class TestVariant(TestCase):
def setUp(self):
header_lines = [
'##fileformat=VCFv4.2',
'##fileDate=20151202',
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
'#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878' ]
self.vcf = Vcf()
self.vcf.add_header(header_lines)
self.variant_line = '1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:SU 0/0:9'
self.variant = Variant(self.variant_line.split('\t'), self.vcf)
def test_set_info(self):
self.variant.set_info('SVTYPE', 'INV')
self.assertEqual(self.variant.info['SVTYPE'], 'INV')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.info['IMAFLAG'], False)
with self.assertRaises(SystemExit) as cm:
self.variant.set_info('SUPER', True)
def test_get_info(self):
self.assertEqual(self.variant.get_info('IMAFLAG'), True)
self.assertEqual(self.variant.get_info('SVTYPE'), 'BND')
with self.assertRaises(KeyError) as cm:
self.variant.get_info('CALI')
def test_get_info_string(self):
self.assertEqual(self.variant.get_info_string(), 'SVTYPE=BND;STRANDS=-+:9;IMAFLAG')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.get_info_string(), 'SVTYPE=BND;STRANDS=-+:9')
def test_get_format_string(self):
self.assertEqual(self.variant.get_format_string(), 'GT:SU')
def test_genotype(self):
self.assertEqual(self.variant.genotype('NA12878').get_gt_string(), '0/0:9')
def test_var_string(self):
self.assertEqual(self.variant.get_var_string(), self.variant_line)
class TestVariant(TestCase):
def setUp(self):
header_lines = [
'##fileformat=VCFv4.2', '##fileDate=20151202',
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
'#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 NA0001'
]
self.vcf = Vcf()
self.vcf.add_header(header_lines)
self.variant_line = '1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:SU 0/0:9 1/1:15'
self.variant = Variant(self.variant_line.split('\t'), self.vcf)
def test_parse_genotypes(self):
genotype_field_strings = ['0/1:20', '0/0:15']
parsed_dict = self.variant._parse_genotypes(genotype_field_strings)
na12878_gt = Genotype(self.variant,
genotype_field_strings[0].split(':'))
na0001_gt = Genotype(self.variant,
genotype_field_strings[1].split(':'))
expected_genotype_dict = {'NA12878': na12878_gt, 'NA0001': na0001_gt}
self.assertEqual(parsed_dict, expected_genotype_dict)
def test_set_info(self):
self.variant.set_info('SVTYPE', 'INV')
self.assertEqual(self.variant.info['SVTYPE'], 'INV')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.info['IMAFLAG'], False)
with self.assertRaises(SystemExit) as cm:
self.variant.set_info('SUPER', True)
def test_get_info(self):
self.assertEqual(self.variant.get_info('IMAFLAG'), True)
self.assertEqual(self.variant.get_info('SVTYPE'), 'BND')
with self.assertRaises(KeyError) as cm:
self.variant.get_info('CALI')
def test_get_info_string(self):
self.assertEqual(self.variant.get_info_string(),
'SVTYPE=BND;STRANDS=-+:9;IMAFLAG')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.get_info_string(),
'SVTYPE=BND;STRANDS=-+:9')
def test_get_format_string(self):
self.assertEqual(self.variant.get_format_string(), 'GT:SU')
def test_get_gt_string(self):
self.assertEqual(self.variant.get_gt_string(), '0/0:9 1/1:15')
def test_genotype(self):
self.assertEqual(
self.variant.genotype('NA12878').get_gt_string(), '0/0:9')
def test_genotypes(self):
self.assertEqual([x.get_gt_string() for x in self.variant.genotypes()],
['0/0:9', '1/1:15'])
def test_var_string(self):
self.assertEqual(self.variant.get_var_string(), self.variant_line)
self.variant.genotype('NA12878').set_format('GT', './.')
