def main():
global prev_lines
#parse ARGs
parser = argparse.ArgumentParser(description='Filter SNP positions by call quality and min. coverage. Awaits filenames for M, P .vcf files, and M, P .sam files.')
parser.add_argument('filenames', type=str, nargs='+', help='paths to .vcf files with M, P SNPs and to corresponding .sam files')
args = parser.parse_args()
if len(args.filenames) != 4: exit("Unexpected number of arguments passed! Expecting 4 filenames.")
#treat these as CONSTANTS!
M = 0; P = 1; MR = 2; PR = 3;
ALL_VCF = [M, P]
ALL = [M, P, MR, PR]
'''
Get union of the M and P SNP positions
'''
print " Stage 1"
#list of input files
in_files = [open(args.filenames[i], "r" ) for i in ALL]
#dictionary to store the union of SNP positions
data = dict()
loci = dict()
#read the first SNP from M, P vcf files
snps = [[] for i in ALL_VCF]
for i in ALL_VCF:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#': line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#get list of all positions in UNION of M and P SNP positions
while len(snps[M])>2 or len(snps[P])>2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in ALL_VCF:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in ALL_VCF:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P]]
for i in [M, P]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3][0], snps[i][4][0]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#check for homozygous alternative sites
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out genotype config info
gt = info[9].split(':')[0]
#if homozygous alternative
if gt[0] == '1':
alleles[i][0] = snps[i][4][0]
if gt[2] == '1':
alleles[i][1] = snps[i][4][0]
if gt[0] == '0':
alleles[i][0] = snps[i][3][0]
if gt[2] == '0':
alleles[i][1] = snps[i][3][0]
#take note that for this position we need to get allele counts in plasma samaples
loci[min_pos] = alleles
sp.add_pos(min_pos, data)
#read input: next SNP
for i in ALL_VCF:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#END WHILE
'''
Get coverage information of the SNP positions from corresponding .sam files
'''
print " Stage 2"
#fetch allele support for the UNION positions in maternal and paternal reads
#set up datastructures for counting allele support in diffrenct SAM files
posInfo = [dict() for i in ALL]
for R in [MR, PR]:
posInfo[R] = copy.deepcopy(data)
#fetch the reads in plasma SAM file and get counts for the positions originally specified in 'data'
for R in [MR, PR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
'''
Filter the SNP positions according to call quality and coverage
'''
print " Stage 3"
#reopen VCF files
for f in in_files: f.close()
in_files = [open(args.filenames[i], "r" ) for i in ALL_VCF]
#list of output files
out_files = [open(args.filenames[i][:-3]+"ftr.vcf", "w") for i in ALL_VCF]
#read the first SNP from M, P vcf files
snps = [[] for i in ALL_VCF]
for i in ALL_VCF:
snps[i] = getlineFromFile(in_files, out_files, i)
#positions ignored from the union of M and P
ignored_pos = 0
while len(snps[M])>2 or len(snps[P])>2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in ALL_VCF:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in ALL_VCF:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get genotype call quality
callQ = [0. for i in ALL_VCF]
for i in ALL_VCF:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out quality info
callQ[i] = float(info[5])
qualityOK = bool(callQ[M] >= 75 or callQ[P] >= 75)
#get coverage info
alleles = loci[min_pos]
coverage = [0 for i in [M, P]]
count_sum = [0 for i in [M, P]]
for i in [M, P]:
a1 = alleles[i][0]
a2 = alleles[i][1]
count_a1 = 0
count_a2 = 0
try: count_a1 = posInfo[i+2][min_pos][a1]
except: print i, min_pos, a1, posInfo[i+2][min_pos], alleles[i]
try: count_a2 = posInfo[i+2][min_pos][a2]
except: print i, min_pos, a2, posInfo[i+2][min_pos], alleles[i]
count_sum[i] = sum(posInfo[i+2][min_pos].values())
coverage[i] = count_a1 + count_a2 #posInfo[i+2][min_pos][a1] + posInfo[i+2][min_pos][a2]
