ccsmeth is built on Python3 and PyTorch.
- Prerequisites:
Python3.* (version>=3.8)
pbccs (version>=6.3.0)
pbmm2 (version>=1.9.0) or minimap2 (version>=2.22-r1101)
samtools (version>=1.12)
CUDA Toolkit (version>=10.2, for GPU only) - Dependencies:
numpy
statsmodels
scikit-learn
PyTorch (version >=1.2.0, <=2.1.0)
tqdm
pysam
pybedtools
pytabix
ccsmeth requires only a standard computer with enough RAM to support the in-memory operations. Using GPU could acceralate the process of methylation calling.
Recommended Hardware: 128 GB RAM, 40 CPU processors, 4 TB disk storage, >=8 GB GPU
Recommended OS: Linux (Ubuntu 16.04, CentOS 7, etc.)
Install ccsmeth, its dependencies, and other required packages in one step using conda and environment.yml:
# download deepsignal-plant
git clone https://github.com/PengNi/ccsmeth.git
# install tools in environment.yml
conda env create --name ccsmethenv -f /path/to/ccsmeth/environment.yml
# then the environment can be activated to use
conda activate ccsmethenvIt is highly recommended installing ccsmeth in a virtual environment.
conda create -n ccsmethenv python=3.8
# activate
conda activate ccsmethenv
# deactivate this environment
conda deactivate
# install ccsmeth after activating ccsmethenv
# install ccsmeth from github (latest version)
git clone https://github.com/PengNi/ccsmeth.git
cd ccsmeth
python setup.py install
# OR, install ccsmeth using pip
pip install ccsmeth
# OR, install ccsmeth using conda
conda install ccsmeth -c biocondaInstall necessary packages (bedtools, and pbccs, pbmm2 or minimap2, samtools) in the same environment. Installing of those packages using Bioconda is recommended:
conda install bedtools -c bioconda # required by pybedtools->ccsmeth:call_mods
conda install pbccs pbmm2 samtools -c biocondaAlso install the cuda version of pytoch and cudatoolkit (>=10.2) if you want use GPU to run ccsmeth in your GPU machine. Uninstall the wrong pytorch first if you have installed it before.
conda install pytorch==2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidiaSee models:
For the ccsmeth call_mods module:
- model_ccsmeth_5mCpG_call_mods_attbigru2s_b21.v3.ckpt: model of ccsmeth call_mods module for 5mCpG detection, trained using NA12898 pcr/MSssI and HG002 native (BS-seq as standard) PacBio Sequel II (kit 2.0) CCS reads. (for version >=0.5.0)
- model_ccsmeth_5mCpG_call_mods_attbigru2s_b21.v2.ckpt: model of ccsmeth call_mods module for 5mCpG detection, trained using NA12898 pcr/MSssI and HG002 native (BS-seq as standard) PacBio Sequel II (kit 2.0) CCS reads. (for version <=0.4.1)
For the aggregate mode of ccsmeth call_freqb module:
- model_ccsmeth_5mCpG_aggregate_attbigru_b11.v2p.ckpt: model of aggregate mode of ccsmeth call_freqb module for 5mCpG detection, trained using HG002 native (BS-seq as standard) PacBio Sequel II (kit 2.0) CCS reads.
Check demo for some demo data to play with:
- hg002.chr20_demo.hifi.bam: HG002 demo hifi reads which are aligned to human genome chr20:10000000-10100000.
- chr20_demo.fa: reference sequence of human chr20:10000000-10100000.
- hg002_bsseq_chr20_demo.bed: HG002 BS-seq results of region chr20:10000000-10100000.
