def sv_genotype(bam_string,
vcf_in,
vcf_out,
min_aligned,
split_weight,
disc_weight,
num_samp,
lib_info_path,
debug,
alignment_outpath,
ref_fasta,
sum_quals,
max_reads,
max_ci_dist):
# parse the comma separated inputs
bam_list = []
for b in bam_string.split(','):
if b.endswith('.bam'):
bam_list.append(pysam.AlignmentFile(b, mode='rb'))
elif b.endswith('.cram'):
bam_list.append(pysam.AlignmentFile(b,
mode='rc',reference_filename=ref_fasta,format_options=["required_fields=7167"]))
else:
sys.stderr.write('Error: %s is not a valid alignment file (*.bam or *.cram)\n' % b)
exit(1)
min_lib_prevalence = 1e-3 # only consider libraries that constitute at least this fraction of the BAM
# parse lib_info_path JSON
lib_info = None
if lib_info_path is not None and os.path.isfile(lib_info_path):
lib_info_file = open(lib_info_path, 'r')
lib_info = json.load(lib_info_file)
if vcf_in is None:
sys.stderr.write('Warning: VCF not found.\n')
# build the sample libraries, either from the lib_info JSON or empirically from the BAMs
sample_list = list()
for i in xrange(len(bam_list)):
if lib_info is None:
logging.info('Calculating library metrics from %s...' % bam_list[i].filename)
sample = Sample.from_bam(bam_list[i], num_samp, min_lib_prevalence)
else:
logging.info('Reading library metrics from %s...' % lib_info_path)
sample = Sample.from_lib_info(bam_list[i], lib_info, min_lib_prevalence)
sample.set_exp_seq_depth(min_aligned)
sample.set_exp_spanning_depth(min_aligned)
sample_list.append(sample)
logging.info('done')
# diagnostic dump of relevant BAM reads
if alignment_outpath is not None:
# create a BAM file of the reads used for genotyping
out_bam_written_reads = set()
template_bam = pysam.AlignmentFile(bam_string.split(',')[0], 'rb')
out_bam = pysam.AlignmentFile(alignment_outpath, 'wb', template_bam)
template_bam.close()
# write the JSON for each sample's libraries
if lib_info_path is not None and not os.path.isfile(lib_info_path):
logging.info('Writing library metrics to %s...' % lib_info_path)
lib_info_file = open(lib_info_path, 'w')
write_sample_json(sample_list, lib_info_file)
lib_info_file.close()
logging.info('done')
# quit early if VCF absent
if vcf_in is None:
if alignment_outpath is not None:
out_bam.close()
return
# set variables for genotyping
z = 3
split_slop = 3 # amount of slop around breakpoint to count splitters
in_header = True
header = []
breakend_dict = {} # cache to hold unmatched generic breakends for genotyping
vcf = Vcf()
# read input VCF
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
if line[1] != '#':
vcf_samples = line.rstrip().split('\t')[9:]
continue
else:
in_header = False
vcf.add_header(header)
# if detailed:
vcf.add_custom_svtyper_headers()
# add the samples in the BAM files to the VCF output
for sample in sample_list:
if sample.name not in vcf.sample_list:
vcf.add_sample(sample.name)
# write the output header
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
var = Variant(v, vcf)
var_length = None # var_length should be None except for deletions
if not sum_quals:
var.qual = 0
# genotype generic breakends
try:
svtype = var.get_info('SVTYPE')
except KeyError:
sys.stderr.write('Warning: SVTYPE missing at variant %s. Skipping.\n' % (var.var_id))
vcf_out.write(var.get_var_string() + '\n')
continue
# print original line if unsupported svtype
if svtype not in ('BND', 'DEL', 'DUP', 'INV'):
sys.stderr.write('Warning: Unsupported SVTYPE at variant %s (%s). Skipping.\n' % (var.var_id, svtype))
vcf_out.write(var.get_var_string() + '\n')
continue
if svtype == 'BND':
if var.info['MATEID'] in breakend_dict:
var2 = var
var = breakend_dict[var.info['MATEID']]
chromA = var.chrom
chromB = var2.chrom
posA = var.pos
posB = var2.pos
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var2, 'CIPOS', 'CIPOS95', max_ci_dist)
# infer the strands from the alt allele
if var.alt[-1] == '[' or var.alt[-1] == ']':
o1_is_reverse = False
else: o1_is_reverse = True
if var2.alt[-1] == '[' or var2.alt[-1] == ']':
o2_is_reverse = False
else: o2_is_reverse = True
# remove the BND from the breakend_dict
# to free up memory
del breakend_dict[var.var_id]
else:
breakend_dict[var.var_id] = var
continue
else:
chromA = var.chrom
chromB = var.chrom
posA = var.pos
posB = int(var.get_info('END'))
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var, 'CIEND', 'CIEND95', max_ci_dist)
if svtype == 'DEL':
var_length = posB - posA
o1_is_reverse, o2_is_reverse = False, True
elif svtype == 'DUP':
o1_is_reverse, o2_is_reverse = True, False
elif svtype == 'INV':
o1_is_reverse, o2_is_reverse = False, False
# increment the negative strand values (note position in VCF should be the base immediately left of the breakpoint junction)
if o1_is_reverse: posA += 1
if o2_is_reverse: posB += 1
for sample in sample_list:
# grab reads from both sides of breakpoint
read_batch, many = gather_all_reads(sample, chromA, posA, ciA, chromB, posB, ciB, z, max_reads)
if many:
var.genotype(sample.name).set_format('GT', './.')