""" #using mpileup to get the coverage info
cmd = 'samtools mpileup -r %(chr)s:%(pos)d-%(pos)d __%(gnm)s.part.bam' % {'chr':min_chr, 'pos':min_pos, 'gnm':'MP'[i]}
process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = process.communicate()
if process.returncode == 0:
fields = out.split('\t')
if len(fields) < 4:
#there is no coverage in the BAM file for this position
coverage[i] = 0
#print "!!! :", out, "|", err, "|", cmd
else:
coverage[i] = int(fields[3])
#print out, '>>coverage>>', coverage[i]
else:
print err
"""
coverageOK = bool(coverage[M] >= 15 and coverage[P] >= 15)
contaminationOK = False
try: contaminationOK = bool(float(coverage[M])/count_sum[M] >= 0.9 and float(coverage[P])/count_sum[P] >= 0.9)
except: contaminationOK = False
if coverageOK and not contaminationOK:
print min_pos, "- contamination M:", coverage[M], posInfo[M+2][min_pos], alleles[M], "P:", coverage[P], posInfo[P+2][min_pos], alleles[P]
#MOK = bool(callQ[M] >= 75 or coverage[M] >= 15)
#POK = bool(callQ[P] >= 75 or coverage[P] >= 15)
#if not (MOK and POK): #ignore positions that are not good enough
if not (qualityOK and coverageOK and contaminationOK): #ignore positions that are not good enough
ignored_pos += 1
for i in ALL_VCF:
#if there is a SNP in the data at this position, skip it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
#print min_pos, callQ[M], callQ[P], coverage[M], coverage[P], prev_lines[i],
prev_lines[i] = ''
#read input: next SNP
for i in ALL_VCF:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = getlineFromFile(in_files, out_files, i)
#END WHILE
print "Low quality positions ignored in the region:", ignored_pos
def main():
global prev_lines
#parse ARGs
parser = argparse.ArgumentParser(
description=
'Filter SNP positions by call quality and min. coverage. Awaits filenames for M, P .vcf files, and M, P .sam files.'
)
parser.add_argument(
'filenames',
type=str,
nargs='+',
help=
'paths to .vcf files with M, P SNPs and to corresponding .sam files')
args = parser.parse_args()
if len(args.filenames) != 4:
exit("Unexpected number of arguments passed! Expecting 4 filenames.")
#treat these as CONSTANTS!
M = 0
P = 1
MR = 2
PR = 3
ALL_VCF = [M, P]
ALL = [M, P, MR, PR]
'''
Get union of the M and P SNP positions
'''
print " Stage 1"
#list of input files
in_files = [open(args.filenames[i], "r") for i in ALL]
#dictionary to store the union of SNP positions
data = dict()
loci = dict()
#read the first SNP from M, P vcf files
snps = [[] for i in ALL_VCF]
for i in ALL_VCF:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#':
line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#get list of all positions in UNION of M and P SNP positions
while len(snps[M]) > 2 or len(
snps[P]) > 2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in ALL_VCF:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in ALL_VCF:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P]]
for i in [M, P]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3][0], snps[i][4][0]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#check for homozygous alternative sites
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out genotype config info
gt = info[9].split(':')[0]
#if homozygous alternative
if gt[0] == '1':
alleles[i][0] = snps[i][4][0]
if gt[2] == '1':
alleles[i][1] = snps[i][4][0]
if gt[0] == '0':
alleles[i][0] = snps[i][3][0]
if gt[2] == '0':
alleles[i][1] = snps[i][3][0]
#take note that for this position we need to get allele counts in plasma samaples
loci[min_pos] = alleles
sp.add_pos(min_pos, data)
#read input: next SNP
for i in ALL_VCF:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#END WHILE
'''
Get coverage information of the SNP positions from corresponding .sam files
'''
print " Stage 2"
#fetch allele support for the UNION positions in maternal and paternal reads
#set up datastructures for counting allele support in diffrenct SAM files