Use denovo mode (first call_mods, then align_hifi):
# 1. call hifi reads with kinetics if needed
# should have added pbccs to $PATH or the used environment
ccsmeth call_hifi --subreads /path/to/subreads.bam \
--threads 10 \
--output /path/to/output.hifi.bam
# 2. call modifications
# output: [--output].modbam.bam
CUDA_VISIBLE_DEVICES=0 ccsmeth call_mods \
--input /path/to/output.hifi.bam \
--model_file /path/to/ccsmeth/models/model_call_mods.ckpt \
--output /path/to/output.hifi.call_mods \
--threads 10 --threads_call 2 --model_type attbigru2s \
--mode denovo
# 3. align hifi reads
# should have added pbmm2 to $PATH or the used environment
ccsmeth align_hifi \
--hifireads /path/to/output.hifi.call_mods.modbam.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.call_mods.modbam.pbmm2.bam \
--threads 10
# 4. call modification frequency
# outputs: [--output].[--call_mode].all.bed
# if the input bam file contains haplotags,
# there will be [--output].[--call_mode].[hp1/hp2].bed in outputs.
# use '--call_mode count' (default):
ccsmeth call_freqb \
--input_bam /path/to/output.hifi.call_mods.modbam.pbmm2.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.call_mods.modbam.pbmm2.freq \
--threads 10 --sort --bed
# OR, use '--call_mode aggregate':
# NOTE: usually is more accurate than 'count' mode
ccsmeth call_freqb \
--input_bam /path/to/output.hifi.call_mods.modbam.pbmm2.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.call_mods.modbam.pbmm2.freq \
--threads 10 --sort --bed \
--call_mode aggregate \
--aggre_model /path/to/ccsmeth/models/model_aggregate.ckptOR, use align mode (first align_hifi, then call_mods):
# 1. call hifi reads with kinetics if needed
# should have added pbccs to $PATH or the used environment
ccsmeth call_hifi --subreads /path/to/subreads.bam \
--threads 10 \
--output /path/to/output.hifi.bam
# 2. align hifi reads
# should have added pbmm2 to $PATH or the used environment
ccsmeth align_hifi \
--hifireads /path/to/output.hifi.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.pbmm2.bam \
--threads 10
# 3. call modifications
# output: [--output].modbam.bam
CUDA_VISIBLE_DEVICES=0 ccsmeth call_mods \
--input /path/to/output.hifi.pbmm2.bam \
--ref /path/to/genome.fa \
--model_file /path/to/ccsmeth/models/model_call_mods.ckpt \
--output /path/to/output.hifi.pbmm2.call_mods \
--threads 10 --threads_call 2 --model_type attbigru2s \
--mode align
# 4. call modification frequency
# outputs: [--output].[--call_mode].all.bed
# if the input bam file contains haplotags,
# there will be [--output].[--call_mode].[hp1/hp2].bed in outputs.
# use '--call_mode count':
ccsmeth call_freqb \
--input_bam /path/to/output.hifi.pbmm2.call_mods.modbam.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.pbmm2.call_mods.modbam.freq \
--threads 10 --sort --bed
# OR, use '--call_mode aggregate':
# NOTE: usually is more accurate than 'count' mode
ccsmeth call_freqb \
--input_bam /path/to/output.hifi.pbmm2.call_mods.modbam.bam \
--ref /path/to/genome.fa \
--output /path/to/output.hifi.pbmm2.call_mods.modbam.freq \
--threads 10 --sort --bed \
--call_mode aggregate \
--aggre_model /path/to/ccsmeth/models/model_aggregate.ckptUsers can use ccsmeth subcommands --help/-h for help.
[the cmds need to be updated]
ccsmeth call_hifi -h
usage: ccsmeth call_hifi [-h] --subreads SUBREADS [--output OUTPUT]
[--path_to_ccs PATH_TO_CCS] [--threads THREADS]
[--min-passes MIN_PASSES] [--by-strand] [--hd-finder]
[--log-level LOG_LEVEL]
[--path_to_samtools PATH_TO_SAMTOOLS]
call hifi reads with kinetics from subreads.bam using CCS, save in bam/sam
format. cmd: ccsmeth call_hifi -i input.subreads.bam
optional arguments:
-h, --help show this help message and exit
--path_to_samtools PATH_TO_SAMTOOLS
full path to the executable binary samtools file. If
not specified, it is assumed that samtools is in the
PATH.
INPUT:
--subreads SUBREADS, -i SUBREADS
path to subreads.bam file as input
OUTPUT:
--output OUTPUT, -o OUTPUT
output file path for alignment results, bam/sam
supported. If not specified, the results will be saved
in input_file_prefix.hifi.bam by default.
