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SSeq_merged.vcf2tsv.py
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SSeq_merged.vcf2tsv.py
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#!/usr/bin/env python3
import sys, argparse, math, gzip, os, pysam
import regex as re
import scipy.stats as stats
import genomic_file_handlers as genome
import pileup_reader as pileup
from read_info_extractor import *
from copy import copy
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
input_sites = parser.add_mutually_exclusive_group()
input_sites.add_argument('-myvcf', '--vcf-format', type=str, help='Input file is VCF formatted.', required=False, default=None)
input_sites.add_argument('-mybed', '--bed-format', type=str, help='Input file is BED formatted.', required=False, default=None)
input_sites.add_argument('-mypos', '--positions-list', type=str, help='A list of positions: tab seperating contig and positions.', required=False, default=None)
parser.add_argument('-nbam', '--normal-bam-file', type=str, help='Normal BAM File', required=True, default=None)
parser.add_argument('-tbam', '--tumor-bam-file', type=str, help='Tumor BAM File', required=True, default=None)
parser.add_argument('-truth', '--ground-truth-vcf', type=str, help='VCF of true hits', required=False, default=None)
parser.add_argument('-dbsnp', '--dbsnp-vcf', type=str, help='dbSNP VCF: do not use if input VCF is annotated', required=False, default=None)
parser.add_argument('-cosmic', '--cosmic-vcf', type=str, help='COSMIC VCF: do not use if input VCF is annotated', required=False, default=None)
parser.add_argument('-mutect', '--mutect-vcf', type=str, help='MuTect VCF', required=False, default=None)
parser.add_argument('-strelka', '--strelka-vcf', type=str, help='Strelka VCF', required=False, default=None)
parser.add_argument('-sniper', '--somaticsniper-vcf', type=str, help='SomaticSniper VCF', required=False, default=None)
parser.add_argument('-varscan', '--varscan-vcf', type=str, help='VarScan2 VCF', required=False, default=None)
parser.add_argument('-jsm', '--jsm-vcf', type=str, help='JointSNVMix2 VCF', required=False, default=None)
parser.add_argument('-vardict', '--vardict-vcf', type=str, help='VarDict VCF', required=False, default=None)
parser.add_argument('-muse', '--muse-vcf', type=str, help='MuSE VCF', required=False, default=None)
parser.add_argument('-lofreq', '--lofreq-vcf', type=str, help='LoFreq VCF', required=False, default=None)
parser.add_argument('-scalpel', '--scalpel-vcf', type=str, help='Scalpel VCF', required=False, default=None)
parser.add_argument('-tnscope', '--tnscope-vcf', type=str, help='TNscope VCF', required=False, default=None)
parser.add_argument('-ref', '--genome-reference', type=str, help='.fasta.fai file to get the contigs', required=True, default=None)
parser.add_argument('-dedup', '--deduplicate', action='store_true', help='Do not consider duplicate reads from BAM files. Default is to count everything', required=False, default=False)
parser.add_argument('-minMQ', '--minimum-mapping-quality',type=float, help='Minimum mapping quality below which is considered poor', required=False, default=1)
parser.add_argument('-minBQ', '--minimum-base-quality', type=float, help='Minimum base quality below which is considered poor', required=False, default=5)
parser.add_argument('-mincaller', '--minimum-num-callers', type=float, help='Minimum number of tools to be considered', required=False, default=0)
parser.add_argument('-scale', '--p-scale', type=str, help='phred, fraction, or none', required=False, default=None)
parser.add_argument('-outfile', '--output-tsv-file', type=str, help='Output TSV Name', required=False, default=os.sys.stdout)
args = parser.parse_args()
# Rename input:
is_vcf = args.vcf_format
is_bed = args.bed_format
is_pos = args.positions_list
nbam_fn = args.normal_bam_file
tbam_fn = args.tumor_bam_file
truth = args.ground_truth_vcf
cosmic = args.cosmic_vcf
dbsnp = args.dbsnp_vcf
mutect = args.mutect_vcf
strelka = args.strelka_vcf
sniper = args.somaticsniper_vcf
varscan = args.varscan_vcf
jsm = args.jsm_vcf
vardict = args.vardict_vcf
muse = args.muse_vcf
lofreq = args.lofreq_vcf
scalpel = args.scalpel_vcf
tnscope = args.tnscope_vcf
min_mq = args.minimum_mapping_quality
min_bq = args.minimum_base_quality
ref_fa = args.genome_reference
p_scale = args.p_scale
outfile = args.output_tsv_file
# Convert contig_sequence to chrom_seq dict:
fai_file = ref_fa + '.fai'
chrom_seq = genome.faiordict2contigorder(fai_file, 'fai')
# Determine input format:
if is_vcf:
mysites = is_vcf
elif is_bed:
mysites = is_bed
elif is_pos:
mysites = is_pos
else:
mysites = fai_file
print('No position supplied. Will evaluate the whole genome.', file=sys.stderr)
# Re-scale output or not:
if p_scale == None:
print('NO RE-SCALING', file=sys.stderr)
elif p_scale.lower() == 'phred':
p_scale = 'phred'
elif p_scale.lower() == 'fraction':
p_scale = 'fraction'
else:
p_scale = None
print('NO RE-SCALING', file=sys.stderr)
def rescale(x, original=None, rescale_to=p_scale, max_phred=1001):
if ( rescale_to == None ) or ( original.lower() == rescale_to.lower() ):
y = x if isinstance(x, int) else '%.2f' % x
elif original.lower() == 'fraction' and rescale_to == 'phred':
y = genome.p2phred(x, max_phred=max_phred)
y = '%.2f' % y
elif original.lower() == 'phred' and rescale_to == 'fraction':
y = genome.phred2p(x)
y = '%.2f' % y
return y
# Define NaN and Inf:
nan = float('nan')
inf = float('inf')