posInfo = [dict() for i in ALL]
for R in [MR, PR]:
posInfo[R] = copy.deepcopy(data)
#fetch the reads in plasma SAM file and get counts for the positions originally specified in 'data'
for R in [MR, PR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
'''
Filter the SNP positions according to call quality and coverage
'''
print " Stage 3"
#reopen VCF files
for f in in_files:
f.close()
in_files = [open(args.filenames[i], "r") for i in ALL_VCF]
#list of output files
out_files = [
open(args.filenames[i][:-3] + "ftr.vcf", "w") for i in ALL_VCF
]
#read the first SNP from M, P vcf files
snps = [[] for i in ALL_VCF]
for i in ALL_VCF:
snps[i] = getlineFromFile(in_files, out_files, i)
#positions ignored from the union of M and P
ignored_pos = 0
while len(snps[M]) > 2 or len(
snps[P]) > 2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in ALL_VCF:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in ALL_VCF:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get genotype call quality
callQ = [0. for i in ALL_VCF]
for i in ALL_VCF:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out quality info
callQ[i] = float(info[5])
qualityOK = bool(callQ[M] >= 75 or callQ[P] >= 75)
#get coverage info
alleles = loci[min_pos]
coverage = [0 for i in [M, P]]
count_sum = [0 for i in [M, P]]
for i in [M, P]:
a1 = alleles[i][0]
a2 = alleles[i][1]
count_a1 = 0
count_a2 = 0
try:
count_a1 = posInfo[i + 2][min_pos][a1]
except:
print i, min_pos, a1, posInfo[i + 2][min_pos], alleles[i]
try:
count_a2 = posInfo[i + 2][min_pos][a2]
except:
print i, min_pos, a2, posInfo[i + 2][min_pos], alleles[i]
count_sum[i] = sum(posInfo[i + 2][min_pos].values())
coverage[
i] = count_a1 + count_a2 #posInfo[i+2][min_pos][a1] + posInfo[i+2][min_pos][a2]
""" #using mpileup to get the coverage info
cmd = 'samtools mpileup -r %(chr)s:%(pos)d-%(pos)d __%(gnm)s.part.bam' % {'chr':min_chr, 'pos':min_pos, 'gnm':'MP'[i]}
process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = process.communicate()
if process.returncode == 0:
fields = out.split('\t')
if len(fields) < 4:
#there is no coverage in the BAM file for this position
coverage[i] = 0
#print "!!! :", out, "|", err, "|", cmd
else:
coverage[i] = int(fields[3])
#print out, '>>coverage>>', coverage[i]
else:
print err
"""
coverageOK = bool(coverage[M] >= 15 and coverage[P] >= 15)
contaminationOK = False
try:
contaminationOK = bool(
float(coverage[M]) / count_sum[M] >= 0.9
and float(coverage[P]) / count_sum[P] >= 0.9)
except:
contaminationOK = False
if coverageOK and not contaminationOK:
print min_pos, "- contamination M:", coverage[M], posInfo[
M + 2][min_pos], alleles[M], "P:", coverage[P], posInfo[
P + 2][min_pos], alleles[P]
#MOK = bool(callQ[M] >= 75 or coverage[M] >= 15)
#POK = bool(callQ[P] >= 75 or coverage[P] >= 15)
#if not (MOK and POK): #ignore positions that are not good enough
if not (qualityOK and coverageOK and
contaminationOK): #ignore positions that are not good enough
ignored_pos += 1
for i in ALL_VCF:
#if there is a SNP in the data at this position, skip it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
#print min_pos, callQ[M], callQ[P], coverage[M], coverage[P], prev_lines[i],
prev_lines[i] = ''
#read input: next SNP
for i in ALL_VCF:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = getlineFromFile(in_files, out_files, i)
#END WHILE
print "Low quality positions ignored in the region:", ignored_pos
def main():
#parse ARGs
parser = argparse.ArgumentParser(description='Prepare SNP support data for FCNV. Read filenames: for joined M&P phased .vcf file; plasma, M, and P .sam files; and for centromeres list.')
parser.add_argument('filenames', type=str, nargs='+', help='paths to 1) .vcf file with phased M & P SNPs; 2) reads in SAM format for plasma, M, and P samples; 3) centromeres list file.')
args = parser.parse_args()
if len(args.filenames) != 5: exit("Unexpected number of arguments passed! Expecting 5 filenames.")
#treat these as CONSTANTS!