CCS ARG:
--path_to_ccs PATH_TO_CCS
full path to the executable binary ccs(PBCCS) file. If
not specified, it is assumed that ccs is in the PATH.
--threads THREADS, -t THREADS
number of threads to call hifi reads, default None ->
means using all available processors
--min-passes MIN_PASSES
CCS: Minimum number of full-length subreads required
to generate CCS for a ZMW. default None -> means using
a default value set by CCS
--by-strand CCS: Generate a consensus for each strand.
--hd-finder CCS: Enable heteroduplex finder and splitting.
--log-level LOG_LEVEL
CCS: Set log level. Valid choices: (TRACE, DEBUG,
INFO, WARN, FATAL). [WARN]ccsmeth align_hifi -h
usage: ccsmeth align_hifi [-h] --hifireads HIFIREADS --ref REF
[--output OUTPUT] [--header]
[--path_to_pbmm2 PATH_TO_PBMM2] [--minimap2]
[--path_to_minimap2 PATH_TO_MINIMAP2]
[--bestn BESTN] [--bwa] [--path_to_bwa PATH_TO_BWA]
[--path_to_samtools PATH_TO_SAMTOOLS]
[--threads THREADS]
align hifi reads using pbmm2/minimap2/bwa, default pbmm2
optional arguments:
-h, --help show this help message and exit
INPUT:
--hifireads HIFIREADS, -i HIFIREADS
path to hifireads.bam/sam/fastq_with_pulseinfo file as
input
--ref REF path to genome reference to be aligned, in fasta/fa
format. If using bwa, the reference must have already
been indexed.
OUTPUT:
--output OUTPUT, -o OUTPUT
output file path for alignment results, bam/sam
supported. If not specified, the results will be saved
in input_file_prefix.bam by default.
--header save header annotations from bam/sam. DEPRECATED
ALIGN:
--path_to_pbmm2 PATH_TO_PBMM2
full path to the executable binary pbmm2 file. If not
specified, it is assumed that pbmm2 is in the PATH.
--minimap2 use minimap2 instead of pbmm2 for alignment
--path_to_minimap2 PATH_TO_MINIMAP2
full path to the executable binary minimap2 file. If
not specified, it is assumed that minimap2 is in the
PATH.
--bestn BESTN, -n BESTN
retain at most n alignments in minimap2. default 3,
which means 2 secondary alignments are retained. Do
not use 2, cause -N1 is not suggested for high
accuracy of alignment. [This arg is for further
extension.]
--bwa use bwa instead of pbmm2 for alignment
--path_to_bwa PATH_TO_BWA
full path to the executable binary bwa file. If not
specified, it is assumed that bwa is in the PATH.
--path_to_samtools PATH_TO_SAMTOOLS
full path to the executable binary samtools file. If
not specified, it is assumed that samtools is in the
PATH.
--threads THREADS, -t THREADS
number of threads, default 5Use CUDA_VISIBLE_DEVICES=${cuda_numbers} ccsmeth call_mods [options] to call modifications with specified GPUs (e.g., CUDA_VISIBLE_DEVICES=0, CUDA_VISIBLE_DEVICES=0,2, or CUDA_VISIBLE_DEVICES=0,1,3, etc).
ccsmeth call_mods -h
usage: ccsmeth call_mods [-h] --input INPUT [--holes_batch HOLES_BATCH]
--output OUTPUT [--gzip] [--keep_pulse] [--no_sort]
--model_file MODEL_FILE
[--model_type {attbilstm2s,attbigru2s,transencoder2s,attbilstm2s2,attbigru2s2}]
[--seq_len SEQ_LEN] [--is_npass IS_NPASS]
[--is_stds IS_STDS] [--is_sn IS_SN] [--is_map IS_MAP]
[--class_num CLASS_NUM] [--dropout_rate DROPOUT_RATE]
[--batch_size BATCH_SIZE] [--layer_rnn LAYER_RNN]
[--hid_rnn HID_RNN] [--layer_trans LAYER_TRANS]
[--nhead NHEAD] [--d_model D_MODEL] [--dim_ff DIM_FF]
[--mode {denovo,align}] [--holeids_e HOLEIDS_E]
[--holeids_ne HOLEIDS_NE] [--motifs MOTIFS]
[--mod_loc MOD_LOC] [--methy_label {1,0}]
[--norm {zscore,min-mean,min-max,mad,none}]
[--no_decode] [--ref REF] [--mapq MAPQ]
[--identity IDENTITY] [--no_supplementary]
[--skip_unmapped SKIP_UNMAPPED] [--threads THREADS]
[--threads_call THREADS_CALL] [--tseed TSEED]
[--use_compile USE_COMPILE]
call modifications
optional arguments:
-h, --help show this help message and exit
--threads THREADS, -p THREADS
number of threads to be used, default 10.