pattern_chr_position = genome.pattern_chr_position
# Normal/Tumor index in the Merged VCF file, or any other VCF file that puts NORMAL first.
idxN,idxT = 0,1
# Normal/Tumor index in VarDict VCF, or any other VCF file that puts TUMOR first.
vdT,vdN = 0,1
# Header for the output data, created here so I won't have to indent this line:
out_header = \
'{CHROM}\t\
{POS}\t\
{ID}\t\
{REF}\t\
{ALT}\t\
{if_MuTect}\t\
{if_VarScan2}\t\
{if_JointSNVMix2}\t\
{if_SomaticSniper}\t\
{if_VarDict}\t\
{MuSE_Tier}\t\
{if_LoFreq}\t\
{if_Scalpel}\t\
{if_Strelka}\t\
{if_TNscope}\t\
{Strelka_Score}\t\
{Strelka_QSS}\t\
{Strelka_TQSS}\t\
{VarScan2_Score}\t\
{SNVMix2_Score}\t\
{Sniper_Score}\t\
{VarDict_Score}\t\
{if_dbsnp}\t\
{COMMON}\t\
{if_COSMIC}\t\
{COSMIC_CNT}\t\
{Consistent_Mates}\t\
{Inconsistent_Mates}\t\
{N_DP}\t\
{nBAM_REF_MQ}\t\
{nBAM_ALT_MQ}\t\
{nBAM_Z_Ranksums_MQ}\t\
{nBAM_REF_BQ}\t\
{nBAM_ALT_BQ}\t\
{nBAM_Z_Ranksums_BQ}\t\
{nBAM_REF_NM}\t\
{nBAM_ALT_NM}\t\
{nBAM_NM_Diff}\t\
{nBAM_REF_Concordant}\t\
{nBAM_REF_Discordant}\t\
{nBAM_ALT_Concordant}\t\
{nBAM_ALT_Discordant}\t\
{nBAM_Concordance_FET}\t\
{N_REF_FOR}\t\
{N_REF_REV}\t\
{N_ALT_FOR}\t\
{N_ALT_REV}\t\
{nBAM_StrandBias_FET}\t\
{nBAM_Z_Ranksums_EndPos}\t\
{nBAM_REF_Clipped_Reads}\t\
{nBAM_ALT_Clipped_Reads}\t\
{nBAM_Clipping_FET}\t\
{nBAM_MQ0}\t\
{nBAM_Other_Reads}\t\
{nBAM_Poor_Reads}\t\
{nBAM_REF_InDel_3bp}\t\
{nBAM_REF_InDel_2bp}\t\
{nBAM_REF_InDel_1bp}\t\
{nBAM_ALT_InDel_3bp}\t\
{nBAM_ALT_InDel_2bp}\t\
{nBAM_ALT_InDel_1bp}\t\
{M2_NLOD}\t\
{M2_TLOD}\t\
{M2_STR}\t\
{M2_ECNT}\t\
{SOR}\t\
{MSI}\t\
{MSILEN}\t\
{SHIFT3}\t\
{MaxHomopolymer_Length}\t\
{SiteHomopolymer_Length}\t\
{T_DP}\t\
{tBAM_REF_MQ}\t\
{tBAM_ALT_MQ}\t\
{tBAM_Z_Ranksums_MQ}\t\
{tBAM_REF_BQ}\t\
{tBAM_ALT_BQ}\t\
{tBAM_Z_Ranksums_BQ}\t\
{tBAM_REF_NM}\t\
{tBAM_ALT_NM}\t\
{tBAM_NM_Diff}\t\
{tBAM_REF_Concordant}\t\
{tBAM_REF_Discordant}\t\
{tBAM_ALT_Concordant}\t\
{tBAM_ALT_Discordant}\t\
{tBAM_Concordance_FET}\t\
{T_REF_FOR}\t\
{T_REF_REV}\t\
{T_ALT_FOR}\t\
{T_ALT_REV}\t\
{tBAM_StrandBias_FET}\t\
{tBAM_Z_Ranksums_EndPos}\t\
{tBAM_REF_Clipped_Reads}\t\
{tBAM_ALT_Clipped_Reads}\t\
{tBAM_Clipping_FET}\t\
{tBAM_MQ0}\t\
{tBAM_Other_Reads}\t\
{tBAM_Poor_Reads}\t\
{tBAM_REF_InDel_3bp}\t\
{tBAM_REF_InDel_2bp}\t\
{tBAM_REF_InDel_1bp}\t\
{tBAM_ALT_InDel_3bp}\t\
{tBAM_ALT_InDel_2bp}\t\
{tBAM_ALT_InDel_1bp}\t\
{InDel_Length}\t\
{TrueVariant_or_False}'
## Running
with genome.open_textfile(mysites) as my_sites, open(outfile, 'w') as outhandle:
my_line = my_sites.readline().rstrip()
nbam = pysam.AlignmentFile(nbam_fn, reference_filename=ref_fa)
tbam = pysam.AlignmentFile(tbam_fn, reference_filename=ref_fa)
ref_fa = pysam.FastaFile(ref_fa)
if truth:
truth = genome.open_textfile(truth)
truth_line = truth.readline().rstrip()
while truth_line.startswith('#'):
truth_line = truth.readline().rstrip()
if cosmic:
cosmic = genome.open_textfile(cosmic)
cosmic_line = cosmic.readline().rstrip()
while cosmic_line.startswith('#'):