MP = 0; PLR = 1; MR = 2; PR = 3; CT = 4; #in_files
ALL = [MP, PLR, MR, PR, CT]
M = 0; P = 1; #maternal, paternal
ALDOC = 0; GT = 1; #out_files: allele DOC and ground truth
#list of input files
in_files = [open(args.filenames[i], "r" ) for i in ALL]
#read centromeres positions
centromeres = dict()
for line in in_files[CT].readlines():
line = line.rstrip('\n').split('\t')
if line[0] not in centromeres.keys(): centromeres[line[0]] = []
centromeres[line[0]] += [(int(line[1]), int(line[2]))]
#allele counts in plasma samples for particular positions
pos_data = dict()
loci = dict()
processed_chr = ''
skipped_in_centromere = 0
print " Getting union of SNP positions and corresponding list of alleles " + datetime.now().strftime('%m-%d-%H-%M')
#read SNPs from M, P, F vcf files
snps = [[] for i in [M, P]]
#get genotypes for all positions in UNION of M and P SNP positions
while True:
line = in_files[MP].readline()
#skip if part of the header
while len(line) > 0 and line[0] == '#': line = in_files[MP].readline()
if not line: break
fields = line.rstrip('\n').split('\t')
if processed_chr == '': processed_chr = 'chr'+fields[0]
if processed_chr != 'chr'+fields[0]:
print "WARNING: multiple chromosomes in the input", processed_chr, "|", 'chr'+fields[0]
pos = int(fields[1])
ref = fields[3]
alt = fields[4]
#get M and P haplotypes
snps[M] = fields[9].split(':')[0].split('|')
for x in [0, 1]:
if snps[M][x] == '0': snps[M][x] = ref
else: snps[M][x] = alt
snps[P] = fields[10].split(':')[0].split('|')
for x in [0, 1]:
if snps[P][x] == '0': snps[P][x] = ref
else: snps[P][x] = alt
#if in centromere region, skip
centromere_regions = centromeres[processed_chr]
if is_within_intervals(pos, centromere_regions):
skipped_in_centromere += 1
continue
#take note that for this position we need to get allele counts in plasma samaples
alleles = (snps[M], snps[P])
loci[pos] = alleles
sp.add_pos(pos, pos_data)
#END WHILE
print " Piling up the reads " + datetime.now().strftime('%m-%d-%H-%M')
#set up datastructures for counting allele support in diffrenct SAM files
posInfo = [dict() for i in ALL]
for R in [PLR, MR, PR]:
posInfo[R] = copy.deepcopy(pos_data)
#fetch the reads in plasma SAM file and get counts for the positions originally specified in 'pos_data'
for R in [PLR, MR, PR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
print " Writing output " + datetime.now().strftime('%m-%d-%H-%M')
#list of output files
out_files = [None for i in [ALDOC, GT]]
out_files[ALDOC] = open(processed_chr + "_alleles_docOWN.txt", "w")
out_files[GT] = open(processed_chr + "_targetOWN.txt", "w")
print >>out_files[ALDOC], '#POS\tA\tC\tG\tT\tM_hapA\tM_hapB\tDP_hapA\tDP_hapB\tP_hapA\tP_hapB\tDP_hapA\tDP_hapB'
skipped_low_doc = 0
#print info / compute stats for each SNP position
for pos in sorted(pos_data.keys()):
alleles = loci[pos]
#print the plasma allele counts
nuc_counts = posInfo[PLR][pos]
tmp = []
for nuc in 'ACGT': #to make sure they are in the right order
try:
tmp.append(str(nuc_counts[nuc]))
except KeyError:
tmp.append('0')
#if the plasma coverage is too low, skip this position
if sum(map(int, tmp)) < 50:
print pos, "- low overall coverage", sum(map(int, tmp))
skipped_low_doc += 1
continue
print >>out_files[ALDOC], str(pos) + '\t' + '\t'.join(tmp),
#output M, P alleles at this SNP locus
for i, r in [(M, MR), (P, PR)]:
a1 = alleles[i][0]
a2 = alleles[i][1]
count_a1 = 0
count_a2 = 0
try: count_a1 = posInfo[r][pos][a1]
except: print i, pos, a1, posInfo[r][pos], alleles[i]
try: count_a2 = posInfo[r][pos][a2]
except: print i, pos, a2, posInfo[r][pos], alleles[i]
if a1 == a2:
count_a1 /= 2.
count_a2 /= 2.
print >>out_files[ALDOC], '\t{0}\t{1}\t{2}\t{3}'.format(a1, a2, count_a1, count_a2),
print >>out_files[ALDOC], '\n',
if 10181440 <= pos and pos <= 10281440:
print >>out_files[GT], '{0}\t{1}\t{2}\t{3}'.format(pos, 'N', 'N', 6)
else:
print >>out_files[GT], '{0}\t{1}\t{2}\t{3}'.format(pos, 'N', 'N', 3)
print "Low overall coverage positions ignored:", skipped_low_doc
print "Ignored positions in centromere regions:", skipped_in_centromere
print "DONE " + datetime.now().strftime('%m-%d-%H-%M')
def main():
#parse ARGs
parser = argparse.ArgumentParser(
description=
'Analyze mixture and allele ratios for SNP positions in union(M, P). Read filenames for M, P .vcf files. Further M, F .sam files with reads that together form plasma reads.'
)
parser.add_argument(
'filenames',
type=str,
nargs='+',
help=
'paths to .vcf files with M, P SNPs and *sorted* M, F reads in SAM format'
)
args = parser.parse_args()
if len(args.filenames) != 4:
die("Unexpected number of arguments passed! Expecting 4 filenames.")
#treat these as CONSTANTS!