--threads_call THREADS_CALL
number of threads used to call modifications with
trained models, no more than threads/3 is suggested.
default 3.
--tseed TSEED random seed for torch
--use_compile USE_COMPILE
if using torch.compile, yes or no, default no ('yes'
only works in pytorch>=2.0)
INPUT:
--input INPUT, -i INPUT
input file, can be bam/sam, or features.tsv generated
by extract_features.py.
--holes_batch HOLES_BATCH
number of holes/hifi-reads in an batch to get/put in
queues, default 50. only used when --input is bam/sam
OUTPUT:
--output OUTPUT, -o OUTPUT
the prefix of output files to save the predicted
results. output files will be
[--output].per_readsite.tsv/.modbam.bam
--gzip if compressing .per_readsite.tsv when --input is not
in bam/sam format.
--keep_pulse if keeping ipd/pw tags in .modbam.bam when --input is
in bam/sam format.
--no_sort don't sort .modbam.bam when --input is in bam/sam
format.
CALL:
--model_file MODEL_FILE, -m MODEL_FILE
file path of the trained model (.ckpt)
--model_type {attbilstm2s,attbigru2s,transencoder2s,attbilstm2s2,attbigru2s2}
type of model to use, 'attbilstm2s', 'attbigru2s',
'transencoder2s', 'attbilstm2s2', 'attbigru2s2',
default: attbigru2s
--seq_len SEQ_LEN len of kmer. default 21
--is_npass IS_NPASS if using num_pass features, yes or no, default yes
--is_stds IS_STDS if using std features, yes or no, default no
--is_sn IS_SN if using signal-to-noise-ratio features, yes or no,
default no. Effects both MODEL input and feature
EXTRACTION
--is_map IS_MAP if using mapping features, yes or no, default no.
Effects both MODEL input and feature EXTRACTION, only
works in EXTRACTION-ALIGN-MODE
--class_num CLASS_NUM
--dropout_rate DROPOUT_RATE
--batch_size BATCH_SIZE, -b BATCH_SIZE
batch size, default 512
CALL MODEL_HYPER RNN:
--layer_rnn LAYER_RNN
BiRNN layer num, default 3
--hid_rnn HID_RNN BiRNN hidden_size, default 256
CALL MODEL_HYPER TRANSFORMER:
--layer_trans LAYER_TRANS
TransformerEncoder nlayers, default 6
--nhead NHEAD TransformerEncoder nhead, default 4
--d_model D_MODEL TransformerEncoder input feature numbers, default 256
--dim_ff DIM_FF TransformerEncoder dim_feedforward, default 512
EXTRACTION:
--mode {denovo,align}
denovo mode: extract features from unaligned/aligned
hifi.bam without reference position info; align mode:
extract features from aligned hifi.bam with reference
position info. default: denovo
--holeids_e HOLEIDS_E
file contains holeids to be extracted, default None
--holeids_ne HOLEIDS_NE
file contains holeids not to be extracted, default
None
--motifs MOTIFS motif seq to be extracted, default: CG. can be multi
motifs splited by comma (no space allowed in the input
str), or use IUPAC alphabet, the mod_loc of all motifs
must be the same
--mod_loc MOD_LOC 0-based location of the targeted base in the motif,
default 0
--methy_label {1,0} the label of the interested modified bases, this is
for training. 0 or 1, default 1
--norm {zscore,min-mean,min-max,mad,none}
method for normalizing ipd/pw in subread level.
zscore, min-mean, min-max, mad, or none. default
zscore
--no_decode not use CodecV1 to decode ipd/pw
EXTRACTION ALIGN_MODE:
--ref REF path to genome reference to be aligned, in fasta/fa
format.