cosmic_line = cosmic.readline().rstrip()
if dbsnp:
dbsnp = genome.open_textfile(dbsnp)
dbsnp_line = dbsnp.readline().rstrip()
while dbsnp_line.startswith('#'):
dbsnp_line = dbsnp.readline().rstrip()
for caller_i in ('mutect', 'varscan', 'jsm', 'sniper', 'vardict', 'muse', 'lofreq', 'scalpel', 'strelka', 'tnscope'):
if vars()[caller_i]:
vars()[caller_i] = genome.open_textfile( vars()[caller_i] )
vars()[caller_i + '_line'] = vars()[caller_i].readline().rstrip()
while vars()[caller_i + '_line'].startswith('#'):
vars()[caller_i + '_line'] = vars()[caller_i].readline().rstrip()
# Get through all the headers:
while my_line.startswith('#') or my_line.startswith('track='):
my_line = my_sites.readline().rstrip()
# First coordinate:
coordinate_i = re.match( genome.pattern_chr_position, my_line )
coordinate_i = coordinate_i.group() if coordinate_i else ''
# First line:
outhandle.write( out_header.replace('{','').replace('}','') + '\n' )
while my_line:
# If VCF, get all the variants with the same coordinate into a list:
if is_vcf:
my_vcf = genome.Vcf_line( my_line )
my_coordinates = [(my_vcf.chromosome, my_vcf.position)]
variants_at_my_coordinate = []
alt_bases = my_vcf.altbase.split(',')
for alt_i in alt_bases:
vcf_i = copy(my_vcf)
vcf_i.altbase = alt_i
variants_at_my_coordinate.append( vcf_i )
# As long as the "coordinate" stays the same, it will keep reading until it's different.
while my_coordinates[0] == (my_vcf.chromosome, my_vcf.position):
my_line = my_sites.readline().rstrip()
my_vcf = genome.Vcf_line( my_line )
coordinate_j = re.match( genome.pattern_chr_position, my_line )
coordinate_j = coordinate_j.group() if coordinate_j else ''
if genome.whoisbehind(coordinate_i, coordinate_j, chrom_seq) == 1:
raise Exception( '{} does not seem to be properly sorted.'.format(mysites) )
coordinate_i = coordinate_j
if my_coordinates[0] == (my_vcf.chromosome, my_vcf.position):
alt_bases = my_vcf.altbase.split(',')
for alt_i in alt_bases:
vcf_i = copy(my_vcf)
vcf_i.altbase = alt_i
variants_at_my_coordinate.append( vcf_i )
elif is_bed:
bed_item = my_line.split('\t')
my_coordinates = genomic_coordinates( bed_item[0], int(bed_item[1])+1, int(bed_item[2]) )
elif is_pos:
pos_item = my_line.split('\t')
my_coordinates = genomic_coordinates( pos_item[0], int(pos_item[1]), int(pos_item[1]) )
elif fai_file:
fai_item = my_line.split('\t')
my_coordinates = genomic_coordinates( fai_item[0], 1, int(fai_item[1]) )
##### ##### ##### ##### ##### #####
for my_coordinate in my_coordinates:
######## If VCF, can get ref base, variant base, as well as other identifying information ########
if is_vcf:
ref_bases = []
alt_bases = []
indel_lengths = []
all_my_identifiers = []
for variant_i in variants_at_my_coordinate:
ref_base = variant_i.refbase
first_alt = variant_i.altbase.split(',')[0]
indel_length = len(first_alt) - len(ref_base)
ref_bases.append( ref_base )
alt_bases.append( first_alt )
indel_lengths.append( indel_length )
