def sort_vcf_file(filename):
from genomicode import vcflib
from genomicode import jmath
from genomicode import AnnotationMatrix
vcf = vcflib.read(filename)
CHROM = vcf.matrix["#CHROM"]
POS = vcf.matrix["POS"]
POS = [int(x) for x in POS]
# Check if POS is sorted. If it's already sorted, then return.
is_sorted = True
for i in range(len(CHROM) - 1):
c1, p1 = CHROM[i], POS[i]
c2, p2 = CHROM[i + 1], POS[i + 1]
if c1 != c2:
continue
if p2 < p1:
is_sorted = False
break
if is_sorted:
return
# Sort by CHROM and POS.
S = ["%s:%d" % (CHROM[i], POS[i]) for i in range(len(CHROM))]
O = jmath.order_list(S, natural=True)
vcf.matrix = AnnotationMatrix.rowslice(vcf.matrix, O)
vcflib.write(filename, vcf)
def fix_vcf_file(sample, infile, outfile):
# JointSNVMix produces VCF files that don't have FORMAT and
# <SAMPLE> columns. Add them.
from genomicode import vcflib
vcf = vcflib.read(infile)
matrix = vcf.matrix
genotype_names = ["DP", "RD", "AD", "FREQ"]
# Get the calls for each variant.
all_genotypes = [] # one for each variant
for i in range(vcf.num_variants()):
var = vcflib.get_variant(vcf, i)
call = vcflib.get_call(var, None)
geno_dict = {
"DP": call.total_reads,
"RD": call.num_ref,
"AD": call.num_alt,
"FREQ": call.vaf,
}
x = vcflib._format_genotype(genotype_names, geno_dict)
all_genotypes.append(x)
# Add FORMAT.
FORMAT_STRING = ":".join(genotype_names)
assert "FORMAT" not in matrix
matrix.headers.append("FORMAT")
matrix.headers_h.append("FORMAT")
matrix.header2annots["FORMAT"] = [FORMAT_STRING] * matrix.num_annots()
# Add the sample.
assert not vcf.samples
assert sample not in matrix
matrix.headers.append(sample)
matrix.headers_h.append(sample)
matrix.header2annots[sample] = all_genotypes
vcf.samples = [sample]
# Add the proper header lines.
lines = [
'##FORMAT=<ID=RD,Number=1,Type=Integer,Description="Allelic depth for the ref allele in the tumor sample">',
'##FORMAT=<ID=AD,Number=1,Type=Integer,Description="Allelic depth for the alt allele in the tumor sample">',
'##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read depth">',
'##FORMAT=<ID=FREQ,Number=1,Type=Integer,Description="Variant allele frequency">',
]
matrix.headerlines.extend(lines)
vcflib.write(outfile, vcf)
def filter_by_vartype(vartype, infile, outfile):
# Filter out snps or indels.
import shutil
from genomicode import vcflib
assert vartype in ["all", "snp", "indel"]
if vartype == "all":
shutil.copy2(infile, outfile)
return
vcf = vcflib.read(infile)
fn = is_snp
if vartype == "indel":
fn = is_indel
vcf = vcflib.select_variants(vcf, fn)
vcflib.write(outfile, vcf)
def backfill_vcf(in_file, bf_file, out_file):
import copy
from genomicode import vcflib
#print in_mvcf_node.identifier
#print back_mvcf_node.identifier
in_vcf = vcflib.read(in_file)
bf_vcf = vcflib.read(bf_file)
# May have multiple samples, e.g. germline and tumor.
#assert len(in_vcf.samples) == 1, "Too many samples: %s" % in_vcf.samples
x = [x for x in in_vcf.samples if x in bf_vcf.samples]
SAMPLES = x
# Parse out the read counts from the backfill vcf.
bf_variants = {} # (sample, chrom, pos) -> ref, alt, Variant, Call
for i in range(bf_vcf.num_variants()):
var = vcflib.get_variant(bf_vcf, i)
for sample in SAMPLES:
call = vcflib.get_call(var, sample)
if call.num_ref is None and call.num_alt is None and \
call.total_reads is None and call.vaf is None:
continue
x = sample, var.chrom, var.pos
assert x not in bf_variants, "Duplicate: %s %s %s" % x
bf_variants[x] = var.ref, var.alt, var, call
# Find the variants that can be backfilled.
# List of (chrom, pos, in_var_num, sample, in_call, bf_var, bf_call)
matches = []
for i in range(in_vcf.num_variants()):
in_var = vcflib.get_variant(in_vcf, i)
for sample in SAMPLES:
# Skip if there is no backfill information.
key = sample, in_var.chrom, in_var.pos
if key not in bf_variants:
continue
bf_ref, bf_alt, bf_var, bf_call = bf_variants[key]