--mapq MAPQ MAPping Quality cutoff for selecting alignment items,
default 1
--identity IDENTITY identity cutoff for selecting alignment items, [0.0,
1.0], default 0.0
--no_supplementary not use supplementary alignment
--skip_unmapped SKIP_UNMAPPED
if skipping unmapped sites in reads, yes or no,
default yesThe [--output].per_readsite.tsv file is a tab-delimited text file in the following format:
- chrom: the chromosome name
- pos: 0-based position of the targeted base in the chromosome
- strand: +/-, the aligned strand of the read to the reference
- read_name: read name of the ccs read
- read_loc: 0-based position of the targeted base in the read
- read_depth: subreads depth of the ccs read, format: fwd_depth,rev_depth
- prob_0: [0, 1], the probability of the targeted base predicted as 0 (unmethylated)
- prob_1: [0, 1], the probability of the targeted base predicted as 1 (methylated)
- called_label: 0/1, unmethylated/methylated
- k_mer: the kmer around the targeted base
ccsmeth call_freqb -h
usage: ccsmeth call_freqb [-h] [--threads THREADS] --input_bam INPUT_BAM --ref
REF [--contigs CONTIGS] [--chunk_len CHUNK_LEN]
--output OUTPUT [--bed] [--sort] [--gzip]
[--modtype {5mC}] [--call_mode {count,aggregate}]
[--prob_cf PROB_CF] [--no_amb_cov]
[--hap_tag HAP_TAG] [--mapq MAPQ]
[--identity IDENTITY] [--no_supplementary]
[--motifs MOTIFS] [--mod_loc MOD_LOC] [--no_comb]
[--refsites_only] [--refsites_all] [--no_hap]
[--base_clip BASE_CLIP] [--aggre_model AGGRE_MODEL]
[--model_type {attbilstm,attbigru}]
[--seq_len SEQ_LEN] [--class_num CLASS_NUM]
[--layer_rnn LAYER_RNN] [--hid_rnn HID_RNN]
[--bin_size BIN_SIZE] [--cov_cf COV_CF]
[--only_close] [--discrete] [--tseed TSEED]
call frequency of modifications at genome level from modbam.bam file
optional arguments:
-h, --help show this help message and exit
--threads THREADS number of subprocesses used. default 5
INPUT:
--input_bam INPUT_BAM
input bam, should be aligned and sorted
--ref REF path to genome reference, in fasta/fa format.
--contigs CONTIGS path of a file containing chromosome/contig names, one
name each line; or a string contains multiple
chromosome names splited by comma.default None, which
means all chromosomes will be processed.
--chunk_len CHUNK_LEN
chunk length, default 500000
OUTPUT:
--output OUTPUT, -o OUTPUT
prefix of output file to save the results
--bed save the result in bedMethyl format
--sort sort items in the result
--gzip if compressing the output using gzip
CALL_FREQ:
--modtype {5mC} modification type, default 5mC.
--call_mode {count,aggregate}
call mode: count, aggregate. default count.
--prob_cf PROB_CF this is to remove ambiguous calls (only for count-mode
now). if abs(prob1-prob0)>=prob_cf, then we use the
call. e.g., proc_cf=0 means use all calls. range [0,
1], default 0.0.
--no_amb_cov when using prob_cf>0, DO NOT count ambiguous calls for
calculating reads coverage
--hap_tag HAP_TAG haplotype tag, default HP
--mapq MAPQ MAPping Quality cutoff for selecting alignment items,
default 1
--identity IDENTITY identity cutoff for selecting alignment items, [0.0,
1.0], default 0.0
--no_supplementary not use supplementary alignment
--motifs MOTIFS motif seq to be extracted, default: CG. can be multi
motifs splited by comma (no space allowed in the input
str), or use IUPAC alphabet, the mod_loc of all motifs
must be the same
--mod_loc MOD_LOC 0-based location of the targeted base in the motif,
default 0
--no_comb don't combine fwd/rev reads of one CG. [Only works
when motifs is CG]
--refsites_only only keep sites which are target motifs in both
reference and reads
--refsites_all output all covered sites which are target motifs in
reference. --refsites_all is True, also means we do
not output sites which are target motifs only in
reads.