self.assertEqual(
self.variant.get_var_string(use_cached_gt_string=True),
self.variant_line)
self.assertNotEqual(self.variant.get_var_string(), self.variant_line)
class TestVariant(TestCase):
def setUp(self):
header_lines = [
"##fileformat=VCFv4.2",
"##fileDate=20151202",
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
"#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 NA0001",
]
self.vcf = Vcf()
self.vcf.add_header(header_lines)
self.variant_line = (
"1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:SU 0/0:9 1/1:15"
)
self.variant = Variant(self.variant_line.split("\t"), self.vcf)
def test_parse_genotypes(self):
genotype_field_strings = ["0/1:20", "0/0:15"]
parsed_dict = self.variant._parse_genotypes(genotype_field_strings)
na12878_gt = Genotype(self.variant, genotype_field_strings[0].split(":"))
na0001_gt = Genotype(self.variant, genotype_field_strings[1].split(":"))
expected_genotype_dict = {"NA12878": na12878_gt, "NA0001": na0001_gt}
self.assertEqual(parsed_dict, expected_genotype_dict)
def test_set_info(self):
self.variant.set_info("SVTYPE", "INV")
self.assertEqual(self.variant.info["SVTYPE"], "INV")
self.variant.set_info("IMAFLAG", False)
self.assertEqual(self.variant.info["IMAFLAG"], False)
with self.assertRaises(SystemExit) as cm:
self.variant.set_info("SUPER", True)
def test_get_info(self):
self.assertEqual(self.variant.get_info("IMAFLAG"), True)
self.assertEqual(self.variant.get_info("SVTYPE"), "BND")
with self.assertRaises(KeyError) as cm:
self.variant.get_info("CALI")
def test_get_info_string(self):
self.assertEqual(self.variant.get_info_string(), "SVTYPE=BND;STRANDS=-+:9;IMAFLAG")
self.variant.set_info("IMAFLAG", False)
self.assertEqual(self.variant.get_info_string(), "SVTYPE=BND;STRANDS=-+:9")
def test_get_format_string(self):
self.assertEqual(self.variant.get_format_string(), "GT:SU")
def test_get_format_string_caching(self):
header_lines = [
"##fileformat=VCFv4.2",
"##fileDate=20151202",
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=AS,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
"#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878",
]
vcf = Vcf()
vcf.add_header(header_lines)
variant_line = "1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:AS:SU 0/0:1:9"
variant = Variant(variant_line.split("\t"), vcf)
self.assertEqual(variant.get_format_string(), "GT:AS:SU")
gts = variant.genotypes()
self.assertEqual(variant.get_format_string(), "GT:SU:AS")
self.assertEqual(variant.get_format_string(True), "GT:AS:SU")
def test_get_gt_string(self):
self.assertEqual(self.variant.get_gt_string(), "0/0:9 1/1:15")
def test_genotype(self):
self.assertEqual(self.variant.genotype("NA12878").get_gt_string(), "0/0:9")
def test_set_genotype(self):
new_genotype = Genotype(self.variant, ["0/1", "9"])
self.variant.set_genotype("NA12878", new_genotype)
self.assertEqual(self.variant.genotype("NA12878").get_gt_string(), "0/1:9")
def test_genotypes(self):
self.assertEqual([x.get_gt_string() for x in self.variant.genotypes()], ["0/0:9", "1/1:15"])
def test_var_string(self):
self.assertEqual(self.variant.get_var_string(), self.variant_line)
self.variant.genotype("NA12878").set_format("GT", "./.")
self.assertEqual(self.variant.get_var_string(use_cached_gt_string=True), self.variant_line)
self.assertNotEqual(self.variant.get_var_string(), self.variant_line)
def test_var_string_format_caching(self):
header_lines = [
"##fileformat=VCFv4.2",
"##fileDate=20151202",
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=AS,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
"#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878",
]
vcf = Vcf()
vcf.add_header(header_lines)
variant_line = "1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:AS:SU 0/0:1:9"
uncached_line = "1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:SU:AS 0/0:9:1"
variant = Variant(variant_line.split("\t"), vcf)
gt = variant.genotypes() # force parsing
self.assertEqual(variant.get_var_string(), uncached_line)
self.assertEqual(variant.get_var_string(use_cached_gt_string=True), variant_line)
def test_add_genotype(self):
header_lines = [
"##fileformat=VCFv4.2",
"##fileDate=20151202",
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
"#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878",
]
vcf = Vcf()
vcf.add_header(header_lines)
variant_line = "1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG SU 9"
variant = Variant(variant_line.split("\t"), vcf)
self.assertEqual(variant.get_gt_string(), "./.:9")