M = 0
P = 1
MR = 2
FR = 3
ALL = [M, P, MR, FR]
#list of input files
in_files = [open(args.filenames[i], "r") for i in ALL]
#list of output files
#out_files = [None for i in ALL]
#out_files[M] = open("M_alleles.txt", "w")
#union of maternal and paternal SNP positions
data = dict()
loci = dict()
#read SNPs from M, P, F vcf files
snps = [[] for i in [M, P]]
for i in [M, P]:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#':
line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#union the maternal and paternals SNPs positions
while len(snps[M]) > 2 or len(
snps[P]) > 2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in [M, P]:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in [M, P]:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P]]
for i in [M, P]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3], snps[i][4]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#organize the haplotypes in M, P (phased VCF files)
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out haplotype config info
ht = map(int, info[9].split('/'))
#get the configuration
phased_alleles = [alleles[i][ht[0]], alleles[i][ht[1]]]
alleles[i] = phased_alleles
#take note that for this position we need to get allele counts in plasma samaples
sp.add_pos(min_pos, data)
#loci[min_pos] = alleles
#print min_pos, ": M:", alleles[M], " P:", alleles[P], " F:", alleles[F]
#read input: next SNP
for i in [M, P]:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#fetch the maternal and fetal portion of plasma reads from SAM files,
# and get counts for the positions specified in 'data'
posInfo = [dict() for i in ALL]
posInfo[MR] = copy.deepcopy(data)
posInfo[FR] = copy.deepcopy(data)
for R in [MR, FR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
#compute and print the stats
for pos in sorted(data.keys()):
MR_nuc_counts = posInfo[MR][pos]
FR_nuc_counts = posInfo[FR][pos]
try:
local_mix_ratio = float(sum(FR_nuc_counts.values())) / (
sum(FR_nuc_counts.values()) + 9 * sum(MR_nuc_counts.values()))
except ZeroDivisionError:
local_mix_ratio = 0
print pos, local_mix_ratio,
'''
for i, NC in enumerate([MR_nuc_counts, FR_nuc_counts]):
tmp = []
for nuc in 'ACGT': #to make sure they are in the right order
try:
tmp.append(NC[nuc])
except KeyError:
tmp.append(0)
try:
summ = float(sum(tmp))
tmp = [tmp[i]/summ for i in range(len(tmp))]
except ZeroDivisionError:
pass
#tmp = [0]
#print sorted(tmp),
try:
ind1 = 'ACGT'.index(loci[pos][i][0].upper())
ind2 = 'ACGT'.index(loci[pos][i][1].upper())
print abs(tmp[ind1] - tmp[ind2]),
except IndexError:
print "X", loci[pos][i][0].upper(), loci[pos][i][1].upper(), tmp,
print 0,
'''
print " "
def main():
#parse ARGs
parser = argparse.ArgumentParser(description='Analyze mixture and allele ratios for SNP positions in union(M, P). Read filenames for M, P .vcf files. Further M, F .sam files with reads that together form plasma reads.')
parser.add_argument('filenames', type=str, nargs='+', help='paths to .vcf files with M, P SNPs and *sorted* M, F reads in SAM format')
args = parser.parse_args()
if len(args.filenames) != 4: die("Unexpected number of arguments passed! Expecting 4 filenames.")
#treat these as CONSTANTS!
M = 0; P = 1; MR = 2; FR = 3;
ALL = [M, P, MR, FR]
#list of input files
in_files = [open(args.filenames[i], "r" ) for i in ALL]
#list of output files
#out_files = [None for i in ALL]
#out_files[M] = open("M_alleles.txt", "w")
#union of maternal and paternal SNP positions
data = dict()
loci = dict()
#read SNPs from M, P, F vcf files
snps = [[] for i in [M, P]]
for i in [M, P]:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#': line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#union the maternal and paternals SNPs positions
while len(snps[M])>2 or len(snps[P])>2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in [M, P]:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
#position
min_pos = 1e15
for i in [M, P]:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P]]
for i in [M, P]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3], snps[i][4]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#organize the haplotypes in M, P (phased VCF files)