continue
# initialize counts to zero
ref_span, alt_span = 0, 0
ref_seq, alt_seq = 0, 0
alt_clip = 0
# ref_ciA = ciA
# ref_ciB = ciB
ref_ciA = [0,0]
ref_ciB = [0,0]
for query_name in sorted(read_batch.keys()):
fragment = read_batch[query_name]
# boolean on whether to write the fragment
write_fragment = False
# -------------------------------------
# Check for split-read evidence
# -------------------------------------
# get reference sequences
for read in fragment.primary_reads:
is_ref_seq_A = fragment.is_ref_seq(read, var, chromA, posA, ciA, min_aligned)
is_ref_seq_B = fragment.is_ref_seq(read, var, chromB, posB, ciB, min_aligned)
if (is_ref_seq_A or is_ref_seq_B):
p_reference = prob_mapq(read)
ref_seq += p_reference
read.set_tag('XV', 'R')
write_fragment = True
# get non-reference split-read support
for split in fragment.split_reads:
split_lr = split.is_split_straddle(chromA, posA, ciA,
chromB, posB, ciB,
o1_is_reverse, o2_is_reverse,
svtype, split_slop)
# p_alt = prob_mapq(split.query_left) * prob_mapq(split.query_right)
p_alt = (prob_mapq(split.query_left) * split_lr[0] + prob_mapq(split.query_right) * split_lr[1]) / 2.0
if split.is_soft_clip:
alt_clip += p_alt
else:
alt_seq += p_alt
if p_alt > 0:
split.tag_split(p_alt)
write_fragment = True
# -------------------------------------
# Check for paired-end evidence
# -------------------------------------
# tally spanning alternate pairs
if svtype == 'DEL' and posB - posA < 2 * fragment.lib.sd:
alt_straddle = False
else:
alt_straddle = fragment.is_pair_straddle(chromA, posA, ciA,
chromB, posB, ciB,
o1_is_reverse, o2_is_reverse,
min_aligned,
fragment.lib)
# check both sides if inversion (perhaps should do this for BND as well?)
if svtype in ('INV'):
alt_straddle_reciprocal = fragment.is_pair_straddle(chromA, posA, ciA,
chromB, posB, ciB,
not o1_is_reverse,
not o2_is_reverse,
min_aligned,
fragment.lib)
else:
alt_straddle_reciprocal = False
if alt_straddle or alt_straddle_reciprocal:
if svtype == 'DEL':
p_conc = fragment.p_concordant(var_length)
if p_conc is not None:
p_alt = (1 - p_conc) * prob_mapq(fragment.readA) * prob_mapq(fragment.readB)
alt_span += p_alt
# # since an alt straddler is by definition also a reference straddler,
# # we can bail out early here to save some time
# p_reference = p_conc * prob_mapq(fragment.readA) * prob_mapq(fragment.readB)
# ref_span += p_reference
# continue
fragment.tag_span(p_alt)
write_fragment = True
else:
p_alt = prob_mapq(fragment.readA) * prob_mapq(fragment.readB)
alt_span += p_alt
fragment.tag_span(p_alt)
write_fragment = True
# # tally spanning reference pairs
if svtype == 'DEL' and posB - posA < 2 * fragment.lib.sd:
ref_straddle_A = False
ref_straddle_B = False
else:
ref_straddle_A = fragment.is_pair_straddle(chromA, posA, ref_ciA,
chromA, posA, ref_ciA,
False, True,
min_aligned,
fragment.lib)
ref_straddle_B = fragment.is_pair_straddle(chromB, posB, ref_ciB,
chromB, posB, ref_ciB,
False, True,
min_aligned,
fragment.lib)
if ref_straddle_A or ref_straddle_B:
# don't allow the pair to jump the entire variant, except for
# length-changing SVs like deletions
if not (ref_straddle_A and ref_straddle_B) or svtype == 'DEL':
p_conc = fragment.p_concordant(var_length)
if p_conc is not None:
p_reference = p_conc * prob_mapq(fragment.readA) * prob_mapq(fragment.readB)
ref_span += (ref_straddle_A + ref_straddle_B) * p_reference / 2
fragment.tag_span(1 - p_conc)
write_fragment = True
# write to BAM if requested
if alignment_outpath is not None and write_fragment:
for read in fragment.primary_reads + [split.read for split in fragment.split_reads]:
out_bam_written_reads = write_alignment(read, out_bam, out_bam_written_reads)
if debug:
print '--------------------------'
print 'ref_span:', ref_span
print 'alt_span:', alt_span
print 'ref_seq:', ref_seq
print 'alt_seq:', alt_seq
print 'alt_clip:', alt_clip
# in the absence of evidence for a particular type, ignore the reference
# support for that type as well
if (alt_seq + alt_clip) < 0.5 and alt_span >= 1:
alt_seq = 0
alt_clip = 0
ref_seq = 0
if alt_span < 0.5 and (alt_seq + alt_clip) >= 1:
alt_span = 0
ref_span = 0
if alt_span + alt_seq == 0 and alt_clip > 0:
# discount any SV that's only supported by clips.
alt_clip = 0
if ref_seq + alt_seq + ref_span + alt_span + alt_clip > 0:
# get bayesian classifier
if var.info['SVTYPE'] == "DUP": is_dup = True
else: is_dup = False
alt_splitters = alt_seq + alt_clip
QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
QA = int(split_weight * alt_splitters) + int(disc_weight * alt_span)
gt_lplist = bayes_gt(QR, QA, is_dup)
best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda(x): x[1], reverse=True)[0:2]
gt_idx = best[0]
# print log probabilities of homref, het, homalt
if debug:
print gt_lplist
# set the overall variant QUAL score and sample specific fields
var.genotype(sample.name).set_format('GL', ','.join(['%.0f' % x for x in gt_lplist]))
var.genotype(sample.name).set_format('DP', int(ref_seq + alt_seq + alt_clip + ref_span + alt_span))
var.genotype(sample.name).set_format('RO', int(ref_seq + ref_span))
var.genotype(sample.name).set_format('AO', int(alt_seq + alt_clip + alt_span))
var.genotype(sample.name).set_format('QR', QR)
var.genotype(sample.name).set_format('QA', QA)
# if detailed:
var.genotype(sample.name).set_format('RS', int(ref_seq))
var.genotype(sample.name).set_format('AS', int(alt_seq))
var.genotype(sample.name).set_format('ASC', int(alt_clip))
var.genotype(sample.name).set_format('RP', int(ref_span))
var.genotype(sample.name).set_format('AP', int(alt_span))
try:
var.genotype(sample.name).set_format('AB', '%.2g' % (QA / float(QR + QA)))
except ZeroDivisionError:
var.genotype(sample.name).set_format('AB', '.')
