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Copy pathconvert_VCF_to_Mutect1.py
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convert_VCF_to_Mutect1.py
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#!/usr/bin/env python
from __future__ import print_function
import sys
import os.path
import argparse
import pysam
import re
"""
Script for parsing a VCF file
"""
epi = ('\
\n\
VCF file parser, turning data into Mutect1-like format for deTIN. Have to give input that has both normal and tumour sample in it\n\
\n\
')
# Describe what the script does
parser = argparse.ArgumentParser(description='This script parses a VCF file and creates a Mutect1 like format for deTIN', epilog= epi, formatter_class=argparse.RawTextHelpFormatter)
# Get inputs
parser.add_argument('-i', '--input', default=None, dest='vcf', action='store', required=True, help="VCF file")
# Check for no input
if len(sys.argv)==1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
# Check if input files exist
if not os.path.isfile(args.vcf)==True:
print("Cannot find input file ",args.vcf)
sys.exit(1)
# Read input
output=args.vcf+".mutect1.stats"
out = open(output, "w")
#print("Input: ",args.vcf, "Output: " , output)
# read the input file
myvcf = pysam.VariantFile(args.vcf, "r")
print ("contig","position","ref_allele","alt_allele","tumor_name","normal_name","t_ref_count","t_alt_count",
"n_ref_count","n_alt_count","failure_reasons","judgement",sep="\t",file=out)
filen = re.split("[/]+", args.vcf)
names = re.split("[._]+", filen[-1])
tumour_name=names[2]+ "_" + names[3]
normal_name=names[0]+ "_" + names[1]
# print (normal_name, tumour_name)
for r in myvcf:
#### FILTER OUT #####
# Shared called total
# Filter out sites which
chr=r.chrom
pos=r.pos
id =r.id
ref = r.ref
alt = r.alts
qual=r.qual
filter=r.filter.keys()
info=r.info.keys()
failure_reasons=str(filter[0])
judgement="KEEP"
# Calculate failure and judgement
# If it is PASS, make it keep
# If it is failed, make it a "fail" which allows for reassignment
if re.match("PASS",failure_reasons):
failure_reasons=''
judgement = "KEEP"
# Check if there is LowQScore fail
elif re.match("LowQscore",failure_reasons):
qss=0
vqsr=0
# If it is, check if INFO['QSS'] > 20 and if INFO['VSQR'] > 2.5
#print (info['QSS'], info['VSQR'])
if "QSS" in r.info.keys():
qss=r.info["QSS"]
else:
qs=0
if "VQSR" in r.info.keys():
vqsr=r.info["VQSR"]
else:
vqsr=0
if (vqsr>2.5) and (qss> 20) :
failure_reasons="alt_allele_in_normal"
judgement = "REJECT"
else:
failure_reasons="str_contraction"
judgement = "REJECT"
else:
failure_reasons="t_lod_fstar"
judgement = "REJECT"
# Make more sane alt
if alt:
alt=str(alt[0])
else:
alt="."
altb = alt
trefd = 0
taltd = 0
nrefd = 0
naltd = 0
# Switch between D1 and D2 reads counts
d0=1
# If is SNP, which Mutect1 outputs, calculate read coverage
if 'CU' in r.samples[0].keys():
if (r.ref == 'A'):
nrefd = r.samples[0]['AU'][d0]
trefd = r.samples[1]['AU'][d0]
elif (r.ref == 'C'):
nrefd = r.samples[0]['CU'][d0]
trefd = r.samples[1]['CU'][d0]
elif (r.ref == 'G'):
nrefd = r.samples[0]['GU'][d0]
trefd = r.samples[1]['GU'][d0]
elif (r.ref == 'T'):
nrefd = r.samples[0]['TU'][d0]
trefd = r.samples[1]['TU'][d0]
else:
print("WARN: ", r.ref, " is not A,C,G or T :", r.id)
if (altb == 'A'):
naltd = r.samples[0]['AU'][d0]
taltd = r.samples[1]['AU'][d0]
elif (altb == 'C'):
naltd = r.samples[0]['CU'][d0]
taltd = r.samples[1]['CU'][d0]
elif (altb == 'G'):
naltd = r.samples[0]['GU'][d0]
taltd = r.samples[1]['GU'][d0]
elif (altb == 'T'):
naltd = r.samples[0]['TU'][d0]
taltd = r.samples[1]['TU'][d0]
# If ALT is unknown/N, add all tier1 counts together and subtract REF counts
elif (altb == '.'):
naltd = r.samples[0]['AU'][d0]+ r.samples[0]['CU'][d0]+ r.samples[0]['GU'][d0]+ r.samples[0]['TU'][d0]-nrefd
taltd = r.samples[1]['AU'][d0]+ r.samples[1]['CU'][d0]+ r.samples[1]['GU'][d0]+ r.samples[1]['TU'][d0]-trefd
else:
print("WARN: ", r.ref, " is not A,C,G or T :", r.id)
t_ref_count=trefd
t_alt_count=taltd
n_ref_count=nrefd
n_alt_count=naltd
#r.info["VT"] = "None"
print (chr,pos,ref,alt,tumour_name,normal_name,t_ref_count,t_alt_count,n_ref_count,n_alt_count,failure_reasons,judgement,sep="\t",file = out)
out.close()
quit()
# Complete mutect1 output
#print ("contig","position","context","ref_allele","alt_allele","tumor_name","normal_name","score","dbsnp_site","covered","power",
# "tumor_power","normal_power","normal_power_nsp","normal_power_wsp","total_reads","map_Q0_reads","init_t_lod","t_lod_fstar",
# "t_lod_fstar_forward","t_lod_fstar_reverse","tumor_f","contaminant_fraction","contaminant_lod","t_q20_count","t_ref_count",
# "t_alt_count","t_ref_sum","t_alt_sum","t_ref_max_mapq","t_alt_max_mapq","t_ins_count","t_del_count","normal_best_gt",
# "init_n_lod","normal_f","n_q20_count","n_ref_count","n_alt_count","n_ref_sum","n_alt_sum",
# "power_to_detect_positive_strand_artifact","power_to_detect_negative_strand_artifact","strand_bias_counts",
# "tumor_alt_fpir_median","tumor_alt_fpir_mad","tumor_alt_rpir_median","tumor_alt_rpir_mad","observed_in_normals_count",
# "failure_reasons","judgement","total_counts_coverage_less_8","total_counts_coverage_greater_8",
# "alt_count_greater1_af_greater_01percent","alt_count_greater2_af_greater_03percent","alt_count_greater3_af_greater_1percent",
# "alt_count_greater3_af_greater_3percent","alt_count_greater3_af_greater_20percent","alt_count_greater10_af_greater_20percent",
# "PoN_Germline","PoN_Artifact","bad",sep="\t")