# Extract these information if they exist in the VCF file, but they could be re-written if dbSNP/COSMIC are supplied.
if_dbsnp = 1 if re.search(r'rs[0-9]+', variant_i.identifier) else 0
if_cosmic = 1 if re.search(r'COS[MN][0-9]+', variant_i.identifier) else 0
if_common = 1 if variant_i.get_info_value('COMMON') == '1' else 0
num_cases = variant_i.get_info_value('CNT') if variant_i.get_info_value('CNT') else nan
if variant_i.identifier == '.':
my_identifier_i = set()
else:
my_identifier_i = variant_i.identifier.split(';')
my_identifier_i = set( my_identifier_i )
all_my_identifiers.append( my_identifier_i )
## If not, 1) get ref_base, first_alt from other VCF files.
# 2) Create placeholders for dbSNP and COSMIC that can be overwritten with dbSNP/COSMIC VCF files (if provided)
else:
variants_at_my_coordinate = [None] # Just to have something to iterate
ref_base = first_alt = indel_length = None
# Could be re-written if dbSNP/COSMIC are supplied. If not, they will remain NaN.
if_dbsnp = if_cosmic = if_common = num_cases = nan
# Keep track of NumCallers:
num_callers = 0
#################################### Find the same coordinate in those VCF files ####################################
if args.mutect_vcf: got_mutect, mutect_variants, mutect_line = genome.find_vcf_at_coordinate(my_coordinate, mutect_line, mutect, chrom_seq)
if args.varscan_vcf: got_varscan, varscan_variants, varscan_line = genome.find_vcf_at_coordinate(my_coordinate, varscan_line, varscan, chrom_seq)
if args.jsm_vcf: got_jsm, jsm_variants, jsm_line = genome.find_vcf_at_coordinate(my_coordinate, jsm_line, jsm, chrom_seq)
if args.somaticsniper_vcf: got_sniper, sniper_variants, sniper_line = genome.find_vcf_at_coordinate(my_coordinate, sniper_line, sniper, chrom_seq)
if args.vardict_vcf: got_vardict, vardict_variants, vardict_line = genome.find_vcf_at_coordinate(my_coordinate, vardict_line, vardict, chrom_seq)
if args.muse_vcf: got_muse, muse_variants, muse_line = genome.find_vcf_at_coordinate(my_coordinate, muse_line, muse, chrom_seq)
if args.lofreq_vcf: got_lofreq, lofreq_variants, lofreq_line = genome.find_vcf_at_coordinate(my_coordinate, lofreq_line, lofreq, chrom_seq)
if args.scalpel_vcf: got_scalpel, scalpel_variants, scalpel_line = genome.find_vcf_at_coordinate(my_coordinate, scalpel_line, scalpel, chrom_seq)
if args.strelka_vcf: got_strelka, strelka_variants, strelka_line = genome.find_vcf_at_coordinate(my_coordinate, strelka_line, strelka, chrom_seq)
if args.tnscope_vcf: got_tnscope, tnscope_variants, tnscope_line = genome.find_vcf_at_coordinate(my_coordinate, tnscope_line, tnscope, chrom_seq)
if args.ground_truth_vcf: got_truth, truth_variants, truth_line = genome.find_vcf_at_coordinate(my_coordinate, truth_line, truth, chrom_seq)
if args.dbsnp_vcf: got_dbsnp, dbsnp_variants, dbsnp_line = genome.find_vcf_at_coordinate(my_coordinate, dbsnp_line, dbsnp, chrom_seq)
if args.cosmic_vcf: got_cosmic, cosmic_variants, cosmic_line = genome.find_vcf_at_coordinate(my_coordinate, cosmic_line, cosmic, chrom_seq)
# Now, use pysam to look into the BAM file(s), variant by variant from the input:
for ith_call, my_call in enumerate( variants_at_my_coordinate ):
if is_vcf:
# The particular line in the input VCF file:
variant_id = ( (my_call.chromosome, my_call.position), my_call.refbase, my_call.altbase )
ref_base = ref_bases[ith_call]
first_alt = alt_bases[ith_call]
indel_length = indel_lengths[ith_call]
my_identifiers = all_my_identifiers[ith_call]
else:
variant_id = ( (my_coordinate[0], my_coordinate[1]), ref_base, first_alt )
#################### Collect MuTect ####################:
if args.mutect_vcf:
if variant_id in mutect_variants:
mutect_variant_i = mutect_variants[variant_id]
mutect_classification = 1 if (mutect_variant_i.get_info_value('SOMATIC') or 'PASS' in mutect_variant_i.filters) else 0
# MuTect2 has some useful information:
nlod = mutect2_nlod(mutect_variant_i)
tlod = mutect2_tlod(mutect_variant_i)
tandem = mutect2_STR(mutect_variant_i)
ecnt = mutect2_ECNT(mutect_variant_i)
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = mutect_variant_i.refbase
if not first_alt: first_alt = mutect_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