def sv_classify(vcf_in, gender_file, exclude_file, ae_dict, f_overlap, slope_threshold, rsquared_threshold, het_del_fit, hom_del_fit, params, diag_outfile):
vcf_out = sys.stdout
vcf = Vcf()
header = []
in_header = True
min_pos_samps_for_regression = 10
sex = {}
# read sample genders
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
if diag_outfile is not None:
outf=open(diag_outfile, 'w', 4096)
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf_out.write(vcf.get_header() + '\n')
# split variant line, quick pre-check if the SVTYPE is BND, and skip if so
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL', 'DUP']:
vcf_out.write(line)
continue
# parse the VCF line
var = Variant(v, vcf, True)
# check intersection with mobile elements
if ae_dict is not None and var.info['SVTYPE'] in ['DEL']:
ae = annotation_intersect(var, ae_dict, f_overlap)
if ae is not None:
if ae.startswith('SINE') or ae.startswith('LINE') or ae.split('|')[2].startswith('SVA'):
ae = 'ME:' + ae
var.alt = '<DEL:%s>' % ae
var.info['SVTYPE'] = 'MEI'
vcf_out.write(var.get_var_string(True) + '\n')
continue
# for now, don't worry about sex chromosomes
if (var.chrom == 'X' or var.chrom == 'Y'):
vcf_out.write(line)
continue
#count positively genotyped samples
num_pos_samps = 0;
for s in var.sample_list:
if s in exclude:
continue
if var.genotype(s).get_format('GT') not in ["./.", "0/0"]:
num_pos_samps += 1
high_freq_support = False
low_freq_support = False
nb_support = False
if num_pos_samps == 0:
vcf_out.write(line)
else:
df=load_df(var, exclude, sex)
if has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params):
nb_support = True
if num_pos_samps < min_pos_samps_for_regression:
if has_low_freq_depth_support(df):
low_freq_support = True
vcf_out.write(line)
else:
for m_var in to_bnd_strings(var, True ):
vcf_out.write(m_var + '\n')
else:
if has_high_freq_depth_support(df, slope_threshold, rsquared_threshold):
high_freq_support = True
vcf_out.write(line)
else:
for m_var in to_bnd_strings(var, True):
vcf_out.write(m_var + '\n')
if diag_outfile is not None:
svlen=df['svlen'][0]
outf.write(var.var_id+"\t"+svtype+"\t"+str(svlen)+"\t"+str(num_pos_samps)+"\t"+str(nb_support)+"\t"+str(high_freq_support)+"\t"+str(low_freq_support)+"\n")
vcf_out.close()
if diag_outfile is not None:
outf.close()
return
def run_gt_refine(vcf_in, vcf_out, diag_outfile, gender_file, exclude_file):
vcf = Vcf()
header = []
in_header = True
sex = {}
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
outf = open(diag_outfile, 'w', 4096)
ct = 1
for line in vcf_in:
if in_header:
if line[0] == "#":
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf.add_info('MEDGQR', '1', 'Float',
'Median quality for refined GT')
vcf.add_info('Q10GQR', '1', 'Float',
'Q10 quality for refined GT')
vcf.add_format('GQR', 1, 'Float',
'Quality of refined genotype.')
vcf.add_format('GTR', 1, 'String', 'Refined genotype.')
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL']:
vcf_out.write(line)
continue
var = Variant(v, vcf)
sys.stderr.write("%s\n" % var.var_id)
sys.stderr.write("%f\n" % float(var.get_info('AF')))
if float(var.get_info('AF')) < 0.01:
vcf_out.write(line)
else:
df = load_df(var, exclude, sex)
recdf = recluster(df)
if ct == 1:
recdf.to_csv(outf, header=True)
ct += 1
else:
recdf.to_csv(outf, header=False)
var.set_info("MEDGQR",
'{:.2f}'.format(recdf.iloc[0, :].loc['med_gq_re']))
var.set_info("Q10GQR",
'{:.2f}'.format(recdf.iloc[0, :].loc['q10_gq_re']))
recdf.set_index('sample', inplace=True)
for s in var.sample_list:
if s in recdf.index:
var.genotype(s).set_format("GTR", recdf.loc[s, 'GTR'])
var.genotype(s).set_format(
"GQR", '{:.2f}'.format(recdf.loc[s, 'gq_re']))
else:
var.genotype(s).set_format("GTR", "./.")
var.genotype(s).set_format("GQR", 0)
vcf_out.write(
var.get_var_string(use_cached_gt_string=False) + '\n')
vcf_out.close()
vcf_in.close()
gender_file.close()
outf.close()
if exclude_file is not None:
exclude_file.close()
return
def run_gt_refine(vcf_in, vcf_out, diag_outfile, gender_file, exclude_file):
vcf = Vcf()
header = []
in_header = True
sex={}
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
outf=open(diag_outfile, 'w', 4096)
ct=1
for line in vcf_in:
if in_header:
if line[0] == "#":
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf.add_info('MEDGQR', '1', 'Float', 'Median quality for refined GT')
vcf.add_info('Q10GQR', '1', 'Float', 'Q10 quality for refined GT')
vcf.add_format('GQR', 1, 'Float', 'Quality of refined genotype.')
vcf.add_format('GTR', 1, 'String', 'Refined genotype.')
