MoCA: Tool for MOtif Conservation Analysis

LICENSE

ISC

Installation

Requirements

• pybedtools
• biopython
• pandas
• scipy
• statsmodels
• pybigwig
• seaborn
• MEME==4.10.2

NOTE: MoCA also relies on fasta-shuffle-letters that was introduced in MEME 4.11.0 hence if you are using 4.10.2 make sure the fasta-shuffle-letters is the updated one.

For a sample script see travis/install_meme.sh

Using Conda

moca is most compatible with the conda environment.

$ conda config --add channels bioconda
$ conda install moca

Using pip

$ pip install moca

For development

$ git clone https://github.com:saketkc/moca.git
$ cd moca
$ conda env create -f environment.yml python=2.7
$ source activate mocadev
$ python setup.py install

Workflow

MoCA makes use of PhyloP/PhastCons/GERP scores to assess the quality of a motif, the hypothesis being a 'true motif' would evolve slower as compared to its surrounding(flanking sequences).

Usage

$ moca
Usage: moca [OPTIONS] COMMAND [ARGS]...

  moca: Motif Conservation Analysis

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  find_motifs  Run meme to locate motifs and create...
  plot         Create stacked conservation plots

Motif analysis using MEME

MoCA can perform motif analysis for you given a bedfile containing ChIP-Seq peaks.

Genome builds and MEME binary locations are specified through a configuraton file. A sample configuration file is available: tests/data/application.cfg and should be self-explanatory.

moca find_motifs

$ moca find_motifs -h
Usage: moca find_motifs [OPTIONS]

  Run meme to locate motifs and create conservation stacked plots

Options:
  -i, --bedfile TEXT            Bed file input  [required]
  -o, --oc TEXT                 Output Directory  [required]
  -c, --configuration TEXT      Configuration file  [required]
  --slop-length INTEGER         Flanking sequence length  [required]
  --flank-motif INTEGER         Length of sequence flanking motif  [required]
  --n-motif INTEGER             Number of motifs
  -t, --cores INTEGER           Number of parallel MEME jobs  [required]
  -g, -gb, --genome-build TEXT  Key denoting genome build to use in
                                configuration file  [required]
  --show-progress               Print progress
  -h, --help                    Show this message and exit.

moca plot

$ moca plot -h
Usage: moca plot [OPTIONS]

  Create stacked conservation plots

Options:
  --meme-dir, --meme_dir TEXT     MEME output directory  [required]
  --centrimo-dir, --centrimo_dir TEXT
                                  Centrimo output directory  [required]
  --fimo-dir-sample, --fimo_dir_sample TEXT
                                  Sample fimo.txt  [required]
  --fimo-dir-control, --fimo_dir_control TEXT
                                  Control fimo.txt  [required]
  --name TEXT                     Plot title
  --flank-motif INTEGER           Length of sequence flanking motif
                                  [required]
  --motif INTEGER                 Motif number
  -o, --oc TEXT                   Output Directory  [required]
  -c, --configuration TEXT        Configuration file  [required]
  --show-progress                 Print progress
  -g, -gb, --genome-build TEXT    Key denoting genome build to use in
                                  configuration file  [required]
  -h, --help                      Show this message and exit.

Example

Most users will require using the command line version only:

$ moca find_motifs -i encode_test_data/ENCFF002DAR.bed\
    -c tests/data/application.cfg -g hg19 --show-progress

Creating plots if you already have run MEME and Centrimo:

$ moca plot -c tests/data/application.cfg -g hg19\
    --meme-dir moca_output/meme_out\
    --centrimo-dir moca_output/centrimo_out\
    --fimo-dir-sample moca_output/meme_out/fimo_out_1\
    --fimo-dir-control moca_output/meme_out/fimo_random_1\
    --name ENCODEID

There is also a structured API available, however it might be missing examples and documentation at places.

API Documentation

http://saketkc.github.io/moca/

Tests

moca is mostly extensively tested. See code-coverage.

Run tests locally

$ ./runtests.sh

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.