def count_gts(sample_list):
# load VCF and choose only samples of interest
vcf = VCF(sys.argv[1])
vcf.set_samples(sample_list)
# initialize lists
ids = []
hets = []
refs = []
alts = []
missing = []
# count # of samples of each genotype for every variant
for variant in vcf:
if (variant.CHROM != "chrX") and (
variant.CHROM != "chrY"): # skip X, Y chromosome variants
ids.append(variant.ID)
hets.append(variant.num_het)
refs.append(variant.num_hom_ref)
alts.append(variant.num_hom_alt)
missing.append(variant.num_unknown)
# create dataframes with gt counts
df = pd.DataFrame({
"SV": ids,
"ref": refs,
"het": hets,
"alt": alts,
"missing": missing
})
return (df)
def count_gts_xchr(sample_list, sex):
# load VCF and choose only samples of interest
vcf = VCF(sys.argv[1])
vcf.set_samples(sample_list)
# initialize lists
ids = []
hets = []
refs = []
alts = []
missing = []
# count # of samples of each genotype for every variant
for variant in vcf:
if variant.CHROM == "chrX": # only look at variants on X chromosome
ids.append(variant.ID)
hets.append(variant.num_het)
refs.append(variant.num_hom_ref)
alts.append(variant.num_hom_alt)
missing.append(variant.num_unknown)
# create dataframes with gt counts
df = pd.DataFrame({
"SV": ids,
"ref": refs,
"het": hets,
"alt": alts,
"missing": missing
})
if sex == "male":
# incorporate het samples into alt allele calls
df["alt"] = df["het"] + df["alt"]
return (df[["SV", "ref", "alt", "missing"]])
else:
return (df[["ref", "het", "alt", "missing"]])
def test_set_samples():
vcf = VCF(VCF_PATH)
assert len(vcf.samples) == 189, len(vcf.samples)
vcf.set_samples([vcf.samples[2]])
assert len(vcf.samples) == 1
v = next(vcf)
assert len(v.gt_types) == 1
def test_set_samples():
vcf = VCF(VCF_PATH)
assert len(vcf.samples) == 189, len(vcf.samples)
vcf.set_samples([vcf.samples[2]])
assert len(vcf.samples) == 1
v = next(vcf)
assert len(v.gt_types) == 1
def get_call_rate_Ychr(sample_list):
vcf = VCF(path_to_vcf)
vcf.set_samples(sample_list)
# initialize lists
ids = []
call_rate = []
# count # of samples of each genotype for every variant
for variant in vcf:
if variant.CHROM == "chrY": # only look at variants on Y chromosome
ids.append(variant.ID)
call_rate.append(variant.call_rate)
# create dataframe
df = pd.DataFrame(
{"SV" : ids,
"call_rate" : call_rate
})
return(df)
if self.freqs[base] != 0.0:
print(self.sample + " Already has a a freq of " +
str(self.freqs[base]) + " at " + self.chrom + ' ' +
str(self.pos))
else:
self.freqs[base] = freq
def print_var(self):
print(self.sample, self.chrom, self.pos, self.ref, self.depth,
self.freqs)
variants = {}
for sample in samples:
vcf = VCF(path)
vcf.set_samples(sample)
for var in vcf:
dp = var.format("DP")[0]
ad = var.format("AD")[0]
alts = var.ALT
bases = [var.REF]
for alt in alts:
bases.append(alt)
name = sample + str(var.CHROM) + str(var.POS)
if dp[0] != -2147483648:
i = 0
while i < len(bases):
if name not in variants:
variants[name] = _var(sample, var.CHROM, var.POS, var.REF,
dp[0])
variants[name].set_freq(bases[i], ad[i])
def vcf_to_fasta_markers(args):
if args.samples:
samples = open(args.samples).read().strip().split("\n")
elif args.nsamples:
samples = get_samples_from_bcf(args.vcf)[:args.nsamples]
else:
samples = get_samples_from_bcf(args.vcf)
if args.fasta == "-":
fasta_in = sys.stdin
else:
fasta_in = open(args.fasta)
pos = 0
if args.save_fasta:
fasta_out = open(args.out + ".from_vcf.fa", "w")
marker_out = open(args.out + ".markers", "wb")
# First, feed the reference sequence to the thing:
for name, seq, qual in readfq(fasta_in):
pos += len(seq)
to_write = ">{}\n{}\n".format(name, seq)
if args.save_fasta:
fasta_out.write(to_write)
yield to_write.encode()
fasta_in.seek(0)
# Then feed in each haplotype
for sample in samples:
for i in range(2): # process two haplotypes
for name, seq, qual in readfq(fasta_in):
prec_end = 0
prec_start = 0
to_write = ">{}.{}.{}\n".format(sample, i + 1, name)
if args.save_fasta:
fasta_out.write(to_write)
yield to_write.encode()
bcf = VCF(args.vcf)
bcf.set_samples([sample])
# recs = bcf.__call__(contig)
recs = bcf.__call__(name)
for rec in recs:
gts = rec.genotypes[0]
if (len(gts) != 3):
die("error: number of genotypes for {} at marker {} is not 2!"
.format(sample, rec.ID))
if gts[i]:
if rec.start in range(prec_start, prec_end):
sys.stderr.write(
"warning: skipping {} at {}:{} because it overlaps a previous marker\n"
.format(rec.ID, rec.CHROM, rec.start))
continue
pos += rec.start - prec_end # ffwd current position
if len(rec.REF) >= len(
rec.ALT[gts[i] - 1]): # del and snp
write_marker(marker_out, pos, rec.start, gts[i],
args.bytes, args.endian)
pos += 1
elif len(rec.REF) < len(rec.ALT[gts[i] - 1]):
# insertion
ins_size = len(rec.ALT[gts[i] - 1]) - len(
rec.REF) + 1
for j in range(ins_size):
write_marker(marker_out, pos, rec.start,
gts[i], args.bytes, args.endian)
pos += 1
# to_write = str(seq[prec_end:rec.start]) + str(rec.ALT[gts[i]-1])
to_write = seq[prec_end:rec.start] + rec.ALT[gts[i] -
1]
if args.save_fasta:
fasta_out.write(to_write)
yield to_write.encode()
prec_start = rec.start
# prec_end skips over to position just after record
prec_end = rec.start + len(rec.REF)
bcf.close()
pos += len(seq) - prec_end
to_write = seq[prec_end:] + "\n"
if args.save_fasta:
fasta_out.write(to_write)
yield to_write.encode()
fasta_in.seek(0)
if args.save_fasta:
fasta_out.close()
marker_out.close()
fasta_in.close()