--no_hap don't call_freq on haplotypes
--base_clip BASE_CLIP
number of base clipped in each read, default 0
AGGREGATE_MODE:
--aggre_model AGGRE_MODEL, -m AGGRE_MODEL
file path of the aggregate model (.ckpt)
--model_type {attbilstm,attbigru}
type of model to use, 'attbigru', 'attbilstm',
default: attbigru
--seq_len SEQ_LEN len of sites used. default 11
--class_num CLASS_NUM
--layer_rnn LAYER_RNN
BiRNN layer num, default 1
--hid_rnn HID_RNN BiRNN hidden_size, default 32
--bin_size BIN_SIZE histogram bin size, default 20
--cov_cf COV_CF coverage cutoff, to consider if use aggregate model to
re-predict the modstate of the site
--only_close [EXPERIMENTAL]
--discrete [EXPERIMENTAL]
--tseed TSEED random seed for torchThe modification_frequency file can be either saved in bedMethyl format (by setting --bed), or saved as a tab-delimited text file in the following format by default:
- chrom: the chromosome name
- pos: 0-based position of the targeted base in the chromosome
- pos_end: pos + 1
- strand: +/-, the aligned strand of the read to the reference
- prob_0_sum: sum of the probabilities of the targeted base predicted as 0 (unmethylated) [DEPRECATED, ONLY meaningful in call_freqt module]
- prob_1_sum: sum of the probabilities of the targeted base predicted as 1 (methylated) [DEPRECATED, ONLY meaningful in call_freqt module]
- count_modified: number of reads in which the targeted base counted as modified
- count_unmodified: number of reads in which the targeted base counted as unmodified
- coverage: number of reads aligned to the targeted base
- modification_frequency: modification frequency
- k_mer: the kmer around the targeted base [DEPRECATED, ONLY meaningful in call_freqt module]
ccsmeth call_freqt -h
usage: ccsmeth call_freqt [-h] --input_path INPUT_PATH [--file_uid FILE_UID]
--result_file RESULT_FILE [--bed] [--sort] [--gzip]
[--prob_cf PROB_CF] [--rm_1strand] [--refsites_only]
[--motifs MOTIFS] [--mod_loc MOD_LOC] [--ref REF]
[--contigs CONTIGS] [--threads THREADS]
call frequency of modifications at genome level from per_readsite text files
optional arguments:
-h, --help show this help message and exit
INPUT:
--input_path INPUT_PATH, -i INPUT_PATH
an output file from call_mods/call_modifications.py,
or a directory contains a bunch of output files. this
arg is in "append" mode, can be used multiple times
--file_uid FILE_UID a unique str which all input files has, this is for
finding all input files and ignoring the not-input-
files in a input directory. if input_path is a file,
ignore this arg.
OUTPUT:
--result_file RESULT_FILE, -o RESULT_FILE
the file path to save the result
--bed save the result in bedMethyl format
--sort sort items in the result
--gzip if compressing the output using gzip
CALL_FREQ:
--prob_cf PROB_CF this is to remove ambiguous calls. if
abs(prob1-prob0)>=prob_cf, then we use the call. e.g.,
proc_cf=0 means use all calls. range [0, 1], default
0.0.
--rm_1strand abandon ccs reads with only 1 strand subreads
[DEPRECATED]
--refsites_only only keep sites which are target motifs in both
reference and reads
--motifs MOTIFS motif seq to be extracted, default: CG. can be multi
motifs splited by comma (no space allowed in the input
str), or use IUPAC alphabet, the mod_loc of all motifs
must be the same. [Only useful when --refsites_only is
True]
--mod_loc MOD_LOC 0-based location of the targeted base in the motif,
default 0. [Only useful when --refsites_only is True]
--ref REF path to genome reference, in fasta/fa format. [Only
useful when --refsites_only is True]
PARALLEL:
--contigs CONTIGS a reference genome file (.fa/.fasta/.fna), used for
extracting all contig names for parallel; or path of a
file containing chromosome/contig names, one name each
line; or a string contains multiple chromosome names
splited by comma.default None, which means all
chromosomes will be processed at one time. If not
None, one chromosome will be processed by one
subprocess.