# Not called by mutect
mutect_classification = 0
nlod = tlod = tandem = ecnt = nan
num_callers += mutect_classification
else:
# Assign a bunch of NaN's
mutect_classification = nan
nlod = tlod = tandem = ecnt = nan
#################### Collect VarScan ####################:
if args.varscan_vcf:
if variant_id in varscan_variants:
varscan_variant_i = varscan_variants[ variant_id ]
varscan_classification = 1 if varscan_variant_i.get_info_value('SOMATIC') else 0
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = varscan_variant_i.refbase
if not first_alt: first_alt = varscan_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
varscan_classification = 0
num_callers += varscan_classification
else:
varscan_classification = nan
#################### Collect JointSNVMix ####################:
if args.jsm_vcf:
if variant_id in jsm_variants:
jsm_variant_i = jsm_variants[ variant_id ]
jointsnvmix2_classification = 1
aaab = float( jsm_variant_i.get_info_value('AAAB') )
aabb = float( jsm_variant_i.get_info_value('AABB') )
jointsnvmix2_p = 1 - aaab - aabb
score_jointsnvmix2 = genome.p2phred(jointsnvmix2_p, max_phred=50)
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = jsm_variant_i.refbase
if not first_alt: first_alt = jsm_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
jointsnvmix2_classification = 0
score_jointsnvmix2 = nan
num_callers += jointsnvmix2_classification
else:
jointsnvmix2_classification = score_jointsnvmix2 = nan
#################### Collect SomaticSniper ####################:
if args.somaticsniper_vcf:
if variant_id in sniper_variants:
sniper_variant_i = sniper_variants[ variant_id ]
sniper_classification = 1 if sniper_variant_i.get_sample_value('SS', idxT) == '2' else 0
if sniper_classification == 1:
score_somaticsniper = sniper_variant_i.get_sample_value('SSC', idxT)
score_somaticsniper = int(score_somaticsniper) if score_somaticsniper else nan
else:
score_somaticsniper = nan
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = sniper_variant_i.refbase
if not first_alt: first_alt = sniper_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
sniper_classification = 0
score_somaticsniper = nan
num_callers += sniper_classification
else:
sniper_classification = score_somaticsniper = nan
#################### Collect VarDict ####################:
if args.vardict_vcf:
if variant_id in vardict_variants:
vardict_variant_i = vardict_variants[ variant_id ]
if (vardict_variant_i.filters == 'PASS') and ('Somatic' in vardict_variant_i.info):
vardict_classification = 1
elif 'Somatic' in vardict_variant_i.info:
vardict_filters = vardict_variant_i.filters.split(';')
disqualifying_filters = ('d7' in vardict_filters or 'd5' in vardict_filters) or \
('DIFF0.2' in vardict_filters) or \
('LongAT' in vardict_filters) or \
('MAF0.05' in vardict_filters) or \
('MSI6' in vardict_filters) or \
('NM4' in vardict_filters or 'NM4.25' in vardict_filters) or \
('pSTD' in vardict_filters) or \
('SN1.5' in vardict_filters) or \
( 'P0.05' in vardict_filters and float(vardict_variant_i.get_info_value('SSF') ) >= 0.15 ) or \
( ('v3' in vardict_filters or 'v4' in vardict_filters) and int(vardict_variant_i.get_sample_value('VD', 0))<3 )
no_bad_filter = not disqualifying_filters
filter_fail_times = len(vardict_filters)
if no_bad_filter and filter_fail_times<=2:
vardict_classification = 0.5
else:
vardict_classification = 0
else:
vardict_classification = 0
# Somatic Score:
score_vardict = vardict_variant_i.get_info_value('SSF')
if score_vardict:
score_vardict = float(score_vardict)
score_vardict = genome.p2phred(score_vardict, max_phred=100)
else:
score_vardict = nan
# MSI, MSILEN, and SHIFT3:
msi = find_MSI(vardict_variant_i)
msilen = find_MSILEN(vardict_variant_i)
shift3 = find_SHIFT3(vardict_variant_i)
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = vardict_variant_i.refbase
if not first_alt: first_alt = vardict_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
vardict_classification = 0
msi = msilen = shift3 = score_vardict = nan
num_callers += vardict_classification
else:
vardict_classification = msi = msilen = shift3 = score_vardict = nan