# assign genotypes
gt_sum = 0
for gt in gt_lplist:
try:
gt_sum += 10**gt
except OverflowError:
gt_sum += 0
if gt_sum > 0:
gt_sum_log = math.log(gt_sum, 10)
sample_qual = abs(-10 * (gt_lplist[0] - gt_sum_log)) # phred-scaled probability site is non-reference in this sample
phred_gq = min(-10 * (second_best[1] - best[1]), 200)
var.genotype(sample.name).set_format('GQ', int(phred_gq))
var.genotype(sample.name).set_format('SQ', sample_qual)
var.qual += sample_qual
if gt_idx == 1:
var.genotype(sample.name).set_format('GT', '0/1')
elif gt_idx == 2:
var.genotype(sample.name).set_format('GT', '1/1')
elif gt_idx == 0:
var.genotype(sample.name).set_format('GT', '0/0')
else:
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GT', './.')
else:
var.genotype(sample.name).set_format('GT', './.')
var.qual = 0
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GL', '.')
var.genotype(sample.name).set_format('DP', 0)
var.genotype(sample.name).set_format('AO', 0)
var.genotype(sample.name).set_format('RO', 0)
# if detailed:
var.genotype(sample.name).set_format('AS', 0)
var.genotype(sample.name).set_format('ASC', 0)
var.genotype(sample.name).set_format('RS', 0)
var.genotype(sample.name).set_format('AP', 0)
var.genotype(sample.name).set_format('RP', 0)
var.genotype(sample.name).set_format('QR', 0)
var.genotype(sample.name).set_format('QA', 0)
var.genotype(sample.name).set_format('AB', '.')
# after all samples have been processed, write
vcf_out.write(var.get_var_string() + '\n')
if var.info['SVTYPE'] == 'BND':
var2.qual = var.qual
var2.active_formats = var.active_formats
var2.genotype = var.genotype
vcf_out.write(var2.get_var_string() + '\n')
# throw warning if we've lost unpaired breakends
if breakend_dict:
logging.warning('Unpaired breakends found in file. These will not be present in output.')
# close the files
vcf_in.close()
vcf_out.close()
if alignment_outpath is not None:
out_bam.close()
return
def sv_genotype(bam_string, vcf_in, vcf_out, min_aligned, split_weight,
disc_weight, num_samp, lib_info_path, debug, alignment_outpath,
ref_fasta, sum_quals, max_reads, max_ci_dist):
# parse the comma separated inputs
bam_list = []
for b in bam_string.split(','):
if b.endswith('.bam'):
bam_list.append(pysam.AlignmentFile(b, mode='rb'))
elif b.endswith('.cram'):
bam_list.append(
pysam.AlignmentFile(b,
mode='rc',
reference_filename=ref_fasta,
format_options=["required_fields=7167"]))
else:
sys.stderr.write(
'Error: %s is not a valid alignment file (*.bam or *.cram)\n' %
b)
exit(1)
min_lib_prevalence = 1e-3 # only consider libraries that constitute at least this fraction of the BAM
# parse lib_info_path JSON
lib_info = None
if lib_info_path is not None and os.path.isfile(lib_info_path):
lib_info_file = open(lib_info_path, 'r')
lib_info = json.load(lib_info_file)
if vcf_in is None:
sys.stderr.write('Warning: VCF not found.\n')
# build the sample libraries, either from the lib_info JSON or empirically from the BAMs
sample_list = list()
for i in xrange(len(bam_list)):
if lib_info is None:
logging.info('Calculating library metrics from %s...' %
bam_list[i].filename)
sample = Sample.from_bam(bam_list[i], num_samp, min_lib_prevalence)
else:
logging.info('Reading library metrics from %s...' % lib_info_path)
sample = Sample.from_lib_info(bam_list[i], lib_info,
min_lib_prevalence)
sample.set_exp_seq_depth(min_aligned)
sample.set_exp_spanning_depth(min_aligned)
sample_list.append(sample)
logging.info('done')
# diagnostic dump of relevant BAM reads
if alignment_outpath is not None:
# create a BAM file of the reads used for genotyping
out_bam_written_reads = set()
template_bam = pysam.AlignmentFile(bam_string.split(',')[0], 'rb')
out_bam = pysam.AlignmentFile(alignment_outpath, 'wb', template_bam)
template_bam.close()
# write the JSON for each sample's libraries
if lib_info_path is not None and not os.path.isfile(lib_info_path):
logging.info('Writing library metrics to %s...' % lib_info_path)
lib_info_file = open(lib_info_path, 'w')
write_sample_json(sample_list, lib_info_file)
lib_info_file.close()
logging.info('done')
# quit early if VCF absent
if vcf_in is None:
if alignment_outpath is not None:
out_bam.close()
return
# set variables for genotyping
z = 3
split_slop = 3 # amount of slop around breakpoint to count splitters
in_header = True
header = []
breakend_dict = {
} # cache to hold unmatched generic breakends for genotyping
vcf = Vcf()
# read input VCF
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
if line[1] != '#':
vcf_samples = line.rstrip().split('\t')[9:]
continue
else:
in_header = False
vcf.add_header(header)
# if detailed:
vcf.add_custom_svtyper_headers()
# add the samples in the BAM files to the VCF output
for sample in sample_list:
if sample.name not in vcf.sample_list:
vcf.add_sample(sample.name)
# write the output header
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
var = Variant(v, vcf)
var_length = None # var_length should be None except for deletions
if not sum_quals:
var.qual = 0
# genotype generic breakends
try:
svtype = var.get_info('SVTYPE')
except KeyError:
sys.stderr.write(
'Warning: SVTYPE missing at variant %s. Skipping.\n' %
(var.var_id))
vcf_out.write(var.get_var_string() + '\n')
continue
# print original line if unsupported svtype
if svtype not in ('BND', 'DEL', 'DUP', 'INV'):
sys.stderr.write(
'Warning: Unsupported SVTYPE at variant %s (%s). Skipping.\n' %
(var.var_id, svtype))
vcf_out.write(var.get_var_string() + '\n')
continue
if svtype == 'BND':
if var.info['MATEID'] in breakend_dict:
var2 = var
var = breakend_dict[var.info['MATEID']]
chromA = var.chrom
chromB = var2.chrom
posA = var.pos
posB = var2.pos
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var2, 'CIPOS', 'CIPOS95',
max_ci_dist)
# infer the strands from the alt allele
if var.alt[-1] == '[' or var.alt[-1] == ']':
o1_is_reverse = False
else:
o1_is_reverse = True
if var2.alt[-1] == '[' or var2.alt[-1] == ']':
o2_is_reverse = False
else:
o2_is_reverse = True
# remove the BND from the breakend_dict
# to free up memory
del breakend_dict[var.var_id]
else:
breakend_dict[var.var_id] = var
continue
else:
chromA = var.chrom
chromB = var.chrom
posA = var.pos
posB = int(var.get_info('END'))
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var, 'CIEND', 'CIEND95', max_ci_dist)
if svtype == 'DEL':
var_length = posB - posA
o1_is_reverse, o2_is_reverse = False, True
elif svtype == 'DUP':
o1_is_reverse, o2_is_reverse = True, False
elif svtype == 'INV':
o1_is_reverse, o2_is_reverse = False, False
# increment the negative strand values (note position in VCF should be the base immediately left of the breakpoint junction)
if o1_is_reverse: posA += 1
if o2_is_reverse: posB += 1
for sample in sample_list:
# grab reads from both sides of breakpoint
read_batch, many = gather_all_reads(sample, chromA, posA, ciA,
chromB, posB, ciB, z,
max_reads)
if many:
var.genotype(sample.name).set_format('GT', './.')