--threads THREADS number of subprocesses used when --contigs is set.
i.e., number of contigs processed in parallel. default
1The format of the output file is the same as of ccsmeth call_freqb.
ccsmeth extract -h
usage: ccsmeth extract [-h] --input INPUT [--holeids_e HOLEIDS_E]
[--holeids_ne HOLEIDS_NE] [--output OUTPUT] [--gzip]
[--mode {denovo,align}] [--seq_len SEQ_LEN]
[--motifs MOTIFS] [--mod_loc MOD_LOC]
[--methy_label {1,0}]
[--norm {zscore,min-mean,min-max,mad,none}]
[--no_decode] [--holes_batch HOLES_BATCH]
[--is_sn IS_SN] [--is_map IS_MAP] [--ref REF]
[--mapq MAPQ] [--identity IDENTITY]
[--no_supplementary] [--skip_unmapped SKIP_UNMAPPED]
[--threads THREADS]
extract features from hifi reads.
optional arguments:
-h, --help show this help message and exit
--threads THREADS number of threads, default 5
INPUT:
--input INPUT, -i INPUT
input file in bam/sam format, can be unaligned
hifi.bam/sam and aligned sorted hifi.bam/sam.
--holeids_e HOLEIDS_E
file contains holeids/hifiids to be extracted, default
None
--holeids_ne HOLEIDS_NE
file contains holeids/hifiids not to be extracted,
default None
OUTPUT:
--output OUTPUT, -o OUTPUT
output file path to save the extracted features. If
not specified, use input_prefix.tsv as default.
--gzip if compressing the output using gzip
EXTRACTION:
--mode {denovo,align}
denovo mode: extract features from unaligned/aligned
hifi.bam without reference position info; align mode:
extract features from aligned hifi.bam with reference
position info. default: denovo
--seq_len SEQ_LEN len of kmer. default 21
--motifs MOTIFS motif seq to be extracted, default: CG. can be multi
motifs splited by comma (no space allowed in the input
str), or use IUPAC alphabet, the mod_loc of all motifs
must be the same
--mod_loc MOD_LOC 0-based location of the targeted base in the motif,
default 0
--methy_label {1,0} the label of the interested modified bases, this is
for training. 0 or 1, default 1
--norm {zscore,min-mean,min-max,mad,none}
method for normalizing ipd/pw in subread level.
zscore, min-mean, min-max, mad, or none. default
zscore
--no_decode not use CodecV1 to decode ipd/pw
--holes_batch HOLES_BATCH
number of holes/hifi-reads in an batch to get/put in
queues, default 50
--is_sn IS_SN if extracting signal-to-noise features, yes or no,
default no
--is_map IS_MAP if extracting mapping features, yes or no, default no.
only works in ALIGN-MODE
EXTRACTION ALIGN_MODE:
--ref REF path to genome reference to be aligned, in fasta/fa
format.
--mapq MAPQ MAPping Quality cutoff for selecting alignment items,
default 1
--identity IDENTITY identity cutoff for selecting alignment items, [0.0,
1.0], default 0.0
--no_supplementary not use supplementary alignment
--skip_unmapped SKIP_UNMAPPED
if skipping unmapped sites in reads, yes or no,
default yesccsmeth train -h
usage: ccsmeth train [-h] --train_file TRAIN_FILE --valid_file VALID_FILE
--model_dir MODEL_DIR
[--model_type {attbilstm2s,attbigru2s,transencoder2s,attbilstm2s2,attbigru2s2}]
[--seq_len SEQ_LEN] [--is_npass IS_NPASS] [--is_sn IS_SN]
[--is_map IS_MAP] [--is_stds IS_STDS]
[--class_num CLASS_NUM] [--dropout_rate DROPOUT_RATE]
[--layer_rnn LAYER_RNN] [--hid_rnn HID_RNN]
[--layer_trans LAYER_TRANS] [--nhead NHEAD]
[--d_model D_MODEL] [--dim_ff DIM_FF]
[--optim_type {Adam,RMSprop,SGD,Ranger,LookaheadAdam}]
[--batch_size BATCH_SIZE]
[--lr_scheduler {StepLR,ReduceLROnPlateau}] [--lr LR]