#################### Collect MuSE ####################:
if args.muse_vcf:
if variant_id in muse_variants:
muse_variant_i = muse_variants[ variant_id ]
if muse_variant_i.filters == 'PASS':
muse_classification = 1
elif muse_variant_i.filters == 'Tier1':
muse_classification = 0.9
elif muse_variant_i.filters == 'Tier2':
muse_classification = 0.8
elif muse_variant_i.filters == 'Tier3':
muse_classification = 0.7
elif muse_variant_i.filters == 'Tier4':
muse_classification = 0.6
elif muse_variant_i.filters == 'Tier5':
muse_classification = 0.5
else:
muse_classification = 0
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = muse_variant_i.refbase
if not first_alt: first_alt = muse_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
muse_classification = 0
num_callers += muse_classification
else:
muse_classification = nan
#################### Collect LoFreq ####################:
if args.lofreq_vcf:
if variant_id in lofreq_variants:
lofreq_variant_i = lofreq_variants[ variant_id ]
lofreq_classification = 1 if lofreq_variant_i.filters == 'PASS' else 0
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = lofreq_variant_i.refbase
if not first_alt: first_alt = lofreq_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
lofreq_classification = 0
num_callers += lofreq_classification
else:
lofreq_classification = nan
#################### Collect Scalpel ####################:
if args.scalpel_vcf:
if variant_id in scalpel_variants:
scalpel_variant_i = scalpel_variants[ variant_id ]
if scalpel_variant_i.get_info_value('SOMATIC'):
if scalpel_variant_i.filters == 'PASS':
scalpel_classification = 1
else:
scalpel_classification = 0.5
else:
scalpel_classification = 0
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = scalpel_variant_i.refbase
if not first_alt: first_alt = scalpel_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
scalpel_classification = 0
num_callers += scalpel_classification
else:
scalpel_classification = nan
#################### Collect Strelka ####################:
if args.strelka_vcf:
if variant_id in strelka_variants:
strelka_variant_i = strelka_variants[variant_id]
strelka_classification = 1 if 'PASS' in strelka_variant_i.filters else 0
somatic_evs = strelka_variant_i.get_info_value('SomaticEVS')
qss = strelka_variant_i.get_info_value('QSS')
tqss = strelka_variant_i.get_info_value('TQSS')
else:
strelka_classification = 0
somatic_evs = qss = tqss = nan
num_callers += strelka_classification
else:
strelka_classification = nan
somatic_evs = qss = tqss = nan
#################### Collect TNScope (similar format as MuTect2) ####################:
if args.tnscope_vcf:
if variant_id in tnscope_variants:
tnscope_variant_i = tnscope_variants[variant_id]
tnscope_classification = 1 if (tnscope_variant_i.get_info_value('SOMATIC') or 'PASS' in tnscope_variant_i.filters) else 0
# If ref_base, first_alt, and indel_length unknown, get it here:
if not ref_base: ref_base = tnscope_variant_i.refbase
if not first_alt: first_alt = tnscope_variant_i.altbase
if indel_length == None: indel_length = len(first_alt) - len(ref_base)
else:
# Not called by TNscope
tnscope_classification = 0
num_callers += tnscope_classification
else:
# Assign a bunch of NaN's
tnscope_classification = nan
# Potentially write the output only if it meets this threshold:
if num_callers >= args.minimum_num_callers:
########## Ground truth file ##########
if args.ground_truth_vcf:
if variant_id in truth_variants.keys():
judgement = 1
my_identifiers.add('TruePositive')
else:
judgement = 0
my_identifiers.add('FalsePositive')
else:
judgement = nan
########## dbSNP ########## Will overwrite dbSNP info from input VCF file
if args.dbsnp_vcf:
if variant_id in dbsnp_variants.keys():
dbsnp_variant_i = dbsnp_variants[variant_id]
if_dbsnp = 1
if_common = 1 if dbsnp_variant_i.get_info_value('COMMON') == '1' else 0
rsID = dbsnp_variant_i.identifier.split(',')
for ID_i in rsID:
my_identifiers.add( ID_i )
else:
if_dbsnp = if_common = 0
########## COSMIC ########## Will overwrite COSMIC info from input VCF file
if args.cosmic_vcf:
if variant_id in cosmic_variants.keys():
cosmic_variant_i = cosmic_variants[variant_id]
