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somaticseq_parallel.py
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somaticseq_parallel.py
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#!/usr/bin/env python3
import sys, os, argparse, shutil, math, re, subprocess
from multiprocessing import Pool
from functools import partial
from shutil import rmtree
import somaticseq.run_somaticseq as run_somaticseq
import utilities.split_Bed_into_equal_regions as split_bed
import genomicFileHandler.concat as concat
def splitRegions(nthreads, outfiles, bed=None, fai=None):
assert bed or fai
if fai and not bed:
bed = split_bed.fai2bed(fai, outfiles)
writtenBeds = split_bed.split(bed, outfiles, nthreads)
return writtenBeds
def runPaired_by_region(inclusion, outdir=None, ref=None, tbam=None, nbam=None, tumor_name='TUMOR', normal_name='NORMAL', truth_snv=None, truth_indel=None, classifier_snv=None, classifier_indel=None, pass_threshold=0.5, lowqual_threshold=0.1, hom_threshold=0.85, het_threshold=0.01, dbsnp=None, cosmic=None, exclusion=None, mutect=None, indelocator=None, mutect2=None, varscan_snv=None, varscan_indel=None, jsm=None, sniper=None, vardict=None, muse=None, lofreq_snv=None, lofreq_indel=None, scalpel=None, strelka_snv=None, strelka_indel=None, tnscope=None, platypus=None, min_mq=1, min_bq=5, min_caller=0.5, somaticseq_train=False, ensembleOutPrefix='Ensemble.', consensusOutPrefix='Consensus.', classifiedOutPrefix='SSeq.Classified.', algo='ada', keep_intermediates=False):
basename = inclusion.split(os.sep)[-1].split('.')[0]
outdir_i = outdir + os.sep + basename
os.makedirs(outdir_i, exist_ok=True)
run_somaticseq.runPaired(outdir_i, ref, tbam, nbam, tumor_name, normal_name, truth_snv, truth_indel, classifier_snv, classifier_indel, pass_threshold, lowqual_threshold, hom_threshold, het_threshold, dbsnp, cosmic, inclusion, exclusion, mutect, indelocator, mutect2, varscan_snv, varscan_indel, jsm, sniper, vardict, muse, lofreq_snv, lofreq_indel, scalpel, strelka_snv, strelka_indel, tnscope, platypus, min_mq, min_bq, min_caller, somaticseq_train, ensembleOutPrefix, consensusOutPrefix, classifiedOutPrefix, algo, keep_intermediates)
return outdir_i
def runSingle_by_region(inclusion, outdir, ref, bam, sample_name='TUMOR', truth_snv=None, truth_indel=None, classifier_snv=None, classifier_indel=None, pass_threshold=0.5, lowqual_threshold=0.1, hom_threshold=0.85, het_threshold=0.01, dbsnp=None, cosmic=None, exclusion=None, mutect=None, mutect2=None, varscan=None, vardict=None, lofreq=None, scalpel=None, strelka=None, min_mq=1, min_bq=5, min_caller=0.5, somaticseq_train=False, ensembleOutPrefix='Ensemble.', consensusOutPrefix='Consensus.', classifiedOutPrefix='SSeq.Classified.', algo='ada', keep_intermediates=False):
basename = inclusion.split(os.sep)[-1].split('.')[0]
outdir_i = outdir + os.sep + basename
os.makedirs(outdir_i, exist_ok=True)
run_somaticseq.runSingle(outdir_i, ref, bam, sample_name, truth_snv, truth_indel, classifier_snv, classifier_indel, pass_threshold, lowqual_threshold, hom_threshold, het_threshold, dbsnp, cosmic, inclusion, exclusion, mutect, mutect2, varscan, vardict, lofreq, scalpel, strelka, min_mq, min_bq, min_caller, somaticseq_train, ensembleOutPrefix, consensusOutPrefix, classifiedOutPrefix, algo, keep_intermediates)
return outdir_i
def mergeSubdirTsv(dirList, filename, outdir=os.curdir):
fileList = ['{}/{}'.format(dir_i, filename) for dir_i in dirList]
concat.tsv(fileList, outdir + os.sep + filename)
def mergeSubdirVcf(dirList, filename, outdir=os.curdir):
fileList = ['{}/{}'.format(dir_i, filename) for dir_i in dirList]
concat.vcf(fileList, outdir + os.sep + filename)
if __name__ == '__main__':
runParameters = run_somaticseq.run()
os.makedirs(runParameters['output_directory'], exist_ok=True)
bed_splitted = splitRegions(runParameters['threads'], runParameters['output_directory']+os.sep+'th.input.bed', runParameters['inclusion_region'], runParameters['genome_reference']+'.fai')
pool = Pool(processes = runParameters['threads'])
if runParameters['which'] == 'paired':
runPaired_by_region_i = partial(runPaired_by_region, \
outdir = runParameters['output_directory'], \
ref = runParameters['genome_reference'], \
tbam = runParameters['tumor_bam_file'], \
nbam = runParameters['normal_bam_file'], \
tumor_name = runParameters['tumor_sample'], \
normal_name = runParameters['normal_sample'], \
truth_snv = runParameters['truth_snv'], \
truth_indel = runParameters['truth_indel'], \
classifier_snv = runParameters['classifier_snv'], \