continue
# initialize counts to zero
ref_span, alt_span = 0, 0
ref_seq, alt_seq = 0, 0
alt_clip = 0
# ref_ciA = ciA
# ref_ciB = ciB
ref_ciA = [0, 0]
ref_ciB = [0, 0]
for query_name in sorted(read_batch.keys()):
fragment = read_batch[query_name]
# boolean on whether to write the fragment
write_fragment = False
# -------------------------------------
# Check for split-read evidence
# -------------------------------------
# get reference sequences
for read in fragment.primary_reads:
is_ref_seq_A = fragment.is_ref_seq(read, var, chromA, posA,
ciA, min_aligned)
is_ref_seq_B = fragment.is_ref_seq(read, var, chromB, posB,
ciB, min_aligned)
if (is_ref_seq_A or is_ref_seq_B):
p_reference = prob_mapq(read)
ref_seq += p_reference
read.set_tag('XV', 'R')
write_fragment = True
# get non-reference split-read support
for split in fragment.split_reads:
split_lr = split.is_split_straddle(chromA, posA, ciA,
chromB, posB, ciB,
o1_is_reverse,
o2_is_reverse, svtype,
split_slop)
# p_alt = prob_mapq(split.query_left) * prob_mapq(split.query_right)
p_alt = (prob_mapq(split.query_left) * split_lr[0] +
prob_mapq(split.query_right) * split_lr[1]) / 2.0
if split.is_soft_clip:
alt_clip += p_alt
else:
alt_seq += p_alt
if p_alt > 0:
split.tag_split(p_alt)
write_fragment = True
# -------------------------------------
# Check for paired-end evidence
# -------------------------------------
# tally spanning alternate pairs
if svtype == 'DEL' and posB - posA < 2 * fragment.lib.sd:
alt_straddle = False
else:
alt_straddle = fragment.is_pair_straddle(
chromA, posA, ciA, chromB, posB, ciB, o1_is_reverse,
o2_is_reverse, min_aligned, fragment.lib)
# check both sides if inversion (perhaps should do this for BND as well?)
if svtype in ('INV'):
alt_straddle_reciprocal = fragment.is_pair_straddle(
chromA, posA, ciA, chromB, posB, ciB,
not o1_is_reverse, not o2_is_reverse, min_aligned,
fragment.lib)
else:
alt_straddle_reciprocal = False
if alt_straddle or alt_straddle_reciprocal:
if svtype == 'DEL':
p_conc = fragment.p_concordant(var_length)
if p_conc is not None:
p_alt = (1 - p_conc) * prob_mapq(
fragment.readA) * prob_mapq(fragment.readB)
alt_span += p_alt
# # since an alt straddler is by definition also a reference straddler,
# # we can bail out early here to save some time
# p_reference = p_conc * prob_mapq(fragment.readA) * prob_mapq(fragment.readB)
# ref_span += p_reference
# continue
fragment.tag_span(p_alt)
write_fragment = True
else:
p_alt = prob_mapq(fragment.readA) * prob_mapq(
fragment.readB)
alt_span += p_alt
fragment.tag_span(p_alt)
write_fragment = True
# # tally spanning reference pairs
if svtype == 'DEL' and posB - posA < 2 * fragment.lib.sd:
ref_straddle_A = False
ref_straddle_B = False
else:
ref_straddle_A = fragment.is_pair_straddle(
chromA, posA, ref_ciA, chromA, posA, ref_ciA, False,
True, min_aligned, fragment.lib)
ref_straddle_B = fragment.is_pair_straddle(
chromB, posB, ref_ciB, chromB, posB, ref_ciB, False,
True, min_aligned, fragment.lib)
if ref_straddle_A or ref_straddle_B:
# don't allow the pair to jump the entire variant, except for
# length-changing SVs like deletions
if not (ref_straddle_A
and ref_straddle_B) or svtype == 'DEL':
p_conc = fragment.p_concordant(var_length)
if p_conc is not None:
p_reference = p_conc * prob_mapq(
fragment.readA) * prob_mapq(fragment.readB)
ref_span += (ref_straddle_A +
ref_straddle_B) * p_reference / 2
fragment.tag_span(1 - p_conc)
write_fragment = True
# write to BAM if requested
if alignment_outpath is not None and write_fragment:
for read in fragment.primary_reads + [
split.read for split in fragment.split_reads
]:
out_bam_written_reads = write_alignment(
read, out_bam, out_bam_written_reads)
if debug:
print '--------------------------'
print 'ref_span:', ref_span
print 'alt_span:', alt_span
print 'ref_seq:', ref_seq
print 'alt_seq:', alt_seq
print 'alt_clip:', alt_clip
# in the absence of evidence for a particular type, ignore the reference
# support for that type as well
if (alt_seq + alt_clip) < 0.5 and alt_span >= 1:
alt_seq = 0
alt_clip = 0
ref_seq = 0
if alt_span < 0.5 and (alt_seq + alt_clip) >= 1:
alt_span = 0
ref_span = 0
if alt_span + alt_seq == 0 and alt_clip > 0:
# discount any SV that's only supported by clips.
alt_clip = 0
if ref_seq + alt_seq + ref_span + alt_span + alt_clip > 0:
# get bayesian classifier
if var.info['SVTYPE'] == "DUP": is_dup = True
else: is_dup = False
alt_splitters = alt_seq + alt_clip
QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
QA = int(split_weight * alt_splitters) + int(
disc_weight * alt_span)
gt_lplist = bayes_gt(QR, QA, is_dup)
best, second_best = sorted([(i, e)
for i, e in enumerate(gt_lplist)],
key=lambda (x): x[1],
reverse=True)[0:2]
gt_idx = best[0]
# print log probabilities of homref, het, homalt
if debug:
print gt_lplist
# set the overall variant QUAL score and sample specific fields
var.genotype(sample.name).set_format(
'GL', ','.join(['%.0f' % x for x in gt_lplist]))
var.genotype(sample.name).set_format(
'DP',
int(ref_seq + alt_seq + alt_clip + ref_span + alt_span))
var.genotype(sample.name).set_format('RO',
int(ref_seq + ref_span))
var.genotype(sample.name).set_format(
'AO', int(alt_seq + alt_clip + alt_span))
var.genotype(sample.name).set_format('QR', QR)
var.genotype(sample.name).set_format('QA', QA)
# if detailed:
var.genotype(sample.name).set_format('RS', int(ref_seq))
var.genotype(sample.name).set_format('AS', int(alt_seq))
var.genotype(sample.name).set_format('ASC', int(alt_clip))
var.genotype(sample.name).set_format('RP', int(ref_span))
var.genotype(sample.name).set_format('AP', int(alt_span))
try:
var.genotype(sample.name).set_format(
'AB', '%.2g' % (QA / float(QR + QA)))
except ZeroDivisionError:
var.genotype(sample.name).set_format('AB', '.')
# assign genotypes
gt_sum = 0
for gt in gt_lplist:
try:
gt_sum += 10**gt
except OverflowError:
gt_sum += 0
if gt_sum > 0:
gt_sum_log = math.log(gt_sum, 10)
sample_qual = abs(
-10 * (gt_lplist[0] - gt_sum_log)
) # phred-scaled probability site is non-reference in this sample
phred_gq = min(-10 * (second_best[1] - best[1]), 200)
var.genotype(sample.name).set_format('GQ', int(phred_gq))
var.genotype(sample.name).set_format('SQ', sample_qual)
var.qual += sample_qual
if gt_idx == 1:
var.genotype(sample.name).set_format('GT', '0/1')
elif gt_idx == 2:
var.genotype(sample.name).set_format('GT', '1/1')
elif gt_idx == 0:
var.genotype(sample.name).set_format('GT', '0/0')
else:
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GT', './.')
else:
var.genotype(sample.name).set_format('GT', './.')
var.qual = 0
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GL', '.')
var.genotype(sample.name).set_format('DP', 0)
var.genotype(sample.name).set_format('AO', 0)
var.genotype(sample.name).set_format('RO', 0)
# if detailed:
var.genotype(sample.name).set_format('AS', 0)
var.genotype(sample.name).set_format('ASC', 0)
var.genotype(sample.name).set_format('RS', 0)
var.genotype(sample.name).set_format('AP', 0)
var.genotype(sample.name).set_format('RP', 0)
var.genotype(sample.name).set_format('QR', 0)
var.genotype(sample.name).set_format('QA', 0)
var.genotype(sample.name).set_format('AB', '.')
# after all samples have been processed, write
vcf_out.write(var.get_var_string() + '\n')
if var.info['SVTYPE'] == 'BND':
var2.qual = var.qual
var2.active_formats = var.active_formats
var2.genotype = var.genotype
vcf_out.write(var2.get_var_string() + '\n')
# throw warning if we've lost unpaired breakends
if breakend_dict:
logging.warning(
'Unpaired breakends found in file. These will not be present in output.'
)
# close the files
vcf_in.close()
vcf_out.close()
if alignment_outpath is not None:
out_bam.close()
return
def bayesian_genotype(breakpoint, counts, split_weight, disc_weight, debug):
is_dup = breakpoint['svtype'] == 'DUP'
elems = ('ref_seq', 'alt_seq', 'alt_clip', 'ref_span', 'alt_span')
(ref_seq, alt_seq, alt_clip, ref_span, alt_span) = \
[counts[i] for i in elems]
# pre-calculations
alt_splitters = alt_seq + alt_clip
QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
QA = int(split_weight * alt_splitters) + int(disc_weight * alt_span)
# the actual bayesian calculation and decision
gt_lplist = bayes_gt(QR, QA, is_dup)
best, second_best = sorted([ (i, e) for i, e in enumerate(gt_lplist) ], key=lambda(x): x[1], reverse=True)[0:2]
gt_idx = best[0]
# print log probabilities of homref, het, homalt
if debug:
msg = ("{} -- "
"log probabilities (homref, het, homalt) : "
"{}").format(breakpoint['id'], gt_lplist)
logit(msg)
result = blank_genotype_result()
result['formats']['GL'] = ','.join(['%.0f' % x for x in gt_lplist])
result['formats']['DP'] = int(ref_seq + alt_seq + alt_clip + ref_span + alt_span)
result['formats']['RO'] = int(ref_seq + ref_span)
result['formats']['AO'] = int(alt_seq + alt_clip + alt_span)
result['formats']['QR'] = QR
result['formats']['QA'] = QA
# if detailed:
result['formats']['RS'] = int(ref_seq)
result['formats']['AS'] = int(alt_seq)
result['formats']['ASC'] = int(alt_clip)
result['formats']['RP'] = int(ref_span)
result['formats']['AP'] = int(alt_span)
try:
result['formats']['AB'] = '%.2g' % (QA / float(QR + QA))
except ZeroDivisionError:
result['formats']['AB'] = '.'