[--lr_decay LR_DECAY] [--lr_decay_step LR_DECAY_STEP]
[--lr_patience LR_PATIENCE]
[--lr_mode_strategy {last,mean,max}]
[--max_epoch_num MAX_EPOCH_NUM]
[--min_epoch_num MIN_EPOCH_NUM] [--pos_weight POS_WEIGHT]
[--step_interval STEP_INTERVAL]
[--dl_num_workers DL_NUM_WORKERS] [--dl_offsets]
[--init_model INIT_MODEL] [--tseed TSEED]
[--use_compile USE_COMPILE]
train a model, need two independent datasets for training and validating
optional arguments:
-h, --help show this help message and exit
INPUT:
--train_file TRAIN_FILE
--valid_file VALID_FILE
OUTPUT:
--model_dir MODEL_DIR
TRAIN MODEL_HYPER:
--model_type {attbilstm2s,attbigru2s,transencoder2s,attbilstm2s2,attbigru2s2}
type of model to use, 'attbilstm2s', 'attbigru2s',
'transencoder2s', 'attbilstm2s2', 'attbigru2s2',
default: attbigru2s
--seq_len SEQ_LEN len of kmer. default 21
--is_npass IS_NPASS if using num_pass features, yes or no, default yes
--is_sn IS_SN if using signal-to-noise-ratio features, yes or no,
default no
--is_map IS_MAP if using mapping features, yes or no, default no
--is_stds IS_STDS if using std features, yes or no, default no
--class_num CLASS_NUM
--dropout_rate DROPOUT_RATE
TRAIN MODEL_HYPER RNN:
--layer_rnn LAYER_RNN
BiRNN layer num, default 3
--hid_rnn HID_RNN BiRNN hidden_size, default 256
TRAIN MODEL_HYPER TRANSFORMER:
--layer_trans LAYER_TRANS
TransformerEncoder nlayers, default 6
--nhead NHEAD TransformerEncoder nhead, default 4
--d_model D_MODEL TransformerEncoder input feature numbers, default 256
--dim_ff DIM_FF TransformerEncoder dim_feedforward, default 512
TRAINING:
--optim_type {Adam,RMSprop,SGD,Ranger,LookaheadAdam}
type of optimizer to use, 'Adam', 'SGD', 'RMSprop',
'Ranger' or 'LookaheadAdam', default Adam
--batch_size BATCH_SIZE
--lr_scheduler {StepLR,ReduceLROnPlateau}
StepLR or ReduceLROnPlateau, default StepLR
--lr LR default 0.001
--lr_decay LR_DECAY default 0.1
--lr_decay_step LR_DECAY_STEP
effective in StepLR. default 1
--lr_patience LR_PATIENCE
effective in ReduceLROnPlateau. default 0
--lr_mode_strategy {last,mean,max}
effective in ReduceLROnPlateau. last, mean, or max,
default last
--max_epoch_num MAX_EPOCH_NUM
max epoch num, default 50
--min_epoch_num MIN_EPOCH_NUM
min epoch num, default 10
--pos_weight POS_WEIGHT
--step_interval STEP_INTERVAL
--dl_num_workers DL_NUM_WORKERS
default 0
--dl_offsets use file offsets loader
--init_model INIT_MODEL
file path of pre-trained model parameters to load
before training
--tseed TSEED random seed for pytorch
--use_compile USE_COMPILE
if using torch.compile, yes or no, default no ('yes'
only works in pytorch>=2.0)See also ccsmeth trainm -h for multi-gpu distributed training.
- We thank Tse et al., The Chinese University of Hong Kong (CUHK) Department of Chemical Pathology, for sharing their code and data, as reported in Proc Natl Acad Sci USA 2021; 118(5): e2019768118. We made use of their data and code for evaluation and comparison.
- We thank Akbari et al., as part of the code for haplotyping were taken from NanoMethPhase of Akbari et al.
- The way to organize features for aggregate model is referenced from pb-CpG-tools.
- Check if the kinetics values (IPD and PW) are encoded or not in the bam file automatically, disable the
--no_decodearg. - Provide a table in README for the illustration of the pre-trained models.
- Reorganize the way of presenting arguments in the help message.