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out haplotype config info
ht = map(int, info[9].split('/'))
#get the configuration
phased_alleles = [alleles[i][ht[0]], alleles[i][ht[1]]]
alleles[i] = phased_alleles
#take note that for this position we need to get allele counts in plasma samaples
sp.add_pos(min_pos, data)
#loci[min_pos] = alleles
#print min_pos, ": M:", alleles[M], " P:", alleles[P], " F:", alleles[F]
#read input: next SNP
for i in [M, P]:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#fetch the maternal and fetal portion of plasma reads from SAM files,
# and get counts for the positions specified in 'data'
posInfo = [dict() for i in ALL]
posInfo[MR] = copy.deepcopy(data)
posInfo[FR] = copy.deepcopy(data)
for R in [MR, FR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
#compute and print the stats
for pos in sorted(data.keys()):
MR_nuc_counts = posInfo[MR][pos]
FR_nuc_counts = posInfo[FR][pos]
try:
local_mix_ratio = float(sum(FR_nuc_counts.values())) / (sum(FR_nuc_counts.values()) + 9*sum(MR_nuc_counts.values()))
except ZeroDivisionError:
local_mix_ratio = 0
print pos, local_mix_ratio,
'''
for i, NC in enumerate([MR_nuc_counts, FR_nuc_counts]):
tmp = []
for nuc in 'ACGT': #to make sure they are in the right order
try:
tmp.append(NC[nuc])
except KeyError:
tmp.append(0)
try:
summ = float(sum(tmp))
tmp = [tmp[i]/summ for i in range(len(tmp))]
except ZeroDivisionError:
pass
#tmp = [0]
#print sorted(tmp),
try:
ind1 = 'ACGT'.index(loci[pos][i][0].upper())
ind2 = 'ACGT'.index(loci[pos][i][1].upper())
print abs(tmp[ind1] - tmp[ind2]),
except IndexError:
print "X", loci[pos][i][0].upper(), loci[pos][i][1].upper(), tmp,
print 0,
'''
print " "
def main():
#parse ARGs
parser = argparse.ArgumentParser(
description=
'Prepare SNP data for FCNV. Read filenames: for M, P, and F .vcf files; plasma, M, and P .sam files; and for centromeres list.'
)
parser.add_argument(
'filenames',
type=str,
nargs='+',
help=
'paths to 1) .vcf files with M, P, F SNPs; 2) reads in SAM format for plasma, M, and P samples; 3) centromeres list file.'
)
args = parser.parse_args()
if len(args.filenames) != 7:
exit("Unexpected number of arguments passed! Expecting 7 filenames.")
#treat these as CONSTANTS!
M = 0
P = 1
F = 2
PLASMA = 3
MR = 4
PR = 5
CT = 6
ALL = [M, P, F, PLASMA, MR, PR, CT]
#list of input files
in_files = [open(args.filenames[i], "r") for i in ALL]
#list of output files
out_files = [None for i in [M, P, F, PLASMA]]
out_files[M] = open("M_alleles.txt", "w")
out_files[P] = open("P_alleles.txt", "w")
out_files[F] = open("F_alleles.txt", "w")
out_files[PLASMA] = open("plasma_samples.txt", "w")
date = datetime.now().strftime('%m-%d-%H-%M')
out_pos_file = open("positions" + date + ".txt", "w")
#read centromeres positions
centromeres = dict()
for line in in_files[CT].readlines():
line = line.rstrip('\n').split('\t')
if line[0] not in centromeres.keys(): centromeres[line[0]] = []
centromeres[line[0]] += [(int(line[1]), int(line[2]))]
#allele counts in plasma samples for particular positions
data = dict()
loci = dict()
processed_chr = ''
print " Getting union of SNP positions and corresponding list of alleles"
#read SNPs from M, P, F vcf files
snps = [[] for i in [M, P, F]]
for i in [M, P, F]:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#':
line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#get genotypes for all positions in UNION of M and P SNP positions
while len(snps[M]) > 2 or len(
snps[P]) > 2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in [M, P, F]:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
if processed_chr == '': processed_chr = min_chr
if processed_chr != min_chr:
print "WARNING: multiple chromosomes in the input", processed_chr, "|", min_chr
#position
min_pos = 1e15
for i in [M, P]:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P, F]]
for i in [M, P, F]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3][0], snps[i][4][0]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P, F]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#check for homozygous alternative sites in Fetal VCF
for i in [F]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out genotype config info
gt = info[9].split(':')[0]