# Don't worry if the variants match. Just want a
# rough estimate of the coverage at this location.
## Make sure the variants match.
##if not is_same_variants(ref, alt, bf_ref, bf_alt):
## continue
in_call = vcflib.get_call(in_var, sample)
x = in_var.chrom, in_var.pos, i, sample, in_call, bf_var, bf_call
matches.append(x)
# Update the read counts from annotated VCF file.
out_vcf = copy.deepcopy(in_vcf)
add_backfill_genotypes(out_vcf)
seen = {}
for x in matches:
chrom, pos, var_num, sample, in_call, bf_var, bf_call = x
seen[(sample, chrom, pos)] = 1
var = vcflib.get_variant(out_vcf, var_num)
GD = var.sample2genodict[sample]
mapping = [
("BFILL_REF", "num_ref"),
("BFILL_ALT", "num_alt"),
("BFILL_COV", "total_reads"),
("BFILL_VAF", "vaf"),
]
changed = False
for gt_key, call_attr in mapping:
x = getattr(bf_call, call_attr)
if x is None:
continue
if type(x) is type([]): # arbitrarily use max
x = max(x)
GD[gt_key] = vcflib._format_vcf_value(x)
changed = True
if changed:
vcflib.set_variant(out_vcf, var_num, var)
# Add the variants that are in bf_file, but not in in_file.
for x in bf_variants:
# sample, chrom, pos = x
if x in seen:
continue
bf_ref, bf_alt, bf_var, bf_call = bf_variants[x]
# VarScan sets the filter_ to "PASS" for everything. Get rid
# of this.
bf_var.filter_ = ["BACKFILL"]
vcflib.add_variant(out_vcf, bf_var)
vcflib.write(out_file, out_vcf)
def make_cancer_samples_file(vcf_file, nc_match, outfile):
# Two column tab-delimited text. No headers.
# <germline> <tumor>
from genomicode import vcflib
from genomicode import hashlib
from genomicode import jmath
# vcf samples (joined with bcftools).
# PIM005_G peak1 2:PIM001_G peak2 3:PIM001_G [...]
germline_samples = [x[0] for x in nc_match]
tumor_samples = [x[1] for x in nc_match]
# Hopefully should be able to find the samples in the first 1000
# rows.
vcf = vcflib.read(vcf_file, nrows=1000)
# Get the samples from the VCF file.
samples = vcf.samples
# HACK: Fix some problems with old files.
#samples = [x.replace("Cap475-5983-19", "PIM001_G") for x in samples]
# HACK: Radia has calls from RNA. Ignore them.
# <tumor_sample>_RNA
rna = {}.fromkeys(["%s_RNA" % x for x in tumor_samples])
samples = [x for x in samples if x not in rna]
# Samples may be hashed, e.g.
# 196B-MG -> X196B_MG
# Need to compare against hashed samples.
germline_samples_h = [hashlib.hash_var(x) for x in germline_samples]
tumor_samples_h = [hashlib.hash_var(x) for x in tumor_samples]
# Make sure hashing does not make duplicate tumor samples.
# Germline may be duplicated.
#assert not _dups(germline_samples)
assert not _dups(tumor_samples)
#assert not _dups(germline_samples_h)
assert not _dups(tumor_samples_h)
# Clean up samples.
clean = [] # list of tuples ("G" or "T", sample_name)
for sample in samples:
if sample in germline_samples:
x = "G", sample
elif sample in germline_samples_h:
# Don't unhash it. Otherwise, snpeff will be confused.
#i = germline_samples_h[sample]
#x = "G", germline_samples[i]
x = "G", sample
elif sample in tumor_samples:
x = "T", sample
elif sample in tumor_samples_h:
#i = tumor_samples_h[sample]
#x = "T", tumor_samples[i]
x = "T", sample
else:
# <num>:<germline sample name>
x = sample.split(":", 1)
assert len(x) == 2, "Unknown sample name (%s) in: %s" % (sample,
vcf_file)
assert jmath.is_int(
x[0]), "Unknown sample name (%s) in: %s" % (sample, vcf_file)
s = x[1]
if s in germline_samples:
x = "G", s
elif s in germline_samples_h:
#i = germline_samples_h[s]
#x = "G", germline_samples[i]
x = "G", s
else:
raise AssertionError, "Unknown sample name: %s" % sample
clean.append(x)
samples = clean
# If there are no germline samples, then don't make a file.
x1 = [x for x in samples if x[0] == "G"]
x2 = [x for x in samples if x[0] == "T"]
if not x1:
return None
# Make sure there are the same number of germline samples.
assert len(x1) == len(x2), "Germline/Tumor mismatch: %s" % vcf_file
assert len(samples) % 2 == 0
# Pairs should contain one "G" and one "T".
for i in range(0, len(samples), 2):
t1, s1 = samples[i]
t2, s2 = samples[i + 1]
assert t1 != t2, "Bad Germline/Tumor ordering: %s" % vcf_file
lines = []
for i in range(0, len(samples), 2):
t1, s1 = samples[i]
t2, s2 = samples[i + 1]
# Want germline, then tumor.
if t1 == "T" and t2 == "G":
t1, s1, t2, s2 = t2, s2, t1, s1
assert t1 == "G" and t2 == "T"
x = "%s\t%s\n" % (s1, s2)
lines.append(x)
open(outfile, 'w').writelines(lines)
def parse_snpeff_file(vcf_filename, out_filename):