# assign genotypes
gt_sum = 0
for gt in gt_lplist:
try:
gt_sum += 10**gt
except OverflowError:
gt_sum += 0
if gt_sum > 0:
gt_sum_log = math.log(gt_sum, 10)
sample_qual = abs(-10 * (gt_lplist[0] - gt_sum_log)) # phred-scaled probability site is non-reference in this sample
phred_gq = min(-10 * (second_best[1] - best[1]), 200)
result['formats']['GQ'] = int(phred_gq)
result['formats']['SQ'] = sample_qual
result['qual'] += sample_qual
if gt_idx == 1:
result['formats']['GT'] = '0/1'
elif gt_idx == 2:
result['formats']['GT'] = '1/1'
elif gt_idx == 0:
result['formats']['GT'] = '0/0'
else:
result['formats']['GQ'] = '.'
result['formats']['SQ'] = '.'
result['formats']['GT'] = './.'
return result
def bayesian_genotype(breakpoint, counts, split_weight, disc_weight, debug):
is_dup = breakpoint['svtype'] == 'DUP'
elems = ('ref_seq', 'alt_seq', 'alt_clip', 'ref_span', 'alt_span')
(ref_seq, alt_seq, alt_clip, ref_span, alt_span) = \
[counts[i] for i in elems]
# pre-calculations
alt_splitters = alt_seq + alt_clip
QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
QA = int(split_weight * alt_splitters) + int(disc_weight * alt_span)
# the actual bayesian calculation and decision
gt_lplist = bayes_gt(QR, QA, is_dup)
best, second_best = sorted([(i, e) for i, e in enumerate(gt_lplist)],
key=lambda x: x[1],
reverse=True)[0:2]
gt_idx = best[0]
# print log probabilities of homref, het, homalt
if debug:
msg = ("{} -- "
"log probabilities (homref, het, homalt) : "
"{}").format(breakpoint['id'], gt_lplist)
logit(msg)
result = blank_genotype_result()
result['formats']['GL'] = ','.join(['%.0f' % x for x in gt_lplist])
result['formats']['DP'] = int(ref_seq + alt_seq + alt_clip + ref_span +
alt_span)
result['formats']['RO'] = int(ref_seq + ref_span)
result['formats']['AO'] = int(alt_seq + alt_clip + alt_span)
result['formats']['QR'] = QR
result['formats']['QA'] = QA
# if detailed:
result['formats']['RS'] = int(ref_seq)
result['formats']['AS'] = int(alt_seq)
result['formats']['ASC'] = int(alt_clip)
result['formats']['RP'] = int(ref_span)
result['formats']['AP'] = int(alt_span)
try:
result['formats']['AB'] = '%.2g' % (QA / float(QR + QA))
except ZeroDivisionError:
result['formats']['AB'] = '.'
# assign genotypes
gt_sum = 0
for gt in gt_lplist:
try:
gt_sum += 10**gt
except OverflowError:
gt_sum += 0
if gt_sum > 0:
gt_sum_log = math.log(gt_sum, 10)
sample_qual = abs(
-10 * (gt_lplist[0] - gt_sum_log)
) # phred-scaled probability site is non-reference in this sample
phred_gq = min(-10 * (second_best[1] - best[1]), 200)
result['formats']['GQ'] = int(phred_gq)
result['formats']['SQ'] = sample_qual
result['qual'] += sample_qual
if gt_idx == 1:
result['formats']['GT'] = '0/1'
elif gt_idx == 2:
result['formats']['GT'] = '1/1'
elif gt_idx == 0:
result['formats']['GT'] = '0/0'
else:
result['formats']['GQ'] = '.'
result['formats']['SQ'] = '.'
result['formats']['GT'] = './.'
return result
def sv_genotype(bam_string, vcf_in, vcf_out, min_aligned, split_weight,
disc_weight, num_samp, lib_info_path, debug, alignment_outpath,
ref_fasta, sum_quals, max_reads, max_ci_dist):
# parse the comma separated inputs
bam_list = []
for b in bam_string.split(','):
if b.endswith('.bam'):
bam_list.append(pysam.AlignmentFile(b, mode='rb'))
elif b.endswith('.cram'):
bam_list.append(
pysam.AlignmentFile(b,
mode='rc',
reference_filename=ref_fasta,
format_options=["required_fields=7167"]))
else:
sys.stderr.write(
'Error: %s is not a valid alignment file (*.bam or *.cram)\n' %
b)
exit(1)
min_lib_prevalence = 1e-3 # only consider libraries that constitute at least this fraction of the BAM
# parse lib_info_path JSON
lib_info = None
if lib_info_path is not None and os.path.isfile(lib_info_path):
lib_info_file = open(lib_info_path, 'r')
lib_info = json.load(lib_info_file)
if vcf_in is None:
sys.stderr.write('Warning: VCF not found.\n')
# build the sample libraries, either from the lib_info JSON or empirically from the BAMs
sample_list = list()
for i in xrange(len(bam_list)):
if lib_info is None:
logging.info('Calculating library metrics from %s...' %
bam_list[i].filename)
sample = Sample.from_bam(bam_list[i], num_samp, min_lib_prevalence)
else:
logging.info('Reading library metrics from %s...' % lib_info_path)
sample = Sample.from_lib_info(bam_list[i], lib_info,
min_lib_prevalence)
sample.set_exp_seq_depth(min_aligned)
sample.set_exp_spanning_depth(min_aligned)
sample_list.append(sample)
logging.info('done')
# diagnostic dump of relevant BAM reads
if alignment_outpath is not None:
# create a BAM file of the reads used for genotyping
template_bam = pysam.AlignmentFile(bam_string.split(',')[0], 'rb')
out_bam = pysam.AlignmentFile(alignment_outpath, 'wb', template_bam)
template_bam.close()
supporting_reads_file = alignment_outpath.replace('bam', 'tsv')
supporting_reads_fh = open(supporting_reads_file, 'w')
supporting_reads_fh.write(
'chromA,chromB,posA,posB,svtype,read_id,read_chrom,read_reference_start,read_reference_end,start_ref_support,start_ref_pe_support,start_alt_sr_support,start_alt_pe_support,end_ref_support,end_ref_pe_support,end_alt_sr_support,end_alt_pe_support\n'
)
else:
out_bam = None