#if homozygous alternative
if gt[0] == '1':
alleles[i][0] = snps[i][4][0]
if gt[2] == '1':
alleles[i][1] = snps[i][4][0]
if gt[0] == '0':
alleles[i][0] = snps[i][3][0]
if gt[2] == '0':
alleles[i][1] = snps[i][3][0]
#organize the haplotypes in M, P (phased VCF files)
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out haplotype config info
ht = map(int, info[9].split('/'))
#get the configuration
phased_alleles = [alleles[i][ht[0]], alleles[i][ht[1]]]
alleles[i] = phased_alleles
#take note that for this position we need to get allele counts in plasma samaples
loci[min_pos] = alleles
sp.add_pos(min_pos, data)
#read input: next SNP
for i in [M, P, F]:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#END WHILE
print " Aligning the reads"
#set up datastructures for counting allele support in diffrenct SAM files
posInfo = [dict() for i in ALL]
for R in [PLASMA, MR, PR]:
posInfo[R] = copy.deepcopy(data)
#fetch the reads in plasma SAM file and get counts for the positions originally specified in 'data'
for R in [PLASMA, MR, PR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
print " Writing output"
skipped_in_centromere = 0
skipped_low = 0
centromere_regions = centromeres[processed_chr]
#print info / compute stats for each SNP position
for pos in sorted(data.keys()):
alleles = loci[pos]
#if alleles[M][0] != alleles[M][1]:
#print the plasma allele counts
nuc_counts = posInfo[PLASMA][pos]
tmp = []
for nuc in 'ACGT': #to make sure they are in the right order
try:
tmp.append(str(nuc_counts[nuc]))
except KeyError:
tmp.append('0')
#if the plasma coverage is too low, skip this position
if sum(map(int, tmp)) < 20:
print pos, "- low overall coverage", sum(map(int, tmp))
skipped_low += 1
continue
#if in centromere region, skip
if is_within_intervals(pos, centromere_regions):
skipped_in_centromere += 1
continue
print >> out_files[PLASMA], ' '.join(tmp)
#output M, P, F alleles at this SNP locus
for i, r in [(M, MR), (P, PR)]:
a1 = alleles[i][0]
a2 = alleles[i][1]
count_a1 = 0
count_a2 = 0
try:
count_a1 = posInfo[r][pos][a1]
except:
print i, pos, a1, posInfo[r][pos], alleles[i]
try:
count_a2 = posInfo[r][pos][a2]
except:
print i, pos, a2, posInfo[r][pos], alleles[i]
if a1 == a2:
count_a1 /= 2.
count_a2 /= 2.
print >> out_files[i], a1, a2, count_a1, count_a2
print >> out_files[F], alleles[F][0], alleles[F][1], 3
print >> out_pos_file, pos, "- M:", alleles[M], " P:", alleles[
P], " F:", alleles[F]
print "Low overall coverage positions ignored:", skipped_low
print "Ignored positions in centromere regions:", skipped_in_centromere
def main():
#parse ARGs
parser = argparse.ArgumentParser(description='Prepare SNP data for FCNV. Read filenames: for M, P, and F .vcf files; plasma, M, and P .sam files; and for centromeres list.')
parser.add_argument('filenames', type=str, nargs='+', help='paths to 1) .vcf files with M, P, F SNPs; 2) reads in SAM format for plasma, M, and P samples; 3) centromeres list file.')
args = parser.parse_args()
if len(args.filenames) != 7: exit("Unexpected number of arguments passed! Expecting 7 filenames.")
#treat these as CONSTANTS!
M = 0; P = 1; F = 2; PLASMA = 3; MR = 4; PR = 5; CT = 6;
ALL = [M, P, F, PLASMA, MR, PR, CT]
#list of input files
in_files = [open(args.filenames[i], "r" ) for i in ALL]
#list of output files
out_files = [None for i in [M, P, F, PLASMA]]
out_files[M] = open("M_alleles.txt", "w")
out_files[P] = open("P_alleles.txt", "w")
out_files[F] = open("F_alleles.txt", "w")
out_files[PLASMA] = open("plasma_samples.txt", "w")
date = datetime.now().strftime('%m-%d-%H-%M')
out_pos_file = open("positions" + date + ".txt", "w")
#read centromeres positions
centromeres = dict()
for line in in_files[CT].readlines():
line = line.rstrip('\n').split('\t')
if line[0] not in centromeres.keys(): centromeres[line[0]] = []
centromeres[line[0]] += [(int(line[1]), int(line[2]))]
#allele counts in plasma samples for particular positions
data = dict()
loci = dict()
processed_chr = ''
print " Getting union of SNP positions and corresponding list of alleles"
#read SNPs from M, P, F vcf files
snps = [[] for i in [M, P, F]]
for i in [M, P, F]:
#skip the header
line = in_files[i].readline()
while len(line) > 0 and line[0] == '#': line = in_files[i].readline()