# If designated as SNP, make it "non-cosmic" and make CNT=nan.
if cosmic_variant_i.get_info_value('SNP'):
if_cosmic = 0
num_cases = nan
else:
if_cosmic = 1
num_cases = cosmic_variant_i.get_info_value('CNT')
num_cases = num_cases if num_cases else nan
# COSMIC ID still intact:
cosmicID = cosmic_variant_i.identifier.split(',')
for ID_i in cosmicID:
my_identifiers.add( ID_i )
else:
if_cosmic = num_cases = 0
########## ######### ######### INFO EXTRACTION FROM BAM FILES ########## ######### #########
# Normal BAM file:
n_reads = nbam.fetch( my_coordinate[0], my_coordinate[1]-1, my_coordinate[1] )
n_ref_read_mq = []
n_alt_read_mq = []
n_ref_read_bq = []
n_alt_read_bq = []
n_ref_edit_distance = []
n_alt_edit_distance = []
n_ref_concordant_reads = n_alt_concordant_reads = n_ref_discordant_reads = n_alt_discordant_reads = 0
n_ref_for = n_ref_rev = n_alt_for = n_alt_rev = N_dp = 0
n_ref_SC_reads = n_alt_SC_reads = n_ref_notSC_reads = n_alt_notSC_reads = 0
n_MQ0 = 0
n_ref_pos_from_end = []
n_alt_pos_from_end = []
n_ref_flanking_indel = []
n_alt_flanking_indel = []
n_noise_read_count = n_poor_read_count = 0
for read_i in n_reads:
if not read_i.is_unmapped and dedup_test(read_i):
N_dp += 1
code_i, ith_base, base_call_i, indel_length_i, flanking_indel_i = position_of_aligned_read(read_i, my_coordinate[1]-1 )
if read_i.mapping_quality < min_mq and mean(read_i.query_qualities) < min_bq:
n_poor_read_count += 1
if read_i.mapping_quality == 0:
n_MQ0 += 1
# Reference calls:
if code_i == 1 and base_call_i == ref_base[0]:
n_ref_read_mq.append( read_i.mapping_quality )
n_ref_read_bq.append( read_i.query_qualities[ith_base] )
try:
n_ref_edit_distance.append( read_i.get_tag('NM') )
except KeyError:
pass
# Concordance
if read_i.is_proper_pair and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_ref_concordant_reads += 1
elif (not read_i.is_proper_pair) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_ref_discordant_reads += 1
# Orientation
if (not read_i.is_reverse) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_ref_for += 1
elif read_i.is_reverse and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_ref_rev += 1
# Soft-clipped reads?
if read_i.cigar[0][0] == cigar_soft_clip or read_i.cigar[-1][0] == cigar_soft_clip:
n_ref_SC_reads += 1
else:
n_ref_notSC_reads += 1
# Distance from the end of the read:
if ith_base != None:
n_ref_pos_from_end.append( min(ith_base, read_i.query_length-ith_base) )
# Flanking indels:
n_ref_flanking_indel.append( flanking_indel_i )
# Alternate calls:
# SNV, or Deletion, or Insertion where I do not check for matching indel length
elif (indel_length == 0 and code_i == 1 and base_call_i == first_alt) or \
(indel_length < 0 and code_i == 2 and indel_length == indel_length_i) or \
(indel_length > 0 and code_i == 3):
n_alt_read_mq.append( read_i.mapping_quality )
n_alt_read_bq.append( read_i.query_qualities[ith_base] )
try:
n_alt_edit_distance.append( read_i.get_tag('NM') )
except KeyError:
pass
# Concordance
if read_i.is_proper_pair and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_alt_concordant_reads += 1
elif (not read_i.is_proper_pair) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_alt_discordant_reads += 1
# Orientation
if (not read_i.is_reverse) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_alt_for += 1