supporting_reads_fh = None
out_bam_written_reads = set()
# write the JSON for each sample's libraries
if lib_info_path is not None and not os.path.isfile(lib_info_path):
logging.info('Writing library metrics to %s...' % lib_info_path)
lib_info_file = open(lib_info_path, 'w')
write_sample_json(sample_list, lib_info_file)
lib_info_file.close()
logging.info('done')
# quit early if VCF absent
if vcf_in is None:
if alignment_outpath is not None:
out_bam.close()
return
# set variables for genotyping
z = 3
in_header = True
header = []
breakend_dict = {
} # cache to hold unmatched generic breakends for genotyping
vcf = Vcf()
# read input VCF
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
if line[1] != '#':
vcf_samples = line.rstrip().split('\t')[9:]
continue
else:
in_header = False
vcf.add_header(header)
# if detailed:
vcf.add_custom_svtyper_headers()
# add the samples in the BAM files to the VCF output
for sample in sample_list:
if sample.name not in vcf.sample_list:
vcf.add_sample(sample.name)
# write the output header
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
var = Variant(v, vcf)
var_length = None # var_length should be None except for deletions
if not sum_quals:
var.qual = 0
# genotype generic breakends
try:
svtype = var.get_info('SVTYPE')
except KeyError:
sys.stderr.write(
'Warning: SVTYPE missing at variant %s. Skipping.\n' %
(var.var_id))
vcf_out.write(var.get_var_string() + '\n')
continue
# print original line if unsupported svtype
if svtype not in ('BND', 'DEL', 'DUP', 'INV'):
sys.stderr.write(
'Warning: Unsupported SVTYPE at variant %s (%s). Skipping.\n' %
(var.var_id, svtype))
vcf_out.write(var.get_var_string() + '\n')
continue
if svtype == 'BND':
if var.info['MATEID'] in breakend_dict:
var2 = var
var = breakend_dict[var.info['MATEID']]
chromA = var.chrom
chromB = var2.chrom
posA = var.pos
posB = var2.pos
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var2, 'CIPOS', 'CIPOS95',
max_ci_dist)
# infer the strands from the alt allele
if var.alt[-1] == '[' or var.alt[-1] == ']':
o1_is_reverse = False
else:
o1_is_reverse = True
if var2.alt[-1] == '[' or var2.alt[-1] == ']':
o2_is_reverse = False
else:
o2_is_reverse = True
# remove the BND from the breakend_dict
# to free up memory
del breakend_dict[var.var_id]
else:
breakend_dict[var.var_id] = var
continue
else:
chromA = var.chrom
chromB = var.chrom
posA = var.pos
posB = int(var.get_info('END'))
# confidence intervals
ciA = confidence_interval(var, 'CIPOS', 'CIPOS95', max_ci_dist)
ciB = confidence_interval(var, 'CIEND', 'CIEND95', max_ci_dist)
if svtype == 'DEL':
var_length = posB - posA
o1_is_reverse, o2_is_reverse = False, True
elif svtype == 'DUP':
o1_is_reverse, o2_is_reverse = True, False
elif svtype == 'INV':
o1_is_reverse, o2_is_reverse = False, False
# increment the negative strand values (note position in VCF should be the base immediately left of the breakpoint junction)
if o1_is_reverse: posA += 1
if o2_is_reverse: posB += 1
for sample in sample_list:
# grab reads for start and end of breakpoints
start_read_batch = {}
end_read_batch = {}
start_read_batch, many = gather_reads(sample, chromA, posA, ciA, z,
start_read_batch, max_reads)
end_read_batch, many = gather_reads(sample, chromB, posB, ciB, z,
end_read_batch, max_reads)
# grab reads from both sides of breakpoint
read_batch, many = gather_all_reads(sample, chromA, posA, ciA,
chromB, posB, ciB, z,
max_reads)
if many:
var.genotype(sample.name).set_format('GT', './.')
continue
metrics = {'start': {}, 'end': {}}
# run metrics for each breakpoint separately
metrics['start'] = calculate_metrics(
start_read_batch, 'start', var, chromA, chromB, posA, posB,
ciA, ciB, min_aligned, o1_is_reverse, o2_is_reverse,
var_length, svtype, out_bam_written_reads, supporting_reads_fh,
out_bam)
metrics['end'] = calculate_metrics(end_read_batch, 'end', var,
chromA, chromB, posA, posB, ciA,
ciB, min_aligned, o1_is_reverse,
o2_is_reverse, var_length,
svtype, out_bam_written_reads,
supporting_reads_fh, out_bam)
# run metrics for both sides of breakpoints
# no bam file and supporting reads files is written here since they are already written in running the method for each breakpoint separately
metrics['both'] = calculate_metrics(
read_batch, 'both', var, chromA, chromB, posA, posB, ciA, ciB,
min_aligned, o1_is_reverse, o2_is_reverse, var_length, svtype,
out_bam_written_reads, None, None)
# set these for metrics from both sides of breakpoints
ref_span = metrics['both']['ref_span']
alt_span = metrics['both']['alt_span']
ref_seq = metrics['both']['ref_seq']
alt_seq = metrics['both']['alt_seq']
alt_clip = metrics['both']['alt_clip']
if debug:
print '--------------------------'
print 'ref_span:', ref_span
print 'alt_span:', alt_span
print 'ref_seq:', ref_seq
print 'alt_seq:', alt_seq
print 'alt_clip:', alt_clip
# in the absence of evidence for a particular type, ignore the reference
# support for that type as well
if (alt_seq + alt_clip) < 0.5 and alt_span >= 1:
alt_seq = 0
alt_clip = 0
ref_seq = 0
if alt_span < 0.5 and (alt_seq + alt_clip) >= 1:
alt_span = 0
ref_span = 0
if alt_span + alt_seq == 0 and alt_clip > 0:
# discount any SV that's only supported by clips.
alt_clip = 0
if ref_seq + alt_seq + ref_span + alt_span + alt_clip > 0:
# get bayesian classifier
if var.info['SVTYPE'] == "DUP":
is_dup = True
else:
is_dup = False
alt_splitters = alt_seq + alt_clip
QR = int(split_weight * ref_seq) + int(disc_weight * ref_span)
QA = int(split_weight * alt_splitters) + int(
disc_weight * alt_span)
gt_lplist = bayes_gt(QR, QA, is_dup)
best, second_best = sorted([(i, e)
for i, e in enumerate(gt_lplist)],
key=lambda (x): x[1],
reverse=True)[0:2]
gt_idx = best[0]
# print log probabilities of homref, het, homalt
if debug:
print gt_lplist
# set the overall variant QUAL score and sample specific fields
var.genotype(sample.name).set_format(
'GL', ','.join(['%.0f' % x for x in gt_lplist]))
var.genotype(sample.name).set_format(
'DP',
int(ref_seq + alt_seq + alt_clip + ref_span + alt_span))
var.genotype(sample.name).set_format('RO',
int(ref_seq + ref_span))
var.genotype(sample.name).set_format(
'AO', int(alt_seq + alt_clip + alt_span))
var.genotype(sample.name).set_format('QR', QR)
var.genotype(sample.name).set_format('QA', QA)
# if detailed:
var.genotype(sample.name).set_format('RS', int(ref_seq))
var.genotype(sample.name).set_format('AS', int(alt_seq))
var.genotype(sample.name).set_format('ASC', int(alt_clip))
var.genotype(sample.name).set_format('RP', int(ref_span))
var.genotype(sample.name).set_format('AP', int(alt_span))
try:
var.genotype(sample.name).set_format(
'AB', '%.2g' % (QA / float(QR + QA)))
except ZeroDivisionError:
var.genotype(sample.name).set_format('AB', '.')
var.genotype(sample.name).set_format(
'SRC', int(metrics['start']['ref_count']))
var.genotype(sample.name).set_format(
'SRPC', int(metrics['start']['ref_pe_count']))
var.genotype(sample.name).set_format(
'SASC', int(metrics['start']['alt_clip_count']))
var.genotype(sample.name).set_format(
'SAPC', int(metrics['start']['alt_pe_count']))
var.genotype(sample.name).set_format(
'ERC', int(metrics['end']['ref_count']))
var.genotype(sample.name).set_format(
'ERPC', int(metrics['end']['ref_pe_count']))
var.genotype(sample.name).set_format(
'EASC', int(metrics['end']['alt_clip_count']))
var.genotype(sample.name).set_format(
'EAPC', int(metrics['end']['alt_pe_count']))
var.genotype(sample.name).set_format(
'BRC', int(metrics['both']['ref_count']))
var.genotype(sample.name).set_format(
'BRPC', int(metrics['both']['ref_pe_count']))
var.genotype(sample.name).set_format(
'BASC', int(metrics['both']['alt_clip_count']))
var.genotype(sample.name).set_format(
'BAPC', int(metrics['both']['alt_pe_count']))
var.genotype(sample.name).set_format(
'ISM', sample.get_mean_insert_size())
var.genotype(sample.name).set_format(
'ISSD', sample.get_stddev_insert_size())
# assign genotypes
gt_sum = 0
for gt in gt_lplist:
try:
gt_sum += 10**gt
except OverflowError:
gt_sum += 0
if gt_sum > 0:
gt_sum_log = math.log(gt_sum, 10)
sample_qual = abs(
-10 * (gt_lplist[0] - gt_sum_log)
) # phred-scaled probability site is non-reference in this sample
phred_gq = min(-10 * (second_best[1] - best[1]), 200)
var.genotype(sample.name).set_format('GQ', int(phred_gq))
var.genotype(sample.name).set_format('SQ', sample_qual)
var.qual += sample_qual
if gt_idx == 1:
var.genotype(sample.name).set_format('GT', '0/1')
elif gt_idx == 2:
var.genotype(sample.name).set_format('GT', '1/1')
elif gt_idx == 0:
var.genotype(sample.name).set_format('GT', '0/0')
else:
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GT', './.')
else:
var.genotype(sample.name).set_format('GT', './.')
var.qual = 0
var.genotype(sample.name).set_format('GQ', '.')
var.genotype(sample.name).set_format('SQ', '.')
var.genotype(sample.name).set_format('GL', '.')
var.genotype(sample.name).set_format('DP', 0)
var.genotype(sample.name).set_format('AO', 0)
var.genotype(sample.name).set_format('RO', 0)
# if detailed:
var.genotype(sample.name).set_format('AS', 0)
var.genotype(sample.name).set_format('ASC', 0)
var.genotype(sample.name).set_format('RS', 0)
var.genotype(sample.name).set_format('AP', 0)
var.genotype(sample.name).set_format('RP', 0)
var.genotype(sample.name).set_format('QR', 0)
var.genotype(sample.name).set_format('QA', 0)
var.genotype(sample.name).set_format('AB', '.')
var.genotype(sample.name).set_format(
'SRC', int(metrics['start']['ref_count']))
var.genotype(sample.name).set_format(
'SRPC', int(metrics['start']['ref_pe_count']))
var.genotype(sample.name).set_format(
'SASC', int(metrics['start']['alt_clip_count']))
var.genotype(sample.name).set_format(
'SAPC', int(metrics['start']['alt_pe_count']))
var.genotype(sample.name).set_format(
'ERC', int(metrics['end']['ref_count']))
var.genotype(sample.name).set_format(
'ERPC', int(metrics['end']['ref_pe_count']))
var.genotype(sample.name).set_format(
'EASC', int(metrics['end']['alt_clip_count']))
var.genotype(sample.name).set_format(
'EAPC', int(metrics['end']['alt_pe_count']))
var.genotype(sample.name).set_format(
'BRC', int(metrics['both']['ref_count']))
var.genotype(sample.name).set_format(
'BRPC', int(metrics['both']['ref_pe_count']))
var.genotype(sample.name).set_format(
'BASC', int(metrics['both']['alt_clip_count']))
var.genotype(sample.name).set_format(
'BAPC', int(metrics['both']['alt_pe_count']))
# after all samples have been processed, write
vcf_out.write(var.get_var_string() + '\n')
if var.info['SVTYPE'] == 'BND':
var2.qual = var.qual
var2.active_formats = var.active_formats
var2.genotype = var.genotype
vcf_out.write(var2.get_var_string() + '\n')
# throw warning if we've lost unpaired breakends
if breakend_dict:
logging.warning(
'Unpaired breakends found in file. These will not be present in output.'
)
# close the files
vcf_in.close()
vcf_out.close()
if alignment_outpath is not None:
out_bam.close()
supporting_reads_fh.close()
return