#split
snps[i] = line.split('\t')
#get genotypes for all positions in UNION of M and P SNP positions
while len(snps[M])>2 or len(snps[P])>2: #while there is a SNP positions in M or P
#get the position of SNP that occure first
for i in [M, P, F]:
if snps[i][0] == '': #if an input files is already at EOF
snps[i][0] = 'chrZZ'
snps[i].append(1e15)
else: #convert to int
snps[i][1] = int(snps[i][1])
#chromosome
min_chr = min(snps[M][0], snps[P][0])
if processed_chr == '': processed_chr = min_chr
if processed_chr != min_chr: print "WARNING: multiple chromosomes in the input", processed_chr, "|", min_chr
#position
min_pos = 1e15
for i in [M, P]:
if min_chr == snps[i][0] and snps[i][1] < min_pos:
min_pos = snps[i][1]
#get alleles
alleles = [['.', '.'] for x in [M, P, F]]
for i in [M, P, F]:
#if there is a SNP in the data at this position, use it
if min_chr == snps[i][0] and min_pos == snps[i][1]:
alleles[i] = [snps[i][3][0], snps[i][4][0]]
#if there is no info on some allele, impute the reference allele
ref_allele = alleles[M][0]
if ref_allele == '.': ref_allele = alleles[P][0]
for i in [M, P, F]:
for a in [0, 1]:
if alleles[i][a] == '.': alleles[i][a] = ref_allele
#check for homozygous alternative sites in Fetal VCF
for i in [F]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out genotype config info
gt = info[9].split(':')[0]
#if homozygous alternative
if gt[0] == '1':
alleles[i][0] = snps[i][4][0]
if gt[2] == '1':
alleles[i][1] = snps[i][4][0]
if gt[0] == '0':
alleles[i][0] = snps[i][3][0]
if gt[2] == '0':
alleles[i][1] = snps[i][3][0]
#organize the haplotypes in M, P (phased VCF files)
for i in [M, P]:
#if there is a SNP in the data at this position
if min_chr == snps[i][0] and min_pos == snps[i][1]:
info = snps[i]
if len(info) <= 2: continue
#parse out haplotype config info
ht = map(int, info[9].split('/'))
#get the configuration
phased_alleles = [alleles[i][ht[0]], alleles[i][ht[1]]]
alleles[i] = phased_alleles
#take note that for this position we need to get allele counts in plasma samaples
loci[min_pos] = alleles
sp.add_pos(min_pos, data)
#read input: next SNP
for i in [M, P, F]:
if min_chr >= snps[i][0] and min_pos >= snps[i][1]:
snps[i] = in_files[i].readline().split('\t')
#END WHILE
print " Aligning the reads"
#set up datastructures for counting allele support in diffrenct SAM files
posInfo = [dict() for i in ALL]
for R in [PLASMA, MR, PR]:
posInfo[R] = copy.deepcopy(data)
#fetch the reads in plasma SAM file and get counts for the positions originally specified in 'data'
for R in [PLASMA, MR, PR]:
while True:
line = in_files[R].readline()
if not line: break
if len(line) > 0 and line[0] == '@': continue
sp.pile_up(sp.mapping_parser(line), posInfo[R])
print " Writing output"
skipped_in_centromere = 0
skipped_low = 0
centromere_regions = centromeres[processed_chr]
#print info / compute stats for each SNP position
for pos in sorted(data.keys()):
alleles = loci[pos]
#if alleles[M][0] != alleles[M][1]:
#print the plasma allele counts
nuc_counts = posInfo[PLASMA][pos]
tmp = []
for nuc in 'ACGT': #to make sure they are in the right order
try:
tmp.append(str(nuc_counts[nuc]))
except KeyError:
tmp.append('0')
#if the plasma coverage is too low, skip this position
if sum(map(int, tmp)) < 20:
print pos, "- low overall coverage", sum(map(int, tmp))
skipped_low += 1
continue
#if in centromere region, skip
if is_within_intervals(pos, centromere_regions):
skipped_in_centromere += 1
continue
print >>out_files[PLASMA], ' '.join(tmp)
#output M, P, F alleles at this SNP locus
for i, r in [(M, MR), (P, PR)]:
a1 = alleles[i][0]
a2 = alleles[i][1]
count_a1 = 0
count_a2 = 0
try: count_a1 = posInfo[r][pos][a1]
except: print i, pos, a1, posInfo[r][pos], alleles[i]
try: count_a2 = posInfo[r][pos][a2]
except: print i, pos, a2, posInfo[r][pos], alleles[i]
if a1 == a2:
count_a1 /= 2.
count_a2 /= 2.
print >>out_files[i], a1, a2, count_a1, count_a2
print >>out_files[F], alleles[F][0], alleles[F][1], 3
print >>out_pos_file, pos, "- M:", alleles[M], " P:", alleles[P], " F:", alleles[F]
print "Low overall coverage positions ignored:", skipped_low
print "Ignored positions in centromere regions:", skipped_in_centromere
