 +# Estimated pi_0=0.929231 +Using motif +M07715_2.00 of width 15. +Using motif -M07715_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8694 +# Estimated pi_0=0.882239 +Using motif +M07716_2.00 of width 14. +Using motif -M07716_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920833 +# Estimated pi_0=0.927305 +Using motif +M07717_2.00 of width 12. +Using motif -M07717_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869474 +# Estimated pi_0=0.874576 +Using motif +M05858_2.00 of width 11. +Using motif -M05858_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917115 +# Estimated pi_0=0.929139 +Using motif +M07718_2.00 of width 21. +Using motif -M07718_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.839 +# Estimated pi_0=0.84375 +Using motif +M07719_2.00 of width 9. +Using motif -M07719_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969178 +# Estimated pi_0=0.976831 +Using motif +M08379_2.00 of width 13. +Using motif -M08379_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950781 +# Estimated pi_0=0.960468 +Using motif +M08950_2.00 of width 12. +Using motif -M08950_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959119 +# Estimated pi_0=0.964205 +Using motif +M07720_2.00 of width 29. +Using motif -M07720_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943604 +# Estimated pi_0=0.952187 +Using motif +M00250_2.00 of width 10. +Using motif -M00250_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00251_2.00 of width 9. +Using motif -M00251_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M00252_2.00 of width 10. +Using motif -M00252_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M08380_2.00 of width 21. +Using motif -M08380_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976517 +# Estimated pi_0=0.981158 +Using motif +M07721_2.00 of width 7. +Using motif -M07721_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932206 +# Estimated pi_0=0.936892 +Using motif +M07722_2.00 of width 27. +Using motif -M07722_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.884854 +# Estimated pi_0=0.892403 +Using motif +M08266_2.00 of width 20. +Using motif -M08266_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992067 +# Estimated pi_0=0.996103 +Using motif +M08381_2.00 of width 21. +Using motif -M08381_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981718 +# Estimated pi_0=0.987553 +Using motif +M07723_2.00 of width 15. +Using motif -M07723_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91438 +# Estimated pi_0=0.91863 +Using motif +M07724_2.00 of width 7. +Using motif -M07724_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973043 +# Estimated pi_0=0.982011 +Using motif +M07725_2.00 of width 12. +Using motif -M07725_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997387 +# Estimated pi_0=0.997387 +Using motif +M07726_2.00 of width 15. +Using motif -M07726_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04627_2.00 of width 13. +Using motif -M04627_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865 +# Estimated pi_0=0.880163 +Using motif +M04628_2.00 of width 13. +Using motif -M04628_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880196 +# Estimated pi_0=0.89761 +Using motif +M07727_2.00 of width 12. +Using motif -M07727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8328 +# Estimated pi_0=0.842407 +Using motif +M07728_2.00 of width 18. +Using motif -M07728_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951429 +# Estimated pi_0=0.955273 +Using motif +M07729_2.00 of width 24. +Using motif -M07729_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88708 +# Estimated pi_0=0.890543 +Using motif +M07730_2.00 of width 24. +Using motif -M07730_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963437 +# Estimated pi_0=0.969677 +Using motif +M07731_2.00 of width 30. +Using motif -M07731_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.844815 +# Estimated pi_0=0.85008 +Using motif +M07732_2.00 of width 8. +Using motif -M07732_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949381 +# Estimated pi_0=0.957108 +Using motif +M08382_2.00 of width 21. +Using motif -M08382_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988804 +# Estimated pi_0=0.990729 +Using motif +M07733_2.00 of width 15. +Using motif -M07733_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933455 +# Estimated pi_0=0.938079 +Using motif +M07734_2.00 of width 21. +Using motif -M07734_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.82233 +# Estimated pi_0=0.828033 +Using motif +M08267_2.00 of width 14. +Using motif -M08267_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991299 +# Estimated pi_0=0.99401 +Using motif +M08383_2.00 of width 24. +Using motif -M08383_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91 +# Estimated pi_0=0.913012 +Using motif +M08384_2.00 of width 24. +Using motif -M08384_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.841416 +# Estimated pi_0=0.849194 +Using motif +M08951_2.00 of width 20. +Using motif -M08951_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907692 +# Estimated pi_0=0.913739 +Using motif +M08952_2.00 of width 22. +Using motif -M08952_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904341 +# Estimated pi_0=0.911608 +Using motif +M07735_2.00 of width 9. +Using motif -M07735_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963537 +# Estimated pi_0=0.974098 +Using motif +M08268_2.00 of width 17. +Using motif -M08268_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910093 +# Estimated pi_0=0.915214 +Using motif +M08385_2.00 of width 12. +Using motif -M08385_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958983 +# Estimated pi_0=0.966667 +Using motif +M07736_2.00 of width 21. +Using motif -M07736_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932857 +# Estimated pi_0=0.935517 +Using motif +M07737_2.00 of width 27. +Using motif -M07737_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944071 +# Estimated pi_0=0.95745 +Using motif +M08386_2.00 of width 18. +Using motif -M08386_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949216 +# Estimated pi_0=0.958529 +Using motif +M08953_2.00 of width 19. +Using motif -M08953_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9175 +# Estimated pi_0=0.928077 +Using motif +M04629_2.00 of width 16. +Using motif -M04629_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8744 +# Estimated pi_0=0.883519 +Using motif +M04630_2.00 of width 16. +Using motif -M04630_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902574 +# Estimated pi_0=0.91 +Using motif +M04631_2.00 of width 16. +Using motif -M04631_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904 +# Estimated pi_0=0.909815 +Using motif +M04632_2.00 of width 16. +Using motif -M04632_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877 +# Estimated pi_0=0.879417 +Using motif +M08387_2.00 of width 9. +Using motif -M08387_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.794257 +# Estimated pi_0=0.794257 +Using motif +M08954_2.00 of width 9. +Using motif -M08954_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.868515 +# Estimated pi_0=0.87283 +Using motif +M07738_2.00 of width 6. +Using motif -M07738_2.00 of width 6. +Computing q-values. +Using motif +M07739_2.00 of width 21. +Using motif -M07739_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M08388_2.00 of width 11. +Using motif -M08388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.911552 +# Estimated pi_0=0.925677 +Using motif +M08955_2.00 of width 22. +Using motif -M08955_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.855085 +# Estimated pi_0=0.8644 +Using motif +M07740_2.00 of width 28. +Using motif -M07740_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998579 +# Estimated pi_0=0.999095 +Using motif +M07741_2.00 of width 15. +Using motif -M07741_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970307 +# Estimated pi_0=0.977444 +Using motif +M07742_2.00 of width 29. +Using motif -M07742_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987159 +# Estimated pi_0=0.989691 +Using motif +M07743_2.00 of width 7. +Using motif -M07743_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986195 +# Estimated pi_0=0.996788 +Using motif +M07744_2.00 of width 11. +Using motif -M07744_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.957143 +Using motif +M07745_2.00 of width 12. +Using motif -M07745_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956387 +# Estimated pi_0=0.962747 +Using motif +M07746_2.00 of width 24. +Using motif -M07746_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893137 +# Estimated pi_0=0.894513 +Using motif +M07747_2.00 of width 21. +Using motif -M07747_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964854 +# Estimated pi_0=0.97775 +Using motif +M07748_2.00 of width 18. +Using motif -M07748_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.857719 +# Estimated pi_0=0.861301 +Using motif +M07749_2.00 of width 18. +Using motif -M07749_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00178 +# Estimated pi_0=1 +Using motif +M07750_2.00 of width 12. +Using motif -M07750_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951126 +# Estimated pi_0=0.962209 +Using motif +M07751_2.00 of width 15. +Using motif -M07751_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899841 +# Estimated pi_0=0.905467 +Using motif +M02938_2.00 of width 13. +Using motif -M02938_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M07752_2.00 of width 15. +Using motif -M07752_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04633_2.00 of width 14. +Using motif -M04633_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8918 +# Estimated pi_0=0.907717 +Using motif +M04634_2.00 of width 14. +Using motif -M04634_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9002 +# Estimated pi_0=0.911367 +Using motif +M04635_2.00 of width 14. +Using motif -M04635_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9032 +# Estimated pi_0=0.913391 +Using motif +M04636_2.00 of width 14. +Using motif -M04636_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9344 +# Estimated pi_0=0.938512 +Using motif +M08269_2.00 of width 17. +Using motif -M08269_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973398 +# Estimated pi_0=0.994966 +Using motif +M08389_2.00 of width 9. +Using motif -M08389_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971089 +# Estimated pi_0=0.986747 +Using motif +M07753_2.00 of width 18. +Using motif -M07753_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929369 +# Estimated pi_0=0.934419 +Using motif +M08390_2.00 of width 15. +Using motif -M08390_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9855 +# Estimated pi_0=0.993333 +Using motif +M07754_2.00 of width 12. +Using motif -M07754_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913108 +# Estimated pi_0=0.920118 +Using motif +M07755_2.00 of width 15. +Using motif -M07755_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94797 +# Estimated pi_0=0.957818 +Using motif +M07756_2.00 of width 6. +Using motif -M07756_2.00 of width 6. +Computing q-values. +Using motif +M08391_2.00 of width 11. +Using motif -M08391_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937794 +# Estimated pi_0=0.943522 +Using motif +M08956_2.00 of width 12. +Using motif -M08956_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950748 +# Estimated pi_0=0.954909 +Using motif +M07757_2.00 of width 8. +Using motif -M07757_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.863853 +# Estimated pi_0=0.87127 +Using motif +M07758_2.00 of width 7. +Using motif -M07758_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905873 +# Estimated pi_0=0.905873 +Using motif +M04388_2.00 of width 11. +Using motif -M04388_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997647 +# Estimated pi_0=1 +Using motif +M04637_2.00 of width 18. +Using motif -M04637_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91817 +# Estimated pi_0=0.92303 +Using motif +M04638_2.00 of width 18. +Using motif -M04638_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950886 +# Estimated pi_0=0.956374 +Using motif +M04639_2.00 of width 19. +Using motif -M04639_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952838 +# Estimated pi_0=0.960559 +Using motif +M04640_2.00 of width 23. +Using motif -M04640_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04641_2.00 of width 19. +Using motif -M04641_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96029 +# Estimated pi_0=0.970814 +Using motif +M04642_2.00 of width 23. +Using motif -M04642_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M07759_2.00 of width 18. +Using motif -M07759_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.817647 +# Estimated pi_0=0.825645 +Using motif +M07760_2.00 of width 12. +Using motif -M07760_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961449 +# Estimated pi_0=0.968391 +Using motif +M08392_2.00 of width 15. +Using motif -M08392_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970828 +# Estimated pi_0=0.974171 +Using motif +M08957_2.00 of width 22. +Using motif -M08957_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942157 +# Estimated pi_0=0.953023 +Using motif +M07761_2.00 of width 15. +Using motif -M07761_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973671 +# Estimated pi_0=0.982667 +Using motif +M07762_2.00 of width 21. +Using motif -M07762_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873269 +# Estimated pi_0=0.885211 +Using motif +M07763_2.00 of width 11. +Using motif -M07763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M02925_2.00 of width 13. +Using motif -M02925_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986986 +# Estimated pi_0=0.992923 +Using motif +M04643_2.00 of width 11. +Using motif -M04643_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979855 +# Estimated pi_0=0.993333 +Using motif +M04644_2.00 of width 11. +Using motif -M04644_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963913 +# Estimated pi_0=0.97686 +Using motif +M04645_2.00 of width 12. +Using motif -M04645_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908252 +# Estimated pi_0=0.910993 +Using motif +M04646_2.00 of width 12. +Using motif -M04646_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921034 +# Estimated pi_0=0.930263 +Using motif +M07764_2.00 of width 27. +Using motif -M07764_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898968 +# Estimated pi_0=0.903815 +Using motif +M07765_2.00 of width 18. +Using motif -M07765_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972818 +# Estimated pi_0=0.977068 +Using motif +M02926_2.00 of width 11. +Using motif -M02926_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983077 +# Estimated pi_0=0.997732 +Using motif +M02927_2.00 of width 11. +Using motif -M02927_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95549 +# Estimated pi_0=0.961481 +Using motif +M02928_2.00 of width 12. +Using motif -M02928_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959804 +# Estimated pi_0=0.9675 +Using motif +M04647_2.00 of width 14. +Using motif -M04647_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991373 +# Estimated pi_0=1 +Using motif +M04648_2.00 of width 14. +Using motif -M04648_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995446 +# Estimated pi_0=1 +Using motif +M07766_2.00 of width 39. +Using motif -M07766_2.00 of width 39. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997889 +# Estimated pi_0=0.997889 +Using motif +M07767_2.00 of width 12. +Using motif -M07767_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984914 +# Estimated pi_0=0.989022 +Using motif +M04649_2.00 of width 19. +Using motif -M04649_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981346 +# Estimated pi_0=0.989834 +Using motif +M04650_2.00 of width 19. +Using motif -M04650_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963774 +# Estimated pi_0=0.972442 +Using motif +M08270_2.00 of width 16. +Using motif -M08270_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M08393_2.00 of width 15. +Using motif -M08393_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973884 +# Estimated pi_0=0.984671 +Using motif +M08958_2.00 of width 18. +Using motif -M08958_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906542 +# Estimated pi_0=0.921529 +Using motif +M04651_2.00 of width 17. +Using motif -M04651_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M04652_2.00 of width 17. +Using motif -M04652_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M07768_2.00 of width 30. +Using motif -M07768_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9336 +# Estimated pi_0=0.946772 +Using motif +M07769_2.00 of width 21. +Using motif -M07769_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956863 +# Estimated pi_0=0.963478 +Using motif +M07770_2.00 of width 9. +Using motif -M07770_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M08394_2.00 of width 21. +Using motif -M08394_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947327 +# Estimated pi_0=0.95748 +Using motif +M07771_2.00 of width 12. +Using motif -M07771_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9114 +# Estimated pi_0=0.916812 +Using motif +M04653_2.00 of width 22. +Using motif -M04653_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951373 +# Estimated pi_0=0.967801 +Using motif +M04654_2.00 of width 22. +Using motif -M04654_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968431 +# Estimated pi_0=0.982209 +Using motif +M07772_2.00 of width 18. +Using motif -M07772_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938605 +# Estimated pi_0=0.945067 +Using motif +M08395_2.00 of width 15. +Using motif -M08395_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.914745 +# Estimated pi_0=0.926108 +Using motif +M08959_2.00 of width 24. +Using motif -M08959_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M07773_2.00 of width 30. +Using motif -M07773_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.860411 +# Estimated pi_0=0.865799 +Using motif +M07774_2.00 of width 9. +Using motif -M07774_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938986 +# Estimated pi_0=0.954886 +Using motif +M07775_2.00 of width 13. +Using motif -M07775_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.893069 +# Estimated pi_0=0.897838 +Using motif +M07776_2.00 of width 28. +Using motif -M07776_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986667 +# Estimated pi_0=0.989697 +Using motif +M08396_2.00 of width 21. +Using motif -M08396_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988541 +# Estimated pi_0=0.992143 +Using motif +M08960_2.00 of width 18. +Using motif -M08960_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98623 +# Estimated pi_0=0.991327 +Using motif +M02929_2.00 of width 15. +Using motif -M02929_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978387 +# Estimated pi_0=0.990777 +Using motif +M04655_2.00 of width 11. +Using motif -M04655_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983235 +# Estimated pi_0=0.992188 +Using motif +M04656_2.00 of width 11. +Using motif -M04656_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980462 +# Estimated pi_0=0.993333 +Using motif +M08397_2.00 of width 12. +Using motif -M08397_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984779 +# Estimated pi_0=0.98663 +Using motif +M04597_2.00 of width 15. +Using motif -M04597_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954815 +# Estimated pi_0=0.963256 +Using motif +M02930_2.00 of width 14. +Using motif -M02930_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925593 +# Estimated pi_0=0.934133 +Using motif +M04657_2.00 of width 13. +Using motif -M04657_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.900163 +# Estimated pi_0=0.909565 +Using motif +M04658_2.00 of width 13. +Using motif -M04658_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91741 +# Estimated pi_0=0.927925 +Using motif +M08398_2.00 of width 14. +Using motif -M08398_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.888966 +# Estimated pi_0=0.902452 +Using motif +M07777_2.00 of width 21. +Using motif -M07777_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997175 +# Estimated pi_0=1 +Using motif +M07778_2.00 of width 36. +Using motif -M07778_2.00 of width 36. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989375 +# Estimated pi_0=0.994316 +Using motif +M08399_2.00 of width 21. +Using motif -M08399_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985806 +# Estimated pi_0=0.993744 +Using motif +M07779_2.00 of width 30. +Using motif -M07779_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.882667 +# Estimated pi_0=0.887626 +Using motif +M08400_2.00 of width 12. +Using motif -M08400_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M07780_2.00 of width 9. +Using motif -M07780_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.869434 +# Estimated pi_0=0.878772 +Using motif +M07781_2.00 of width 12. +Using motif -M07781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9608 +# Estimated pi_0=0.978676 +Using motif +M08401_2.00 of width 18. +Using motif -M08401_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9608 +# Estimated pi_0=0.973613 +Using motif +M08961_2.00 of width 22. +Using motif -M08961_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M00650_2.00 of width 8. +Using motif -M00650_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M08104_2.00 of width 11. +Using motif -M08104_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972545 +# Estimated pi_0=0.984571 +Using motif +M09018_2.00 of width 14. +Using motif -M09018_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977757 +# Estimated pi_0=0.993446 +Using motif +M10405_2.00 of width 14. +Using motif -M10405_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999457 +# Estimated pi_0=0.999598 +Using motif +M04659_2.00 of width 12. +Using motif -M04659_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953711 +# Estimated pi_0=0.962184 +Using motif +M04660_2.00 of width 12. +Using motif -M04660_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958642 +# Estimated pi_0=0.96538 +Using motif +M02939_2.00 of width 15. +Using motif -M02939_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967921 +# Estimated pi_0=0.988447 +Using motif +M01205_2.00 of width 9. +Using motif -M01205_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M01203_2.00 of width 9. +Using motif -M01203_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M01206_2.00 of width 10. +Using motif -M01206_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M01204_2.00 of width 10. +Using motif -M01204_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987027 +# Estimated pi_0=0.995722 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989153 +# Estimated pi_0=0.995635 +Using motif +M04661_2.00 of width 9. +Using motif -M04661_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996226 +# Estimated pi_0=1 +Using motif +M04662_2.00 of width 9. +Using motif -M04662_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988617 +# Estimated pi_0=0.99125 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.919029 +# Estimated pi_0=0.922 +Using motif +M04663_2.00 of width 15. +Using motif -M04663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901765 +# Estimated pi_0=0.901765 +Using motif +M04664_2.00 of width 15. +Using motif -M04664_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903168 +# Estimated pi_0=0.917368 +Using motif +M09519_2.00 of width 10. +Using motif -M09519_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985 +# Estimated pi_0=0.990995 +Using motif +M00735_2.00 of width 10. +Using motif -M00735_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971358 +# Estimated pi_0=0.974091 +Using motif +M00736_2.00 of width 10. +Using motif -M00736_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980168 +# Estimated pi_0=0.984503 +Using motif +M00737_2.00 of width 10. +Using motif -M00737_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968182 +# Estimated pi_0=0.973933 +Using motif +M00738_2.00 of width 8. +Using motif -M00738_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973486 +# Estimated pi_0=0.977447 +Using motif +M00739_2.00 of width 9. +Using motif -M00739_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979055 +# Estimated pi_0=0.982623 +Using motif +M00740_2.00 of width 10. +Using motif -M00740_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988617 +# Estimated pi_0=0.995204 +Using motif +M00741_2.00 of width 10. +Using motif -M00741_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968 +# Estimated pi_0=0.970989 +Using motif +M00742_2.00 of width 9. +Using motif -M00742_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974308 +# Estimated pi_0=0.979006 +Using motif +M08105_2.00 of width 10. +Using motif -M08105_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975147 +# Estimated pi_0=0.984382 +Using motif +M09020_2.00 of width 11. +Using motif -M09020_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928 +# Estimated pi_0=0.933103 +Using motif +M01499_2.00 of width 9. +Using motif -M01499_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M01500_2.00 of width 9. +Using motif -M01500_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M02940_2.00 of width 18. +Using motif -M02940_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M02941_2.00 of width 10. +Using motif -M02941_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M04665_2.00 of width 12. +Using motif -M04665_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M04666_2.00 of width 12. +Using motif -M04666_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0012 +# Estimated pi_0=1 +Using motif +M02942_2.00 of width 14. +Using motif -M02942_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03714_2.00 of width 14. +Using motif -M03714_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04667_2.00 of width 10. +Using motif -M04667_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M04668_2.00 of width 10. +Using motif -M04668_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04669_2.00 of width 12. +Using motif -M04669_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978416 +# Estimated pi_0=1 +Using motif +M04670_2.00 of width 11. +Using motif -M04670_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M04671_2.00 of width 12. +Using motif -M04671_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04672_2.00 of width 11. +Using motif -M04672_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M02943_2.00 of width 14. +Using motif -M02943_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M02944_2.00 of width 14. +Using motif -M02944_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03715_2.00 of width 11. +Using motif -M03715_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00392 +# Estimated pi_0=1 +Using motif +M04673_2.00 of width 10. +Using motif -M04673_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0016 +# Estimated pi_0=1 +Using motif +M04674_2.00 of width 10. +Using motif -M04674_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04675_2.00 of width 12. +Using motif -M04675_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9746 +# Estimated pi_0=1 +Using motif +M04676_2.00 of width 12. +Using motif -M04676_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09022_2.00 of width 11. +Using motif -M09022_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M02945_2.00 of width 14. +Using motif -M02945_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04677_2.00 of width 10. +Using motif -M04677_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M04678_2.00 of width 10. +Using motif -M04678_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M01501_2.00 of width 9. +Using motif -M01501_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M01502_2.00 of width 9. +Using motif -M01502_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M02946_2.00 of width 17. +Using motif -M02946_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M02947_2.00 of width 18. +Using motif -M02947_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M02948_2.00 of width 10. +Using motif -M02948_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04679_2.00 of width 10. +Using motif -M04679_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M04680_2.00 of width 10. +Using motif -M04680_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M09023_2.00 of width 14. +Using motif -M09023_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9636 +# Estimated pi_0=0.96549 +Using motif +M10422_2.00 of width 12. +Using motif -M10422_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M10423_2.00 of width 15. +Using motif -M10423_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M10424_2.00 of width 10. +Using motif -M10424_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968317 +# Estimated pi_0=0.979298 +Using motif +M10425_2.00 of width 15. +Using motif -M10425_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9512 +# Estimated pi_0=0.963652 +Using motif +M10426_2.00 of width 10. +Using motif -M10426_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9504 +# Estimated pi_0=0.957358 +Using motif +M01911_2.00 of width 9. +Using motif -M01911_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9324 +# Estimated pi_0=0.947907 +Using motif +M01916_2.00 of width 8. +Using motif -M01916_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934355 +# Estimated pi_0=0.937926 +Using motif +M01912_2.00 of width 10. +Using motif -M01912_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8318 +# Estimated pi_0=0.83619 +Using motif +M01913_2.00 of width 10. +Using motif -M01913_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.794455 +# Estimated pi_0=0.7958 +Using motif +M01914_2.00 of width 9. +Using motif -M01914_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.871453 +# Estimated pi_0=0.87648 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982692 +# Estimated pi_0=0.990552 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988477 +# Estimated pi_0=1 +Using motif +M01915_2.00 of width 10. +Using motif -M01915_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88932 +# Estimated pi_0=0.88932 +Using motif +M01210_2.00 of width 8. +Using motif -M01210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985974 +# Estimated pi_0=0.989317 +Using motif +M01927_2.00 of width 11. +Using motif -M01927_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04681_2.00 of width 13. +Using motif -M04681_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04682_2.00 of width 13. +Using motif -M04682_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M01928_2.00 of width 10. +Using motif -M01928_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M01929_2.00 of width 8. +Using motif -M01929_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04683_2.00 of width 12. +Using motif -M04683_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04684_2.00 of width 12. +Using motif -M04684_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04685_2.00 of width 10. +Using motif -M04685_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04686_2.00 of width 12. +Using motif -M04686_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04687_2.00 of width 12. +Using motif -M04687_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00152 +# Estimated pi_0=1 +Using motif +M04688_2.00 of width 10. +Using motif -M04688_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04689_2.00 of width 12. +Using motif -M04689_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04690_2.00 of width 10. +Using motif -M04690_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M04691_2.00 of width 12. +Using motif -M04691_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M01930_2.00 of width 10. +Using motif -M01930_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04692_2.00 of width 12. +Using motif -M04692_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04693_2.00 of width 12. +Using motif -M04693_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09028_2.00 of width 16. +Using motif -M09028_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M01931_2.00 of width 10. +Using motif -M01931_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04694_2.00 of width 12. +Using motif -M04694_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04695_2.00 of width 12. +Using motif -M04695_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M01961_2.00 of width 10. +Using motif -M01961_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.829714 +# Estimated pi_0=0.831569 +Using motif +M02949_2.00 of width 18. +Using motif -M02949_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M02950_2.00 of width 18. +Using motif -M02950_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M02951_2.00 of width 16. +Using motif -M02951_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04696_2.00 of width 11. +Using motif -M04696_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8248 +# Estimated pi_0=0.832571 +Using motif +M04697_2.00 of width 16. +Using motif -M04697_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9316 +# Estimated pi_0=0.933922 +Using motif +M04698_2.00 of width 11. +Using motif -M04698_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.796832 +# Estimated pi_0=0.803738 +Using motif +M04699_2.00 of width 16. +Using motif -M04699_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M09029_2.00 of width 10. +Using motif -M09029_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8638 +# Estimated pi_0=0.873945 +Using motif +M02952_2.00 of width 12. +Using motif -M02952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9158 +# Estimated pi_0=0.930727 +Using motif +M02953_2.00 of width 12. +Using motif -M02953_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929 +# Estimated pi_0=0.942114 +Using motif +M02954_2.00 of width 14. +Using motif -M02954_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964112 +# Estimated pi_0=0.964112 +Using motif +M02955_2.00 of width 14. +Using motif -M02955_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04700_2.00 of width 16. +Using motif -M04700_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9682 +# Estimated pi_0=0.974955 +Using motif +M07938_2.00 of width 15. +Using motif -M07938_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.79 +# Estimated pi_0=0.79757 +Using motif +M09030_2.00 of width 14. +Using motif -M09030_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877 +# Estimated pi_0=0.889538 +Using motif +M09521_2.00 of width 10. +Using motif -M09521_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8886 +# Estimated pi_0=0.894038 +Using motif +M10444_2.00 of width 15. +Using motif -M10444_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973704 +# Estimated pi_0=0.9832 +Using motif +M10445_2.00 of width 12. +Using motif -M10445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8592 +# Estimated pi_0=0.866957 +Using motif +M02956_2.00 of width 18. +Using motif -M02956_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M02957_2.00 of width 18. +Using motif -M02957_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M02958_2.00 of width 18. +Using motif -M02958_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04701_2.00 of width 16. +Using motif -M04701_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04702_2.00 of width 16. +Using motif -M04702_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M09031_2.00 of width 11. +Using motif -M09031_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933285 +# Estimated pi_0=0.938344 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8658 +# Estimated pi_0=0.874766 +Using motif +M02959_2.00 of width 12. +Using motif -M02959_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982162 +# Estimated pi_0=0.990829 +Using motif +M04703_2.00 of width 14. +Using motif -M04703_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9156 +# Estimated pi_0=0.916471 +Using motif +M04704_2.00 of width 14. +Using motif -M04704_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987925 +# Estimated pi_0=1 +Using motif +M09032_2.00 of width 10. +Using motif -M09032_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.890196 +# Estimated pi_0=0.890196 +Using motif +M02960_2.00 of width 14. +Using motif -M02960_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04705_2.00 of width 14. +Using motif -M04705_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04706_2.00 of width 14. +Using motif -M04706_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09033_2.00 of width 13. +Using motif -M09033_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905816 +# Estimated pi_0=0.91509 +Using motif +M09522_2.00 of width 12. +Using motif -M09522_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8902 +# Estimated pi_0=0.908167 +Using motif +M07939_2.00 of width 11. +Using motif -M07939_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9086 +# Estimated pi_0=0.915194 +Using motif +M07940_2.00 of width 11. +Using motif -M07940_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.905517 +# Estimated pi_0=0.913566 +Using motif +M07941_2.00 of width 11. +Using motif -M07941_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928794 +# Estimated pi_0=0.93575 +Using motif +M08107_2.00 of width 11. +Using motif -M08107_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922946 +# Estimated pi_0=0.933248 +Using motif +M09034_2.00 of width 13. +Using motif -M09034_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910092 +# Estimated pi_0=0.918 +Using motif +M09523_2.00 of width 10. +Using motif -M09523_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906019 +# Estimated pi_0=0.916694 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8556 +# Estimated pi_0=0.861714 +Using motif +M08108_2.00 of width 11. +Using motif -M08108_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.870196 +# Estimated pi_0=0.874909 +Using motif +M09035_2.00 of width 14. +Using motif -M09035_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.88537 +# Estimated pi_0=0.895852 +Using motif +M02961_2.00 of width 12. +Using motif -M02961_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990074 +# Estimated pi_0=0.999192 +Using motif +M02962_2.00 of width 12. +Using motif -M02962_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04707_2.00 of width 14. +Using motif -M04707_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925347 +# Estimated pi_0=0.936909 +Using motif +M07942_2.00 of width 15. +Using motif -M07942_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8314 +# Estimated pi_0=0.836762 +Using motif +M07943_2.00 of width 15. +Using motif -M07943_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8374 +# Estimated pi_0=0.84283 +Using motif +M08109_2.00 of width 11. +Using motif -M08109_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.879 +# Estimated pi_0=0.88 +Using motif +M09036_2.00 of width 13. +Using motif -M09036_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.866415 +# Estimated pi_0=0.875772 +Using motif +M09524_2.00 of width 10. +Using motif -M09524_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8524 +# Estimated pi_0=0.8524 +Using motif +M02963_2.00 of width 14. +Using motif -M02963_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96 +# Estimated pi_0=0.96662 +Using motif +M09044_2.00 of width 15. +Using motif -M09044_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939623 +# Estimated pi_0=0.947733 +Using motif +M02964_2.00 of width 10. +Using motif -M02964_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958476 +# Estimated pi_0=0.972121 +Using motif +M04708_2.00 of width 11. +Using motif -M04708_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9194 +# Estimated pi_0=0.927179 +Using motif +M04709_2.00 of width 11. +Using motif -M04709_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918846 +# Estimated pi_0=0.923571 +Using motif +M04710_2.00 of width 11. +Using motif -M04710_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9642 +# Estimated pi_0=0.978 +Using motif +M04711_2.00 of width 11. +Using motif -M04711_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9452 +# Estimated pi_0=0.954516 +Using motif +M09046_2.00 of width 11. +Using motif -M09046_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9368 +# Estimated pi_0=0.945103 +Using motif +M09528_2.00 of width 10. +Using motif -M09528_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9422 +# Estimated pi_0=0.954915 +Using motif +M04712_2.00 of width 16. +Using motif -M04712_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947 +# Estimated pi_0=0.952143 +Using motif +M04713_2.00 of width 13. +Using motif -M04713_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9884 +# Estimated pi_0=0.999598 +Using motif +M04714_2.00 of width 16. +Using motif -M04714_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988 +# Estimated pi_0=0.995025 +Using motif +M04715_2.00 of width 13. +Using motif -M04715_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M02965_2.00 of width 14. +Using motif -M02965_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989888 +# Estimated pi_0=0.992386 +Using motif +M07944_2.00 of width 19. +Using motif -M07944_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958065 +# Estimated pi_0=0.965464 +Using motif +M07945_2.00 of width 19. +Using motif -M07945_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963007 +# Estimated pi_0=0.968087 +Using motif +M07946_2.00 of width 15. +Using motif -M07946_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960231 +# Estimated pi_0=0.966111 +Using motif +M08026_2.00 of width 15. +Using motif -M08026_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961375 +# Estimated pi_0=0.965081 +Using motif +M09047_2.00 of width 17. +Using motif -M09047_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955621 +# Estimated pi_0=0.96113 +Using motif +M02966_2.00 of width 12. +Using motif -M02966_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.960308 +Using motif +M04716_2.00 of width 12. +Using motif -M04716_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969818 +# Estimated pi_0=0.981954 +Using motif +M04717_2.00 of width 12. +Using motif -M04717_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961709 +# Estimated pi_0=0.971515 +Using motif +M04718_2.00 of width 12. +Using motif -M04718_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992771 +# Estimated pi_0=0.996142 +Using motif +M04719_2.00 of width 12. +Using motif -M04719_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969254 +# Estimated pi_0=0.975867 +Using motif +M02967_2.00 of width 11. +Using motif -M02967_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983025 +# Estimated pi_0=0.998191 +Using motif +M04720_2.00 of width 11. +Using motif -M04720_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9502 +# Estimated pi_0=0.968951 +Using motif +M04721_2.00 of width 11. +Using motif -M04721_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967258 +# Estimated pi_0=0.973533 +Using motif +M09048_2.00 of width 16. +Using motif -M09048_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941449 +# Estimated pi_0=0.952242 +Using motif +M02968_2.00 of width 10. +Using motif -M02968_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9606 +# Estimated pi_0=0.976618 +Using motif +M04722_2.00 of width 12. +Using motif -M04722_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955701 +# Estimated pi_0=0.968872 +Using motif +M04723_2.00 of width 12. +Using motif -M04723_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945882 +# Estimated pi_0=0.958939 +Using motif +M04724_2.00 of width 12. +Using motif -M04724_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986526 +# Estimated pi_0=0.988923 +Using motif +M04725_2.00 of width 12. +Using motif -M04725_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985497 +# Estimated pi_0=0.990476 +Using motif +M04726_2.00 of width 12. +Using motif -M04726_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9626 +# Estimated pi_0=0.974375 +Using motif +M04727_2.00 of width 12. +Using motif -M04727_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977812 +# Estimated pi_0=0.984138 +Using motif +M04728_2.00 of width 12. +Using motif -M04728_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994632 +# Estimated pi_0=0.997475 +Using motif +M04729_2.00 of width 12. +Using motif -M04729_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98895 +# Estimated pi_0=0.99164 +Using motif +M09049_2.00 of width 13. +Using motif -M09049_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928348 +# Estimated pi_0=0.93863 +Using motif +M02969_2.00 of width 10. +Using motif -M02969_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966471 +# Estimated pi_0=0.98383 +Using motif +M04730_2.00 of width 11. +Using motif -M04730_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9352 +# Estimated pi_0=0.939057 +Using motif +M04731_2.00 of width 12. +Using motif -M04731_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9776 +# Estimated pi_0=0.981391 +Using motif +M04732_2.00 of width 11. +Using motif -M04732_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9386 +# Estimated pi_0=0.952143 +Using motif +M04733_2.00 of width 10. +Using motif -M04733_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9446 +# Estimated pi_0=0.9608 +Using motif +M04734_2.00 of width 12. +Using motif -M04734_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04735_2.00 of width 11. +Using motif -M04735_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955146 +# Estimated pi_0=0.958 +Using motif +M02970_2.00 of width 10. +Using motif -M02970_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983922 +# Estimated pi_0=0.999899 +Using motif +M04736_2.00 of width 16. +Using motif -M04736_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981132 +# Estimated pi_0=1 +Using motif +M04737_2.00 of width 21. +Using motif -M04737_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979406 +# Estimated pi_0=0.993054 +Using motif +M04738_2.00 of width 16. +Using motif -M04738_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970182 +# Estimated pi_0=0.97292 +Using motif +M04739_2.00 of width 21. +Using motif -M04739_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994595 +# Estimated pi_0=0.998081 +Using motif +M02971_2.00 of width 12. +Using motif -M02971_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96198 +# Estimated pi_0=0.97122 +Using motif +M02972_2.00 of width 12. +Using motif -M02972_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M04740_2.00 of width 12. +Using motif -M04740_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952816 +# Estimated pi_0=0.971842 +Using motif +M04741_2.00 of width 12. +Using motif -M04741_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961111 +# Estimated pi_0=0.973203 +Using motif +M04742_2.00 of width 12. +Using motif -M04742_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98213 +# Estimated pi_0=0.989362 +Using motif +M04743_2.00 of width 12. +Using motif -M04743_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960746 +# Estimated pi_0=0.97025 +Using motif +M04744_2.00 of width 12. +Using motif -M04744_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970714 +# Estimated pi_0=0.973974 +Using motif +M04745_2.00 of width 12. +Using motif -M04745_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956752 +# Estimated pi_0=0.96062 +Using motif +M04746_2.00 of width 12. +Using motif -M04746_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974737 +# Estimated pi_0=0.981274 +Using motif +M04747_2.00 of width 12. +Using motif -M04747_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972 +# Estimated pi_0=0.982874 +Using motif +M07947_2.00 of width 15. +Using motif -M07947_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.875922 +# Estimated pi_0=0.876078 +Using motif +M07948_2.00 of width 14. +Using motif -M07948_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903366 +# Estimated pi_0=0.91969 +Using motif +M07949_2.00 of width 11. +Using motif -M07949_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.908713 +# Estimated pi_0=0.920702 +Using motif +M08198_2.00 of width 10. +Using motif -M08198_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968908 +# Estimated pi_0=0.972518 +Using motif +M09050_2.00 of width 14. +Using motif -M09050_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947852 +# Estimated pi_0=0.956735 +Using motif +M09529_2.00 of width 10. +Using motif -M09529_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9166 +# Estimated pi_0=0.933139 +Using motif +M02973_2.00 of width 11. +Using motif -M02973_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97767 +# Estimated pi_0=0.993711 +Using motif +M02974_2.00 of width 15. +Using motif -M02974_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9684 +# Estimated pi_0=0.990875 +Using motif +M02975_2.00 of width 16. +Using motif -M02975_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99608 +# Estimated pi_0=0.99608 +Using motif +M02976_2.00 of width 11. +Using motif -M02976_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959273 +# Estimated pi_0=0.97359 +Using motif +M02977_2.00 of width 15. +Using motif -M02977_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9614 +# Estimated pi_0=0.978169 +Using motif +M02978_2.00 of width 16. +Using motif -M02978_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997143 +# Estimated pi_0=0.999497 +Using motif +M04748_2.00 of width 12. +Using motif -M04748_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968627 +# Estimated pi_0=0.980752 +Using motif +M04749_2.00 of width 12. +Using motif -M04749_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967327 +# Estimated pi_0=0.994432 +Using motif +M04750_2.00 of width 12. +Using motif -M04750_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961 +# Estimated pi_0=0.96992 +Using motif +M04751_2.00 of width 12. +Using motif -M04751_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9886 +# Estimated pi_0=0.998283 +Using motif +M09530_2.00 of width 10. +Using motif -M09530_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987808 +# Estimated pi_0=0.994468 +Using motif +M02979_2.00 of width 10. +Using motif -M02979_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9586 +# Estimated pi_0=0.976711 +Using motif +M02980_2.00 of width 10. +Using motif -M02980_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944 +# Estimated pi_0=0.953233 +Using motif +M02981_2.00 of width 17. +Using motif -M02981_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9118 +# Estimated pi_0=0.926833 +Using motif +M02982_2.00 of width 10. +Using motif -M02982_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957714 +# Estimated pi_0=0.966667 +Using motif +M04752_2.00 of width 11. +Using motif -M04752_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956238 +# Estimated pi_0=0.957 +Using motif +M04753_2.00 of width 11. +Using motif -M04753_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964237 +# Estimated pi_0=0.972692 +Using motif +M04754_2.00 of width 11. +Using motif -M04754_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940784 +# Estimated pi_0=0.949217 +Using motif +M04755_2.00 of width 11. +Using motif -M04755_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956333 +# Estimated pi_0=0.965 +Using motif +M09051_2.00 of width 11. +Using motif -M09051_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943922 +# Estimated pi_0=0.952174 +Using motif +M09531_2.00 of width 10. +Using motif -M09531_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946 +# Estimated pi_0=0.95562 +Using motif +M10488_2.00 of width 14. +Using motif -M10488_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9478 +# Estimated pi_0=0.960142 +Using motif +M02645_2.00 of width 9. +Using motif -M02645_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971215 +# Estimated pi_0=0.982899 +Using motif +M02983_2.00 of width 10. +Using motif -M02983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975135 +# Estimated pi_0=0.990435 +Using motif +M02984_2.00 of width 18. +Using motif -M02984_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9512 +# Estimated pi_0=0.955725 +Using motif +M02985_2.00 of width 10. +Using motif -M02985_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960777 +# Estimated pi_0=0.979006 +Using motif +M02986_2.00 of width 18. +Using motif -M02986_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951538 +# Estimated pi_0=0.967239 +Using motif +M07950_2.00 of width 21. +Using motif -M07950_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.949606 +Using motif +M07951_2.00 of width 21. +Using motif -M07951_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949298 +# Estimated pi_0=0.963014 +Using motif +M09052_2.00 of width 13. +Using motif -M09052_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948689 +# Estimated pi_0=0.953517 +Using motif +M09532_2.00 of width 10. +Using motif -M09532_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978301 +# Estimated pi_0=0.985054 +Using motif +M10493_2.00 of width 10. +Using motif -M10493_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984068 +# Estimated pi_0=0.995833 +Using motif +M10494_2.00 of width 13. +Using motif -M10494_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998265 +# Estimated pi_0=0.999196 +Using motif +M02733_2.00 of width 10. +Using motif -M02733_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985833 +# Estimated pi_0=0.995838 +Using motif +M02987_2.00 of width 12. +Using motif -M02987_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971048 +# Estimated pi_0=0.99 +Using motif +M09053_2.00 of width 15. +Using motif -M09053_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977195 +# Estimated pi_0=0.981196 +Using motif +M09533_2.00 of width 10. +Using motif -M09533_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975753 +# Estimated pi_0=0.982246 +Using motif +M02988_2.00 of width 11. +Using motif -M02988_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977945 +# Estimated pi_0=0.985635 +Using motif +M02989_2.00 of width 11. +Using motif -M02989_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988447 +# Estimated pi_0=0.997688 +Using motif +M04756_2.00 of width 12. +Using motif -M04756_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949649 +# Estimated pi_0=0.966242 +Using motif +M04757_2.00 of width 12. +Using motif -M04757_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985806 +# Estimated pi_0=0.986979 +Using motif +M04758_2.00 of width 12. +Using motif -M04758_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952277 +# Estimated pi_0=0.968029 +Using motif +M04759_2.00 of width 12. +Using motif -M04759_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972143 +# Estimated pi_0=0.977826 +Using motif +M09054_2.00 of width 15. +Using motif -M09054_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9596 +# Estimated pi_0=0.965652 +Using motif +M09534_2.00 of width 10. +Using motif -M09534_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9842 +# Estimated pi_0=0.99697 +Using motif +M02990_2.00 of width 15. +Using motif -M02990_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.927414 +# Estimated pi_0=0.942837 +Using motif +M02991_2.00 of width 10. +Using motif -M02991_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95626 +# Estimated pi_0=0.963649 +Using motif +M02992_2.00 of width 10. +Using motif -M02992_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955614 +# Estimated pi_0=0.955614 +Using motif +M02993_2.00 of width 14. +Using motif -M02993_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940187 +# Estimated pi_0=0.955789 +Using motif +M02994_2.00 of width 10. +Using motif -M02994_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972545 +# Estimated pi_0=0.986118 +Using motif +M02995_2.00 of width 14. +Using motif -M02995_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.933929 +Using motif +M04760_2.00 of width 11. +Using motif -M04760_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9412 +# Estimated pi_0=0.951579 +Using motif +M04761_2.00 of width 16. +Using motif -M04761_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95 +# Estimated pi_0=0.96 +Using motif +M04762_2.00 of width 16. +Using motif -M04762_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9536 +# Estimated pi_0=0.958843 +Using motif +M04763_2.00 of width 11. +Using motif -M04763_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969063 +# Estimated pi_0=0.976114 +Using motif +M04764_2.00 of width 11. +Using motif -M04764_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969369 +# Estimated pi_0=0.985535 +Using motif +M04765_2.00 of width 16. +Using motif -M04765_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9312 +# Estimated pi_0=0.938349 +Using motif +M04766_2.00 of width 16. +Using motif -M04766_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961188 +# Estimated pi_0=0.972913 +Using motif +M04767_2.00 of width 11. +Using motif -M04767_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961053 +# Estimated pi_0=0.966761 +Using motif +M05864_2.00 of width 10. +Using motif -M05864_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985366 +# Estimated pi_0=0.993405 +Using motif +M08111_2.00 of width 18. +Using motif -M08111_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938272 +# Estimated pi_0=0.946517 +Using motif +M09055_2.00 of width 18. +Using motif -M09055_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90446 +# Estimated pi_0=0.907792 +Using motif +M02996_2.00 of width 10. +Using motif -M02996_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950291 +# Estimated pi_0=0.967424 +Using motif +M04768_2.00 of width 11. +Using motif -M04768_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947129 +# Estimated pi_0=0.962987 +Using motif +M04769_2.00 of width 11. +Using motif -M04769_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96367 +# Estimated pi_0=0.966724 +Using motif +M05865_2.00 of width 11. +Using motif -M05865_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94098 +# Estimated pi_0=0.954688 +Using motif +M07952_2.00 of width 11. +Using motif -M07952_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910485 +# Estimated pi_0=0.916964 +Using motif +M07953_2.00 of width 11. +Using motif -M07953_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8988 +# Estimated pi_0=0.906783 +Using motif +M07954_2.00 of width 14. +Using motif -M07954_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8924 +# Estimated pi_0=0.901333 +Using motif +M07955_2.00 of width 15. +Using motif -M07955_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880594 +# Estimated pi_0=0.881553 +Using motif +M07956_2.00 of width 13. +Using motif -M07956_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.90396 +# Estimated pi_0=0.907966 +Using motif +M09056_2.00 of width 14. +Using motif -M09056_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9128 +# Estimated pi_0=0.923333 +Using motif +M09535_2.00 of width 10. +Using motif -M09535_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.928932 +# Estimated pi_0=0.94096 +Using motif +M02734_2.00 of width 10. +Using motif -M02734_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9666 +# Estimated pi_0=0.98 +Using motif +M02735_2.00 of width 9. +Using motif -M02735_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9626 +# Estimated pi_0=0.980414 +Using motif +M02997_2.00 of width 10. +Using motif -M02997_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9688 +# Estimated pi_0=0.983333 +Using motif +M02998_2.00 of width 14. +Using motif -M02998_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946733 +# Estimated pi_0=0.95431 +Using motif +M02999_2.00 of width 10. +Using motif -M02999_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9634 +# Estimated pi_0=0.979716 +Using motif +M03000_2.00 of width 14. +Using motif -M03000_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947327 +# Estimated pi_0=0.955088 +Using motif +M04770_2.00 of width 11. +Using motif -M04770_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957885 +# Estimated pi_0=0.976711 +Using motif +M04771_2.00 of width 16. +Using motif -M04771_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953148 +# Estimated pi_0=0.958136 +Using motif +M04772_2.00 of width 11. +Using motif -M04772_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988068 +# Estimated pi_0=0.992698 +Using motif +M04773_2.00 of width 16. +Using motif -M04773_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951262 +# Estimated pi_0=0.958929 +Using motif +M04774_2.00 of width 11. +Using motif -M04774_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953158 +# Estimated pi_0=0.962585 +Using motif +M04775_2.00 of width 16. +Using motif -M04775_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940784 +# Estimated pi_0=0.944906 +Using motif +M04776_2.00 of width 16. +Using motif -M04776_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949109 +# Estimated pi_0=0.95375 +Using motif +M04777_2.00 of width 11. +Using motif -M04777_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984833 +# Estimated pi_0=0.993085 +Using motif +M09057_2.00 of width 13. +Using motif -M09057_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930394 +# Estimated pi_0=0.936319 +Using motif +M09536_2.00 of width 10. +Using motif -M09536_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973571 +# Estimated pi_0=0.979667 +Using motif +M04778_2.00 of width 20. +Using motif -M04778_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935 +# Estimated pi_0=0.943167 +Using motif +M04779_2.00 of width 20. +Using motif -M04779_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987211 +# Estimated pi_0=0.997273 +Using motif +M09058_2.00 of width 13. +Using motif -M09058_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946625 +# Estimated pi_0=0.952111 +Using motif +M03001_2.00 of width 10. +Using motif -M03001_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938889 +# Estimated pi_0=0.943604 +Using motif +M04780_2.00 of width 11. +Using motif -M04780_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949703 +# Estimated pi_0=0.961818 +Using motif +M04781_2.00 of width 12. +Using motif -M04781_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982136 +# Estimated pi_0=1 +Using motif +M04782_2.00 of width 11. +Using motif -M04782_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948077 +# Estimated pi_0=0.96604 +Using motif +M07957_2.00 of width 10. +Using motif -M07957_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940893 +# Estimated pi_0=0.957333 +Using motif +M07958_2.00 of width 11. +Using motif -M07958_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9316 +# Estimated pi_0=0.94144 +Using motif +M08112_2.00 of width 11. +Using motif -M08112_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932545 +# Estimated pi_0=0.942517 +Using motif +M09059_2.00 of width 12. +Using motif -M09059_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9334 +# Estimated pi_0=0.93811 +Using motif +M03002_2.00 of width 12. +Using motif -M03002_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988714 +# Estimated pi_0=0.992418 +Using motif +M03003_2.00 of width 13. +Using motif -M03003_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=0.986552 +Using motif +M04783_2.00 of width 23. +Using motif -M04783_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999192 +# Estimated pi_0=1 +Using motif +M04784_2.00 of width 13. +Using motif -M04784_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957624 +# Estimated pi_0=0.96432 +Using motif +M04785_2.00 of width 13. +Using motif -M04785_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975044 +# Estimated pi_0=0.986171 +Using motif +M04786_2.00 of width 12. +Using motif -M04786_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998022 +# Estimated pi_0=0.998794 +Using motif +M04787_2.00 of width 23. +Using motif -M04787_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.998995 +Using motif +M04788_2.00 of width 23. +Using motif -M04788_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998687 +# Estimated pi_0=0.999397 +Using motif +M04789_2.00 of width 13. +Using motif -M04789_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942178 +# Estimated pi_0=0.94625 +Using motif +M04790_2.00 of width 23. +Using motif -M04790_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998693 +# Estimated pi_0=0.998693 +Using motif +M09060_2.00 of width 14. +Using motif -M09060_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976914 +# Estimated pi_0=0.979551 +Using motif +M03004_2.00 of width 10. +Using motif -M03004_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988855 +# Estimated pi_0=0.997475 +Using motif +M04791_2.00 of width 11. +Using motif -M04791_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954653 +# Estimated pi_0=0.962878 +Using motif +M04792_2.00 of width 11. +Using motif -M04792_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950727 +# Estimated pi_0=0.971007 +Using motif +M09061_2.00 of width 10. +Using motif -M09061_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929748 +# Estimated pi_0=0.940411 +Using motif +M03005_2.00 of width 14. +Using motif -M03005_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993505 +# Estimated pi_0=0.997186 +Using motif +M03006_2.00 of width 10. +Using motif -M03006_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9652 +# Estimated pi_0=0.977748 +Using motif +M04793_2.00 of width 11. +Using motif -M04793_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948302 +# Estimated pi_0=0.955573 +Using motif +M04794_2.00 of width 14. +Using motif -M04794_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953565 +# Estimated pi_0=0.95507 +Using motif +M04795_2.00 of width 11. +Using motif -M04795_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96717 +# Estimated pi_0=0.982024 +Using motif +M04796_2.00 of width 11. +Using motif -M04796_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949057 +# Estimated pi_0=0.957444 +Using motif +M04797_2.00 of width 12. +Using motif -M04797_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04798_2.00 of width 14. +Using motif -M04798_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975479 +# Estimated pi_0=0.983316 +Using motif +M04799_2.00 of width 11. +Using motif -M04799_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944 +# Estimated pi_0=0.95584 +Using motif +M09062_2.00 of width 11. +Using motif -M09062_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943788 +# Estimated pi_0=0.949167 +Using motif +M03007_2.00 of width 10. +Using motif -M03007_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9552 +# Estimated pi_0=0.971268 +Using motif +M04800_2.00 of width 11. +Using motif -M04800_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9264 +# Estimated pi_0=0.931743 +Using motif +M04801_2.00 of width 14. +Using motif -M04801_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941379 +# Estimated pi_0=0.951765 +Using motif +M04802_2.00 of width 11. +Using motif -M04802_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949839 +# Estimated pi_0=0.958322 +Using motif +M09063_2.00 of width 14. +Using motif -M09063_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939264 +# Estimated pi_0=0.945 +Using motif +M01477_2.00 of width 10. +Using motif -M01477_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992517 +# Estimated pi_0=0.99809 +Using motif +M02678_2.00 of width 7. +Using motif -M02678_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99117 +# Estimated pi_0=0.99608 +Using motif +M03008_2.00 of width 14. +Using motif -M03008_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988152 +# Estimated pi_0=0.989436 +Using motif +M04803_2.00 of width 12. +Using motif -M04803_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971469 +# Estimated pi_0=0.982209 +Using motif +M04804_2.00 of width 12. +Using motif -M04804_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984366 +# Estimated pi_0=0.990421 +Using motif +M04805_2.00 of width 13. +Using motif -M04805_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994586 +# Estimated pi_0=0.997286 +Using motif +M04806_2.00 of width 13. +Using motif -M04806_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986737 +# Estimated pi_0=0.991878 +Using motif +M09064_2.00 of width 17. +Using motif -M09064_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961412 +# Estimated pi_0=0.965934 +Using motif +M09537_2.00 of width 12. +Using motif -M09537_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974382 +# Estimated pi_0=0.977901 +Using motif +M03012_2.00 of width 7. +Using motif -M03012_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04807_2.00 of width 17. +Using motif -M04807_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04808_2.00 of width 17. +Using motif -M04808_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M01227_2.00 of width 8. +Using motif -M01227_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998602 +# Estimated pi_0=1 +Using motif +M00253_2.00 of width 11. +Using motif -M00253_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00254_2.00 of width 10. +Using motif -M00254_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997879 +# Estimated pi_0=1 +Using motif +M00255_2.00 of width 12. +Using motif -M00255_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00256_2.00 of width 13. +Using motif -M00256_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00257_2.00 of width 10. +Using motif -M00257_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M00258_2.00 of width 11. +Using motif -M00258_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999697 +# Estimated pi_0=0.999899 +Using motif +M00259_2.00 of width 8. +Using motif -M00259_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03013_2.00 of width 13. +Using motif -M03013_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998291 +Using motif +M03014_2.00 of width 14. +Using motif -M03014_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03015_2.00 of width 11. +Using motif -M03015_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M09083_2.00 of width 15. +Using motif -M09083_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M10526_2.00 of width 16. +Using motif -M10526_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03016_2.00 of width 14. +Using motif -M03016_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998144 +# Estimated pi_0=0.998693 +Using motif +M03017_2.00 of width 8. +Using motif -M03017_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999798 +# Estimated pi_0=1 +Using motif +M03018_2.00 of width 13. +Using motif -M03018_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998593 +# Estimated pi_0=0.998593 +Using motif +M04809_2.00 of width 14. +Using motif -M04809_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04810_2.00 of width 14. +Using motif -M04810_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09084_2.00 of width 10. +Using motif -M09084_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997475 +# Estimated pi_0=0.99809 +Using motif +M10527_2.00 of width 18. +Using motif -M10527_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998191 +# Estimated pi_0=0.998191 +Using motif +M10528_2.00 of width 14. +Using motif -M10528_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M10530_2.00 of width 13. +Using motif -M10530_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M09085_2.00 of width 12. +Using motif -M09085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989783 +# Estimated pi_0=0.993807 +Using motif +M00836_2.00 of width 10. +Using motif -M00836_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M00837_2.00 of width 9. +Using motif -M00837_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00838_2.00 of width 8. +Using motif -M00838_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00839_2.00 of width 11. +Using motif -M00839_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M01976_2.00 of width 8. +Using motif -M01976_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04811_2.00 of width 30. +Using motif -M04811_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04812_2.00 of width 30. +Using motif -M04812_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M08115_2.00 of width 12. +Using motif -M08115_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998446 +# Estimated pi_0=0.999095 +Using motif +M09086_2.00 of width 9. +Using motif -M09086_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988353 +# Estimated pi_0=0.992755 +Using motif +M09541_2.00 of width 12. +Using motif -M09541_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03019_2.00 of width 14. +Using motif -M03019_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03020_2.00 of width 8. +Using motif -M03020_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03021_2.00 of width 11. +Using motif -M03021_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959728 +# Estimated pi_0=0.96631 +Using motif +M04813_2.00 of width 11. +Using motif -M04813_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957671 +# Estimated pi_0=0.966966 +Using motif +M04814_2.00 of width 11. +Using motif -M04814_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921765 +# Estimated pi_0=0.931373 +Using motif +M09087_2.00 of width 10. +Using motif -M09087_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996907 +# Estimated pi_0=0.999293 +Using motif +M10537_2.00 of width 14. +Using motif -M10537_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997949 +# Estimated pi_0=0.998392 +Using motif +M04815_2.00 of width 20. +Using motif -M04815_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04816_2.00 of width 20. +Using motif -M04816_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M07959_2.00 of width 15. +Using motif -M07959_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M09088_2.00 of width 12. +Using motif -M09088_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999175 +# Estimated pi_0=0.999598 +Using motif +M09542_2.00 of width 12. +Using motif -M09542_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M10540_2.00 of width 15. +Using motif -M10540_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998883 +# Estimated pi_0=0.999397 +Using motif +M05866_2.00 of width 10. +Using motif -M05866_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M08116_2.00 of width 11. +Using motif -M08116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995895 +# Estimated pi_0=0.998794 +Using motif +M09089_2.00 of width 9. +Using motif -M09089_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988901 +# Estimated pi_0=0.988901 +Using motif +M04817_2.00 of width 12. +Using motif -M04817_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999487 +# Estimated pi_0=1 +Using motif +M04818_2.00 of width 12. +Using motif -M04818_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04819_2.00 of width 16. +Using motif -M04819_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M04820_2.00 of width 20. +Using motif -M04820_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04821_2.00 of width 16. +Using motif -M04821_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04822_2.00 of width 20. +Using motif -M04822_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M07960_2.00 of width 15. +Using motif -M07960_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995228 +# Estimated pi_0=0.998492 +Using motif +M07961_2.00 of width 11. +Using motif -M07961_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997857 +# Estimated pi_0=1 +Using motif +M07962_2.00 of width 11. +Using motif -M07962_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=0.999598 +Using motif +M08028_2.00 of width 10. +Using motif -M08028_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M08117_2.00 of width 15. +Using motif -M08117_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997157 +# Estimated pi_0=0.998693 +Using motif +M08199_2.00 of width 13. +Using motif -M08199_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998446 +# Estimated pi_0=0.999397 +Using motif +M09090_2.00 of width 12. +Using motif -M09090_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998883 +# Estimated pi_0=0.999497 +Using motif +M09543_2.00 of width 10. +Using motif -M09543_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M09544_2.00 of width 16. +Using motif -M09544_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997949 +# Estimated pi_0=0.998265 +Using motif +M09545_2.00 of width 10. +Using motif -M09545_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M00793_2.00 of width 9. +Using motif -M00793_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02680_2.00 of width 14. +Using motif -M02680_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M10548_2.00 of width 16. +Using motif -M10548_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M01011_2.00 of width 10. +Using motif -M01011_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902376 +# Estimated pi_0=0.913675 +Using motif +M08118_2.00 of width 11. +Using motif -M08118_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03022_2.00 of width 8. +Using motif -M03022_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03023_2.00 of width 14. +Using motif -M03023_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03024_2.00 of width 12. +Using motif -M03024_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951071 +# Estimated pi_0=0.954559 +Using motif +M09091_2.00 of width 12. +Using motif -M09091_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97061 +# Estimated pi_0=0.97861 +Using motif +M10549_2.00 of width 10. +Using motif -M10549_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998376 +# Estimated pi_0=0.999095 +Using motif +M10550_2.00 of width 14. +Using motif -M10550_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998788 +# Estimated pi_0=0.999196 +Using motif +M08119_2.00 of width 11. +Using motif -M08119_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M09092_2.00 of width 9. +Using motif -M09092_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99734 +# Estimated pi_0=0.999296 +Using motif +M09546_2.00 of width 12. +Using motif -M09546_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04823_2.00 of width 12. +Using motif -M04823_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04824_2.00 of width 12. +Using motif -M04824_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M09093_2.00 of width 12. +Using motif -M09093_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M01977_2.00 of width 10. +Using motif -M01977_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998673 +# Estimated pi_0=1 +Using motif +M03025_2.00 of width 10. +Using motif -M03025_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979298 +# Estimated pi_0=1 +Using motif +M04825_2.00 of width 11. +Using motif -M04825_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04826_2.00 of width 11. +Using motif -M04826_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M08120_2.00 of width 14. +Using motif -M08120_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99866 +# Estimated pi_0=1 +Using motif +M09094_2.00 of width 10. +Using motif -M09094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992812 +# Estimated pi_0=0.995859 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03027_2.00 of width 17. +Using motif -M03027_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04827_2.00 of width 13. +Using motif -M04827_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04828_2.00 of width 13. +Using motif -M04828_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04829_2.00 of width 12. +Using motif -M04829_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04830_2.00 of width 12. +Using motif -M04830_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M09095_2.00 of width 12. +Using motif -M09095_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997202 +# Estimated pi_0=0.998894 +Using motif +M10556_2.00 of width 13. +Using motif -M10556_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997333 +# Estimated pi_0=0.999397 +Using motif +M04831_2.00 of width 16. +Using motif -M04831_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M04832_2.00 of width 20. +Using motif -M04832_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04833_2.00 of width 16. +Using motif -M04833_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M04834_2.00 of width 20. +Using motif -M04834_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09096_2.00 of width 13. +Using motif -M09096_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99732 +# Estimated pi_0=0.999799 +Using motif +M01007_2.00 of width 9. +Using motif -M01007_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95678 +# Estimated pi_0=0.962047 +Using motif +M04835_2.00 of width 19. +Using motif -M04835_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.864158 +# Estimated pi_0=0.869455 +Using motif +M04836_2.00 of width 17. +Using motif -M04836_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9886 +# Estimated pi_0=1 +Using motif +M03028_2.00 of width 14. +Using motif -M03028_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978039 +# Estimated pi_0=0.995294 +Using motif +M03029_2.00 of width 18. +Using motif -M03029_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03030_2.00 of width 11. +Using motif -M03030_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03031_2.00 of width 9. +Using motif -M03031_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04837_2.00 of width 20. +Using motif -M04837_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04838_2.00 of width 20. +Using motif -M04838_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M01978_2.00 of width 8. +Using motif -M01978_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03032_2.00 of width 17. +Using motif -M03032_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.999598 +Using motif +M03033_2.00 of width 12. +Using motif -M03033_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989231 +# Estimated pi_0=1 +Using motif +M04839_2.00 of width 11. +Using motif -M04839_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99525 +# Estimated pi_0=0.998693 +Using motif +M04840_2.00 of width 11. +Using motif -M04840_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988911 +# Estimated pi_0=1 +Using motif +M01008_2.00 of width 8. +Using motif -M01008_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M03034_2.00 of width 7. +Using motif -M03034_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03035_2.00 of width 13. +Using motif -M03035_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M10558_2.00 of width 16. +Using motif -M10558_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03036_2.00 of width 14. +Using motif -M03036_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03037_2.00 of width 11. +Using motif -M03037_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997789 +# Estimated pi_0=0.997789 +Using motif +M03038_2.00 of width 12. +Using motif -M03038_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M04841_2.00 of width 12. +Using motif -M04841_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995025 +# Estimated pi_0=0.998492 +Using motif +M04842_2.00 of width 12. +Using motif -M04842_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997172 +# Estimated pi_0=0.998191 +Using motif +M04843_2.00 of width 12. +Using motif -M04843_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M04844_2.00 of width 12. +Using motif -M04844_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998359 +# Estimated pi_0=0.999095 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M04846_2.00 of width 15. +Using motif -M04846_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M00164_2.00 of width 10. +Using motif -M00164_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M04847_2.00 of width 11. +Using motif -M04847_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999032 +# Estimated pi_0=1 +Using motif +M04848_2.00 of width 11. +Using motif -M04848_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983269 +# Estimated pi_0=0.999799 +Using motif +M03039_2.00 of width 14. +Using motif -M03039_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03040_2.00 of width 7. +Using motif -M03040_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00072 +# Estimated pi_0=1 +Using motif +M03041_2.00 of width 12. +Using motif -M03041_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969593 +# Estimated pi_0=0.977019 +Using motif +M04849_2.00 of width 10. +Using motif -M04849_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9724 +# Estimated pi_0=0.981429 +Using motif +M04850_2.00 of width 10. +Using motif -M04850_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09097_2.00 of width 9. +Using motif -M09097_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998571 +# Estimated pi_0=0.998794 +Using motif +M10562_2.00 of width 11. +Using motif -M10562_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999798 +# Estimated pi_0=1 +Using motif +M10563_2.00 of width 14. +Using motif -M10563_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03042_2.00 of width 14. +Using motif -M03042_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03043_2.00 of width 7. +Using motif -M03043_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04851_2.00 of width 14. +Using motif -M04851_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04852_2.00 of width 13. +Using motif -M04852_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999898 +# Estimated pi_0=1 +Using motif +M04853_2.00 of width 14. +Using motif -M04853_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04854_2.00 of width 13. +Using motif -M04854_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997526 +# Estimated pi_0=0.998283 +Using motif +M04845_2.00 of width 15. +Using motif -M04845_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999184 +# Estimated pi_0=1 +Using motif +M03044_2.00 of width 14. +Using motif -M03044_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03045_2.00 of width 7. +Using motif -M03045_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04855_2.00 of width 12. +Using motif -M04855_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04856_2.00 of width 12. +Using motif -M04856_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M10565_2.00 of width 12. +Using motif -M10565_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M04857_2.00 of width 17. +Using motif -M04857_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M04858_2.00 of width 17. +Using motif -M04858_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M02736_2.00 of width 12. +Using motif -M02736_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996263 +# Estimated pi_0=0.997688 +Using motif +M02737_2.00 of width 8. +Using motif -M02737_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998889 +# Estimated pi_0=1 +Using motif +M03046_2.00 of width 8. +Using motif -M03046_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998182 +# Estimated pi_0=0.998593 +Using motif +M03047_2.00 of width 14. +Using motif -M03047_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998492 +# Estimated pi_0=0.998492 +Using motif +M03048_2.00 of width 13. +Using motif -M03048_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999082 +# Estimated pi_0=0.999296 +Using motif +M04859_2.00 of width 10. +Using motif -M04859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04860_2.00 of width 10. +Using motif -M04860_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M09098_2.00 of width 13. +Using motif -M09098_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997588 +# Estimated pi_0=0.997588 +Using motif +M03049_2.00 of width 14. +Using motif -M03049_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=1 +Using motif +M03050_2.00 of width 7. +Using motif -M03050_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03051_2.00 of width 14. +Using motif -M03051_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953333 +# Estimated pi_0=0.957945 +Using motif +M01983_2.00 of width 10. +Using motif -M01983_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03026_2.00 of width 7. +Using motif -M03026_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M02681_2.00 of width 8. +Using motif -M02681_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M02738_2.00 of width 8. +Using motif -M02738_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M02739_2.00 of width 6. +Using motif -M02739_2.00 of width 6. +Computing q-values. +Using motif +M04861_2.00 of width 15. +Using motif -M04861_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04862_2.00 of width 15. +Using motif -M04862_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M07963_2.00 of width 11. +Using motif -M07963_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=0.999698 +Using motif +M07964_2.00 of width 18. +Using motif -M07964_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995487 +# Estimated pi_0=0.99809 +Using motif +M08202_2.00 of width 20. +Using motif -M08202_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9498 +# Estimated pi_0=0.962047 +Using motif +M08203_2.00 of width 10. +Using motif -M08203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09115_2.00 of width 19. +Using motif -M09115_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988889 +# Estimated pi_0=0.99513 +Using motif +M09549_2.00 of width 10. +Using motif -M09549_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992739 +# Estimated pi_0=1 +Using motif +M10577_2.00 of width 10. +Using motif -M10577_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9412 +# Estimated pi_0=0.94887 +Using motif +M10578_2.00 of width 14. +Using motif -M10578_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10579_2.00 of width 14. +Using motif -M10579_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943529 +# Estimated pi_0=0.954035 +Using motif +M10580_2.00 of width 13. +Using motif -M10580_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M10581_2.00 of width 10. +Using motif -M10581_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M10582_2.00 of width 10. +Using motif -M10582_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03063_2.00 of width 8. +Using motif -M03063_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00182 +# Estimated pi_0=1 +Using motif +M03064_2.00 of width 8. +Using motif -M03064_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04863_2.00 of width 12. +Using motif -M04863_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04864_2.00 of width 11. +Using motif -M04864_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04865_2.00 of width 12. +Using motif -M04865_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04866_2.00 of width 11. +Using motif -M04866_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M07965_2.00 of width 15. +Using motif -M07965_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999798 +# Estimated pi_0=1 +Using motif +M09116_2.00 of width 11. +Using motif -M09116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983536 +# Estimated pi_0=0.986283 +Using motif +M10586_2.00 of width 9. +Using motif -M10586_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994167 +# Estimated pi_0=1 +Using motif +M10587_2.00 of width 10. +Using motif -M10587_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M10588_2.00 of width 10. +Using motif -M10588_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03065_2.00 of width 8. +Using motif -M03065_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M04867_2.00 of width 10. +Using motif -M04867_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04868_2.00 of width 12. +Using motif -M04868_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04869_2.00 of width 10. +Using motif -M04869_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04870_2.00 of width 12. +Using motif -M04870_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M04871_2.00 of width 10. +Using motif -M04871_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04872_2.00 of width 12. +Using motif -M04872_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04873_2.00 of width 10. +Using motif -M04873_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04874_2.00 of width 12. +Using motif -M04874_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03066_2.00 of width 8. +Using motif -M03066_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04875_2.00 of width 10. +Using motif -M04875_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04876_2.00 of width 10. +Using motif -M04876_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M09117_2.00 of width 10. +Using motif -M09117_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990405 +# Estimated pi_0=0.995187 +Using motif +M04877_2.00 of width 12. +Using motif -M04877_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04878_2.00 of width 12. +Using motif -M04878_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M08124_2.00 of width 13. +Using motif -M08124_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996684 +# Estimated pi_0=0.999596 +Using motif +M09118_2.00 of width 13. +Using motif -M09118_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M10592_2.00 of width 10. +Using motif -M10592_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04879_2.00 of width 9. +Using motif -M04879_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989703 +# Estimated pi_0=1 +Using motif +M04880_2.00 of width 9. +Using motif -M04880_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M07966_2.00 of width 11. +Using motif -M07966_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996062 +# Estimated pi_0=0.999497 +Using motif +M07967_2.00 of width 18. +Using motif -M07967_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976667 +# Estimated pi_0=0.984545 +Using motif +M07968_2.00 of width 18. +Using motif -M07968_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986711 +# Estimated pi_0=0.998021 +Using motif +M07969_2.00 of width 10. +Using motif -M07969_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M07970_2.00 of width 10. +Using motif -M07970_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998289 +# Estimated pi_0=1 +Using motif +M08125_2.00 of width 11. +Using motif -M08125_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M09119_2.00 of width 19. +Using motif -M09119_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996484 +# Estimated pi_0=0.999899 +Using motif +M09550_2.00 of width 10. +Using motif -M09550_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992299 +# Estimated pi_0=0.999497 +Using motif +M10594_2.00 of width 10. +Using motif -M10594_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9034 +# Estimated pi_0=0.915636 +Using motif +M10595_2.00 of width 10. +Using motif -M10595_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M10596_2.00 of width 10. +Using motif -M10596_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03067_2.00 of width 10. +Using motif -M03067_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942524 +# Estimated pi_0=0.956389 +Using motif +M04881_2.00 of width 9. +Using motif -M04881_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961905 +# Estimated pi_0=0.968481 +Using motif +M04882_2.00 of width 9. +Using motif -M04882_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976601 +# Estimated pi_0=0.98071 +Using motif +M02020_2.00 of width 9. +Using motif -M02020_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.903529 +# Estimated pi_0=0.910894 +Using motif +M03068_2.00 of width 10. +Using motif -M03068_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949709 +# Estimated pi_0=0.961029 +Using motif +M03069_2.00 of width 16. +Using motif -M03069_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932 +# Estimated pi_0=0.952653 +Using motif +M03070_2.00 of width 11. +Using motif -M03070_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9182 +# Estimated pi_0=0.932773 +Using motif +M04883_2.00 of width 9. +Using motif -M04883_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93504 +# Estimated pi_0=0.938182 +Using motif +M04884_2.00 of width 9. +Using motif -M04884_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973701 +# Estimated pi_0=0.981963 +Using motif +M05868_2.00 of width 9. +Using motif -M05868_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9344 +# Estimated pi_0=0.936078 +Using motif +M08129_2.00 of width 15. +Using motif -M08129_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999485 +# Estimated pi_0=1 +Using motif +M09125_2.00 of width 12. +Using motif -M09125_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970317 +# Estimated pi_0=0.984358 +Using motif +M09558_2.00 of width 20. +Using motif -M09558_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981635 +# Estimated pi_0=0.986188 +Using motif +M04885_2.00 of width 16. +Using motif -M04885_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937248 +# Estimated pi_0=0.944895 +Using motif +M04886_2.00 of width 16. +Using motif -M04886_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953636 +# Estimated pi_0=0.959613 +Using motif +M03071_2.00 of width 17. +Using motif -M03071_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992222 +# Estimated pi_0=1 +Using motif +M03072_2.00 of width 10. +Using motif -M03072_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03073_2.00 of width 12. +Using motif -M03073_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M04887_2.00 of width 10. +Using motif -M04887_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04888_2.00 of width 10. +Using motif -M04888_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03074_2.00 of width 10. +Using motif -M03074_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03075_2.00 of width 16. +Using motif -M03075_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9952 +# Estimated pi_0=0.999899 +Using motif +M04889_2.00 of width 18. +Using motif -M04889_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968429 +# Estimated pi_0=0.975556 +Using motif +M04890_2.00 of width 18. +Using motif -M04890_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9632 +# Estimated pi_0=0.974061 +Using motif +M04891_2.00 of width 18. +Using motif -M04891_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00137 +# Estimated pi_0=1 +Using motif +M04892_2.00 of width 18. +Using motif -M04892_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M10610_2.00 of width 11. +Using motif -M10610_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926337 +# Estimated pi_0=0.941699 +Using motif +M04893_2.00 of width 18. +Using motif -M04893_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.912903 +# Estimated pi_0=0.923889 +Using motif +M04894_2.00 of width 18. +Using motif -M04894_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935556 +# Estimated pi_0=0.941752 +Using motif +M00260_2.00 of width 11. +Using motif -M00260_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00261_2.00 of width 9. +Using motif -M00261_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00262_2.00 of width 8. +Using motif -M00262_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M00263_2.00 of width 10. +Using motif -M00263_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M00264_2.00 of width 9. +Using motif -M00264_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00265_2.00 of width 8. +Using motif -M00265_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03076_2.00 of width 13. +Using motif -M03076_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04895_2.00 of width 8. +Using motif -M04895_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04896_2.00 of width 8. +Using motif -M04896_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04897_2.00 of width 11. +Using motif -M04897_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04898_2.00 of width 11. +Using motif -M04898_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04899_2.00 of width 11. +Using motif -M04899_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04900_2.00 of width 11. +Using motif -M04900_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03077_2.00 of width 10. +Using motif -M03077_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04901_2.00 of width 8. +Using motif -M04901_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968544 +# Estimated pi_0=0.972762 +Using motif +M04902_2.00 of width 8. +Using motif -M04902_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04903_2.00 of width 8. +Using motif -M04903_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04904_2.00 of width 8. +Using motif -M04904_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03078_2.00 of width 8. +Using motif -M03078_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04905_2.00 of width 8. +Using motif -M04905_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04906_2.00 of width 8. +Using motif -M04906_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04907_2.00 of width 8. +Using motif -M04907_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04908_2.00 of width 8. +Using motif -M04908_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04909_2.00 of width 8. +Using motif -M04909_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04910_2.00 of width 8. +Using motif -M04910_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04911_2.00 of width 8. +Using motif -M04911_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09128_2.00 of width 9. +Using motif -M09128_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990828 +# Estimated pi_0=0.993264 +Using motif +M04912_2.00 of width 8. +Using motif -M04912_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04913_2.00 of width 8. +Using motif -M04913_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04914_2.00 of width 8. +Using motif -M04914_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04915_2.00 of width 12. +Using motif -M04915_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04916_2.00 of width 12. +Using motif -M04916_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03079_2.00 of width 13. +Using motif -M03079_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04917_2.00 of width 8. +Using motif -M04917_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04918_2.00 of width 8. +Using motif -M04918_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04919_2.00 of width 8. +Using motif -M04919_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994 +# Estimated pi_0=1 +Using motif +M03080_2.00 of width 10. +Using motif -M03080_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04920_2.00 of width 8. +Using motif -M04920_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04921_2.00 of width 8. +Using motif -M04921_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9976 +# Estimated pi_0=1 +Using motif +M01503_2.00 of width 8. +Using motif -M01503_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M01504_2.00 of width 8. +Using motif -M01504_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03081_2.00 of width 8. +Using motif -M03081_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04922_2.00 of width 8. +Using motif -M04922_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04923_2.00 of width 8. +Using motif -M04923_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04924_2.00 of width 8. +Using motif -M04924_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04925_2.00 of width 8. +Using motif -M04925_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04926_2.00 of width 8. +Using motif -M04926_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04927_2.00 of width 8. +Using motif -M04927_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M09129_2.00 of width 10. +Using motif -M09129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03082_2.00 of width 9. +Using motif -M03082_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03083_2.00 of width 9. +Using motif -M03083_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03084_2.00 of width 8. +Using motif -M03084_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04928_2.00 of width 8. +Using motif -M04928_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M04929_2.00 of width 8. +Using motif -M04929_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04930_2.00 of width 8. +Using motif -M04930_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04931_2.00 of width 8. +Using motif -M04931_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989333 +# Estimated pi_0=1 +Using motif +M03085_2.00 of width 12. +Using motif -M03085_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03086_2.00 of width 11. +Using motif -M03086_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04932_2.00 of width 10. +Using motif -M04932_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04933_2.00 of width 10. +Using motif -M04933_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04934_2.00 of width 10. +Using motif -M04934_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04935_2.00 of width 10. +Using motif -M04935_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M09130_2.00 of width 12. +Using motif -M09130_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M09131_2.00 of width 11. +Using motif -M09131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04936_2.00 of width 8. +Using motif -M04936_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04937_2.00 of width 8. +Using motif -M04937_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M04938_2.00 of width 8. +Using motif -M04938_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04939_2.00 of width 10. +Using motif -M04939_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9236 +# Estimated pi_0=0.924808 +Using motif +M04940_2.00 of width 10. +Using motif -M04940_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9174 +# Estimated pi_0=0.921765 +Using motif +M00266_2.00 of width 9. +Using motif -M00266_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M00267_2.00 of width 9. +Using motif -M00267_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00268_2.00 of width 9. +Using motif -M00268_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00269_2.00 of width 9. +Using motif -M00269_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03087_2.00 of width 8. +Using motif -M03087_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03088_2.00 of width 11. +Using motif -M03088_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04941_2.00 of width 8. +Using motif -M04941_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04942_2.00 of width 8. +Using motif -M04942_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M04943_2.00 of width 8. +Using motif -M04943_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04944_2.00 of width 8. +Using motif -M04944_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04945_2.00 of width 8. +Using motif -M04945_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04946_2.00 of width 8. +Using motif -M04946_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05869_2.00 of width 10. +Using motif -M05869_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03089_2.00 of width 9. +Using motif -M03089_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03090_2.00 of width 8. +Using motif -M03090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998788 +# Estimated pi_0=1 +Using motif +M03091_2.00 of width 9. +Using motif -M03091_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994677 +# Estimated pi_0=1 +Using motif +M03092_2.00 of width 8. +Using motif -M03092_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M04947_2.00 of width 8. +Using motif -M04947_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990617 +# Estimated pi_0=0.995833 +Using motif +M04948_2.00 of width 8. +Using motif -M04948_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999479 +# Estimated pi_0=1 +Using motif +M00270_2.00 of width 7. +Using motif -M00270_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00272_2.00 of width 8. +Using motif -M00272_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00273_2.00 of width 11. +Using motif -M00273_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M00274_2.00 of width 10. +Using motif -M00274_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00275_2.00 of width 10. +Using motif -M00275_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M04949_2.00 of width 8. +Using motif -M04949_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996952 +# Estimated pi_0=1 +Using motif +M04950_2.00 of width 8. +Using motif -M04950_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M09132_2.00 of width 13. +Using motif -M09132_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992781 +# Estimated pi_0=0.998579 +Using motif +M03093_2.00 of width 8. +Using motif -M03093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985362 +# Estimated pi_0=0.994184 +Using motif +M03094_2.00 of width 12. +Using motif -M03094_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976615 +# Estimated pi_0=0.986257 +Using motif +M04951_2.00 of width 12. +Using motif -M04951_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9846 +# Estimated pi_0=0.997113 +Using motif +M04952_2.00 of width 12. +Using motif -M04952_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940388 +# Estimated pi_0=0.95808 +Using motif +M00276_2.00 of width 11. +Using motif -M00276_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00277_2.00 of width 10. +Using motif -M00277_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03095_2.00 of width 8. +Using motif -M03095_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04953_2.00 of width 8. +Using motif -M04953_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M04954_2.00 of width 8. +Using motif -M04954_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04955_2.00 of width 8. +Using motif -M04955_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04956_2.00 of width 8. +Using motif -M04956_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04957_2.00 of width 8. +Using motif -M04957_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M04958_2.00 of width 8. +Using motif -M04958_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03096_2.00 of width 10. +Using motif -M03096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04959_2.00 of width 8. +Using motif -M04959_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04960_2.00 of width 8. +Using motif -M04960_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04961_2.00 of width 8. +Using motif -M04961_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09133_2.00 of width 10. +Using motif -M09133_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993146 +# Estimated pi_0=0.998782 +Using motif +M02083_2.00 of width 9. +Using motif -M02083_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03097_2.00 of width 10. +Using motif -M03097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M04962_2.00 of width 8. +Using motif -M04962_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04963_2.00 of width 8. +Using motif -M04963_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M04964_2.00 of width 8. +Using motif -M04964_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04965_2.00 of width 8. +Using motif -M04965_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04966_2.00 of width 8. +Using motif -M04966_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04967_2.00 of width 8. +Using motif -M04967_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M10651_2.00 of width 9. +Using motif -M10651_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04968_2.00 of width 8. +Using motif -M04968_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00242 +# Estimated pi_0=1 +Using motif +M04969_2.00 of width 8. +Using motif -M04969_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04970_2.00 of width 8. +Using motif -M04970_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04971_2.00 of width 8. +Using motif -M04971_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04972_2.00 of width 8. +Using motif -M04972_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04973_2.00 of width 8. +Using motif -M04973_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M08457_2.00 of width 8. +Using motif -M08457_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M10652_2.00 of width 30. +Using motif -M10652_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996479 +# Estimated pi_0=0.999799 +Using motif +M04974_2.00 of width 10. +Using motif -M04974_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M04975_2.00 of width 10. +Using motif -M04975_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M04976_2.00 of width 8. +Using motif -M04976_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03098_2.00 of width 10. +Using motif -M03098_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03099_2.00 of width 11. +Using motif -M03099_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03100_2.00 of width 10. +Using motif -M03100_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03101_2.00 of width 11. +Using motif -M03101_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M04977_2.00 of width 11. +Using motif -M04977_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00122 +# Estimated pi_0=1 +Using motif +M04978_2.00 of width 11. +Using motif -M04978_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04979_2.00 of width 11. +Using motif -M04979_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04980_2.00 of width 11. +Using motif -M04980_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M04981_2.00 of width 11. +Using motif -M04981_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04982_2.00 of width 11. +Using motif -M04982_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M04983_2.00 of width 11. +Using motif -M04983_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M04984_2.00 of width 11. +Using motif -M04984_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M09134_2.00 of width 11. +Using motif -M09134_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03102_2.00 of width 10. +Using motif -M03102_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M04985_2.00 of width 8. +Using motif -M04985_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04986_2.00 of width 8. +Using motif -M04986_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M04987_2.00 of width 8. +Using motif -M04987_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M02084_2.00 of width 10. +Using motif -M02084_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09135_2.00 of width 9. +Using motif -M09135_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03103_2.00 of width 10. +Using motif -M03103_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03104_2.00 of width 17. +Using motif -M03104_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03105_2.00 of width 14. +Using motif -M03105_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04988_2.00 of width 8. +Using motif -M04988_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M04989_2.00 of width 8. +Using motif -M04989_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M04990_2.00 of width 8. +Using motif -M04990_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M04991_2.00 of width 8. +Using motif -M04991_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M02085_2.00 of width 10. +Using motif -M02085_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03106_2.00 of width 10. +Using motif -M03106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03107_2.00 of width 15. +Using motif -M03107_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M09136_2.00 of width 12. +Using motif -M09136_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M02086_2.00 of width 8. +Using motif -M02086_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03108_2.00 of width 10. +Using motif -M03108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03109_2.00 of width 16. +Using motif -M03109_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03110_2.00 of width 12. +Using motif -M03110_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M04992_2.00 of width 8. +Using motif -M04992_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M04993_2.00 of width 12. +Using motif -M04993_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M04994_2.00 of width 8. +Using motif -M04994_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04995_2.00 of width 8. +Using motif -M04995_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M04996_2.00 of width 12. +Using motif -M04996_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05870_2.00 of width 8. +Using motif -M05870_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M09137_2.00 of width 13. +Using motif -M09137_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03111_2.00 of width 9. +Using motif -M03111_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M04997_2.00 of width 8. +Using motif -M04997_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M04998_2.00 of width 8. +Using motif -M04998_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M04999_2.00 of width 8. +Using motif -M04999_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05000_2.00 of width 8. +Using motif -M05000_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05001_2.00 of width 8. +Using motif -M05001_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03112_2.00 of width 8. +Using motif -M03112_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05002_2.00 of width 8. +Using motif -M05002_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05003_2.00 of width 8. +Using motif -M05003_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05004_2.00 of width 8. +Using motif -M05004_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05005_2.00 of width 8. +Using motif -M05005_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05006_2.00 of width 8. +Using motif -M05006_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05007_2.00 of width 8. +Using motif -M05007_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00278_2.00 of width 7. +Using motif -M00278_2.00 of width 7. +Computing q-values. +Using motif +M00279_2.00 of width 11. +Using motif -M00279_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00280_2.00 of width 13. +Using motif -M00280_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00281_2.00 of width 10. +Using motif -M00281_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8984 +# Estimated pi_0=0.908679 +Using motif +M00282_2.00 of width 8. +Using motif -M00282_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03113_2.00 of width 11. +Using motif -M03113_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03114_2.00 of width 11. +Using motif -M03114_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05008_2.00 of width 8. +Using motif -M05008_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05009_2.00 of width 8. +Using motif -M05009_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05010_2.00 of width 11. +Using motif -M05010_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05011_2.00 of width 11. +Using motif -M05011_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05012_2.00 of width 10. +Using motif -M05012_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996484 +# Estimated pi_0=0.999799 +Using motif +M05013_2.00 of width 10. +Using motif -M05013_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99898 +# Estimated pi_0=0.999196 +Using motif +M05014_2.00 of width 8. +Using motif -M05014_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9692 +# Estimated pi_0=0.9692 +Using motif +M09138_2.00 of width 10. +Using motif -M09138_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M00451_2.00 of width 8. +Using motif -M00451_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9576 +# Estimated pi_0=0.967925 +Using motif +M05016_2.00 of width 7. +Using motif -M05016_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05017_2.00 of width 8. +Using motif -M05017_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M09139_2.00 of width 17. +Using motif -M09139_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M00416_2.00 of width 8. +Using motif -M00416_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03115_2.00 of width 9. +Using motif -M03115_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05018_2.00 of width 11. +Using motif -M05018_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05019_2.00 of width 11. +Using motif -M05019_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09140_2.00 of width 8. +Using motif -M09140_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M02087_2.00 of width 10. +Using motif -M02087_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05020_2.00 of width 8. +Using motif -M05020_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05021_2.00 of width 8. +Using motif -M05021_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M10672_2.00 of width 10. +Using motif -M10672_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966078 +# Estimated pi_0=0.977586 +Using motif +M03116_2.00 of width 15. +Using motif -M03116_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03117_2.00 of width 8. +Using motif -M03117_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05022_2.00 of width 8. +Using motif -M05022_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05023_2.00 of width 8. +Using motif -M05023_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05024_2.00 of width 10. +Using motif -M05024_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05025_2.00 of width 10. +Using motif -M05025_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03118_2.00 of width 8. +Using motif -M03118_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05026_2.00 of width 8. +Using motif -M05026_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05027_2.00 of width 8. +Using motif -M05027_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05028_2.00 of width 8. +Using motif -M05028_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05029_2.00 of width 8. +Using motif -M05029_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00283_2.00 of width 10. +Using motif -M00283_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00284_2.00 of width 8. +Using motif -M00284_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03119_2.00 of width 8. +Using motif -M03119_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05030_2.00 of width 8. +Using motif -M05030_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05031_2.00 of width 8. +Using motif -M05031_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03120_2.00 of width 8. +Using motif -M03120_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03121_2.00 of width 8. +Using motif -M03121_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03122_2.00 of width 11. +Using motif -M03122_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03123_2.00 of width 12. +Using motif -M03123_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987636 +# Estimated pi_0=0.993196 +Using motif +M05032_2.00 of width 12. +Using motif -M05032_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973861 +# Estimated pi_0=0.989888 +Using motif +M05033_2.00 of width 12. +Using motif -M05033_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9844 +# Estimated pi_0=0.998794 +Using motif +M05034_2.00 of width 8. +Using motif -M05034_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991515 +# Estimated pi_0=1 +Using motif +M05035_2.00 of width 8. +Using motif -M05035_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998089 +# Estimated pi_0=0.999598 +Using motif +M00285_2.00 of width 12. +Using motif -M00285_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00286_2.00 of width 7. +Using motif -M00286_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M00287_2.00 of width 10. +Using motif -M00287_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03124_2.00 of width 8. +Using motif -M03124_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05036_2.00 of width 8. +Using motif -M05036_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05037_2.00 of width 8. +Using motif -M05037_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05038_2.00 of width 8. +Using motif -M05038_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05039_2.00 of width 9. +Using motif -M05039_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05040_2.00 of width 8. +Using motif -M05040_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05041_2.00 of width 9. +Using motif -M05041_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M10676_2.00 of width 14. +Using motif -M10676_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05042_2.00 of width 10. +Using motif -M05042_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9795 +# Estimated pi_0=0.987104 +Using motif +M05043_2.00 of width 10. +Using motif -M05043_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979259 +# Estimated pi_0=0.991005 +Using motif +M05044_2.00 of width 9. +Using motif -M05044_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.951132 +Using motif +M05045_2.00 of width 10. +Using motif -M05045_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9798 +# Estimated pi_0=0.997487 +Using motif +M05046_2.00 of width 10. +Using motif -M05046_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972353 +# Estimated pi_0=0.987869 +Using motif +M05047_2.00 of width 9. +Using motif -M05047_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9912 +# Estimated pi_0=1 +Using motif +M05048_2.00 of width 8. +Using motif -M05048_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05049_2.00 of width 8. +Using motif -M05049_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05050_2.00 of width 8. +Using motif -M05050_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M09141_2.00 of width 11. +Using motif -M09141_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03125_2.00 of width 10. +Using motif -M03125_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05051_2.00 of width 8. +Using motif -M05051_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05052_2.00 of width 8. +Using motif -M05052_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05053_2.00 of width 8. +Using motif -M05053_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05054_2.00 of width 8. +Using motif -M05054_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05055_2.00 of width 8. +Using motif -M05055_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05056_2.00 of width 8. +Using motif -M05056_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M03126_2.00 of width 10. +Using motif -M03126_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05057_2.00 of width 8. +Using motif -M05057_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05058_2.00 of width 8. +Using motif -M05058_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9734 +# Estimated pi_0=1 +Using motif +M05059_2.00 of width 8. +Using motif -M05059_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9804 +# Estimated pi_0=1 +Using motif +M00288_2.00 of width 8. +Using motif -M00288_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00289_2.00 of width 8. +Using motif -M00289_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00290_2.00 of width 8. +Using motif -M00290_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03127_2.00 of width 18. +Using motif -M03127_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03128_2.00 of width 8. +Using motif -M03128_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05060_2.00 of width 8. +Using motif -M05060_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05061_2.00 of width 8. +Using motif -M05061_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05062_2.00 of width 8. +Using motif -M05062_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05063_2.00 of width 8. +Using motif -M05063_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05064_2.00 of width 8. +Using motif -M05064_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05065_2.00 of width 8. +Using motif -M05065_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9944 +# Estimated pi_0=1 +Using motif +M05066_2.00 of width 8. +Using motif -M05066_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05067_2.00 of width 8. +Using motif -M05067_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05068_2.00 of width 8. +Using motif -M05068_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05069_2.00 of width 8. +Using motif -M05069_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05070_2.00 of width 8. +Using motif -M05070_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05071_2.00 of width 8. +Using motif -M05071_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03129_2.00 of width 10. +Using motif -M03129_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03130_2.00 of width 11. +Using motif -M03130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05072_2.00 of width 11. +Using motif -M05072_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05073_2.00 of width 11. +Using motif -M05073_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05074_2.00 of width 11. +Using motif -M05074_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03131_2.00 of width 11. +Using motif -M03131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03132_2.00 of width 11. +Using motif -M03132_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03133_2.00 of width 11. +Using motif -M03133_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03134_2.00 of width 11. +Using motif -M03134_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05075_2.00 of width 11. +Using motif -M05075_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05076_2.00 of width 11. +Using motif -M05076_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05077_2.00 of width 11. +Using motif -M05077_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05078_2.00 of width 11. +Using motif -M05078_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05079_2.00 of width 11. +Using motif -M05079_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05080_2.00 of width 11. +Using motif -M05080_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05081_2.00 of width 11. +Using motif -M05081_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M05082_2.00 of width 11. +Using motif -M05082_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03135_2.00 of width 9. +Using motif -M03135_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M03136_2.00 of width 11. +Using motif -M03136_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05083_2.00 of width 11. +Using motif -M05083_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05084_2.00 of width 11. +Using motif -M05084_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05085_2.00 of width 11. +Using motif -M05085_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05086_2.00 of width 11. +Using motif -M05086_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00291_2.00 of width 9. +Using motif -M00291_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00292_2.00 of width 9. +Using motif -M00292_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03137_2.00 of width 10. +Using motif -M03137_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03138_2.00 of width 10. +Using motif -M03138_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05087_2.00 of width 8. +Using motif -M05087_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05088_2.00 of width 8. +Using motif -M05088_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05089_2.00 of width 8. +Using motif -M05089_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05090_2.00 of width 8. +Using motif -M05090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05091_2.00 of width 8. +Using motif -M05091_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05092_2.00 of width 8. +Using motif -M05092_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05093_2.00 of width 8. +Using motif -M05093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00385_2.00 of width 8. +Using motif -M00385_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994525 +# Estimated pi_0=0.999698 +Using motif +M10681_2.00 of width 10. +Using motif -M10681_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05094_2.00 of width 10. +Using motif -M05094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9828 +# Estimated pi_0=1 +Using motif +M05095_2.00 of width 10. +Using motif -M05095_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9964 +# Estimated pi_0=1 +Using motif +M05096_2.00 of width 10. +Using motif -M05096_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973 +# Estimated pi_0=1 +Using motif +M05097_2.00 of width 10. +Using motif -M05097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9428 +# Estimated pi_0=0.94932 +Using motif +M08130_2.00 of width 11. +Using motif -M08130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M09142_2.00 of width 14. +Using motif -M09142_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05098_2.00 of width 8. +Using motif -M05098_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00132 +# Estimated pi_0=1 +Using motif +M05099_2.00 of width 8. +Using motif -M05099_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05100_2.00 of width 8. +Using motif -M05100_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05101_2.00 of width 8. +Using motif -M05101_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05102_2.00 of width 8. +Using motif -M05102_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05103_2.00 of width 8. +Using motif -M05103_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05104_2.00 of width 10. +Using motif -M05104_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05105_2.00 of width 10. +Using motif -M05105_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05106_2.00 of width 10. +Using motif -M05106_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05107_2.00 of width 10. +Using motif -M05107_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05108_2.00 of width 10. +Using motif -M05108_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05109_2.00 of width 10. +Using motif -M05109_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05110_2.00 of width 10. +Using motif -M05110_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05111_2.00 of width 10. +Using motif -M05111_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05112_2.00 of width 10. +Using motif -M05112_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05113_2.00 of width 10. +Using motif -M05113_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05114_2.00 of width 10. +Using motif -M05114_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05115_2.00 of width 10. +Using motif -M05115_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05116_2.00 of width 11. +Using motif -M05116_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05117_2.00 of width 11. +Using motif -M05117_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05118_2.00 of width 11. +Using motif -M05118_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05119_2.00 of width 11. +Using motif -M05119_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03139_2.00 of width 10. +Using motif -M03139_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03140_2.00 of width 10. +Using motif -M03140_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05120_2.00 of width 16. +Using motif -M05120_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05121_2.00 of width 16. +Using motif -M05121_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00293_2.00 of width 10. +Using motif -M00293_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00294_2.00 of width 8. +Using motif -M00294_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00295_2.00 of width 9. +Using motif -M00295_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M00296_2.00 of width 8. +Using motif -M00296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00297_2.00 of width 10. +Using motif -M00297_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M00298_2.00 of width 9. +Using motif -M00298_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00299_2.00 of width 9. +Using motif -M00299_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00300_2.00 of width 8. +Using motif -M00300_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03141_2.00 of width 10. +Using motif -M03141_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03142_2.00 of width 11. +Using motif -M03142_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05122_2.00 of width 11. +Using motif -M05122_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993455 +# Estimated pi_0=0.998763 +Using motif +M05123_2.00 of width 11. +Using motif -M05123_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05124_2.00 of width 11. +Using motif -M05124_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05125_2.00 of width 11. +Using motif -M05125_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03143_2.00 of width 10. +Using motif -M03143_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05126_2.00 of width 8. +Using motif -M05126_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05127_2.00 of width 8. +Using motif -M05127_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05128_2.00 of width 8. +Using motif -M05128_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05129_2.00 of width 11. +Using motif -M05129_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05130_2.00 of width 11. +Using motif -M05130_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05131_2.00 of width 11. +Using motif -M05131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03144_2.00 of width 17. +Using motif -M03144_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03145_2.00 of width 8. +Using motif -M03145_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00092 +# Estimated pi_0=1 +Using motif +M05133_2.00 of width 8. +Using motif -M05133_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03146_2.00 of width 10. +Using motif -M03146_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05134_2.00 of width 8. +Using motif -M05134_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05135_2.00 of width 8. +Using motif -M05135_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05136_2.00 of width 8. +Using motif -M05136_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M05137_2.00 of width 8. +Using motif -M05137_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M02740_2.00 of width 11. +Using motif -M02740_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953388 +# Estimated pi_0=0.958906 +Using motif +M02741_2.00 of width 8. +Using motif -M02741_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949 +# Estimated pi_0=0.958919 +Using motif +M03147_2.00 of width 14. +Using motif -M03147_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989076 +# Estimated pi_0=0.997665 +Using motif +M03148_2.00 of width 8. +Using motif -M03148_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972857 +# Estimated pi_0=0.978636 +Using motif +M05138_2.00 of width 12. +Using motif -M05138_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967455 +# Estimated pi_0=0.983375 +Using motif +M05139_2.00 of width 12. +Using motif -M05139_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929907 +# Estimated pi_0=0.942877 +Using motif +M05140_2.00 of width 12. +Using motif -M05140_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980645 +# Estimated pi_0=0.986452 +Using motif +M05141_2.00 of width 12. +Using motif -M05141_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967009 +# Estimated pi_0=0.983312 +Using motif +M09143_2.00 of width 13. +Using motif -M09143_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991465 +# Estimated pi_0=0.99641 +Using motif +M03149_2.00 of width 10. +Using motif -M03149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05142_2.00 of width 8. +Using motif -M05142_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05143_2.00 of width 8. +Using motif -M05143_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05144_2.00 of width 8. +Using motif -M05144_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03150_2.00 of width 15. +Using motif -M03150_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09144_2.00 of width 15. +Using motif -M09144_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M10690_2.00 of width 15. +Using motif -M10690_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M10693_2.00 of width 17. +Using motif -M10693_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M02088_2.00 of width 10. +Using motif -M02088_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03151_2.00 of width 10. +Using motif -M03151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03152_2.00 of width 14. +Using motif -M03152_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05145_2.00 of width 8. +Using motif -M05145_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05146_2.00 of width 8. +Using motif -M05146_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05147_2.00 of width 8. +Using motif -M05147_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05148_2.00 of width 8. +Using motif -M05148_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00301_2.00 of width 8. +Using motif -M00301_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996458 +# Estimated pi_0=0.998794 +Using motif +M00302_2.00 of width 8. +Using motif -M00302_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993333 +# Estimated pi_0=0.996633 +Using motif +M05149_2.00 of width 10. +Using motif -M05149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981852 +# Estimated pi_0=0.993107 +Using motif +M05150_2.00 of width 14. +Using motif -M05150_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9926 +# Estimated pi_0=1 +Using motif +M05151_2.00 of width 10. +Using motif -M05151_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981964 +# Estimated pi_0=0.99949 +Using motif +M05152_2.00 of width 9. +Using motif -M05152_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9364 +# Estimated pi_0=0.945524 +Using motif +M09145_2.00 of width 9. +Using motif -M09145_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986667 +# Estimated pi_0=0.99898 +Using motif +M09146_2.00 of width 10. +Using motif -M09146_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963125 +# Estimated pi_0=0.975847 +Using motif +M02089_2.00 of width 10. +Using motif -M02089_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00485_2.00 of width 7. +Using motif -M00485_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03153_2.00 of width 8. +Using motif -M03153_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03154_2.00 of width 8. +Using motif -M03154_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05153_2.00 of width 11. +Using motif -M05153_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05154_2.00 of width 11. +Using motif -M05154_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05155_2.00 of width 8. +Using motif -M05155_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05156_2.00 of width 11. +Using motif -M05156_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05157_2.00 of width 8. +Using motif -M05157_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00382 +# Estimated pi_0=1 +Using motif +M05158_2.00 of width 11. +Using motif -M05158_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05159_2.00 of width 10. +Using motif -M05159_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0026 +# Estimated pi_0=1 +Using motif +M05160_2.00 of width 10. +Using motif -M05160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9716 +# Estimated pi_0=0.990413 +Using motif +M02090_2.00 of width 8. +Using motif -M02090_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03155_2.00 of width 18. +Using motif -M03155_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03156_2.00 of width 8. +Using motif -M03156_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05161_2.00 of width 8. +Using motif -M05161_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05162_2.00 of width 8. +Using motif -M05162_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05163_2.00 of width 8. +Using motif -M05163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M09147_2.00 of width 9. +Using motif -M09147_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03157_2.00 of width 10. +Using motif -M03157_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03158_2.00 of width 10. +Using motif -M03158_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03159_2.00 of width 16. +Using motif -M03159_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03160_2.00 of width 10. +Using motif -M03160_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03161_2.00 of width 10. +Using motif -M03161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03162_2.00 of width 16. +Using motif -M03162_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05164_2.00 of width 9. +Using motif -M05164_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05165_2.00 of width 9. +Using motif -M05165_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05166_2.00 of width 9. +Using motif -M05166_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05167_2.00 of width 8. +Using motif -M05167_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05168_2.00 of width 8. +Using motif -M05168_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05169_2.00 of width 8. +Using motif -M05169_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M01505_2.00 of width 9. +Using motif -M01505_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M01506_2.00 of width 9. +Using motif -M01506_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03163_2.00 of width 8. +Using motif -M03163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03164_2.00 of width 13. +Using motif -M03164_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05170_2.00 of width 8. +Using motif -M05170_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05171_2.00 of width 8. +Using motif -M05171_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05172_2.00 of width 8. +Using motif -M05172_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05173_2.00 of width 8. +Using motif -M05173_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05174_2.00 of width 8. +Using motif -M05174_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05175_2.00 of width 8. +Using motif -M05175_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M03165_2.00 of width 7. +Using motif -M03165_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977692 +# Estimated pi_0=0.983077 +Using motif +M09148_2.00 of width 12. +Using motif -M09148_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M10709_2.00 of width 12. +Using motif -M10709_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973108 +# Estimated pi_0=0.981547 +Using motif +M03166_2.00 of width 10. +Using motif -M03166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05176_2.00 of width 8. +Using motif -M05176_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05177_2.00 of width 8. +Using motif -M05177_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05178_2.00 of width 8. +Using motif -M05178_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05179_2.00 of width 8. +Using motif -M05179_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03167_2.00 of width 10. +Using motif -M03167_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05180_2.00 of width 9. +Using motif -M05180_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9986 +# Estimated pi_0=1 +Using motif +M05181_2.00 of width 9. +Using motif -M05181_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03168_2.00 of width 8. +Using motif -M03168_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05182_2.00 of width 8. +Using motif -M05182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05183_2.00 of width 8. +Using motif -M05183_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05184_2.00 of width 8. +Using motif -M05184_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05185_2.00 of width 8. +Using motif -M05185_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05186_2.00 of width 8. +Using motif -M05186_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05187_2.00 of width 8. +Using motif -M05187_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M00303_2.00 of width 11. +Using motif -M00303_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00304_2.00 of width 9. +Using motif -M00304_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M00305_2.00 of width 8. +Using motif -M00305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M02091_2.00 of width 8. +Using motif -M02091_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03169_2.00 of width 9. +Using motif -M03169_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03170_2.00 of width 21. +Using motif -M03170_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05188_2.00 of width 9. +Using motif -M05188_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99604 +# Estimated pi_0=1 +Using motif +M05189_2.00 of width 10. +Using motif -M05189_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05190_2.00 of width 10. +Using motif -M05190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M02184_2.00 of width 10. +Using motif -M02184_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9536 +# Estimated pi_0=0.958235 +Using motif +M03171_2.00 of width 12. +Using motif -M03171_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982047 +# Estimated pi_0=0.991489 +Using motif +M05191_2.00 of width 12. +Using motif -M05191_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969714 +# Estimated pi_0=0.976234 +Using motif +M05192_2.00 of width 12. +Using motif -M05192_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967723 +# Estimated pi_0=0.978207 +Using motif +M05193_2.00 of width 12. +Using motif -M05193_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9494 +# Estimated pi_0=0.958779 +Using motif +M05194_2.00 of width 12. +Using motif -M05194_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964228 +# Estimated pi_0=0.968092 +Using motif +M03172_2.00 of width 10. +Using motif -M03172_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03173_2.00 of width 10. +Using motif -M03173_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03174_2.00 of width 13. +Using motif -M03174_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05195_2.00 of width 8. +Using motif -M05195_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05196_2.00 of width 8. +Using motif -M05196_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05197_2.00 of width 8. +Using motif -M05197_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05198_2.00 of width 8. +Using motif -M05198_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05199_2.00 of width 8. +Using motif -M05199_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05200_2.00 of width 8. +Using motif -M05200_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M02092_2.00 of width 10. +Using motif -M02092_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03175_2.00 of width 10. +Using motif -M03175_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03176_2.00 of width 11. +Using motif -M03176_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05201_2.00 of width 11. +Using motif -M05201_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05202_2.00 of width 11. +Using motif -M05202_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05203_2.00 of width 11. +Using motif -M05203_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09149_2.00 of width 10. +Using motif -M09149_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999598 +# Estimated pi_0=0.999598 +Using motif +M06305_2.00 of width 8. +Using motif -M06305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99665 +# Estimated pi_0=0.997475 +Using motif +M03177_2.00 of width 8. +Using motif -M03177_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05204_2.00 of width 8. +Using motif -M05204_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05205_2.00 of width 8. +Using motif -M05205_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03178_2.00 of width 12. +Using motif -M03178_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987066 +# Estimated pi_0=0.991895 +Using motif +M05206_2.00 of width 12. +Using motif -M05206_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958378 +# Estimated pi_0=0.967891 +Using motif +M05207_2.00 of width 12. +Using motif -M05207_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945882 +# Estimated pi_0=0.956788 +Using motif +M05208_2.00 of width 12. +Using motif -M05208_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977009 +# Estimated pi_0=0.989462 +Using motif +M05209_2.00 of width 12. +Using motif -M05209_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9682 +# Estimated pi_0=0.974087 +Using motif +M09150_2.00 of width 11. +Using motif -M09150_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946857 +# Estimated pi_0=0.954615 +Using motif +M05210_2.00 of width 8. +Using motif -M05210_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05211_2.00 of width 12. +Using motif -M05211_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05212_2.00 of width 8. +Using motif -M05212_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05213_2.00 of width 8. +Using motif -M05213_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05214_2.00 of width 12. +Using motif -M05214_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03179_2.00 of width 8. +Using motif -M03179_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05215_2.00 of width 8. +Using motif -M05215_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05216_2.00 of width 8. +Using motif -M05216_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05217_2.00 of width 8. +Using motif -M05217_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03180_2.00 of width 10. +Using motif -M03180_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03181_2.00 of width 14. +Using motif -M03181_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03182_2.00 of width 8. +Using motif -M03182_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03183_2.00 of width 14. +Using motif -M03183_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05218_2.00 of width 8. +Using motif -M05218_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05219_2.00 of width 8. +Using motif -M05219_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05220_2.00 of width 8. +Using motif -M05220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05871_2.00 of width 8. +Using motif -M05871_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M10714_2.00 of width 7. +Using motif -M10714_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03184_2.00 of width 18. +Using motif -M03184_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03185_2.00 of width 8. +Using motif -M03185_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03186_2.00 of width 8. +Using motif -M03186_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05221_2.00 of width 8. +Using motif -M05221_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05222_2.00 of width 8. +Using motif -M05222_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05223_2.00 of width 8. +Using motif -M05223_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05224_2.00 of width 8. +Using motif -M05224_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05225_2.00 of width 8. +Using motif -M05225_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05226_2.00 of width 8. +Using motif -M05226_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M10715_2.00 of width 9. +Using motif -M10715_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09151_2.00 of width 19. +Using motif -M09151_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M10718_2.00 of width 13. +Using motif -M10718_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M00306_2.00 of width 8. +Using motif -M00306_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00307_2.00 of width 8. +Using motif -M00307_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00308_2.00 of width 9. +Using motif -M00308_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00309_2.00 of width 9. +Using motif -M00309_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03187_2.00 of width 10. +Using motif -M03187_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03188_2.00 of width 15. +Using motif -M03188_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05227_2.00 of width 8. +Using motif -M05227_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05228_2.00 of width 8. +Using motif -M05228_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00310_2.00 of width 8. +Using motif -M00310_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00311_2.00 of width 8. +Using motif -M00311_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00312_2.00 of width 13. +Using motif -M00312_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00313_2.00 of width 8. +Using motif -M00313_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00314_2.00 of width 8. +Using motif -M00314_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05229_2.00 of width 8. +Using motif -M05229_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05230_2.00 of width 8. +Using motif -M05230_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05231_2.00 of width 8. +Using motif -M05231_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05232_2.00 of width 8. +Using motif -M05232_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05233_2.00 of width 25. +Using motif -M05233_2.00 of width 25. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05234_2.00 of width 23. +Using motif -M05234_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05235_2.00 of width 22. +Using motif -M05235_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05236_2.00 of width 21. +Using motif -M05236_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03189_2.00 of width 10. +Using motif -M03189_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03190_2.00 of width 10. +Using motif -M03190_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05237_2.00 of width 8. +Using motif -M05237_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05238_2.00 of width 9. +Using motif -M05238_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05239_2.00 of width 8. +Using motif -M05239_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03191_2.00 of width 13. +Using motif -M03191_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03192_2.00 of width 8. +Using motif -M03192_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05240_2.00 of width 8. +Using motif -M05240_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05241_2.00 of width 8. +Using motif -M05241_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983407 +# Estimated pi_0=0.988933 +Using motif +M03193_2.00 of width 10. +Using motif -M03193_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05242_2.00 of width 9. +Using motif -M05242_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05243_2.00 of width 9. +Using motif -M05243_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M02093_2.00 of width 8. +Using motif -M02093_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M01246_2.00 of width 7. +Using motif -M01246_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99914 +# Estimated pi_0=1 +Using motif +M03194_2.00 of width 11. +Using motif -M03194_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05244_2.00 of width 8. +Using motif -M05244_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05245_2.00 of width 8. +Using motif -M05245_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03195_2.00 of width 12. +Using motif -M03195_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965191 +# Estimated pi_0=0.976988 +Using motif +M05246_2.00 of width 12. +Using motif -M05246_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9672 +# Estimated pi_0=0.980593 +Using motif +M05247_2.00 of width 12. +Using motif -M05247_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9376 +# Estimated pi_0=0.942804 +Using motif +M03196_2.00 of width 9. +Using motif -M03196_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05248_2.00 of width 10. +Using motif -M05248_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05249_2.00 of width 9. +Using motif -M05249_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05250_2.00 of width 12. +Using motif -M05250_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00392 +# Estimated pi_0=1 +Using motif +M08131_2.00 of width 11. +Using motif -M08131_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00061 +# Estimated pi_0=1 +Using motif +M09152_2.00 of width 12. +Using motif -M09152_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03197_2.00 of width 15. +Using motif -M03197_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03198_2.00 of width 8. +Using motif -M03198_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05251_2.00 of width 8. +Using motif -M05251_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05252_2.00 of width 8. +Using motif -M05252_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M09153_2.00 of width 11. +Using motif -M09153_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991145 +# Estimated pi_0=1 +Using motif +M03199_2.00 of width 13. +Using motif -M03199_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05253_2.00 of width 8. +Using motif -M05253_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05254_2.00 of width 8. +Using motif -M05254_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05255_2.00 of width 10. +Using motif -M05255_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990877 +# Estimated pi_0=0.995233 +Using motif +M05256_2.00 of width 10. +Using motif -M05256_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998351 +# Estimated pi_0=0.999598 +Using motif +M05257_2.00 of width 8. +Using motif -M05257_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998824 +# Estimated pi_0=1 +Using motif +M09154_2.00 of width 12. +Using motif -M09154_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998564 +# Estimated pi_0=0.999397 +Using motif +M09559_2.00 of width 10. +Using motif -M09559_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992994 +# Estimated pi_0=0.997665 +Using motif +M10730_2.00 of width 12. +Using motif -M10730_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M07971_2.00 of width 15. +Using motif -M07971_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935146 +# Estimated pi_0=0.940177 +Using motif +M08132_2.00 of width 17. +Using motif -M08132_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945 +# Estimated pi_0=0.965629 +Using motif +M09155_2.00 of width 11. +Using motif -M09155_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9296 +# Estimated pi_0=0.935556 +Using motif +M09560_2.00 of width 12. +Using motif -M09560_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975128 +# Estimated pi_0=0.983261 +Using motif +M03200_2.00 of width 8. +Using motif -M03200_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05258_2.00 of width 8. +Using motif -M05258_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954 +# Estimated pi_0=0.961887 +Using motif +M05259_2.00 of width 8. +Using motif -M05259_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05260_2.00 of width 8. +Using motif -M05260_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99578 +# Estimated pi_0=1 +Using motif +M05261_2.00 of width 8. +Using motif -M05261_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99619 +# Estimated pi_0=1 +Using motif +M05262_2.00 of width 8. +Using motif -M05262_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05263_2.00 of width 8. +Using motif -M05263_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05264_2.00 of width 8. +Using motif -M05264_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M09156_2.00 of width 7. +Using motif -M09156_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03201_2.00 of width 10. +Using motif -M03201_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03202_2.00 of width 14. +Using motif -M03202_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03203_2.00 of width 10. +Using motif -M03203_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05265_2.00 of width 8. +Using motif -M05265_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05266_2.00 of width 8. +Using motif -M05266_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05267_2.00 of width 8. +Using motif -M05267_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05268_2.00 of width 8. +Using motif -M05268_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05269_2.00 of width 8. +Using motif -M05269_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05270_2.00 of width 8. +Using motif -M05270_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00372 +# Estimated pi_0=1 +Using motif +M03204_2.00 of width 8. +Using motif -M03204_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03205_2.00 of width 10. +Using motif -M03205_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05271_2.00 of width 8. +Using motif -M05271_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05272_2.00 of width 8. +Using motif -M05272_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05273_2.00 of width 8. +Using motif -M05273_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05274_2.00 of width 8. +Using motif -M05274_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05275_2.00 of width 8. +Using motif -M05275_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05276_2.00 of width 8. +Using motif -M05276_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05277_2.00 of width 8. +Using motif -M05277_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05278_2.00 of width 8. +Using motif -M05278_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03206_2.00 of width 9. +Using motif -M03206_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03207_2.00 of width 11. +Using motif -M03207_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03208_2.00 of width 11. +Using motif -M03208_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03209_2.00 of width 9. +Using motif -M03209_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05279_2.00 of width 10. +Using motif -M05279_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05280_2.00 of width 10. +Using motif -M05280_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03210_2.00 of width 10. +Using motif -M03210_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03211_2.00 of width 14. +Using motif -M03211_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05281_2.00 of width 8. +Using motif -M05281_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05282_2.00 of width 8. +Using motif -M05282_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05283_2.00 of width 18. +Using motif -M05283_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05284_2.00 of width 18. +Using motif -M05284_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.929 +# Estimated pi_0=0.929 +Using motif +M03212_2.00 of width 12. +Using motif -M03212_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05285_2.00 of width 10. +Using motif -M05285_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92835 +# Estimated pi_0=0.936937 +Using motif +M05286_2.00 of width 10. +Using motif -M05286_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.938857 +Using motif +M05287_2.00 of width 10. +Using motif -M05287_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91703 +# Estimated pi_0=0.918252 +Using motif +M05288_2.00 of width 10. +Using motif -M05288_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9414 +# Estimated pi_0=0.955327 +Using motif +M08133_2.00 of width 16. +Using motif -M08133_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09157_2.00 of width 13. +Using motif -M09157_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05289_2.00 of width 10. +Using motif -M05289_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05290_2.00 of width 10. +Using motif -M05290_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05291_2.00 of width 10. +Using motif -M05291_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9914 +# Estimated pi_0=1 +Using motif +M00417_2.00 of width 8. +Using motif -M00417_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M00422_2.00 of width 7. +Using motif -M00422_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03213_2.00 of width 10. +Using motif -M03213_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05292_2.00 of width 8. +Using motif -M05292_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05293_2.00 of width 8. +Using motif -M05293_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05294_2.00 of width 8. +Using motif -M05294_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03214_2.00 of width 8. +Using motif -M03214_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03215_2.00 of width 10. +Using motif -M03215_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05295_2.00 of width 8. +Using motif -M05295_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05296_2.00 of width 8. +Using motif -M05296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05297_2.00 of width 8. +Using motif -M05297_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09158_2.00 of width 13. +Using motif -M09158_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M00315_2.00 of width 8. +Using motif -M00315_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9938 +# Estimated pi_0=1 +Using motif +M00316_2.00 of width 8. +Using motif -M00316_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00317_2.00 of width 10. +Using motif -M00317_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03216_2.00 of width 11. +Using motif -M03216_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03217_2.00 of width 11. +Using motif -M03217_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05298_2.00 of width 11. +Using motif -M05298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05299_2.00 of width 11. +Using motif -M05299_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00242 +# Estimated pi_0=1 +Using motif +M05300_2.00 of width 11. +Using motif -M05300_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05301_2.00 of width 11. +Using motif -M05301_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05302_2.00 of width 11. +Using motif -M05302_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05303_2.00 of width 11. +Using motif -M05303_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M00318_2.00 of width 8. +Using motif -M00318_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00319_2.00 of width 8. +Using motif -M00319_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03218_2.00 of width 13. +Using motif -M03218_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03219_2.00 of width 8. +Using motif -M03219_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M03220_2.00 of width 8. +Using motif -M03220_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05304_2.00 of width 8. +Using motif -M05304_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05305_2.00 of width 8. +Using motif -M05305_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05306_2.00 of width 8. +Using motif -M05306_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05307_2.00 of width 8. +Using motif -M05307_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05308_2.00 of width 8. +Using motif -M05308_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M05309_2.00 of width 8. +Using motif -M05309_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M03221_2.00 of width 10. +Using motif -M03221_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05310_2.00 of width 8. +Using motif -M05310_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05311_2.00 of width 8. +Using motif -M05311_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05312_2.00 of width 8. +Using motif -M05312_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05313_2.00 of width 12. +Using motif -M05313_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.969589 +Using motif +M05314_2.00 of width 12. +Using motif -M05314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964752 +# Estimated pi_0=0.97988 +Using motif +M03222_2.00 of width 12. +Using motif -M03222_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05315_2.00 of width 15. +Using motif -M05315_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05316_2.00 of width 15. +Using motif -M05316_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08208_2.00 of width 10. +Using motif -M08208_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M08209_2.00 of width 8. +Using motif -M08209_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965891 +# Estimated pi_0=0.971379 +Using motif +M03223_2.00 of width 12. +Using motif -M03223_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982276 +# Estimated pi_0=0.986738 +Using motif +M05317_2.00 of width 12. +Using motif -M05317_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988718 +# Estimated pi_0=0.996 +Using motif +M05318_2.00 of width 12. +Using motif -M05318_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9916 +# Estimated pi_0=0.999394 +Using motif +M09159_2.00 of width 7. +Using motif -M09159_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952597 +# Estimated pi_0=0.961697 +Using motif +M10740_2.00 of width 11. +Using motif -M10740_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996477 +# Estimated pi_0=0.998693 +Using motif +M05319_2.00 of width 18. +Using motif -M05319_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05320_2.00 of width 18. +Using motif -M05320_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M00271_2.00 of width 7. +Using motif -M00271_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03224_2.00 of width 13. +Using motif -M03224_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9662 +# Estimated pi_0=0.975413 +Using motif +M03225_2.00 of width 10. +Using motif -M03225_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05321_2.00 of width 8. +Using motif -M05321_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05322_2.00 of width 12. +Using motif -M05322_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05323_2.00 of width 10. +Using motif -M05323_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05324_2.00 of width 8. +Using motif -M05324_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05325_2.00 of width 12. +Using motif -M05325_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05326_2.00 of width 10. +Using motif -M05326_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00496_2.00 of width 10. +Using motif -M00496_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M03226_2.00 of width 13. +Using motif -M03226_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05327_2.00 of width 11. +Using motif -M05327_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05328_2.00 of width 11. +Using motif -M05328_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M09160_2.00 of width 12. +Using motif -M09160_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M10741_2.00 of width 18. +Using motif -M10741_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03227_2.00 of width 10. +Using motif -M03227_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05329_2.00 of width 8. +Using motif -M05329_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05330_2.00 of width 8. +Using motif -M05330_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05331_2.00 of width 10. +Using motif -M05331_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05332_2.00 of width 10. +Using motif -M05332_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05333_2.00 of width 10. +Using motif -M05333_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05334_2.00 of width 10. +Using motif -M05334_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09161_2.00 of width 10. +Using motif -M09161_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03228_2.00 of width 10. +Using motif -M03228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03229_2.00 of width 10. +Using motif -M03229_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03230_2.00 of width 10. +Using motif -M03230_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05335_2.00 of width 11. +Using motif -M05335_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05336_2.00 of width 11. +Using motif -M05336_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05337_2.00 of width 11. +Using motif -M05337_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05338_2.00 of width 11. +Using motif -M05338_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05339_2.00 of width 11. +Using motif -M05339_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05340_2.00 of width 11. +Using motif -M05340_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05341_2.00 of width 11. +Using motif -M05341_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M05342_2.00 of width 11. +Using motif -M05342_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05343_2.00 of width 8. +Using motif -M05343_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05344_2.00 of width 8. +Using motif -M05344_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05345_2.00 of width 8. +Using motif -M05345_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M09162_2.00 of width 8. +Using motif -M09162_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M00320_2.00 of width 9. +Using motif -M00320_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M00321_2.00 of width 10. +Using motif -M00321_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M00322_2.00 of width 7. +Using motif -M00322_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966443 +# Estimated pi_0=0.973789 +Using motif +M00323_2.00 of width 11. +Using motif -M00323_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999568 +# Estimated pi_0=1 +Using motif +M00324_2.00 of width 7. +Using motif -M00324_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986195 +# Estimated pi_0=1 +Using motif +M01507_2.00 of width 7. +Using motif -M01507_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M01508_2.00 of width 7. +Using motif -M01508_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998542 +# Estimated pi_0=1 +Using motif +M05346_2.00 of width 10. +Using motif -M05346_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994 +# Estimated pi_0=0.996429 +Using motif +M05347_2.00 of width 10. +Using motif -M05347_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979811 +# Estimated pi_0=0.991538 +Using motif +M05348_2.00 of width 9. +Using motif -M05348_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975769 +# Estimated pi_0=1 +Using motif +M09163_2.00 of width 8. +Using motif -M09163_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977159 +# Estimated pi_0=0.980645 +Using motif +M10742_2.00 of width 7. +Using motif -M10742_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M10743_2.00 of width 8. +Using motif -M10743_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M02094_2.00 of width 10. +Using motif -M02094_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05349_2.00 of width 19. +Using motif -M05349_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05350_2.00 of width 14. +Using motif -M05350_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05351_2.00 of width 10. +Using motif -M05351_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05352_2.00 of width 13. +Using motif -M05352_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05353_2.00 of width 14. +Using motif -M05353_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05354_2.00 of width 10. +Using motif -M05354_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05355_2.00 of width 14. +Using motif -M05355_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05356_2.00 of width 16. +Using motif -M05356_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M10746_2.00 of width 11. +Using motif -M10746_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00325_2.00 of width 8. +Using motif -M00325_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00326_2.00 of width 7. +Using motif -M00326_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M00327_2.00 of width 9. +Using motif -M00327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9912 +# Estimated pi_0=1 +Using motif +M05357_2.00 of width 10. +Using motif -M05357_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9514 +# Estimated pi_0=0.963519 +Using motif +M05358_2.00 of width 10. +Using motif -M05358_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8772 +# Estimated pi_0=0.886606 +Using motif +M03231_2.00 of width 10. +Using motif -M03231_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05359_2.00 of width 11. +Using motif -M05359_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05360_2.00 of width 11. +Using motif -M05360_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05361_2.00 of width 11. +Using motif -M05361_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00499_2.00 of width 7. +Using motif -M00499_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M02685_2.00 of width 12. +Using motif -M02685_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05362_2.00 of width 10. +Using motif -M05362_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997917 +# Estimated pi_0=1 +Using motif +M05363_2.00 of width 10. +Using motif -M05363_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9452 +# Estimated pi_0=0.9452 +Using motif +M05364_2.00 of width 10. +Using motif -M05364_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981391 +# Estimated pi_0=1 +Using motif +M05365_2.00 of width 10. +Using motif -M05365_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996567 +# Estimated pi_0=1 +Using motif +M09164_2.00 of width 10. +Using motif -M09164_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958318 +# Estimated pi_0=0.966115 +Using motif +M09561_2.00 of width 12. +Using motif -M09561_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963333 +# Estimated pi_0=0.969349 +Using motif +M10748_2.00 of width 9. +Using motif -M10748_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M10749_2.00 of width 15. +Using motif -M10749_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M02095_2.00 of width 8. +Using motif -M02095_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03232_2.00 of width 8. +Using motif -M03232_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05366_2.00 of width 8. +Using motif -M05366_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05367_2.00 of width 8. +Using motif -M05367_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05368_2.00 of width 8. +Using motif -M05368_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05369_2.00 of width 10. +Using motif -M05369_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05370_2.00 of width 13. +Using motif -M05370_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05371_2.00 of width 10. +Using motif -M05371_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05372_2.00 of width 13. +Using motif -M05372_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03233_2.00 of width 11. +Using motif -M03233_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M10754_2.00 of width 10. +Using motif -M10754_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963281 +# Estimated pi_0=0.965429 +Using motif +M03234_2.00 of width 11. +Using motif -M03234_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05373_2.00 of width 9. +Using motif -M05373_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9676 +# Estimated pi_0=0.984122 +Using motif +M05374_2.00 of width 9. +Using motif -M05374_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05375_2.00 of width 10. +Using motif -M05375_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05376_2.00 of width 10. +Using motif -M05376_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03235_2.00 of width 8. +Using motif -M03235_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05377_2.00 of width 8. +Using motif -M05377_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9406 +# Estimated pi_0=0.950577 +Using motif +M05378_2.00 of width 8. +Using motif -M05378_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05379_2.00 of width 8. +Using motif -M05379_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05380_2.00 of width 8. +Using motif -M05380_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05381_2.00 of width 8. +Using motif -M05381_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05382_2.00 of width 8. +Using motif -M05382_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03236_2.00 of width 10. +Using motif -M03236_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05383_2.00 of width 8. +Using motif -M05383_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9768 +# Estimated pi_0=1 +Using motif +M05384_2.00 of width 8. +Using motif -M05384_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00392_2.00 of width 8. +Using motif -M00392_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00328_2.00 of width 8. +Using motif -M00328_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00329_2.00 of width 9. +Using motif -M00329_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00330_2.00 of width 8. +Using motif -M00330_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05385_2.00 of width 8. +Using motif -M05385_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05386_2.00 of width 8. +Using motif -M05386_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05387_2.00 of width 8. +Using motif -M05387_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00362 +# Estimated pi_0=1 +Using motif +M05132_2.00 of width 8. +Using motif -M05132_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M08134_2.00 of width 12. +Using motif -M08134_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993642 +# Estimated pi_0=0.996531 +Using motif +M09165_2.00 of width 15. +Using motif -M09165_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03237_2.00 of width 10. +Using motif -M03237_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03238_2.00 of width 11. +Using motif -M03238_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9876 +# Estimated pi_0=1 +Using motif +M05388_2.00 of width 10. +Using motif -M05388_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05389_2.00 of width 10. +Using motif -M05389_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M01509_2.00 of width 7. +Using motif -M01509_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M01510_2.00 of width 7. +Using motif -M01510_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03239_2.00 of width 9. +Using motif -M03239_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03240_2.00 of width 12. +Using motif -M03240_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03241_2.00 of width 10. +Using motif -M03241_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05390_2.00 of width 8. +Using motif -M05390_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05391_2.00 of width 8. +Using motif -M05391_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03242_2.00 of width 12. +Using motif -M03242_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00182 +# Estimated pi_0=1 +Using motif +M03243_2.00 of width 11. +Using motif -M03243_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05392_2.00 of width 9. +Using motif -M05392_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05393_2.00 of width 9. +Using motif -M05393_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M02096_2.00 of width 8. +Using motif -M02096_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M00503_2.00 of width 9. +Using motif -M00503_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00442_2.00 of width 10. +Using motif -M00442_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05394_2.00 of width 11. +Using motif -M05394_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05395_2.00 of width 11. +Using motif -M05395_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05396_2.00 of width 11. +Using motif -M05396_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00392 +# Estimated pi_0=1 +Using motif +M05397_2.00 of width 11. +Using motif -M05397_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05398_2.00 of width 11. +Using motif -M05398_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05399_2.00 of width 11. +Using motif -M05399_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05400_2.00 of width 11. +Using motif -M05400_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05401_2.00 of width 11. +Using motif -M05401_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05015_2.00 of width 8. +Using motif -M05015_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9982 +# Estimated pi_0=1 +Using motif +M01247_2.00 of width 9. +Using motif -M01247_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03244_2.00 of width 13. +Using motif -M03244_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05402_2.00 of width 11. +Using motif -M05402_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M05403_2.00 of width 11. +Using motif -M05403_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M00331_2.00 of width 7. +Using motif -M00331_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M00332_2.00 of width 10. +Using motif -M00332_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M00333_2.00 of width 10. +Using motif -M00333_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00334_2.00 of width 7. +Using motif -M00334_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M05404_2.00 of width 8. +Using motif -M05404_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05405_2.00 of width 8. +Using motif -M05405_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05406_2.00 of width 8. +Using motif -M05406_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05407_2.00 of width 10. +Using motif -M05407_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05408_2.00 of width 10. +Using motif -M05408_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05409_2.00 of width 8. +Using motif -M05409_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09166_2.00 of width 10. +Using motif -M09166_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M08135_2.00 of width 11. +Using motif -M08135_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M09167_2.00 of width 11. +Using motif -M09167_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05410_2.00 of width 8. +Using motif -M05410_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05411_2.00 of width 8. +Using motif -M05411_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9882 +# Estimated pi_0=1 +Using motif +M02097_2.00 of width 10. +Using motif -M02097_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03245_2.00 of width 13. +Using motif -M03245_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03246_2.00 of width 15. +Using motif -M03246_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M09168_2.00 of width 15. +Using motif -M09168_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M00335_2.00 of width 8. +Using motif -M00335_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8794 +# Estimated pi_0=0.887477 +Using motif +M00336_2.00 of width 8. +Using motif -M00336_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949764 +# Estimated pi_0=0.953333 +Using motif +M00337_2.00 of width 11. +Using motif -M00337_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00342 +# Estimated pi_0=1 +Using motif +M00338_2.00 of width 8. +Using motif -M00338_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964314 +# Estimated pi_0=0.972987 +Using motif +M00339_2.00 of width 13. +Using motif -M00339_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8442 +# Estimated pi_0=0.8442 +Using motif +M00340_2.00 of width 7. +Using motif -M00340_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9468 +# Estimated pi_0=0.956226 +Using motif +M00341_2.00 of width 10. +Using motif -M00341_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.84396 +# Estimated pi_0=0.857586 +Using motif +M00342_2.00 of width 14. +Using motif -M00342_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00343_2.00 of width 13. +Using motif -M00343_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8282 +# Estimated pi_0=0.837143 +Using motif +M00344_2.00 of width 12. +Using motif -M00344_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9074 +# Estimated pi_0=0.917692 +Using motif +M02689_2.00 of width 14. +Using motif -M02689_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03291_2.00 of width 19. +Using motif -M03291_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989787 +# Estimated pi_0=0.999188 +Using motif +M05412_2.00 of width 17. +Using motif -M05412_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9144 +# Estimated pi_0=0.920374 +Using motif +M05413_2.00 of width 17. +Using motif -M05413_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92396 +# Estimated pi_0=0.928108 +Using motif +M05414_2.00 of width 17. +Using motif -M05414_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.889109 +# Estimated pi_0=0.89283 +Using motif +M05415_2.00 of width 17. +Using motif -M05415_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9358 +# Estimated pi_0=0.937624 +Using motif +M09216_2.00 of width 12. +Using motif -M09216_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988829 +# Estimated pi_0=0.997879 +Using motif +M10806_2.00 of width 21. +Using motif -M10806_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M00345_2.00 of width 10. +Using motif -M00345_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8946 +# Estimated pi_0=0.902667 +Using motif +M00346_2.00 of width 10. +Using motif -M00346_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9678 +# Estimated pi_0=0.979115 +Using motif +M03292_2.00 of width 10. +Using motif -M03292_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03293_2.00 of width 10. +Using motif -M03293_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05416_2.00 of width 15. +Using motif -M05416_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8914 +# Estimated pi_0=0.896699 +Using motif +M05417_2.00 of width 16. +Using motif -M05417_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9686 +# Estimated pi_0=0.97719 +Using motif +M05418_2.00 of width 12. +Using motif -M05418_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05419_2.00 of width 15. +Using motif -M05419_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.972963 +Using motif +M05420_2.00 of width 16. +Using motif -M05420_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00125 +# Estimated pi_0=1 +Using motif +M05421_2.00 of width 12. +Using motif -M05421_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05422_2.00 of width 15. +Using motif -M05422_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9404 +# Estimated pi_0=0.959231 +Using motif +M05423_2.00 of width 16. +Using motif -M05423_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976762 +# Estimated pi_0=1 +Using motif +M05424_2.00 of width 12. +Using motif -M05424_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00312 +# Estimated pi_0=1 +Using motif +M05425_2.00 of width 15. +Using motif -M05425_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.967227 +Using motif +M05426_2.00 of width 16. +Using motif -M05426_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9914 +# Estimated pi_0=1 +Using motif +M05427_2.00 of width 12. +Using motif -M05427_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05874_2.00 of width 13. +Using motif -M05874_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03294_2.00 of width 18. +Using motif -M03294_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9222 +# Estimated pi_0=0.939344 +Using motif +M05428_2.00 of width 20. +Using motif -M05428_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.881 +# Estimated pi_0=0.889623 +Using motif +M05429_2.00 of width 20. +Using motif -M05429_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9274 +# Estimated pi_0=0.941695 +Using motif +M10808_2.00 of width 19. +Using motif -M10808_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00079 +# Estimated pi_0=1 +Using motif +M10809_2.00 of width 9. +Using motif -M10809_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998593 +# Estimated pi_0=0.998593 +Using motif +M00347_2.00 of width 7. +Using motif -M00347_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M00348_2.00 of width 8. +Using motif -M00348_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00349_2.00 of width 9. +Using motif -M00349_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M00350_2.00 of width 8. +Using motif -M00350_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M00351_2.00 of width 8. +Using motif -M00351_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03295_2.00 of width 8. +Using motif -M03295_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M03296_2.00 of width 8. +Using motif -M03296_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M03883_2.00 of width 15. +Using motif -M03883_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9422 +# Estimated pi_0=0.955118 +Using motif +M03884_2.00 of width 16. +Using motif -M03884_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05430_2.00 of width 20. +Using motif -M05430_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M05431_2.00 of width 21. +Using motif -M05431_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9966 +# Estimated pi_0=1 +Using motif +M05432_2.00 of width 18. +Using motif -M05432_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9318 +# Estimated pi_0=0.946379 +Using motif +M05433_2.00 of width 13. +Using motif -M05433_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M05434_2.00 of width 14. +Using motif -M05434_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05435_2.00 of width 21. +Using motif -M05435_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05436_2.00 of width 18. +Using motif -M05436_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9666 +# Estimated pi_0=0.987376 +Using motif +M05437_2.00 of width 13. +Using motif -M05437_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M10811_2.00 of width 21. +Using motif -M10811_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8074 +# Estimated pi_0=0.8074 +Using motif +M10812_2.00 of width 11. +Using motif -M10812_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M10813_2.00 of width 12. +Using motif -M10813_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940367 +# Estimated pi_0=0.946475 +Using motif +M10814_2.00 of width 30. +Using motif -M10814_2.00 of width 30. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992139 +# Estimated pi_0=0.996884 +Using motif +M00352_2.00 of width 8. +Using motif -M00352_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00353_2.00 of width 9. +Using motif -M00353_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9822 +# Estimated pi_0=1 +Using motif +M00354_2.00 of width 7. +Using motif -M00354_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9624 +# Estimated pi_0=0.9624 +Using motif +M00355_2.00 of width 9. +Using motif -M00355_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9798 +# Estimated pi_0=1 +Using motif +M00356_2.00 of width 11. +Using motif -M00356_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00357_2.00 of width 9. +Using motif -M00357_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9974 +# Estimated pi_0=1 +Using motif +M00358_2.00 of width 7. +Using motif -M00358_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00359_2.00 of width 8. +Using motif -M00359_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03297_2.00 of width 10. +Using motif -M03297_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03885_2.00 of width 14. +Using motif -M03885_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M03886_2.00 of width 13. +Using motif -M03886_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998131 +# Estimated pi_0=1 +Using motif +M05438_2.00 of width 16. +Using motif -M05438_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991089 +# Estimated pi_0=1 +Using motif +M05439_2.00 of width 12. +Using motif -M05439_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05440_2.00 of width 16. +Using motif -M05440_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970857 +# Estimated pi_0=0.982735 +Using motif +M05441_2.00 of width 16. +Using motif -M05441_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989 +# Estimated pi_0=1 +Using motif +M05442_2.00 of width 12. +Using motif -M05442_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05443_2.00 of width 16. +Using motif -M05443_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979 +# Estimated pi_0=1 +Using motif +M09585_2.00 of width 15. +Using motif -M09585_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M10816_2.00 of width 13. +Using motif -M10816_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00038 +# Estimated pi_0=1 +Using motif +M02742_2.00 of width 10. +Using motif -M02742_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M02743_2.00 of width 10. +Using motif -M02743_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03298_2.00 of width 11. +Using motif -M03298_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03299_2.00 of width 14. +Using motif -M03299_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05444_2.00 of width 15. +Using motif -M05444_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05445_2.00 of width 12. +Using motif -M05445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05446_2.00 of width 11. +Using motif -M05446_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05447_2.00 of width 12. +Using motif -M05447_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05448_2.00 of width 12. +Using motif -M05448_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998559 +# Estimated pi_0=1 +Using motif +M05449_2.00 of width 11. +Using motif -M05449_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05450_2.00 of width 12. +Using motif -M05450_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05451_2.00 of width 12. +Using motif -M05451_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05452_2.00 of width 12. +Using motif -M05452_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M07972_2.00 of width 10. +Using motif -M07972_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M07973_2.00 of width 10. +Using motif -M07973_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M07974_2.00 of width 13. +Using motif -M07974_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M07975_2.00 of width 13. +Using motif -M07975_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M08140_2.00 of width 13. +Using motif -M08140_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M09218_2.00 of width 11. +Using motif -M09218_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M03300_2.00 of width 17. +Using motif -M03300_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03301_2.00 of width 14. +Using motif -M03301_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05453_2.00 of width 14. +Using motif -M05453_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05454_2.00 of width 13. +Using motif -M05454_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05455_2.00 of width 14. +Using motif -M05455_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05456_2.00 of width 14. +Using motif -M05456_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05457_2.00 of width 13. +Using motif -M05457_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05458_2.00 of width 14. +Using motif -M05458_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M00360_2.00 of width 7. +Using motif -M00360_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M00361_2.00 of width 8. +Using motif -M00361_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03302_2.00 of width 16. +Using motif -M03302_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05459_2.00 of width 13. +Using motif -M05459_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05460_2.00 of width 13. +Using motif -M05460_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05461_2.00 of width 13. +Using motif -M05461_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05462_2.00 of width 10. +Using motif -M05462_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05463_2.00 of width 13. +Using motif -M05463_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05464_2.00 of width 13. +Using motif -M05464_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05465_2.00 of width 13. +Using motif -M05465_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05466_2.00 of width 13. +Using motif -M05466_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00362_2.00 of width 10. +Using motif -M00362_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M00363_2.00 of width 8. +Using motif -M00363_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00332 +# Estimated pi_0=1 +Using motif +M03303_2.00 of width 10. +Using motif -M03303_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03304_2.00 of width 16. +Using motif -M03304_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03305_2.00 of width 10. +Using motif -M03305_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M03306_2.00 of width 9. +Using motif -M03306_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03307_2.00 of width 12. +Using motif -M03307_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05467_2.00 of width 11. +Using motif -M05467_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971402 +# Estimated pi_0=0.980342 +Using motif +M05468_2.00 of width 11. +Using motif -M05468_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05469_2.00 of width 12. +Using motif -M05469_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M00377_2.00 of width 10. +Using motif -M00377_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M03308_2.00 of width 12. +Using motif -M03308_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03309_2.00 of width 14. +Using motif -M03309_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M09219_2.00 of width 16. +Using motif -M09219_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10826_2.00 of width 19. +Using motif -M10826_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00251 +# Estimated pi_0=1 +Using motif +M10827_2.00 of width 15. +Using motif -M10827_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M10828_2.00 of width 13. +Using motif -M10828_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M10829_2.00 of width 23. +Using motif -M10829_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M10830_2.00 of width 14. +Using motif -M10830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10831_2.00 of width 14. +Using motif -M10831_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M10832_2.00 of width 12. +Using motif -M10832_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M10835_2.00 of width 15. +Using motif -M10835_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M03310_2.00 of width 16. +Using motif -M03310_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03311_2.00 of width 16. +Using motif -M03311_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03312_2.00 of width 14. +Using motif -M03312_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05470_2.00 of width 13. +Using motif -M05470_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05471_2.00 of width 13. +Using motif -M05471_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05472_2.00 of width 13. +Using motif -M05472_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M03313_2.00 of width 13. +Using motif -M03313_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03314_2.00 of width 12. +Using motif -M03314_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05473_2.00 of width 14. +Using motif -M05473_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05474_2.00 of width 13. +Using motif -M05474_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05475_2.00 of width 12. +Using motif -M05475_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M05476_2.00 of width 13. +Using motif -M05476_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05477_2.00 of width 18. +Using motif -M05477_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05478_2.00 of width 14. +Using motif -M05478_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05479_2.00 of width 13. +Using motif -M05479_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05480_2.00 of width 12. +Using motif -M05480_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9936 +# Estimated pi_0=1 +Using motif +M05481_2.00 of width 13. +Using motif -M05481_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05482_2.00 of width 17. +Using motif -M05482_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09220_2.00 of width 17. +Using motif -M09220_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998307 +# Estimated pi_0=1 +Using motif +M10843_2.00 of width 16. +Using motif -M10843_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M10844_2.00 of width 14. +Using motif -M10844_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M10845_2.00 of width 10. +Using motif -M10845_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03315_2.00 of width 12. +Using motif -M03315_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03316_2.00 of width 12. +Using motif -M03316_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05483_2.00 of width 17. +Using motif -M05483_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05484_2.00 of width 14. +Using motif -M05484_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05485_2.00 of width 13. +Using motif -M05485_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05486_2.00 of width 13. +Using motif -M05486_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05487_2.00 of width 14. +Using motif -M05487_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05488_2.00 of width 13. +Using motif -M05488_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05489_2.00 of width 12. +Using motif -M05489_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05490_2.00 of width 13. +Using motif -M05490_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05491_2.00 of width 17. +Using motif -M05491_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M09221_2.00 of width 14. +Using motif -M09221_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M10849_2.00 of width 15. +Using motif -M10849_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M00364_2.00 of width 7. +Using motif -M00364_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M00365_2.00 of width 7. +Using motif -M00365_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M00366_2.00 of width 7. +Using motif -M00366_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03317_2.00 of width 9. +Using motif -M03317_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M03318_2.00 of width 11. +Using motif -M03318_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05492_2.00 of width 12. +Using motif -M05492_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05493_2.00 of width 14. +Using motif -M05493_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05494_2.00 of width 13. +Using motif -M05494_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00231 +# Estimated pi_0=1 +Using motif +M05495_2.00 of width 12. +Using motif -M05495_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05496_2.00 of width 13. +Using motif -M05496_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05497_2.00 of width 14. +Using motif -M05497_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05498_2.00 of width 13. +Using motif -M05498_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05499_2.00 of width 12. +Using motif -M05499_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05500_2.00 of width 13. +Using motif -M05500_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03319_2.00 of width 13. +Using motif -M03319_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03320_2.00 of width 12. +Using motif -M03320_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03321_2.00 of width 12. +Using motif -M03321_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M05501_2.00 of width 16. +Using motif -M05501_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05502_2.00 of width 12. +Using motif -M05502_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05503_2.00 of width 16. +Using motif -M05503_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05504_2.00 of width 15. +Using motif -M05504_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05875_2.00 of width 12. +Using motif -M05875_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M07976_2.00 of width 17. +Using motif -M07976_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998958 +# Estimated pi_0=0.999397 +Using motif +M08141_2.00 of width 11. +Using motif -M08141_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09222_2.00 of width 16. +Using motif -M09222_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M02744_2.00 of width 12. +Using motif -M02744_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03324_2.00 of width 13. +Using motif -M03324_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M05505_2.00 of width 15. +Using motif -M05505_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9792 +# Estimated pi_0=1 +Using motif +M05506_2.00 of width 15. +Using motif -M05506_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998696 +# Estimated pi_0=0.999899 +Using motif +M05507_2.00 of width 15. +Using motif -M05507_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9862 +# Estimated pi_0=1 +Using motif +M05508_2.00 of width 15. +Using motif -M05508_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993552 +# Estimated pi_0=0.998492 +Using motif +M09229_2.00 of width 14. +Using motif -M09229_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9818 +# Estimated pi_0=1 +Using motif +M10859_2.00 of width 10. +Using motif -M10859_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9608 +# Estimated pi_0=0.976935 +Using motif +M03325_2.00 of width 13. +Using motif -M03325_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05509_2.00 of width 15. +Using motif -M05509_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9624 +# Estimated pi_0=0.967048 +Using motif +M05510_2.00 of width 15. +Using motif -M05510_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992 +# Estimated pi_0=0.996345 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03327_2.00 of width 9. +Using motif -M03327_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M03328_2.00 of width 15. +Using motif -M03328_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05511_2.00 of width 19. +Using motif -M05511_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00392 +# Estimated pi_0=1 +Using motif +M05512_2.00 of width 17. +Using motif -M05512_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M05513_2.00 of width 19. +Using motif -M05513_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05514_2.00 of width 17. +Using motif -M05514_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00322 +# Estimated pi_0=1 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00291 +# Estimated pi_0=1 +Using motif +M05515_2.00 of width 19. +Using motif -M05515_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05516_2.00 of width 17. +Using motif -M05516_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05517_2.00 of width 19. +Using motif -M05517_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05518_2.00 of width 17. +Using motif -M05518_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M05519_2.00 of width 16. +Using motif -M05519_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988911 +# Estimated pi_0=1 +Using motif +M05520_2.00 of width 27. +Using motif -M05520_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8936 +# Estimated pi_0=0.8936 +Using motif +M05521_2.00 of width 16. +Using motif -M05521_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05522_2.00 of width 27. +Using motif -M05522_2.00 of width 27. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973725 +# Estimated pi_0=1 +Using motif +M03329_2.00 of width 13. +Using motif -M03329_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03330_2.00 of width 13. +Using motif -M03330_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05523_2.00 of width 15. +Using motif -M05523_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977778 +# Estimated pi_0=0.98 +Using motif +M05524_2.00 of width 15. +Using motif -M05524_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995243 +# Estimated pi_0=0.998061 +Using motif +M05525_2.00 of width 15. +Using motif -M05525_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988627 +# Estimated pi_0=1 +Using motif +M05526_2.00 of width 15. +Using motif -M05526_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996979 +# Estimated pi_0=1 +Using motif +M07977_2.00 of width 18. +Using motif -M07977_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9254 +# Estimated pi_0=0.935676 +Using motif +M09230_2.00 of width 15. +Using motif -M09230_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994149 +# Estimated pi_0=0.998693 +Using motif +M09592_2.00 of width 20. +Using motif -M09592_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9402 +# Estimated pi_0=0.952552 +Using motif +M10863_2.00 of width 10. +Using motif -M10863_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03326_2.00 of width 15. +Using motif -M03326_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00352 +# Estimated pi_0=1 +Using motif +M05527_2.00 of width 20. +Using motif -M05527_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05528_2.00 of width 20. +Using motif -M05528_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05529_2.00 of width 9. +Using motif -M05529_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M05530_2.00 of width 9. +Using motif -M05530_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957308 +# Estimated pi_0=0.965932 +Using motif +M08142_2.00 of width 21. +Using motif -M08142_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984649 +# Estimated pi_0=0.989072 +Using motif +M09233_2.00 of width 20. +Using motif -M09233_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987749 +# Estimated pi_0=0.991414 +Using motif +M09594_2.00 of width 12. +Using motif -M09594_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991979 +# Estimated pi_0=0.99404 +Using motif +M10879_2.00 of width 13. +Using motif -M10879_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997857 +# Estimated pi_0=0.999698 +Using motif +M03331_2.00 of width 21. +Using motif -M03331_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992746 +# Estimated pi_0=0.995556 +Using motif +M05531_2.00 of width 23. +Using motif -M05531_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993774 +# Estimated pi_0=0.998125 +Using motif +M05532_2.00 of width 22. +Using motif -M05532_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994769 +# Estimated pi_0=0.998794 +Using motif +M05533_2.00 of width 20. +Using motif -M05533_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994227 +# Estimated pi_0=0.997085 +Using motif +M05534_2.00 of width 20. +Using motif -M05534_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994824 +# Estimated pi_0=0.996382 +Using motif +M09234_2.00 of width 20. +Using motif -M09234_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945172 +# Estimated pi_0=0.951444 +Using motif +M03332_2.00 of width 14. +Using motif -M03332_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03333_2.00 of width 11. +Using motif -M03333_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05535_2.00 of width 14. +Using motif -M05535_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982975 +# Estimated pi_0=1 +Using motif +M05536_2.00 of width 14. +Using motif -M05536_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999495 +# Estimated pi_0=1 +Using motif +M05537_2.00 of width 14. +Using motif -M05537_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992613 +# Estimated pi_0=1 +Using motif +M05538_2.00 of width 14. +Using motif -M05538_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03334_2.00 of width 15. +Using motif -M03334_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05539_2.00 of width 15. +Using motif -M05539_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05540_2.00 of width 15. +Using motif -M05540_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.999397 +Using motif +M07978_2.00 of width 18. +Using motif -M07978_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986111 +# Estimated pi_0=0.990363 +Using motif +M09235_2.00 of width 18. +Using motif -M09235_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957317 +# Estimated pi_0=0.963503 +Using motif +M09595_2.00 of width 10. +Using motif -M09595_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03335_2.00 of width 14. +Using motif -M03335_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=1 +Using motif +M03336_2.00 of width 14. +Using motif -M03336_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M05541_2.00 of width 15. +Using motif -M05541_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999585 +# Estimated pi_0=1 +Using motif +M05542_2.00 of width 15. +Using motif -M05542_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998925 +# Estimated pi_0=0.999497 +Using motif +M05543_2.00 of width 15. +Using motif -M05543_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05544_2.00 of width 15. +Using motif -M05544_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997755 +# Estimated pi_0=1 +Using motif +M09236_2.00 of width 20. +Using motif -M09236_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969605 +# Estimated pi_0=0.972787 +Using motif +M02691_2.00 of width 18. +Using motif -M02691_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05545_2.00 of width 12. +Using motif -M05545_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99697 +# Estimated pi_0=0.998492 +Using motif +M05546_2.00 of width 12. +Using motif -M05546_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997872 +# Estimated pi_0=0.998492 +Using motif +M09237_2.00 of width 20. +Using motif -M09237_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988191 +# Estimated pi_0=0.991168 +Using motif +M09596_2.00 of width 12. +Using motif -M09596_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9704 +# Estimated pi_0=0.986742 +Using motif +M10892_2.00 of width 13. +Using motif -M10892_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999086 +# Estimated pi_0=1 +Using motif +M03337_2.00 of width 14. +Using motif -M03337_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997273 +# Estimated pi_0=0.998593 +Using motif +M03338_2.00 of width 17. +Using motif -M03338_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05547_2.00 of width 15. +Using motif -M05547_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998454 +# Estimated pi_0=1 +Using motif +M05548_2.00 of width 15. +Using motif -M05548_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994315 +# Estimated pi_0=0.994747 +Using motif +M09238_2.00 of width 10. +Using motif -M09238_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993617 +# Estimated pi_0=0.997487 +Using motif +M10894_2.00 of width 18. +Using motif -M10894_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M03339_2.00 of width 15. +Using motif -M03339_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998889 +# Estimated pi_0=0.999095 +Using motif +M05549_2.00 of width 15. +Using motif -M05549_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999196 +# Estimated pi_0=0.999196 +Using motif +M05550_2.00 of width 15. +Using motif -M05550_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998769 +# Estimated pi_0=1 +Using motif +M09239_2.00 of width 12. +Using motif -M09239_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M01259_2.00 of width 10. +Using motif -M01259_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978904 +# Estimated pi_0=0.983333 +Using motif +M01260_2.00 of width 10. +Using motif -M01260_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9332 +# Estimated pi_0=0.938667 +Using motif +M03344_2.00 of width 12. +Using motif -M03344_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03340_2.00 of width 12. +Using motif -M03340_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M07979_2.00 of width 15. +Using motif -M07979_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M08212_2.00 of width 10. +Using motif -M08212_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M08213_2.00 of width 8. +Using motif -M08213_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996684 +# Estimated pi_0=0.999497 +Using motif +M09247_2.00 of width 13. +Using motif -M09247_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M10935_2.00 of width 16. +Using motif -M10935_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10936_2.00 of width 22. +Using motif -M10936_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M10937_2.00 of width 22. +Using motif -M10937_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M10938_2.00 of width 22. +Using motif -M10938_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M10943_2.00 of width 16. +Using motif -M10943_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M02745_2.00 of width 12. +Using motif -M02745_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M05551_2.00 of width 10. +Using motif -M05551_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05552_2.00 of width 10. +Using motif -M05552_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M07980_2.00 of width 15. +Using motif -M07980_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M08149_2.00 of width 15. +Using motif -M08149_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M09248_2.00 of width 13. +Using motif -M09248_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M09597_2.00 of width 12. +Using motif -M09597_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999286 +# Estimated pi_0=1 +Using motif +M03341_2.00 of width 12. +Using motif -M03341_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03342_2.00 of width 16. +Using motif -M03342_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05553_2.00 of width 10. +Using motif -M05553_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05554_2.00 of width 10. +Using motif -M05554_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M07981_2.00 of width 19. +Using motif -M07981_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M07982_2.00 of width 14. +Using motif -M07982_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998794 +# Estimated pi_0=0.998794 +Using motif +M07983_2.00 of width 18. +Using motif -M07983_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998477 +# Estimated pi_0=0.998693 +Using motif +M07984_2.00 of width 17. +Using motif -M07984_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09249_2.00 of width 18. +Using motif -M09249_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M10947_2.00 of width 18. +Using motif -M10947_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M10951_2.00 of width 15. +Using motif -M10951_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M10956_2.00 of width 14. +Using motif -M10956_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M03343_2.00 of width 12. +Using motif -M03343_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05555_2.00 of width 10. +Using motif -M05555_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05556_2.00 of width 10. +Using motif -M05556_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09250_2.00 of width 12. +Using motif -M09250_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03344_2.00 of width 12. +Using motif -M03344_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05557_2.00 of width 10. +Using motif -M05557_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05558_2.00 of width 10. +Using motif -M05558_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999289 +# Estimated pi_0=1 +Using motif +M05559_2.00 of width 15. +Using motif -M05559_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05560_2.00 of width 15. +Using motif -M05560_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M09251_2.00 of width 14. +Using motif -M09251_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M09256_2.00 of width 11. +Using motif -M09256_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.842 +# Estimated pi_0=0.849074 +Using motif +M09257_2.00 of width 7. +Using motif -M09257_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.899652 +# Estimated pi_0=0.911507 +Using motif +M01272_2.00 of width 11. +Using motif -M01272_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M11049_2.00 of width 12. +Using motif -M11049_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03345_2.00 of width 14. +Using motif -M03345_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976 +# Estimated pi_0=1 +Using motif +M03346_2.00 of width 16. +Using motif -M03346_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983366 +# Estimated pi_0=1 +Using motif +M03347_2.00 of width 11. +Using motif -M03347_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9764 +# Estimated pi_0=0.986207 +Using motif +M03348_2.00 of width 15. +Using motif -M03348_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98 +# Estimated pi_0=1 +Using motif +M05561_2.00 of width 11. +Using motif -M05561_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9746 +# Estimated pi_0=1 +Using motif +M05562_2.00 of width 11. +Using motif -M05562_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988571 +# Estimated pi_0=1 +Using motif +M09600_2.00 of width 10. +Using motif -M09600_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M08150_2.00 of width 10. +Using motif -M08150_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9878 +# Estimated pi_0=0.994869 +Using motif +M09260_2.00 of width 12. +Using motif -M09260_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98037 +# Estimated pi_0=0.987742 +Using motif +M11050_2.00 of width 18. +Using motif -M11050_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906078 +# Estimated pi_0=0.908155 +Using motif +M11054_2.00 of width 10. +Using motif -M11054_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960594 +# Estimated pi_0=0.970588 +Using motif +M02323_2.00 of width 9. +Using motif -M02323_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974098 +# Estimated pi_0=0.98 +Using motif +M00808_2.00 of width 10. +Using motif -M00808_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9356 +# Estimated pi_0=0.951094 +Using motif +M03349_2.00 of width 12. +Using motif -M03349_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M03350_2.00 of width 12. +Using motif -M03350_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9866 +# Estimated pi_0=1 +Using motif +M03351_2.00 of width 11. +Using motif -M03351_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9798 +# Estimated pi_0=1 +Using motif +M03352_2.00 of width 17. +Using motif -M03352_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99505 +# Estimated pi_0=1 +Using motif +M02385_2.00 of width 8. +Using motif -M02385_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939429 +# Estimated pi_0=0.951129 +Using motif +M00367_2.00 of width 13. +Using motif -M00367_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M00368_2.00 of width 10. +Using motif -M00368_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960198 +# Estimated pi_0=0.964 +Using motif +M05876_2.00 of width 11. +Using motif -M05876_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999783 +# Estimated pi_0=1 +Using motif +M08152_2.00 of width 11. +Using motif -M08152_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995489 +# Estimated pi_0=1 +Using motif +M09263_2.00 of width 19. +Using motif -M09263_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.880484 +# Estimated pi_0=0.887862 +Using motif +M09264_2.00 of width 19. +Using motif -M09264_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962734 +# Estimated pi_0=0.96875 +Using motif +M02705_2.00 of width 10. +Using motif -M02705_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M02706_2.00 of width 14. +Using motif -M02706_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03353_2.00 of width 20. +Using motif -M03353_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980746 +# Estimated pi_0=0.988642 +Using motif +M03354_2.00 of width 19. +Using motif -M03354_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09265_2.00 of width 13. +Using motif -M09265_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938519 +# Estimated pi_0=0.942237 +Using motif +M11090_2.00 of width 13. +Using motif -M11090_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999798 +# Estimated pi_0=1 +Using motif +M11091_2.00 of width 13. +Using motif -M11091_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M03355_2.00 of width 16. +Using motif -M03355_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998421 +# Estimated pi_0=1 +Using motif +M05563_2.00 of width 10. +Using motif -M05563_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9804 +# Estimated pi_0=1 +Using motif +M05564_2.00 of width 16. +Using motif -M05564_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964463 +# Estimated pi_0=0.97907 +Using motif +M05565_2.00 of width 14. +Using motif -M05565_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95505 +# Estimated pi_0=0.966838 +Using motif +M05566_2.00 of width 10. +Using motif -M05566_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9776 +# Estimated pi_0=0.995699 +Using motif +M05567_2.00 of width 16. +Using motif -M05567_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95514 +# Estimated pi_0=0.963057 +Using motif +M05568_2.00 of width 14. +Using motif -M05568_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94297 +# Estimated pi_0=0.949016 +Using motif +M03923_2.00 of width 15. +Using motif -M03923_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.984328 +Using motif +M09266_2.00 of width 16. +Using motif -M09266_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993889 +# Estimated pi_0=0.999899 +Using motif +M09603_2.00 of width 15. +Using motif -M09603_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984076 +# Estimated pi_0=0.99401 +Using motif +M03356_2.00 of width 17. +Using motif -M03356_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955726 +# Estimated pi_0=0.96137 +Using motif +M05569_2.00 of width 17. +Using motif -M05569_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9554 +# Estimated pi_0=0.967381 +Using motif +M05570_2.00 of width 17. +Using motif -M05570_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.888 +# Estimated pi_0=0.899826 +Using motif +M05571_2.00 of width 17. +Using motif -M05571_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938 +# Estimated pi_0=0.942975 +Using motif +M05572_2.00 of width 17. +Using motif -M05572_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.909515 +# Estimated pi_0=0.910385 +Using motif +M07985_2.00 of width 14. +Using motif -M07985_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.920784 +# Estimated pi_0=0.929836 +Using motif +M08217_2.00 of width 18. +Using motif -M08217_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938318 +# Estimated pi_0=0.945229 +Using motif +M08218_2.00 of width 10. +Using motif -M08218_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923 +# Estimated pi_0=0.93415 +Using motif +M09267_2.00 of width 15. +Using motif -M09267_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954857 +# Estimated pi_0=0.962036 +Using motif +M09604_2.00 of width 15. +Using motif -M09604_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96048 +# Estimated pi_0=0.967066 +Using motif +M11102_2.00 of width 19. +Using motif -M11102_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950732 +# Estimated pi_0=0.955969 +Using motif +M03357_2.00 of width 16. +Using motif -M03357_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03358_2.00 of width 16. +Using motif -M03358_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965743 +# Estimated pi_0=0.979886 +Using motif +M03359_2.00 of width 16. +Using motif -M03359_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9924 +# Estimated pi_0=1 +Using motif +M03360_2.00 of width 14. +Using motif -M03360_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976 +# Estimated pi_0=0.989881 +Using motif +M03361_2.00 of width 16. +Using motif -M03361_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983492 +# Estimated pi_0=0.995714 +Using motif +M03362_2.00 of width 15. +Using motif -M03362_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983433 +# Estimated pi_0=0.996111 +Using motif +M05573_2.00 of width 15. +Using motif -M05573_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948 +# Estimated pi_0=0.954394 +Using motif +M05574_2.00 of width 15. +Using motif -M05574_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971 +# Estimated pi_0=0.980662 +Using motif +M05575_2.00 of width 15. +Using motif -M05575_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9754 +# Estimated pi_0=0.996598 +Using motif +M05576_2.00 of width 15. +Using motif -M05576_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986275 +# Estimated pi_0=0.99369 +Using motif +M05577_2.00 of width 15. +Using motif -M05577_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979114 +# Estimated pi_0=0.986077 +Using motif +M05578_2.00 of width 15. +Using motif -M05578_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964356 +# Estimated pi_0=0.971825 +Using motif +M05579_2.00 of width 15. +Using motif -M05579_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.978923 +# Estimated pi_0=0.991705 +Using motif +M05580_2.00 of width 15. +Using motif -M05580_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984706 +# Estimated pi_0=0.995491 +Using motif +M08219_2.00 of width 15. +Using motif -M08219_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952913 +# Estimated pi_0=0.968554 +Using motif +M08220_2.00 of width 6. +Using motif -M08220_2.00 of width 6. +Computing q-values. +Using motif +M09268_2.00 of width 14. +Using motif -M09268_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974646 +# Estimated pi_0=0.981778 +Using motif +M09605_2.00 of width 16. +Using motif -M09605_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982166 +# Estimated pi_0=0.989479 +Using motif +M11105_2.00 of width 15. +Using motif -M11105_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973881 +# Estimated pi_0=0.981798 +Using motif +M11106_2.00 of width 19. +Using motif -M11106_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971679 +# Estimated pi_0=0.981749 +Using motif +M03363_2.00 of width 16. +Using motif -M03363_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966019 +# Estimated pi_0=0.966612 +Using motif +M05877_2.00 of width 8. +Using motif -M05877_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998378 +# Estimated pi_0=1 +Using motif +M09269_2.00 of width 16. +Using motif -M09269_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938319 +# Estimated pi_0=0.946667 +Using motif +M09606_2.00 of width 20. +Using motif -M09606_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945902 +# Estimated pi_0=0.955 +Using motif +M05581_2.00 of width 16. +Using motif -M05581_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9738 +# Estimated pi_0=0.988526 +Using motif +M05582_2.00 of width 16. +Using motif -M05582_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936538 +# Estimated pi_0=0.941404 +Using motif +M05583_2.00 of width 16. +Using motif -M05583_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961584 +# Estimated pi_0=0.975765 +Using motif +M05584_2.00 of width 16. +Using motif -M05584_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9046 +# Estimated pi_0=0.917876 +Using motif +M02394_2.00 of width 10. +Using motif -M02394_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983919 +# Estimated pi_0=0.997677 +Using motif +M03364_2.00 of width 9. +Using motif -M03364_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998071 +# Estimated pi_0=1 +Using motif +M03365_2.00 of width 14. +Using motif -M03365_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00081 +# Estimated pi_0=1 +Using motif +M05585_2.00 of width 9. +Using motif -M05585_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973398 +# Estimated pi_0=0.985844 +Using motif +M05586_2.00 of width 9. +Using motif -M05586_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9764 +# Estimated pi_0=1 +Using motif +M03366_2.00 of width 17. +Using motif -M03366_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M05587_2.00 of width 15. +Using motif -M05587_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05588_2.00 of width 15. +Using motif -M05588_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M07986_2.00 of width 14. +Using motif -M07986_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979477 +# Estimated pi_0=0.987727 +Using motif +M07987_2.00 of width 21. +Using motif -M07987_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976053 +# Estimated pi_0=0.98 +Using motif +M09270_2.00 of width 15. +Using motif -M09270_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991644 +# Estimated pi_0=0.999095 +Using motif +M09607_2.00 of width 16. +Using motif -M09607_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973 +# Estimated pi_0=0.98185 +Using motif +M11120_2.00 of width 16. +Using motif -M11120_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986494 +# Estimated pi_0=0.993717 +Using motif +M11124_2.00 of width 19. +Using motif -M11124_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98875 +# Estimated pi_0=0.99809 +Using motif +M05589_2.00 of width 18. +Using motif -M05589_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936 +# Estimated pi_0=0.943636 +Using motif +M05590_2.00 of width 11. +Using motif -M05590_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983922 +# Estimated pi_0=0.992979 +Using motif +M05591_2.00 of width 19. +Using motif -M05591_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949558 +# Estimated pi_0=0.952308 +Using motif +M05592_2.00 of width 18. +Using motif -M05592_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9446 +# Estimated pi_0=0.950167 +Using motif +M05593_2.00 of width 11. +Using motif -M05593_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975726 +# Estimated pi_0=0.995521 +Using motif +M05594_2.00 of width 19. +Using motif -M05594_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9422 +# Estimated pi_0=0.955 +Using motif +M09271_2.00 of width 11. +Using motif -M09271_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953333 +# Estimated pi_0=0.970182 +Using motif +M00818_2.00 of width 8. +Using motif -M00818_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M03367_2.00 of width 11. +Using motif -M03367_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05595_2.00 of width 11. +Using motif -M05595_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976 +# Estimated pi_0=0.985424 +Using motif +M05596_2.00 of width 11. +Using motif -M05596_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975965 +# Estimated pi_0=0.992588 +Using motif +M09272_2.00 of width 9. +Using motif -M09272_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969831 +# Estimated pi_0=0.981786 +Using motif +M05597_2.00 of width 14. +Using motif -M05597_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9458 +# Estimated pi_0=0.958347 +Using motif +M05598_2.00 of width 9. +Using motif -M05598_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931287 +# Estimated pi_0=0.951159 +Using motif +M05599_2.00 of width 9. +Using motif -M05599_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96705 +# Estimated pi_0=0.97085 +Using motif +M05600_2.00 of width 14. +Using motif -M05600_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950435 +# Estimated pi_0=0.960435 +Using motif +M09273_2.00 of width 13. +Using motif -M09273_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96 +# Estimated pi_0=0.978 +Using motif +M05601_2.00 of width 10. +Using motif -M05601_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05602_2.00 of width 10. +Using motif -M05602_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M05603_2.00 of width 10. +Using motif -M05603_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05604_2.00 of width 10. +Using motif -M05604_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M08153_2.00 of width 10. +Using motif -M08153_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999372 +# Estimated pi_0=1 +Using motif +M09274_2.00 of width 9. +Using motif -M09274_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973158 +# Estimated pi_0=0.978065 +Using motif +M09608_2.00 of width 12. +Using motif -M09608_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03368_2.00 of width 18. +Using motif -M03368_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984423 +# Estimated pi_0=0.997744 +Using motif +M09275_2.00 of width 17. +Using motif -M09275_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.936832 +# Estimated pi_0=0.942148 +Using motif +M05605_2.00 of width 20. +Using motif -M05605_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967961 +# Estimated pi_0=0.98 +Using motif +M05606_2.00 of width 20. +Using motif -M05606_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962982 +# Estimated pi_0=0.977966 +Using motif +M09276_2.00 of width 19. +Using motif -M09276_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951126 +# Estimated pi_0=0.959432 +Using motif +M09620_2.00 of width 16. +Using motif -M09620_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995519 +# Estimated pi_0=0.998283 +Using motif +M03369_2.00 of width 18. +Using motif -M03369_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98687 +# Estimated pi_0=0.996869 +Using motif +M03370_2.00 of width 18. +Using motif -M03370_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998703 +# Estimated pi_0=0.999799 +Using motif +M03371_2.00 of width 17. +Using motif -M03371_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966847 +# Estimated pi_0=0.980241 +Using motif +M03372_2.00 of width 15. +Using motif -M03372_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996837 +# Estimated pi_0=0.999497 +Using motif +M03373_2.00 of width 18. +Using motif -M03373_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949322 +# Estimated pi_0=0.957606 +Using motif +M03374_2.00 of width 17. +Using motif -M03374_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974775 +# Estimated pi_0=0.988602 +Using motif +M05607_2.00 of width 16. +Using motif -M05607_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9056 +# Estimated pi_0=0.92 +Using motif +M05608_2.00 of width 14. +Using motif -M05608_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938 +# Estimated pi_0=0.950794 +Using motif +M05609_2.00 of width 10. +Using motif -M05609_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9504 +# Estimated pi_0=0.966957 +Using motif +M05610_2.00 of width 16. +Using motif -M05610_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941053 +# Estimated pi_0=0.946182 +Using motif +M05611_2.00 of width 14. +Using motif -M05611_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.906226 +# Estimated pi_0=0.923226 +Using motif +M05612_2.00 of width 10. +Using motif -M05612_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9554 +# Estimated pi_0=0.969737 +Using motif +M05878_2.00 of width 7. +Using motif -M05878_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976436 +# Estimated pi_0=0.98325 +Using motif +M09277_2.00 of width 20. +Using motif -M09277_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.916262 +# Estimated pi_0=0.925532 +Using motif +M02707_2.00 of width 20. +Using motif -M02707_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967525 +# Estimated pi_0=0.973636 +Using motif +M09278_2.00 of width 17. +Using motif -M09278_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945223 +# Estimated pi_0=0.946627 +Using motif +M11138_2.00 of width 21. +Using motif -M11138_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970097 +# Estimated pi_0=0.979868 +Using motif +M11139_2.00 of width 23. +Using motif -M11139_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998534 +# Estimated pi_0=1 +Using motif +M11140_2.00 of width 17. +Using motif -M11140_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985037 +# Estimated pi_0=0.994256 +Using motif +M05613_2.00 of width 11. +Using motif -M05613_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959083 +# Estimated pi_0=0.962794 +Using motif +M05614_2.00 of width 11. +Using motif -M05614_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.926154 +# Estimated pi_0=0.931803 +Using motif +M08154_2.00 of width 15. +Using motif -M08154_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949106 +# Estimated pi_0=0.959497 +Using motif +M09279_2.00 of width 15. +Using motif -M09279_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.9 +Using motif +M02746_2.00 of width 12. +Using motif -M02746_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993788 +# Estimated pi_0=0.998985 +Using motif +M03375_2.00 of width 14. +Using motif -M03375_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987516 +# Estimated pi_0=0.994316 +Using motif +M03376_2.00 of width 14. +Using motif -M03376_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93505 +# Estimated pi_0=0.940305 +Using motif +M03377_2.00 of width 14. +Using motif -M03377_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962178 +# Estimated pi_0=0.970738 +Using motif +M03378_2.00 of width 14. +Using motif -M03378_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941961 +# Estimated pi_0=0.958657 +Using motif +M05615_2.00 of width 15. +Using motif -M05615_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97549 +# Estimated pi_0=0.986557 +Using motif +M05616_2.00 of width 14. +Using motif -M05616_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913333 +# Estimated pi_0=0.921441 +Using motif +M05617_2.00 of width 15. +Using motif -M05617_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956774 +# Estimated pi_0=0.964368 +Using motif +M05618_2.00 of width 14. +Using motif -M05618_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930588 +# Estimated pi_0=0.940303 +Using motif +M05879_2.00 of width 15. +Using motif -M05879_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970635 +# Estimated pi_0=0.981818 +Using motif +M09280_2.00 of width 13. +Using motif -M09280_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987895 +# Estimated pi_0=0.996043 +Using motif +M03924_2.00 of width 16. +Using motif -M03924_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950962 +# Estimated pi_0=0.963947 +Using motif +M05619_2.00 of width 9. +Using motif -M05619_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05620_2.00 of width 9. +Using motif -M05620_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M09281_2.00 of width 18. +Using motif -M09281_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971304 +# Estimated pi_0=0.982099 +Using motif +M05621_2.00 of width 14. +Using motif -M05621_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952277 +# Estimated pi_0=0.958814 +Using motif +M05622_2.00 of width 20. +Using motif -M05622_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971525 +# Estimated pi_0=0.97988 +Using motif +M05623_2.00 of width 14. +Using motif -M05623_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932475 +# Estimated pi_0=0.945289 +Using motif +M05624_2.00 of width 20. +Using motif -M05624_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983922 +# Estimated pi_0=0.995243 +Using motif +M05880_2.00 of width 12. +Using motif -M05880_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M09282_2.00 