elif read_i.is_reverse and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
n_alt_rev += 1
# Soft-clipped reads?
if read_i.cigar[0][0] == cigar_soft_clip or read_i.cigar[-1][0] == cigar_soft_clip:
n_alt_SC_reads += 1
else:
n_alt_notSC_reads += 1
# Distance from the end of the read:
if ith_base != None:
n_alt_pos_from_end.append( min(ith_base, read_i.query_length-ith_base) )
# Flanking indels:
n_alt_flanking_indel.append( flanking_indel_i )
# Inconsistent read or 2nd alternate calls:
else:
n_noise_read_count += 1
# Done extracting info from tumor BAM. Now tally them:
n_ref_mq = mean(n_ref_read_mq)
n_alt_mq = mean(n_alt_read_mq)
n_z_ranksums_mq = stats.ranksums(n_alt_read_mq, n_ref_read_mq)[0]
n_ref_bq = mean(n_ref_read_bq)
n_alt_bq = mean(n_alt_read_bq)
n_z_ranksums_bq = stats.ranksums(n_alt_read_bq, n_ref_read_bq)[0]
n_ref_NM = mean(n_ref_edit_distance)
n_alt_NM = mean(n_alt_edit_distance)
n_z_ranksums_NM = stats.ranksums(n_alt_edit_distance, n_ref_edit_distance)[0]
n_NM_Diff = n_alt_NM - n_ref_NM - abs(indel_length)
n_concordance_fet = stats.fisher_exact(( (n_ref_concordant_reads, n_alt_concordant_reads), (n_ref_discordant_reads, n_alt_discordant_reads) ))[1]
n_strandbias_fet = stats.fisher_exact(( (n_ref_for, n_alt_for), (n_ref_rev, n_alt_rev) ))[1]
n_clipping_fet = stats.fisher_exact(( (n_ref_notSC_reads, n_alt_notSC_reads), (n_ref_SC_reads, n_alt_SC_reads) ))[1]
n_z_ranksums_endpos = stats.ranksums(n_alt_pos_from_end, n_ref_pos_from_end)[0]
n_ref_indel_1bp = n_ref_flanking_indel.count(1)
n_ref_indel_2bp = n_ref_flanking_indel.count(2) + n_ref_indel_1bp
n_ref_indel_3bp = n_ref_flanking_indel.count(3) + n_ref_indel_2bp + n_ref_indel_1bp
n_alt_indel_1bp = n_alt_flanking_indel.count(1)
n_alt_indel_2bp = n_alt_flanking_indel.count(2) + n_alt_indel_1bp
n_alt_indel_3bp = n_alt_flanking_indel.count(3) + n_alt_indel_2bp + n_alt_indel_1bp
########################################################################################
# Tumor BAM file:
t_reads = tbam.fetch( my_coordinate[0], my_coordinate[1]-1, my_coordinate[1] )
t_ref_read_mq = []
t_alt_read_mq = []
t_ref_read_bq = []
t_alt_read_bq = []
t_ref_edit_distance = []
t_alt_edit_distance = []
t_ref_concordant_reads = t_alt_concordant_reads = t_ref_discordant_reads = t_alt_discordant_reads = 0
t_ref_for = t_ref_rev = t_alt_for = t_alt_rev = T_dp = 0
t_ref_SC_reads = t_alt_SC_reads = t_ref_notSC_reads = t_alt_notSC_reads = 0
t_MQ0 = 0
t_ref_pos_from_end = []
t_alt_pos_from_end = []
t_ref_flanking_indel = []
t_alt_flanking_indel = []
t_noise_read_count = t_poor_read_count = 0
qname_collector = {}
for read_i in t_reads:
if not read_i.is_unmapped and dedup_test(read_i):
T_dp += 1
code_i, ith_base, base_call_i, indel_length_i, flanking_indel_i = position_of_aligned_read(read_i, my_coordinate[1]-1 )
if read_i.mapping_quality < min_mq and mean(read_i.query_qualities) < min_bq:
t_poor_read_count += 1
if read_i.mapping_quality == 0:
t_MQ0 += 1
# Reference calls:
if code_i == 1 and base_call_i == ref_base[0]:
try:
qname_collector[read_i.qname].append(0)
except KeyError:
qname_collector[read_i.qname] = [0]
t_ref_read_mq.append( read_i.mapping_quality )
t_ref_read_bq.append( read_i.query_qualities[ith_base] )
try:
t_ref_edit_distance.append( read_i.get_tag('NM') )
except KeyError:
pass
# Concordance
if read_i.is_proper_pair and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
t_ref_concordant_reads += 1
elif (not read_i.is_proper_pair) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
t_ref_discordant_reads += 1
# Orientation
if (not read_i.is_reverse) and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
t_ref_for += 1
elif read_i.is_reverse and read_i.mapping_quality >= min_mq and read_i.query_qualities[ith_base] >= min_bq:
t_ref_rev += 1
# Soft-clipped reads?
if read_i.cigar[0][0] == cigar_soft_clip or read_i.cigar[-1][0] == cigar_soft_clip:
t_ref_SC_reads += 1
else:
t_ref_notSC_reads += 1
# Distance from the end of the read:
if ith_base != None:
t_ref_pos_from_end.append( min(ith_base, read_i.query_length-ith_base) )
# Flanking indels:
t_ref_flanking_indel.append( flanking_indel_i )
# Alternate calls:
# SNV, or Deletion, or Insertion where I do not check for matching indel length
elif (indel_length == 0 and code_i == 1 and base_call_i == first_alt) or \