from genomicode import vcflib
# Parse out the snpEff annotations. Should have ANN in INFO.
# Make a tab-delimited text file containin columns:
# Chrom Pos Ref Alt <snpEff-specific columns>
#
# ##INFO=<ID=ANN,Number=.,Type=String,
# Description="Functional annotations: '
# Allele |
# Annotation |
# Annotation_Impact |
# Gene_Name |
# Gene_ID |
# Feature_Type |
# Feature_ID |
# Transcript_BioType |
# Rank |
# HGVS.c |
# HGVS.p |
# cDNA.pos / cDNA.length |
# CDS.pos / CDS.length |
# AA.pos / AA.length |
# Distance | ERRORS / WARNINGS / INFO' ">
vcf = vcflib.read(vcf_filename)
# Figure out the Functional annotations.
assert vcf.matrix.headerlines, "No header lines"
x = [x for x in vcf.matrix.headerlines if x.find("<ID=ANN,") >= 0]
if not x:
return
# No duplicates.
# The ANN line can end with:
# ERRORS / WARNINGS / INFO'">
# ERRORS / WARNINGS / INFO' ">
# I encountered a VCF file that contained two ANN lines differing
# by this spacing. Normalize these lines and make sure there are
# no duplicates.
x = [
x.replace("ERRORS / WARNINGS / INFO' \">",
"ERRORS / WARNINGS / INFO'\">") for x in x
]
x = {}.fromkeys(x).keys()
assert len(x) == 1, "Multiple ANN headers: %s" % vcf_filename
header = x[0]
x = header.strip()
TEXT = "Functional annotations:"
assert TEXT in x
x = x[x.index(TEXT) + len(TEXT):] # Get rid of "Functional annotations:"
assert x.endswith('">') # No ">
x = x[:-2].strip()
assert x.startswith("'") and x.endswith("'") # No ''
x = x[1:-1]
x = x.split("|")
x = [x.strip() for x in x]
annotations = x
handle = open(out_filename, 'w')
header = ["Chrom", "Pos", "Ref", "Alt"] + annotations
print >> handle, "\t".join(header)
for i in range(vcf.num_variants()):
var = vcflib.get_variant(vcf, i)
if "ANN" not in var.infodict:
continue
# Can have multiple annotations if there are more than one allele.
# <ALLELE>|...|...|,<ALLELE>|...|...|
# If this happens, just add them to the file.
x = var.infodict["ANN"]
annots = x.split(",")
for annot in annots:
x = annot.split("|")
x = [x.strip() for x in x]
values = x
assert len(values) == len(annotations), \
"Mismatch annotations %d %d: %s %s %d" % (
len(annotations), len(values), vcf_filename,
var.chrom, var.pos)
alt = ",".join(var.alt)
x = [var.chrom, var.pos, var.ref, alt] + values
assert len(x) == len(header)
print >> handle, "\t".join(map(str, x))
def summarize_vcf_file(filename, filestem, header, outfilename, lock):
from genomicode import hashlib
from genomicode import vcflib
vcf = vcflib.read(filename)
lines = []
for i in range(vcf.num_variants()):
var = vcflib.get_variant(vcf, i)
caller_name = var.caller.name
ref = var.ref
alt = ",".join(var.alt)
filter_str = vcf.caller.get_filter(var)
for sample in var.samples:
# If sample begins with an integer, there may be a
# "X" pre-pended to it. Try to detect this case
# and fix it.
clean_sample = sample
if sample == hashlib.hash_var(filestem):
clean_sample = filestem
source = "DNA"
if caller_name == "Radia":
# DNA <clean_sample> 196B-lung
# RNA <clean_sample>_RNA 196B-lung_RNA
# Figure out whether this is RNA and fix it.
if clean_sample.endswith("_RNA"):
clean_sample = clean_sample[:-4]
source = "RNA"
genodict = var.sample2genodict[sample]
call = vcflib.get_call(var, sample)
num_ref = vcflib._format_vcf_value(call.num_ref, None_char="")
num_alt = vcflib._format_vcf_value(call.num_alt, None_char="")
total_reads = vcflib._format_vcf_value(call.total_reads,
None_char="")
vaf = vcflib._format_vcf_value(call.vaf, None_char="")
call_str = vcflib._format_vcf_value(call.call, None_char="")
GQ = genodict.get("GQ", "")
if GQ in [None, "."]:
GQ = ""
x = caller_name, filestem, clean_sample, var.chrom, var.pos, \
ref, alt, source, \
num_ref, num_alt, total_reads, vaf, filter_str, call_str, GQ
assert len(x) == len(header)
x = "\t".join(map(str, x))
lines.append(x)
if len(lines) >= 100000:
x = "\n".join(lines) + "\n"
lock.acquire()
handle = open(outfilename, 'a')
handle.write(x)
handle.close()
lock.release()
lines = []
x = "\n".join(lines) + "\n"
lock.acquire()
handle = open(outfilename, 'a')
handle.write(x)
handle.close()
lock.release()