of width 12. +Using motif -M09282_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986298 +# Estimated pi_0=0.992228 +Using motif +M03925_2.00 of width 15. +Using motif -M03925_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9316 +# Estimated pi_0=0.938815 +Using motif +M05625_2.00 of width 17. +Using motif -M05625_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949216 +# Estimated pi_0=0.955455 +Using motif +M05626_2.00 of width 17. +Using motif -M05626_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9474 +# Estimated pi_0=0.961307 +Using motif +M09283_2.00 of width 19. +Using motif -M09283_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97215 +# Estimated pi_0=0.983145 +Using motif +M05627_2.00 of width 17. +Using motif -M05627_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9854 +# Estimated pi_0=0.996649 +Using motif +M05628_2.00 of width 17. +Using motif -M05628_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98944 +# Estimated pi_0=0.999485 +Using motif +M09284_2.00 of width 13. +Using motif -M09284_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999492 +# Estimated pi_0=0.999899 +Using motif +M11148_2.00 of width 18. +Using motif -M11148_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993974 +# Estimated pi_0=1 +Using motif +M03379_2.00 of width 18. +Using motif -M03379_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9692 +# Estimated pi_0=0.981067 +Using motif +M03380_2.00 of width 19. +Using motif -M03380_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979104 +# Estimated pi_0=0.992044 +Using motif +M03381_2.00 of width 20. +Using motif -M03381_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976111 +# Estimated pi_0=0.981494 +Using motif +M05629_2.00 of width 15. +Using motif -M05629_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9534 +# Estimated pi_0=0.964699 +Using motif +M05630_2.00 of width 16. +Using motif -M05630_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94297 +# Estimated pi_0=0.952881 +Using motif +M05631_2.00 of width 15. +Using motif -M05631_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94495 +# Estimated pi_0=0.95619 +Using motif +M05632_2.00 of width 16. +Using motif -M05632_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941373 +# Estimated pi_0=0.952672 +Using motif +M05633_2.00 of width 19. +Using motif -M05633_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930693 +# Estimated pi_0=0.939853 +Using motif +M05634_2.00 of width 19. +Using motif -M05634_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.923168 +# Estimated pi_0=0.927407 +Using motif +M05635_2.00 of width 19. +Using motif -M05635_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91802 +# Estimated pi_0=0.93009 +Using motif +M05636_2.00 of width 19. +Using motif -M05636_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995868 +# Estimated pi_0=1 +Using motif +M09285_2.00 of width 17. +Using motif -M09285_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935852 +# Estimated pi_0=0.938273 +Using motif +M02395_2.00 of width 9. +Using motif -M02395_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03382_2.00 of width 17. +Using motif -M03382_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M05637_2.00 of width 15. +Using motif -M05637_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.921782 +# Estimated pi_0=0.929455 +Using motif +M05638_2.00 of width 15. +Using motif -M05638_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953077 +# Estimated pi_0=0.969333 +Using motif +M03383_2.00 of width 17. +Using motif -M03383_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9894 +# Estimated pi_0=0.999296 +Using motif +M03384_2.00 of width 16. +Using motif -M03384_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03385_2.00 of width 11. +Using motif -M03385_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05639_2.00 of width 10. +Using motif -M05639_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05640_2.00 of width 10. +Using motif -M05640_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M05881_2.00 of width 9. +Using motif -M05881_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M08461_2.00 of width 8. +Using motif -M08461_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99525 +# Estimated pi_0=0.997688 +Using motif +M09286_2.00 of width 9. +Using motif -M09286_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988769 +# Estimated pi_0=0.999698 +Using motif +M02396_2.00 of width 8. +Using motif -M02396_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934857 +# Estimated pi_0=0.944255 +Using motif +M03386_2.00 of width 15. +Using motif -M03386_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977043 +# Estimated pi_0=0.986111 +Using motif +M03387_2.00 of width 14. +Using motif -M03387_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970291 +# Estimated pi_0=0.986897 +Using motif +M03388_2.00 of width 14. +Using motif -M03388_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974851 +# Estimated pi_0=0.987322 +Using motif +M05641_2.00 of width 15. +Using motif -M05641_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968214 +# Estimated pi_0=0.98426 +Using motif +M05642_2.00 of width 15. +Using motif -M05642_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940714 +# Estimated pi_0=0.961224 +Using motif +M05643_2.00 of width 15. +Using motif -M05643_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969703 +# Estimated pi_0=0.971655 +Using motif +M05644_2.00 of width 15. +Using motif -M05644_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924615 +# Estimated pi_0=0.924615 +Using motif +M05645_2.00 of width 15. +Using motif -M05645_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.934576 +# Estimated pi_0=0.941589 +Using motif +M05646_2.00 of width 15. +Using motif -M05646_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9034 +# Estimated pi_0=0.906226 +Using motif +M05647_2.00 of width 15. +Using motif -M05647_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.933857 +# Estimated pi_0=0.941988 +Using motif +M05648_2.00 of width 15. +Using motif -M05648_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9196 +# Estimated pi_0=0.929244 +Using motif +M08155_2.00 of width 15. +Using motif -M08155_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965075 +# Estimated pi_0=0.970751 +Using motif +M09287_2.00 of width 14. +Using motif -M09287_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965778 +# Estimated pi_0=0.973082 +Using motif +M03389_2.00 of width 17. +Using motif -M03389_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9784 +# Estimated pi_0=0.992614 +Using motif +M03390_2.00 of width 17. +Using motif -M03390_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97301 +# Estimated pi_0=0.98575 +Using motif +M09288_2.00 of width 18. +Using motif -M09288_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994639 +# Estimated pi_0=0.997085 +Using motif +M09609_2.00 of width 10. +Using motif -M09609_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954175 +# Estimated pi_0=0.958851 +Using motif +M09610_2.00 of width 16. +Using motif -M09610_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9775 +# Estimated pi_0=0.9808 +Using motif +M02397_2.00 of width 8. +Using motif -M02397_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963529 +# Estimated pi_0=0.974591 +Using motif +M03391_2.00 of width 17. +Using motif -M03391_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987759 +# Estimated pi_0=0.999397 +Using motif +M03392_2.00 of width 18. +Using motif -M03392_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.98176 +# Estimated pi_0=0.993711 +Using motif +M03393_2.00 of width 17. +Using motif -M03393_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974028 +# Estimated pi_0=0.981125 +Using motif +M03394_2.00 of width 16. +Using motif -M03394_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979477 +# Estimated pi_0=0.988701 +Using motif +M03395_2.00 of width 18. +Using motif -M03395_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976731 +# Estimated pi_0=0.991512 +Using motif +M03396_2.00 of width 17. +Using motif -M03396_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932475 +# Estimated pi_0=0.952296 +Using motif +M05649_2.00 of width 16. +Using motif -M05649_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986232 +# Estimated pi_0=0.995707 +Using motif +M05650_2.00 of width 14. +Using motif -M05650_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993 +# Estimated pi_0=0.998071 +Using motif +M05651_2.00 of width 16. +Using motif -M05651_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946496 +# Estimated pi_0=0.954717 +Using motif +M05652_2.00 of width 14. +Using motif -M05652_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945941 +# Estimated pi_0=0.950631 +Using motif +M05653_2.00 of width 9. +Using motif -M05653_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950192 +# Estimated pi_0=0.963974 +Using motif +M05654_2.00 of width 9. +Using motif -M05654_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950189 +# Estimated pi_0=0.959329 +Using motif +M09289_2.00 of width 18. +Using motif -M09289_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.910357 +# Estimated pi_0=0.91781 +Using motif +M03397_2.00 of width 11. +Using motif -M03397_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9818 +# Estimated pi_0=0.991803 +Using motif +M03398_2.00 of width 17. +Using motif -M03398_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985942 +# Estimated pi_0=1 +Using motif +M03399_2.00 of width 19. +Using motif -M03399_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967387 +# Estimated pi_0=0.974621 +Using motif +M03400_2.00 of width 11. +Using motif -M03400_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9514 +# Estimated pi_0=0.964265 +Using motif +M03401_2.00 of width 17. +Using motif -M03401_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995761 +# Estimated pi_0=0.999196 +Using motif +M03402_2.00 of width 19. +Using motif -M03402_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979024 +# Estimated pi_0=0.987119 +Using motif +M05655_2.00 of width 11. +Using motif -M05655_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962178 +# Estimated pi_0=0.966786 +Using motif +M05656_2.00 of width 11. +Using motif -M05656_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.959238 +# Estimated pi_0=0.971829 +Using motif +M05657_2.00 of width 11. +Using motif -M05657_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950476 +# Estimated pi_0=0.953509 +Using motif +M05658_2.00 of width 11. +Using motif -M05658_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968871 +# Estimated pi_0=0.981509 +Using motif +M07988_2.00 of width 15. +Using motif -M07988_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.940198 +# Estimated pi_0=0.948387 +Using motif +M07989_2.00 of width 19. +Using motif -M07989_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9228 +# Estimated pi_0=0.926972 +Using motif +M09290_2.00 of width 15. +Using motif -M09290_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965938 +# Estimated pi_0=0.974713 +Using motif +M05659_2.00 of width 20. +Using motif -M05659_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989102 +# Estimated pi_0=0.997653 +Using motif +M05660_2.00 of width 20. +Using motif -M05660_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962517 +# Estimated pi_0=0.968571 +Using motif +M03403_2.00 of width 15. +Using motif -M03403_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975762 +# Estimated pi_0=0.981622 +Using motif +M03404_2.00 of width 16. +Using motif -M03404_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9675 +# Estimated pi_0=0.972717 +Using motif +M03405_2.00 of width 8. +Using motif -M03405_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9526 +# Estimated pi_0=0.963262 +Using motif +M03406_2.00 of width 13. +Using motif -M03406_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.957181 +Using motif +M05661_2.00 of width 15. +Using motif -M05661_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942692 +# Estimated pi_0=0.946165 +Using motif +M05662_2.00 of width 15. +Using motif -M05662_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938632 +# Estimated pi_0=0.953425 +Using motif +M05663_2.00 of width 15. +Using motif -M05663_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966299 +# Estimated pi_0=0.979401 +Using motif +M05664_2.00 of width 15. +Using motif -M05664_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951481 +# Estimated pi_0=0.964539 +Using motif +M05665_2.00 of width 15. +Using motif -M05665_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95938 +# Estimated pi_0=0.969172 +Using motif +M05666_2.00 of width 15. +Using motif -M05666_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953036 +# Estimated pi_0=0.963571 +Using motif +M05667_2.00 of width 15. +Using motif -M05667_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9486 +# Estimated pi_0=0.963537 +Using motif +M05668_2.00 of width 15. +Using motif -M05668_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.945794 +# Estimated pi_0=0.957343 +Using motif +M09291_2.00 of width 17. +Using motif -M09291_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.917266 +# Estimated pi_0=0.928686 +Using motif +M03407_2.00 of width 14. +Using motif -M03407_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966102 +# Estimated pi_0=0.979239 +Using motif +M05669_2.00 of width 8. +Using motif -M05669_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969032 +# Estimated pi_0=0.982597 +Using motif +M05670_2.00 of width 8. +Using motif -M05670_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970073 +# Estimated pi_0=0.97729 +Using motif +M07990_2.00 of width 15. +Using motif -M07990_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.924505 +# Estimated pi_0=0.932727 +Using motif +M07991_2.00 of width 15. +Using motif -M07991_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930891 +# Estimated pi_0=0.937544 +Using motif +M07992_2.00 of width 15. +Using motif -M07992_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943333 +# Estimated pi_0=0.956596 +Using motif +M08156_2.00 of width 15. +Using motif -M08156_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.93 +# Estimated pi_0=0.940479 +Using motif +M08221_2.00 of width 15. +Using motif -M08221_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954 +# Estimated pi_0=0.963359 +Using motif +M08222_2.00 of width 10. +Using motif -M08222_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9752 +# Estimated pi_0=0.982699 +Using motif +M09292_2.00 of width 12. +Using motif -M09292_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9062 +# Estimated pi_0=0.915207 +Using motif +M09611_2.00 of width 14. +Using motif -M09611_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.950631 +# Estimated pi_0=0.961205 +Using motif +M08157_2.00 of width 11. +Using motif -M08157_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993226 +# Estimated pi_0=1 +Using motif +M08223_2.00 of width 8. +Using motif -M08223_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97283 +# Estimated pi_0=0.978095 +Using motif +M08224_2.00 of width 10. +Using motif -M08224_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963826 +# Estimated pi_0=0.967143 +Using motif +M09293_2.00 of width 13. +Using motif -M09293_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944414 +# Estimated pi_0=0.952393 +Using motif +M11180_2.00 of width 16. +Using motif -M11180_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96496 +# Estimated pi_0=0.976686 +Using motif +M03408_2.00 of width 14. +Using motif -M03408_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97322 +# Estimated pi_0=0.98763 +Using motif +M03409_2.00 of width 14. +Using motif -M03409_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9208 +# Estimated pi_0=0.931298 +Using motif +M03410_2.00 of width 14. +Using motif -M03410_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985347 +# Estimated pi_0=0.99701 +Using motif +M03411_2.00 of width 14. +Using motif -M03411_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922524 +# Estimated pi_0=0.930079 +Using motif +M05671_2.00 of width 15. +Using motif -M05671_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96699 +# Estimated pi_0=0.975294 +Using motif +M05672_2.00 of width 15. +Using motif -M05672_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988 +# Estimated pi_0=0.996548 +Using motif +M05882_2.00 of width 15. +Using motif -M05882_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965752 +# Estimated pi_0=0.973953 +Using motif +M07993_2.00 of width 8. +Using motif -M07993_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9708 +# Estimated pi_0=0.982195 +Using motif +M07994_2.00 of width 15. +Using motif -M07994_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932419 +# Estimated pi_0=0.940839 +Using motif +M09294_2.00 of width 20. +Using motif -M09294_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.907015 +# Estimated pi_0=0.909281 +Using motif +M09295_2.00 of width 17. +Using motif -M09295_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949867 +# Estimated pi_0=0.957791 +Using motif +M11183_2.00 of width 20. +Using motif -M11183_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995138 +# Estimated pi_0=1 +Using motif +M03412_2.00 of width 18. +Using motif -M03412_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975179 +# Estimated pi_0=0.996702 +Using motif +M03413_2.00 of width 17. +Using motif -M03413_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995337 +# Estimated pi_0=0.996327 +Using motif +M03414_2.00 of width 10. +Using motif -M03414_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983019 +# Estimated pi_0=0.998462 +Using motif +M05673_2.00 of width 11. +Using motif -M05673_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96735 +# Estimated pi_0=0.977284 +Using motif +M05674_2.00 of width 11. +Using motif -M05674_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971845 +# Estimated pi_0=0.983483 +Using motif +M05675_2.00 of width 11. +Using motif -M05675_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973451 +# Estimated pi_0=0.984286 +Using motif +M05676_2.00 of width 11. +Using motif -M05676_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.952137 +# Estimated pi_0=0.966988 +Using motif +M09296_2.00 of width 9. +Using motif -M09296_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05677_2.00 of width 20. +Using motif -M05677_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97 +# Estimated pi_0=0.986012 +Using motif +M05678_2.00 of width 22. +Using motif -M05678_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9936 +# Estimated pi_0=1 +Using motif +M05679_2.00 of width 20. +Using motif -M05679_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984336 +# Estimated pi_0=0.990235 +Using motif +M05680_2.00 of width 22. +Using motif -M05680_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998557 +# Estimated pi_0=1 +Using motif +M05883_2.00 of width 11. +Using motif -M05883_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03415_2.00 of width 14. +Using motif -M03415_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995036 +# Estimated pi_0=0.999497 +Using motif +M05681_2.00 of width 15. +Using motif -M05681_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979704 +# Estimated pi_0=0.98763 +Using motif +M05682_2.00 of width 14. +Using motif -M05682_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.948224 +# Estimated pi_0=0.952212 +Using motif +M05683_2.00 of width 15. +Using motif -M05683_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958776 +# Estimated pi_0=0.964881 +Using motif +M05684_2.00 of width 14. +Using motif -M05684_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.931881 +# Estimated pi_0=0.94619 +Using motif +M09297_2.00 of width 10. +Using motif -M09297_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955398 +# Estimated pi_0=0.97131 +Using motif +M00369_2.00 of width 10. +Using motif -M00369_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9538 +# Estimated pi_0=0.966061 +Using motif +M00370_2.00 of width 13. +Using motif -M00370_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M00371_2.00 of width 13. +Using motif -M00371_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00281 +# Estimated pi_0=1 +Using motif +M00372_2.00 of width 8. +Using motif -M00372_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00065 +# Estimated pi_0=1 +Using motif +M00373_2.00 of width 10. +Using motif -M00373_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M09298_2.00 of width 14. +Using motif -M09298_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993667 +# Estimated pi_0=0.997374 +Using motif +M03437_2.00 of width 18. +Using motif -M03437_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M09335_2.00 of width 19. +Using motif -M09335_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943438 +# Estimated pi_0=0.94859 +Using motif +M08035_2.00 of width 15. +Using motif -M08035_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955147 +# Estimated pi_0=0.95975 +Using motif +M08036_2.00 of width 19. +Using motif -M08036_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958915 +# Estimated pi_0=0.9688 +Using motif +M08037_2.00 of width 15. +Using motif -M08037_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.938609 +# Estimated pi_0=0.946667 +Using motif +M09336_2.00 of width 19. +Using motif -M09336_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955909 +# Estimated pi_0=0.96506 +Using motif +M09337_2.00 of width 20. +Using motif -M09337_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972389 +# Estimated pi_0=0.980581 +Using motif +M09621_2.00 of width 20. +Using motif -M09621_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9624 +# Estimated pi_0=0.966275 +Using motif +M11197_2.00 of width 20. +Using motif -M11197_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918148 +# Estimated pi_0=0.93411 +Using motif +M11198_2.00 of width 10. +Using motif -M11198_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971071 +# Estimated pi_0=0.977572 +Using motif +M05685_2.00 of width 20. +Using motif -M05685_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9062 +# Estimated pi_0=0.91 +Using motif +M05686_2.00 of width 20. +Using motif -M05686_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91802 +# Estimated pi_0=0.921538 +Using motif +M03442_2.00 of width 17. +Using motif -M03442_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9372 +# Estimated pi_0=0.9372 +Using motif +M05687_2.00 of width 17. +Using motif -M05687_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.901 +# Estimated pi_0=0.912389 +Using motif +M05688_2.00 of width 17. +Using motif -M05688_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9032 +# Estimated pi_0=0.912661 +Using motif +M03443_2.00 of width 18. +Using motif -M03443_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9318 +# Estimated pi_0=0.94018 +Using motif +M07995_2.00 of width 15. +Using motif -M07995_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902 +# Estimated pi_0=0.914876 +Using motif +M07996_2.00 of width 15. +Using motif -M07996_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8916 +# Estimated pi_0=0.909134 +Using motif +M07997_2.00 of width 15. +Using motif -M07997_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9118 +# Estimated pi_0=0.921724 +Using motif +M07998_2.00 of width 15. +Using motif -M07998_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8532 +# Estimated pi_0=0.867627 +Using motif +M08160_2.00 of width 12. +Using motif -M08160_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8952 +# Estimated pi_0=0.897667 +Using motif +M08227_2.00 of width 15. +Using motif -M08227_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.878 +# Estimated pi_0=0.883704 +Using motif +M08228_2.00 of width 10. +Using motif -M08228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.877087 +# Estimated pi_0=0.884655 +Using motif +M09341_2.00 of width 21. +Using motif -M09341_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.873654 +# Estimated pi_0=0.883103 +Using motif +M09622_2.00 of width 16. +Using motif -M09622_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9088 +# Estimated pi_0=0.91469 +Using motif +M09623_2.00 of width 14. +Using motif -M09623_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913663 +# Estimated pi_0=0.925414 +Using motif +M11208_2.00 of width 28. +Using motif -M11208_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.865 +# Estimated pi_0=0.874815 +Using motif +M11209_2.00 of width 28. +Using motif -M11209_2.00 of width 28. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943652 +# Estimated pi_0=0.963377 +Using motif +M03444_2.00 of width 17. +Using motif -M03444_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9166 +# Estimated pi_0=0.92 +Using motif +M05689_2.00 of width 17. +Using motif -M05689_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.904356 +# Estimated pi_0=0.908952 +Using motif +M05690_2.00 of width 17. +Using motif -M05690_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9 +# Estimated pi_0=0.903364 +Using motif +M01304_2.00 of width 8. +Using motif -M01304_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M02433_2.00 of width 10. +Using motif -M02433_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999397 +# Estimated pi_0=0.999397 +Using motif +M03445_2.00 of width 12. +Using motif -M03445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988868 +# Estimated pi_0=1 +Using motif +M05691_2.00 of width 12. +Using motif -M05691_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977426 +# Estimated pi_0=0.989405 +Using motif +M05692_2.00 of width 12. +Using motif -M05692_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97514 +# Estimated pi_0=0.98975 +Using motif +M03445_2.00 of width 12. +Using motif -M03445_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02440_2.00 of width 10. +Using motif -M02440_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997946 +# Estimated pi_0=1 +Using motif +M05693_2.00 of width 10. +Using motif -M05693_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998769 +# Estimated pi_0=0.999698 +Using motif +M05694_2.00 of width 14. +Using motif -M05694_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05695_2.00 of width 21. +Using motif -M05695_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997216 +# Estimated pi_0=0.999095 +Using motif +M05696_2.00 of width 10. +Using motif -M05696_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M05697_2.00 of width 14. +Using motif -M05697_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05698_2.00 of width 21. +Using motif -M05698_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M09343_2.00 of width 9. +Using motif -M09343_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990414 +# Estimated pi_0=0.992746 +Using motif +M03446_2.00 of width 13. +Using motif -M03446_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9396 +# Estimated pi_0=0.953286 +Using motif +M05699_2.00 of width 13. +Using motif -M05699_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9532 +# Estimated pi_0=0.963494 +Using motif +M05700_2.00 of width 13. +Using motif -M05700_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.954737 +# Estimated pi_0=0.970857 +Using motif +M09344_2.00 of width 11. +Using motif -M09344_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.930275 +# Estimated pi_0=0.944823 +Using motif +M01306_2.00 of width 10. +Using motif -M01306_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05701_2.00 of width 10. +Using motif -M05701_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998359 +# Estimated pi_0=0.999397 +Using motif +M05702_2.00 of width 14. +Using motif -M05702_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05703_2.00 of width 10. +Using motif -M05703_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M05704_2.00 of width 14. +Using motif -M05704_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M09345_2.00 of width 10. +Using motif -M09345_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994759 +# Estimated pi_0=0.997576 +Using motif +M01511_2.00 of width 10. +Using motif -M01511_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996596 +# Estimated pi_0=0.999497 +Using motif +M01512_2.00 of width 10. +Using motif -M01512_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998343 +# Estimated pi_0=1 +Using motif +M02441_2.00 of width 10. +Using motif -M02441_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05705_2.00 of width 14. +Using motif -M05705_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05706_2.00 of width 18. +Using motif -M05706_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05707_2.00 of width 14. +Using motif -M05707_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05708_2.00 of width 18. +Using motif -M05708_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M08462_2.00 of width 7. +Using motif -M08462_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990222 +# Estimated pi_0=0.99665 +Using motif +M09346_2.00 of width 9. +Using motif -M09346_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995208 +# Estimated pi_0=0.998492 +Using motif +M03447_2.00 of width 14. +Using motif -M03447_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05709_2.00 of width 13. +Using motif -M05709_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.949107 +# Estimated pi_0=0.961528 +Using motif +M05710_2.00 of width 13. +Using motif -M05710_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999487 +# Estimated pi_0=1 +Using motif +M08161_2.00 of width 11. +Using motif -M08161_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970923 +# Estimated pi_0=0.980674 +Using motif +M09347_2.00 of width 12. +Using motif -M09347_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969444 +# Estimated pi_0=0.989888 +Using motif +M03448_2.00 of width 13. +Using motif -M03448_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.925243 +# Estimated pi_0=0.932403 +Using motif +M05887_2.00 of width 13. +Using motif -M05887_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9148 +# Estimated pi_0=0.922056 +Using motif +M09348_2.00 of width 8. +Using motif -M09348_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9664 +# Estimated pi_0=0.968261 +Using motif +M11228_2.00 of width 10. +Using motif -M11228_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.897426 +# Estimated pi_0=0.905565 +Using motif +M11229_2.00 of width 12. +Using motif -M11229_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962376 +# Estimated pi_0=0.97894 +Using motif +M11230_2.00 of width 14. +Using motif -M11230_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9828 +# Estimated pi_0=0.988342 +Using motif +M02747_2.00 of width 11. +Using motif -M02747_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991977 +# Estimated pi_0=0.996995 +Using motif +M02748_2.00 of width 10. +Using motif -M02748_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M02749_2.00 of width 9. +Using motif -M02749_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998974 +# Estimated pi_0=0.999799 +Using motif +M03449_2.00 of width 20. +Using motif -M03449_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M03450_2.00 of width 15. +Using motif -M03450_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=1 +Using motif +M03451_2.00 of width 14. +Using motif -M03451_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M05711_2.00 of width 14. +Using motif -M05711_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05712_2.00 of width 18. +Using motif -M05712_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999796 +# Estimated pi_0=1 +Using motif +M05713_2.00 of width 14. +Using motif -M05713_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05714_2.00 of width 18. +Using motif -M05714_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M09349_2.00 of width 15. +Using motif -M09349_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.971073 +# Estimated pi_0=0.974054 +Using motif +M09625_2.00 of width 10. +Using motif -M09625_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998054 +# Estimated pi_0=0.999397 +Using motif +M02710_2.00 of width 10. +Using motif -M02710_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999084 +# Estimated pi_0=1 +Using motif +M09350_2.00 of width 15. +Using motif -M09350_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960265 +# Estimated pi_0=0.96474 +Using motif +M11235_2.00 of width 10. +Using motif -M11235_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999239 +# Estimated pi_0=1 +Using motif +M02711_2.00 of width 10. +Using motif -M02711_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9532 +# Estimated pi_0=0.956333 +Using motif +M07999_2.00 of width 14. +Using motif -M07999_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963214 +# Estimated pi_0=0.978035 +Using motif +M08000_2.00 of width 14. +Using motif -M08000_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969159 +# Estimated pi_0=0.977225 +Using motif +M08001_2.00 of width 15. +Using motif -M08001_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963276 +# Estimated pi_0=0.968143 +Using motif +M08002_2.00 of width 15. +Using motif -M08002_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966538 +# Estimated pi_0=0.98 +Using motif +M08003_2.00 of width 14. +Using motif -M08003_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964034 +# Estimated pi_0=0.966788 +Using motif +M08004_2.00 of width 15. +Using motif -M08004_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967273 +# Estimated pi_0=0.98221 +Using motif +M08005_2.00 of width 15. +Using motif -M08005_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967931 +# Estimated pi_0=0.979326 +Using motif +M08006_2.00 of width 11. +Using motif -M08006_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965333 +# Estimated pi_0=0.974598 +Using motif +M08007_2.00 of width 13. +Using motif -M08007_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9642 +# Estimated pi_0=0.969512 +Using motif +M08008_2.00 of width 15. +Using motif -M08008_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951132 +# Estimated pi_0=0.959739 +Using motif +M09351_2.00 of width 14. +Using motif -M09351_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990385 +# Estimated pi_0=0.995385 +Using motif +M09626_2.00 of width 12. +Using motif -M09626_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983537 +# Estimated pi_0=0.99 +Using motif +M11237_2.00 of width 10. +Using motif -M11237_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9556 +# Estimated pi_0=0.966667 +Using motif +M02750_2.00 of width 10. +Using motif -M02750_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03452_2.00 of width 16. +Using motif -M03452_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03453_2.00 of width 15. +Using motif -M03453_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9804 +# Estimated pi_0=1 +Using motif +M05715_2.00 of width 16. +Using motif -M05715_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05716_2.00 of width 15. +Using motif -M05716_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9476 +# Estimated pi_0=0.962131 +Using motif +M05717_2.00 of width 16. +Using motif -M05717_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9804 +# Estimated pi_0=1 +Using motif +M05718_2.00 of width 15. +Using motif -M05718_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.969403 +Using motif +M09361_2.00 of width 14. +Using motif -M09361_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M09627_2.00 of width 16. +Using motif -M09627_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.939 +# Estimated pi_0=0.951967 +Using motif +M02449_2.00 of width 10. +Using motif -M02449_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9946 +# Estimated pi_0=1 +Using motif +M03454_2.00 of width 16. +Using motif -M03454_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999695 +# Estimated pi_0=1 +Using motif +M03455_2.00 of width 15. +Using motif -M03455_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958627 +# Estimated pi_0=0.962362 +Using motif +M05719_2.00 of width 16. +Using motif -M05719_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M05720_2.00 of width 16. +Using motif -M05720_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M09362_2.00 of width 18. +Using motif -M09362_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955091 +# Estimated pi_0=0.961486 +Using motif +M09628_2.00 of width 15. +Using motif -M09628_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982393 +# Estimated pi_0=0.988984 +Using motif +M03456_2.00 of width 16. +Using motif -M03456_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999293 +# Estimated pi_0=0.999497 +Using motif +M03457_2.00 of width 15. +Using motif -M03457_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961165 +# Estimated pi_0=0.972867 +Using motif +M05721_2.00 of width 16. +Using motif -M05721_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M05722_2.00 of width 16. +Using motif -M05722_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00085 +# Estimated pi_0=1 +Using motif +M05723_2.00 of width 16. +Using motif -M05723_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990943 +# Estimated pi_0=1 +Using motif +M05724_2.00 of width 16. +Using motif -M05724_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M09363_2.00 of width 22. +Using motif -M09363_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.856154 +# Estimated pi_0=0.869344 +Using motif +M11246_2.00 of width 17. +Using motif -M11246_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961714 +# Estimated pi_0=0.975634 +Using motif +M11247_2.00 of width 18. +Using motif -M11247_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982832 +# Estimated pi_0=0.990698 +Using motif +M03458_2.00 of width 16. +Using motif -M03458_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983119 +# Estimated pi_0=1 +Using motif +M03459_2.00 of width 16. +Using motif -M03459_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M03460_2.00 of width 16. +Using motif -M03460_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976667 +# Estimated pi_0=0.987632 +Using motif +M05725_2.00 of width 19. +Using motif -M05725_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975098 +# Estimated pi_0=1 +Using motif +M05726_2.00 of width 17. +Using motif -M05726_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.957885 +# Estimated pi_0=0.976 +Using motif +M05727_2.00 of width 19. +Using motif -M05727_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.946078 +# Estimated pi_0=0.951455 +Using motif +M05728_2.00 of width 17. +Using motif -M05728_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9328 +# Estimated pi_0=0.939655 +Using motif +M08009_2.00 of width 15. +Using motif -M08009_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977576 +# Estimated pi_0=0.985269 +Using motif +M08010_2.00 of width 19. +Using motif -M08010_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.972714 +# Estimated pi_0=0.977175 +Using motif +M08011_2.00 of width 15. +Using motif -M08011_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961029 +# Estimated pi_0=0.970465 +Using motif +M08012_2.00 of width 15. +Using motif -M08012_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968952 +# Estimated pi_0=0.974625 +Using motif +M09364_2.00 of width 20. +Using motif -M09364_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913465 +# Estimated pi_0=0.924961 +Using motif +M09629_2.00 of width 12. +Using motif -M09629_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981379 +# Estimated pi_0=0.987869 +Using motif +M05729_2.00 of width 9. +Using motif -M05729_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9478 +# Estimated pi_0=0.953739 +Using motif +M05730_2.00 of width 9. +Using motif -M05730_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953333 +# Estimated pi_0=0.960741 +Using motif +M09365_2.00 of width 11. +Using motif -M09365_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.943091 +# Estimated pi_0=0.95645 +Using motif +M02454_2.00 of width 10. +Using motif -M02454_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9832 +# Estimated pi_0=1 +Using motif +M02751_2.00 of width 8. +Using motif -M02751_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980137 +# Estimated pi_0=0.987978 +Using motif +M03464_2.00 of width 16. +Using motif -M03464_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922178 +# Estimated pi_0=0.935667 +Using motif +M03465_2.00 of width 10. +Using motif -M03465_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.967103 +# Estimated pi_0=0.983185 +Using motif +M03466_2.00 of width 18. +Using motif -M03466_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935534 +# Estimated pi_0=0.947534 +Using motif +M03467_2.00 of width 10. +Using motif -M03467_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.980734 +# Estimated pi_0=0.995959 +Using motif +M05731_2.00 of width 20. +Using motif -M05731_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988594 +# Estimated pi_0=0.996429 +Using motif +M05732_2.00 of width 21. +Using motif -M05732_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991285 +# Estimated pi_0=0.994792 +Using motif +M09369_2.00 of width 10. +Using motif -M09369_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.974507 +# Estimated pi_0=0.984918 +Using motif +M03468_2.00 of width 16. +Using motif -M03468_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.915294 +# Estimated pi_0=0.931318 +Using motif +M03469_2.00 of width 18. +Using motif -M03469_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9432 +# Estimated pi_0=0.949402 +Using motif +M03470_2.00 of width 9. +Using motif -M03470_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977168 +# Estimated pi_0=0.990549 +Using motif +M05733_2.00 of width 20. +Using motif -M05733_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9608 +# Estimated pi_0=0.978519 +Using motif +M05734_2.00 of width 20. +Using motif -M05734_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989913 +# Estimated pi_0=0.998894 +Using motif +M09370_2.00 of width 14. +Using motif -M09370_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964874 +# Estimated pi_0=0.973018 +Using motif +M09630_2.00 of width 12. +Using motif -M09630_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976364 +# Estimated pi_0=0.982811 +Using motif +M09371_2.00 of width 11. +Using motif -M09371_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976974 +# Estimated pi_0=0.987059 +Using motif +M09631_2.00 of width 12. +Using motif -M09631_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953684 +# Estimated pi_0=0.963529 +Using motif +M09632_2.00 of width 10. +Using motif -M09632_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986548 +# Estimated pi_0=0.99266 +Using motif +M11258_2.00 of width 6. +Using motif -M11258_2.00 of width 6. +Computing q-values. +Using motif +M03471_2.00 of width 8. +Using motif -M03471_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9522 +# Estimated pi_0=0.956415 +Using motif +M03472_2.00 of width 14. +Using motif -M03472_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9782 +# Estimated pi_0=1 +Using motif +M03473_2.00 of width 15. +Using motif -M03473_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951 +# Estimated pi_0=0.955185 +Using motif +M03474_2.00 of width 8. +Using motif -M03474_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9638 +# Estimated pi_0=0.975596 +Using motif +M05735_2.00 of width 17. +Using motif -M05735_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.922353 +# Estimated pi_0=0.930095 +Using motif +M05736_2.00 of width 17. +Using motif -M05736_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8732 +# Estimated pi_0=0.875146 +Using motif +M09375_2.00 of width 18. +Using motif -M09375_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M02460_2.00 of width 9. +Using motif -M02460_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8542 +# Estimated pi_0=0.854455 +Using motif +M05737_2.00 of width 17. +Using motif -M05737_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.97505 +# Estimated pi_0=1 +Using motif +M05738_2.00 of width 16. +Using motif -M05738_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.902772 +# Estimated pi_0=0.906019 +Using motif +M05739_2.00 of width 14. +Using motif -M05739_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M05740_2.00 of width 14. +Using motif -M05740_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00302 +# Estimated pi_0=1 +Using motif +M03475_2.00 of width 15. +Using motif -M03475_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947327 +# Estimated pi_0=0.958431 +Using motif +M03476_2.00 of width 9. +Using motif -M03476_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9242 +# Estimated pi_0=0.941702 +Using motif +M03477_2.00 of width 9. +Using motif -M03477_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.937966 +# Estimated pi_0=0.9395 +Using motif +M03478_2.00 of width 15. +Using motif -M03478_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975873 +# Estimated pi_0=0.986064 +Using motif +M05741_2.00 of width 17. +Using motif -M05741_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.894 +# Estimated pi_0=0.904138 +Using motif +M05742_2.00 of width 17. +Using motif -M05742_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.91604 +# Estimated pi_0=0.923 +Using motif +M05743_2.00 of width 10. +Using motif -M05743_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9674 +# Estimated pi_0=0.979847 +Using motif +M05744_2.00 of width 10. +Using motif -M05744_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.970667 +# Estimated pi_0=0.987945 +Using motif +M09377_2.00 of width 13. +Using motif -M09377_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94832 +# Estimated pi_0=0.958382 +Using motif +M09634_2.00 of width 10. +Using motif -M09634_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953333 +# Estimated pi_0=0.97141 +Using motif +M05745_2.00 of width 17. +Using motif -M05745_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9324 +# Estimated pi_0=0.950738 +Using motif +M05746_2.00 of width 17. +Using motif -M05746_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92375 +# Estimated pi_0=0.925789 +Using motif +M08164_2.00 of width 11. +Using motif -M08164_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.963762 +# Estimated pi_0=0.975754 +Using motif +M09378_2.00 of width 17. +Using motif -M09378_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.92766 +# Estimated pi_0=0.938375 +Using motif +M09635_2.00 of width 8. +Using motif -M09635_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947414 +# Estimated pi_0=0.95881 +Using motif +M09636_2.00 of width 16. +Using motif -M09636_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.951544 +# Estimated pi_0=0.957586 +Using motif +M03479_2.00 of width 15. +Using motif -M03479_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.956727 +# Estimated pi_0=0.972078 +Using motif +M05747_2.00 of width 17. +Using motif -M05747_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9044 +# Estimated pi_0=0.91313 +Using motif +M05748_2.00 of width 17. +Using motif -M05748_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9298 +# Estimated pi_0=0.946377 +Using motif +M03480_2.00 of width 15. +Using motif -M03480_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961111 +# Estimated pi_0=0.970064 +Using motif +M03481_2.00 of width 10. +Using motif -M03481_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.966029 +# Estimated pi_0=0.974359 +Using motif +M09379_2.00 of width 15. +Using motif -M09379_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.944706 +# Estimated pi_0=0.956622 +Using motif +M03482_2.00 of width 10. +Using motif -M03482_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987429 +# Estimated pi_0=1 +Using motif +M09380_2.00 of width 12. +Using motif -M09380_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.953291 +# Estimated pi_0=0.957 +Using motif +M03483_2.00 of width 16. +Using motif -M03483_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M03484_2.00 of width 13. +Using motif -M03484_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03485_2.00 of width 17. +Using motif -M03485_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998995 +# Estimated pi_0=0.998995 +Using motif +M03486_2.00 of width 17. +Using motif -M03486_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M03487_2.00 of width 14. +Using motif -M03487_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M03488_2.00 of width 15. +Using motif -M03488_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999091 +# Estimated pi_0=0.999196 +Using motif +M03489_2.00 of width 13. +Using motif -M03489_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03490_2.00 of width 17. +Using motif -M03490_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05749_2.00 of width 10. +Using motif -M05749_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M05750_2.00 of width 10. +Using motif -M05750_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05751_2.00 of width 16. +Using motif -M05751_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05752_2.00 of width 16. +Using motif -M05752_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999394 +# Estimated pi_0=1 +Using motif +M05753_2.00 of width 8. +Using motif -M05753_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M05754_2.00 of width 8. +Using motif -M05754_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02487_2.00 of width 11. +Using motif -M02487_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00195_2.00 of width 10. +Using motif -M00195_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983145 +# Estimated pi_0=0.987684 +Using motif +M05755_2.00 of width 9. +Using motif -M05755_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05756_2.00 of width 15. +Using motif -M05756_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932075 +# Estimated pi_0=0.94371 +Using motif +M05757_2.00 of width 9. +Using motif -M05757_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998324 +# Estimated pi_0=0.999095 +Using motif +M05758_2.00 of width 15. +Using motif -M05758_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94566 +# Estimated pi_0=0.95344 +Using motif +M05890_2.00 of width 13. +Using motif -M05890_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997629 +# Estimated pi_0=0.999598 +Using motif +M09386_2.00 of width 16. +Using motif -M09386_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985244 +# Estimated pi_0=0.991224 +Using motif +M03491_2.00 of width 15. +Using motif -M03491_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M03492_2.00 of width 16. +Using motif -M03492_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999167 +# Estimated pi_0=0.999497 +Using motif +M03493_2.00 of width 15. +Using motif -M03493_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03494_2.00 of width 15. +Using motif -M03494_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03495_2.00 of width 14. +Using motif -M03495_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M05759_2.00 of width 17. +Using motif -M05759_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05760_2.00 of width 17. +Using motif -M05760_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M05761_2.00 of width 18. +Using motif -M05761_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M05762_2.00 of width 18. +Using motif -M05762_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05891_2.00 of width 10. +Using motif -M05891_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997563 +# Estimated pi_0=1 +Using motif +M08165_2.00 of width 11. +Using motif -M08165_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990395 +# Estimated pi_0=0.992551 +Using motif +M09387_2.00 of width 16. +Using motif -M09387_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997816 +# Estimated pi_0=1 +Using motif +M05892_2.00 of width 10. +Using motif -M05892_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999072 +# Estimated pi_0=1 +Using motif +M05763_2.00 of width 13. +Using motif -M05763_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05764_2.00 of width 13. +Using motif -M05764_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M01026_2.00 of width 8. +Using motif -M01026_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993838 +# Estimated pi_0=0.997778 +Using motif +M03496_2.00 of width 16. +Using motif -M03496_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05765_2.00 of width 10. +Using motif -M05765_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994737 +# Estimated pi_0=0.999497 +Using motif +M05766_2.00 of width 10. +Using motif -M05766_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992193 +# Estimated pi_0=0.99551 +Using motif +M09388_2.00 of width 12. +Using motif -M09388_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960261 +# Estimated pi_0=0.966552 +Using motif +M03497_2.00 of width 15. +Using motif -M03497_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M03498_2.00 of width 16. +Using motif -M03498_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03499_2.00 of width 13. +Using motif -M03499_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03500_2.00 of width 13. +Using motif -M03500_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M02716_2.00 of width 9. +Using motif -M02716_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M03501_2.00 of width 9. +Using motif -M03501_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03502_2.00 of width 16. +Using motif -M03502_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00091 +# Estimated pi_0=1 +Using motif +M03503_2.00 of width 16. +Using motif -M03503_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M03504_2.00 of width 16. +Using motif -M03504_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03505_2.00 of width 17. +Using motif -M03505_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03506_2.00 of width 17. +Using motif -M03506_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03507_2.00 of width 17. +Using motif -M03507_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05767_2.00 of width 10. +Using motif -M05767_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M05768_2.00 of width 10. +Using motif -M05768_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9804 +# Estimated pi_0=1 +Using motif +M09389_2.00 of width 16. +Using motif -M09389_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.988024 +# Estimated pi_0=0.99267 +Using motif +M11305_2.00 of width 14. +Using motif -M11305_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00071 +# Estimated pi_0=1 +Using motif +M03508_2.00 of width 15. +Using motif -M03508_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03509_2.00 of width 15. +Using motif -M03509_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M03510_2.00 of width 15. +Using motif -M03510_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05893_2.00 of width 10. +Using motif -M05893_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=1 +Using motif +M05894_2.00 of width 10. +Using motif -M05894_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999899 +# Estimated pi_0=0.999899 +Using motif +M09390_2.00 of width 8. +Using motif -M09390_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999492 +# Estimated pi_0=1 +Using motif +M11309_2.00 of width 10. +Using motif -M11309_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05769_2.00 of width 10. +Using motif -M05769_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M05770_2.00 of width 10. +Using motif -M05770_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0008 +# Estimated pi_0=1 +Using motif +M09391_2.00 of width 11. +Using motif -M09391_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976384 +# Estimated pi_0=0.982526 +Using motif +M03511_2.00 of width 15. +Using motif -M03511_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995337 +# Estimated pi_0=0.99736 +Using motif +M05771_2.00 of width 9. +Using motif -M05771_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00083 +# Estimated pi_0=1 +Using motif +M05772_2.00 of width 15. +Using motif -M05772_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.913 +# Estimated pi_0=0.921239 +Using motif +M05773_2.00 of width 9. +Using motif -M05773_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999799 +# Estimated pi_0=0.999799 +Using motif +M05774_2.00 of width 15. +Using motif -M05774_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.941 +# Estimated pi_0=0.95374 +Using motif +M05775_2.00 of width 9. +Using motif -M05775_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998012 +# Estimated pi_0=1 +Using motif +M05776_2.00 of width 15. +Using motif -M05776_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.932 +# Estimated pi_0=0.950833 +Using motif +M05777_2.00 of width 9. +Using motif -M05777_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05778_2.00 of width 15. +Using motif -M05778_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9396 +# Estimated pi_0=0.941359 +Using motif +M05895_2.00 of width 10. +Using motif -M05895_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997382 +# Estimated pi_0=0.999799 +Using motif +M09392_2.00 of width 14. +Using motif -M09392_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960667 +# Estimated pi_0=0.968508 +Using motif +M02487_2.00 of width 11. +Using motif -M02487_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M08166_2.00 of width 11. +Using motif -M08166_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00075 +# Estimated pi_0=1 +Using motif +M08167_2.00 of width 14. +Using motif -M08167_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996831 +# Estimated pi_0=1 +Using motif +M09393_2.00 of width 10. +Using motif -M09393_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.935333 +# Estimated pi_0=0.944571 +Using motif +M09394_2.00 of width 13. +Using motif -M09394_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982207 +# Estimated pi_0=0.989255 +Using motif +M09638_2.00 of width 10. +Using motif -M09638_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.989811 +# Estimated pi_0=0.998376 +Using motif +M03512_2.00 of width 12. +Using motif -M03512_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996043 +# Estimated pi_0=1 +Using motif +M05779_2.00 of width 9. +Using motif -M05779_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M05780_2.00 of width 15. +Using motif -M05780_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9354 +# Estimated pi_0=0.946567 +Using motif +M05781_2.00 of width 9. +Using motif -M05781_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00063 +# Estimated pi_0=1 +Using motif +M05782_2.00 of width 15. +Using motif -M05782_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9146 +# Estimated pi_0=0.928092 +Using motif +M09395_2.00 of width 13. +Using motif -M09395_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994078 +# Estimated pi_0=0.998291 +Using motif +M05896_2.00 of width 10. +Using motif -M05896_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00122 +# Estimated pi_0=1 +Using motif +M09396_2.00 of width 11. +Using motif -M09396_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.969126 +# Estimated pi_0=0.976951 +Using motif +M03513_2.00 of width 12. +Using motif -M03513_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M03514_2.00 of width 15. +Using motif -M03514_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00182 +# Estimated pi_0=1 +Using motif +M03515_2.00 of width 13. +Using motif -M03515_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M05783_2.00 of width 10. +Using motif -M05783_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00051 +# Estimated pi_0=1 +Using motif +M05784_2.00 of width 10. +Using motif -M05784_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05785_2.00 of width 10. +Using motif -M05785_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M05786_2.00 of width 10. +Using motif -M05786_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M03516_2.00 of width 16. +Using motif -M03516_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M03517_2.00 of width 17. +Using motif -M03517_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999296 +# Estimated pi_0=0.999296 +Using motif +M03518_2.00 of width 17. +Using motif -M03518_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M05787_2.00 of width 13. +Using motif -M05787_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00161 +# Estimated pi_0=1 +Using motif +M05788_2.00 of width 13. +Using motif -M05788_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0001 +# Estimated pi_0=1 +Using motif +M05789_2.00 of width 11. +Using motif -M05789_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997806 +# Estimated pi_0=1 +Using motif +M05790_2.00 of width 11. +Using motif -M05790_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03519_2.00 of width 17. +Using motif -M03519_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0007 +# Estimated pi_0=1 +Using motif +M03520_2.00 of width 15. +Using motif -M03520_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03521_2.00 of width 17. +Using motif -M03521_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00241 +# Estimated pi_0=1 +Using motif +M03522_2.00 of width 17. +Using motif -M03522_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0004 +# Estimated pi_0=1 +Using motif +M03523_2.00 of width 17. +Using motif -M03523_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03524_2.00 of width 15. +Using motif -M03524_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M05897_2.00 of width 10. +Using motif -M05897_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998485 +# Estimated pi_0=0.999598 +Using motif +M09397_2.00 of width 13. +Using motif -M09397_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976949 +# Estimated pi_0=0.980741 +Using motif +M00214_2.00 of width 10. +Using motif -M00214_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00211 +# Estimated pi_0=1 +Using motif +M02717_2.00 of width 9. +Using motif -M02717_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998883 +# Estimated pi_0=1 +Using motif +M03525_2.00 of width 15. +Using motif -M03525_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0003 +# Estimated pi_0=1 +Using motif +M03526_2.00 of width 16. +Using motif -M03526_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M03527_2.00 of width 13. +Using motif -M03527_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00151 +# Estimated pi_0=1 +Using motif +M03528_2.00 of width 13. +Using motif -M03528_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M05898_2.00 of width 8. +Using motif -M05898_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M09398_2.00 of width 9. +Using motif -M09398_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997959 +# Estimated pi_0=0.999497 +Using motif +M11331_2.00 of width 7. +Using motif -M11331_2.00 of width 7. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996954 +# Estimated pi_0=0.999196 +Using motif +M11332_2.00 of width 12. +Using motif -M11332_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999596 +# Estimated pi_0=0.999799 +Using motif +M03529_2.00 of width 15. +Using motif -M03529_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00101 +# Estimated pi_0=1 +Using motif +M03530_2.00 of width 15. +Using motif -M03530_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00111 +# Estimated pi_0=1 +Using motif +M03531_2.00 of width 15. +Using motif -M03531_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05791_2.00 of width 8. +Using motif -M05791_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9226 +# Estimated pi_0=0.924706 +Using motif +M05792_2.00 of width 8. +Using motif -M05792_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998947 +# Estimated pi_0=1 +Using motif +M08013_2.00 of width 21. +Using motif -M08013_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982901 +# Estimated pi_0=0.991649 +Using motif +M08014_2.00 of width 11. +Using motif -M08014_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995359 +# Estimated pi_0=0.998894 +Using motif +M08171_2.00 of width 11. +Using motif -M08171_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997538 +# Estimated pi_0=0.998693 +Using motif +M08229_2.00 of width 10. +Using motif -M08229_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999495 +# Estimated pi_0=1 +Using motif +M08230_2.00 of width 10. +Using motif -M08230_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0002 +# Estimated pi_0=1 +Using motif +M09411_2.00 of width 19. +Using motif -M09411_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985089 +# Estimated pi_0=0.994564 +Using motif +M09642_2.00 of width 14. +Using motif -M09642_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00142 +# Estimated pi_0=1 +Using motif +M11348_2.00 of width 21. +Using motif -M11348_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9322 +# Estimated pi_0=0.941525 +Using motif +M11350_2.00 of width 8. +Using motif -M11350_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999095 +# Estimated pi_0=0.999095 +Using motif +M09412_2.00 of width 10. +Using motif -M09412_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997879 +# Estimated pi_0=0.998894 +Using motif +M11355_2.00 of width 15. +Using motif -M11355_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999689 +# Estimated pi_0=1 +Using motif +M11356_2.00 of width 24. +Using motif -M11356_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0006 +# Estimated pi_0=1 +Using motif +M11358_2.00 of width 8. +Using motif -M11358_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991913 +# Estimated pi_0=0.997778 +Using motif +M09413_2.00 of width 11. +Using motif -M09413_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.977179 +# Estimated pi_0=0.986632 +Using motif +M11361_2.00 of width 8. +Using motif -M11361_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998492 +Using motif +M09414_2.00 of width 11. +Using motif -M09414_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995 +# Estimated pi_0=0.99809 +Using motif +M11365_2.00 of width 8. +Using motif -M11365_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983429 +# Estimated pi_0=0.990306 +Using motif +M08015_2.00 of width 20. +Using motif -M08015_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997202 +# Estimated pi_0=0.998995 +Using motif +M08016_2.00 of width 13. +Using motif -M08016_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998492 +# Estimated pi_0=0.998492 +Using motif +M08017_2.00 of width 15. +Using motif -M08017_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992928 +# Estimated pi_0=0.996364 +Using motif +M08018_2.00 of width 15. +Using motif -M08018_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.992356 +# Estimated pi_0=0.995051 +Using motif +M08172_2.00 of width 11. +Using motif -M08172_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993886 +# Estimated pi_0=0.997186 +Using motif +M09415_2.00 of width 12. +Using motif -M09415_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998061 +# Estimated pi_0=0.998693 +Using motif +M11367_2.00 of width 21. +Using motif -M11367_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9476 +# Estimated pi_0=0.951532 +Using motif +M11368_2.00 of width 8. +Using motif -M11368_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.973786 +# Estimated pi_0=0.984253 +Using motif +M08019_2.00 of width 15. +Using motif -M08019_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993368 +# Estimated pi_0=0.996181 +Using motif +M09416_2.00 of width 19. +Using motif -M09416_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.982766 +# Estimated pi_0=0.986186 +Using motif +M09417_2.00 of width 12. +Using motif -M09417_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999497 +# Estimated pi_0=0.999497 +Using motif +M11371_2.00 of width 15. +Using motif -M11371_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999698 +# Estimated pi_0=0.999698 +Using motif +M03546_2.00 of width 16. +Using motif -M03546_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983462 +# Estimated pi_0=0.994839 +Using motif +M03547_2.00 of width 10. +Using motif -M03547_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995 +# Estimated pi_0=0.998485 +Using motif +M03548_2.00 of width 19. +Using motif -M03548_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962778 +# Estimated pi_0=0.979745 +Using motif +M03549_2.00 of width 16. +Using motif -M03549_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99381 +# Estimated pi_0=0.998593 +Using motif +M03550_2.00 of width 10. +Using motif -M03550_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997436 +# Estimated pi_0=0.999598 +Using motif +M03551_2.00 of width 19. +Using motif -M03551_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998342 +# Estimated pi_0=0.999497 +Using motif +M05793_2.00 of width 10. +Using motif -M05793_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.981805 +# Estimated pi_0=0.992967 +Using motif +M05794_2.00 of width 10. +Using motif -M05794_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9822 +# Estimated pi_0=1 +Using motif +M09425_2.00 of width 13. +Using motif -M09425_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.947945 +# Estimated pi_0=0.954545 +Using motif +M03552_2.00 of width 8. +Using motif -M03552_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.964238 +# Estimated pi_0=0.972778 +Using motif +M03553_2.00 of width 20. +Using motif -M03553_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997778 +# Estimated pi_0=0.998392 +Using motif +M03554_2.00 of width 19. +Using motif -M03554_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998586 +# Estimated pi_0=0.998693 +Using motif +M03555_2.00 of width 8. +Using motif -M03555_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.96512 +# Estimated pi_0=0.970253 +Using motif +M05795_2.00 of width 10. +Using motif -M05795_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.942569 +# Estimated pi_0=0.955267 +Using motif +M05796_2.00 of width 10. +Using motif -M05796_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9698 +# Estimated pi_0=0.994946 +Using motif +M05797_2.00 of width 12. +Using motif -M05797_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.962432 +# Estimated pi_0=0.965229 +Using motif +M05798_2.00 of width 12. +Using motif -M05798_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984143 +# Estimated pi_0=0.994409 +Using motif +M03556_2.00 of width 18. +Using motif -M03556_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9596 +# Estimated pi_0=0.966032 +Using motif +M03557_2.00 of width 11. +Using motif -M03557_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999251 +# Estimated pi_0=1 +Using motif +M05799_2.00 of width 10. +Using motif -M05799_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.853529 +# Estimated pi_0=0.856893 +Using motif +M05800_2.00 of width 10. +Using motif -M05800_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9112 +# Estimated pi_0=0.918039 +Using motif +M03558_2.00 of width 8. +Using motif -M03558_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965 +# Estimated pi_0=0.989836 +Using motif +M03559_2.00 of width 20. +Using motif -M03559_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960194 +# Estimated pi_0=0.991459 +Using motif +M05801_2.00 of width 10. +Using motif -M05801_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9154 +# Estimated pi_0=0.917282 +Using motif +M05802_2.00 of width 10. +Using motif -M05802_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961 +# Estimated pi_0=0.965484 +Using motif +M09426_2.00 of width 11. +Using motif -M09426_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.94303 +# Estimated pi_0=0.945977 +Using motif +M03560_2.00 of width 10. +Using motif -M03560_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997396 +# Estimated pi_0=0.999095 +Using motif +M03561_2.00 of width 11. +Using motif -M03561_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995729 +# Estimated pi_0=0.999196 +Using motif +M03562_2.00 of width 20. +Using motif -M03562_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M05803_2.00 of width 21. +Using motif -M05803_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.985634 +# Estimated pi_0=1 +Using motif +M05804_2.00 of width 21. +Using motif -M05804_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M05805_2.00 of width 10. +Using motif -M05805_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9626 +# Estimated pi_0=0.975571 +Using motif +M05806_2.00 of width 10. +Using motif -M05806_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9654 +# Estimated pi_0=0.975478 +Using motif +M02752_2.00 of width 9. +Using motif -M02752_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.991111 +# Estimated pi_0=0.997462 +Using motif +M03563_2.00 of width 13. +Using motif -M03563_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.996933 +# Estimated pi_0=0.999192 +Using motif +M03564_2.00 of width 20. +Using motif -M03564_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.958627 +# Estimated pi_0=0.969402 +Using motif +M05807_2.00 of width 10. +Using motif -M05807_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.983363 +# Estimated pi_0=0.993053 +Using motif +M05808_2.00 of width 10. +Using motif -M05808_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9664 +# Estimated pi_0=0.98775 +Using motif +M09647_2.00 of width 10. +Using motif -M09647_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995714 +# Estimated pi_0=0.997778 +Using motif +M05809_2.00 of width 22. +Using motif -M05809_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999 +# Estimated pi_0=1 +Using motif +M05810_2.00 of width 18. +Using motif -M05810_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00121 +# Estimated pi_0=1 +Using motif +M05811_2.00 of width 22. +Using motif -M05811_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979057 +# Estimated pi_0=1 +Using motif +M05812_2.00 of width 18. +Using motif -M05812_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994118 +# Estimated pi_0=1 +Using motif +M09427_2.00 of width 20. +Using motif -M09427_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.961695 +# Estimated pi_0=0.972121 +Using motif +M11384_2.00 of width 24. +Using motif -M11384_2.00 of width 24. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M03565_2.00 of width 16. +Using motif -M03565_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.975842 +# Estimated pi_0=0.988354 +Using motif +M03566_2.00 of width 19. +Using motif -M03566_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993019 +# Estimated pi_0=0.999095 +Using motif +M03567_2.00 of width 15. +Using motif -M03567_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.993927 +# Estimated pi_0=0.998191 +Using motif +M03568_2.00 of width 11. +Using motif -M03568_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.994144 +# Estimated pi_0=0.999497 +Using motif +M03569_2.00 of width 16. +Using motif -M03569_2.00 of width 16. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.986706 +# Estimated pi_0=0.993789 +Using motif +M05813_2.00 of width 18. +Using motif -M05813_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987363 +# Estimated pi_0=0.990802 +Using motif +M05814_2.00 of width 21. +Using motif -M05814_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998367 +# Estimated pi_0=1 +Using motif +M05815_2.00 of width 18. +Using motif -M05815_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9825 +# Estimated pi_0=0.999082 +Using motif +M05816_2.00 of width 21. +Using motif -M05816_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998447 +# Estimated pi_0=1 +Using motif +M05817_2.00 of width 18. +Using motif -M05817_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.968769 +# Estimated pi_0=0.976296 +Using motif +M05818_2.00 of width 18. +Using motif -M05818_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0005 +# Estimated pi_0=1 +Using motif +M05819_2.00 of width 18. +Using motif -M05819_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955 +# Estimated pi_0=0.962556 +Using motif +M05820_2.00 of width 18. +Using motif -M05820_2.00 of width 18. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00141 +# Estimated pi_0=1 +Using motif +M00832_2.00 of width 11. +Using motif -M00832_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997566 +# Estimated pi_0=0.999598 +Using motif +M03570_2.00 of width 20. +Using motif -M03570_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99544 +# Estimated pi_0=0.999095 +Using motif +M03571_2.00 of width 19. +Using motif -M03571_2.00 of width 19. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999077 +# Estimated pi_0=1 +Using motif +M03572_2.00 of width 8. +Using motif -M03572_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979802 +# Estimated pi_0=0.991913 +Using motif +M03573_2.00 of width 23. +Using motif -M03573_2.00 of width 23. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.984741 +# Estimated pi_0=0.989364 +Using motif +M03574_2.00 of width 20. +Using motif -M03574_2.00 of width 20. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.979444 +# Estimated pi_0=0.986203 +Using motif +M05821_2.00 of width 12. +Using motif -M05821_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.976491 +# Estimated pi_0=0.989206 +Using motif +M05822_2.00 of width 12. +Using motif -M05822_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99694 +# Estimated pi_0=0.999397 +Using motif +M08231_2.00 of width 10. +Using motif -M08231_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M09432_2.00 of width 10. +Using motif -M09432_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00171 +# Estimated pi_0=1 +Using motif +M11388_2.00 of width 15. +Using motif -M11388_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00181 +# Estimated pi_0=1 +Using motif +M11389_2.00 of width 10. +Using motif -M11389_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.0009 +# Estimated pi_0=1 +Using motif +M11390_2.00 of width 8. +Using motif -M11390_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00191 +# Estimated pi_0=1 +Using motif +M11491_2.00 of width 15. +Using motif -M11491_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00261 +# Estimated pi_0=1 +Using motif +M00216_2.00 of width 7. +Using motif -M00216_2.00 of width 7. +Computing q-values. +Using motif +M02527_2.00 of width 9. +Using motif -M02527_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00201 +# Estimated pi_0=1 +Using motif +M03575_2.00 of width 17. +Using motif -M03575_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00032 +# Estimated pi_0=1 +Using motif +M03576_2.00 of width 8. +Using motif -M03576_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.998919 +# Estimated pi_0=1 +Using motif +M05823_2.00 of width 10. +Using motif -M05823_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1 +# Estimated pi_0=1 +Using motif +M05824_2.00 of width 10. +Using motif -M05824_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00131 +# Estimated pi_0=1 +Using motif +M08175_2.00 of width 13. +Using motif -M08175_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987119 +# Estimated pi_0=0.989843 +Using motif +M03577_2.00 of width 10. +Using motif -M03577_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00042 +# Estimated pi_0=1 +Using motif +M03578_2.00 of width 17. +Using motif -M03578_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.99899 +# Estimated pi_0=0.999095 +Using motif +M05825_2.00 of width 9. +Using motif -M05825_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9788 +# Estimated pi_0=1 +Using motif +M05826_2.00 of width 9. +Using motif -M05826_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00012 +# Estimated pi_0=1 +Using motif +M09434_2.00 of width 12. +Using motif -M09434_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.995273 +# Estimated pi_0=0.999799 +Using motif +M03579_2.00 of width 10. +Using motif -M03579_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999062 +# Estimated pi_0=0.999899 +Using motif +M09435_2.00 of width 13. +Using motif -M09435_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.997556 +# Estimated pi_0=1 +Using motif +M08020_2.00 of width 21. +Using motif -M08020_2.00 of width 21. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.95 +# Estimated pi_0=0.964028 +Using motif +M08176_2.00 of width 9. +Using motif -M08176_2.00 of width 9. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.960485 +# Estimated pi_0=0.962209 +Using motif +M09439_2.00 of width 22. +Using motif -M09439_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.780388 +# Estimated pi_0=0.784828 +Using motif +M02538_2.00 of width 12. +Using motif -M02538_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8886 +# Estimated pi_0=0.892673 +Using motif +M09440_2.00 of width 22. +Using motif -M09440_2.00 of width 22. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.965124 +# Estimated pi_0=0.968608 +Using motif +M09442_2.00 of width 14. +Using motif -M09442_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9436 +# Estimated pi_0=0.950566 +Using motif +M03580_2.00 of width 12. +Using motif -M03580_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9142 +# Estimated pi_0=0.923429 +Using motif +M08177_2.00 of width 11. +Using motif -M08177_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8376 +# Estimated pi_0=0.843619 +Using motif +M09443_2.00 of width 17. +Using motif -M09443_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8774 +# Estimated pi_0=0.88931 +Using motif +M09655_2.00 of width 12. +Using motif -M09655_2.00 of width 12. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.84297 +# Estimated pi_0=0.861416 +Using motif +M00967_2.00 of width 10. +Using motif -M00967_2.00 of width 10. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8658 +# Estimated pi_0=0.8785 +Using motif +M08178_2.00 of width 15. +Using motif -M08178_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.9442 +# Estimated pi_0=0.951132 +Using motif +M09444_2.00 of width 13. +Using motif -M09444_2.00 of width 13. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.955 +# Estimated pi_0=0.963387 +Using motif +M05827_2.00 of width 11. +Using motif -M05827_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.918053 +# Estimated pi_0=0.928333 +Using motif +M05828_2.00 of width 11. +Using motif -M05828_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.898218 +# Estimated pi_0=0.906102 +Using motif +M00968_2.00 of width 11. +Using motif -M00968_2.00 of width 11. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.7972 +# Estimated pi_0=0.801359 +Using motif +M11433_2.00 of width 15. +Using motif -M11433_2.00 of width 15. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.8768 +# Estimated pi_0=0.884037 +Using motif +M11435_2.00 of width 14. +Using motif -M11435_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990435 +# Estimated pi_0=0.996382 +Using motif +M05829_2.00 of width 14. +Using motif -M05829_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00221 +# Estimated pi_0=1 +Using motif +M05830_2.00 of width 14. +Using motif -M05830_2.00 of width 14. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 1.00271 +# Estimated pi_0=1 +Using motif +M11437_2.00 of width 17. +Using motif -M11437_2.00 of width 17. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.987143 +# Estimated pi_0=0.998144 +Using motif +M03581_2.00 of width 8. +Using motif -M03581_2.00 of width 8. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.999192 +# Estimated pi_0=0.999296 +Using motif +M07782_2.00 of width 29. +Using motif -M07782_2.00 of width 29. +Computing q-values. +# Computing q-values. +# Estimating pi_0 from a uniformly sampled set of 10000 p-values. +# Estimating pi_0. +# Minimal pi_zero = 0.990204 +# Estimated pi_0=0.992563