classifier_indel = runParameters['classifier_indel'], \
pass_threshold = runParameters['pass_threshold'], \
lowqual_threshold = runParameters['lowqual_threshold'], \
hom_threshold = runParameters['homozygous_threshold'], \
het_threshold = runParameters['heterozygous_threshold'], \
min_mq = runParameters['minimum_mapping_quality'], \
min_bq = runParameters['minimum_base_quality'], \
min_caller = runParameters['minimum_num_callers'], \
dbsnp = runParameters['dbsnp_vcf'], \
cosmic = runParameters['cosmic_vcf'], \
exclusion = runParameters['exclusion_region'], \
mutect = runParameters['mutect_vcf'], \
indelocator = runParameters['indelocator_vcf'], \
mutect2 = runParameters['mutect2_vcf'], \
varscan_snv = runParameters['varscan_snv'], \
varscan_indel = runParameters['varscan_indel'], \
jsm = runParameters['jsm_vcf'], \
sniper = runParameters['somaticsniper_vcf'], \
vardict = runParameters['vardict_vcf'], \
muse = runParameters['muse_vcf'], \
lofreq_snv = runParameters['lofreq_snv'], \
lofreq_indel = runParameters['lofreq_indel'], \
scalpel = runParameters['scalpel_vcf'], \
strelka_snv = runParameters['strelka_snv'], \
strelka_indel = runParameters['strelka_indel'], \
tnscope = runParameters['tnscope_vcf'], \
platypus = runParameters['platypus_vcf'], \
algo = runParameters['algorithm'], \
somaticseq_train = False, \
keep_intermediates = runParameters['keep_intermediates'] )
subdirs = pool.map(runPaired_by_region_i, bed_splitted)
elif runParameters['which'] == 'single':
runSingle_by_region_i = partial(runSingle_by_region, \
outdir = runParameters['output_directory'], \
ref = runParameters['genome_reference'], \
bam = runParameters['bam_file'], \
sample_name = runParameters['sample_name'], \
truth_snv = runParameters['truth_snv'], \
truth_indel = runParameters['truth_indel'], \
classifier_snv = runParameters['classifier_snv'], \
classifier_indel = runParameters['classifier_indel'], \
pass_threshold = runParameters['pass_threshold'], \
lowqual_threshold = runParameters['lowqual_threshold'], \
hom_threshold = runParameters['homozygous_threshold'], \
het_threshold = runParameters['heterozygous_threshold'], \
min_mq = runParameters['minimum_mapping_quality'], \
min_bq = runParameters['minimum_base_quality'], \
min_caller = runParameters['minimum_num_callers'], \
dbsnp = runParameters['dbsnp_vcf'], \
cosmic = runParameters['cosmic_vcf'], \
exclusion = runParameters['exclusion_region'], \
mutect = runParameters['mutect_vcf'], \
mutect2 = runParameters['mutect2_vcf'], \
varscan = runParameters['varscan_vcf'], \
vardict = runParameters['vardict_vcf'], \
lofreq = runParameters['lofreq_vcf'], \
scalpel = runParameters['scalpel_vcf'], \
strelka = runParameters['strelka_vcf'], \
algo = runParameters['algorithm'], \
somaticseq_train = False, \
keep_intermediates = runParameters['keep_intermediates'] )
subdirs = pool.map(runSingle_by_region_i, bed_splitted)
run_somaticseq.logger.info('Sub-directories created: {}'.format(', '.join(subdirs)) )
# Merge sub-results
mergeSubdirTsv(subdirs, 'Ensemble.sSNV.tsv', runParameters['output_directory'])
mergeSubdirTsv(subdirs, 'Ensemble.sINDEL.tsv', runParameters['output_directory'])
if runParameters['classifier_snv']:
mergeSubdirTsv(subdirs, 'SSeq.Classified.sSNV.tsv', runParameters['output_directory'])
mergeSubdirVcf(subdirs, 'SSeq.Classified.sSNV.vcf', runParameters['output_directory'])
else:
mergeSubdirVcf(subdirs, 'Consensus.sSNV.vcf', runParameters['output_directory'])
if runParameters['classifier_indel']:
mergeSubdirTsv(subdirs, 'SSeq.Classified.sINDEL.tsv', runParameters['output_directory'])
mergeSubdirVcf(subdirs, 'SSeq.Classified.sINDEL.vcf', runParameters['output_directory'])
else:
mergeSubdirVcf(subdirs, 'Consensus.sINDEL.vcf', runParameters['output_directory'])
if runParameters['somaticseq_train']:
subprocess.call( (run_somaticseq.modelTrainer(runParameters['algorithm']), runParameters['output_directory'] + os.sep + 'Ensemble.sSNV.tsv', 'Consistent_Mates', 'Inconsistent_Mates') )
subprocess.call( (run_somaticseq.modelTrainer(runParameters['algorithm']), runParameters['output_directory'] + os.sep + 'Ensemble.sINDEL.tsv', 'Strelka_QSS', 'Strelka_TQSS','Consistent_Mates', 'Inconsistent_Mates') )
# Clean up after yourself
if not runParameters['keep_intermediates']:
for bed_i in bed_splitted:
os.remove( bed_i )
run_somaticseq.logger.info('Removed: {}'.format( bed_i ) )
for dir_i in subdirs:
rmtree( dir_i )
run_somaticseq.logger.info('Removed sub-directory: {}'.format( dir_i ) )