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL']:
vcf_out.write(line)
continue
var = Variant(v, vcf)
sys.stderr.write("%s\n" % var.var_id)
sys.stderr.write("%f\n" % float(var.get_info('AF')))
if float(var.get_info('AF'))<0.01:
vcf_out.write(line)
else:
df=load_df(var, exclude, sex)
recdf=recluster(df)
if ct==1:
recdf.to_csv(outf, header=True)
ct += 1
else:
recdf.to_csv(outf, header=False)
var.set_info("MEDGQR", '{:.2f}'.format(recdf.iloc[0,:].loc['med_gq_re']))
var.set_info("Q10GQR", '{:.2f}'.format(recdf.iloc[0,:].loc['q10_gq_re']))
recdf.set_index('sample', inplace=True)
for s in var.sample_list:
if s in recdf.index:
var.genotype(s).set_format("GTR", recdf.loc[s,'GTR'])
var.genotype(s).set_format("GQR", '{:.2f}'.format(recdf.loc[s,'gq_re']))
else:
var.genotype(s).set_format("GTR", "./.")
var.genotype(s).set_format("GQR", 0)
vcf_out.write(var.get_var_string(use_cached_gt_string=False) + '\n')
vcf_out.close()
vcf_in.close()
gender_file.close()
outf.close()
if exclude_file is not None:
exclude_file.close()
return
def sv_classify(vcf_in, vcf_out, gender_file, exclude_file, ae_dict, f_overlap, slope_threshold, rsquared_threshold, p_cnv, het_del_fit, hom_del_fit, params, diag_outfile, method):
vcf = Vcf()
header = []
in_header = True
sex = {}
# read sample genders
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
if diag_outfile is not None:
outf=open(diag_outfile, 'w', 4096)
outf.write("varid\torig_svtype\tsvlen\tnum_pos_samps\tnb_support\tls_support\thybrid_support\thas_rd_support\n")
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL', 'DUP']:
vcf_out.write(line)
continue
var = Variant(v, vcf)
# check intersection with mobile elements
if ae_dict is not None and var.info['SVTYPE'] in ['DEL']:
ae = annotation_intersect(var, ae_dict, f_overlap)
if ae is not None:
if ae.startswith('SINE') or ae.startswith('LINE') or ae.split('|')[2].startswith('SVA'):
ae = 'ME:' + ae
var.alt = '<DEL:%s>' % ae
var.info['SVTYPE'] = 'MEI'
vcf_out.write(var.get_var_string(True) + '\n')
continue
#count positively genotyped samples
num_pos_samps = 0
num_total_samps=len(var.sample_list)
for s in var.sample_list:
if var.genotype(s).get_format('GT') not in ["./.", "0/0"]:
num_pos_samps += 1
nb_support = False
ls_support = False
hybrid_support = False
has_rd_support = False
if num_pos_samps == 0:
vcf_out.write(line)
else:
df=load_df(var, exclude, sex)
if method=='large_sample':
ls_support = has_rd_support_by_ls(df, slope_threshold, rsquared_threshold, num_pos_samps)
has_rd_support=ls_support
elif method=='naive_bayes':
nb_support = has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params, p_cnv)
has_rd_support=nb_support
elif method=='hybrid':
ls_support, nb_support, hybrid_support = has_rd_support_hybrid(df, het_del_fit, hom_del_fit, params, p_cnv, slope_threshold, rsquared_threshold, num_pos_samps)
has_rd_support=hybrid_support
if has_rd_support:
vcf_out.write(line)
else:
for m_var in to_bnd_strings(var, True):
vcf_out.write(m_var + '\n')
if diag_outfile is not None:
svlen=df['svlen'][0]
outf.write(var.var_id+"\t"+svtype+"\t"+str(svlen)+"\t"+str(num_pos_samps)+"\t"+str(nb_support)+"\t"+str(ls_support)+"\t"+str(hybrid_support)+"\t"+str(has_rd_support)+"\n")
vcf_out.close()
if diag_outfile is not None:
outf.close()
vcf_in.close()
vcf_out.close()
gender_file.close()
if exclude_file is not None:
exclude_file.close()
return
def calc_params(vcf_path):
tSet = list()
epsilon=0.1
header=[]
in_header = True
vcf = Vcf()
if vcf_path.endswith('.gz'):
vcf_file = gzip.open(vcf_path, 'rb')
else:
vcf_file = open(vcf_path, 'r')
for line in vcf_file:
if in_header:
if line[0] == '#':
header.append(line)
if line[1] != '#':
vcf_samples = line.rstrip().split('\t')[9:]
in_header = False
vcf.add_header(header)
continue
else:
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
if svtype not in ['DEL', 'DUP'] or v[0]=="X" or v[0]=="Y":
continue
var = Variant(v, vcf)
for sample in vcf_samples:
sample_genotype = var.genotype(sample)
if sample_genotype.get_format('GT') != './.':
log2r = math.log((float(sample_genotype.get_format('CN'))+ epsilon)/2,2) #to avoid log(0)
tSet.append(CN_rec(var.var_id, sample, var.info['SVTYPE'], abs(float(var.info['SVLEN'])), var.info['AF'],
sample_genotype.get_format('GT'), sample_genotype.get_format('CN'), sample_genotype.get_format('AB'), math.log(abs(float(var.info['SVLEN']))), log2r))
df=pd.DataFrame(tSet, columns=CN_rec._fields)
#exclude from training data, DELs and DUPs with CN in the tails of the distribution
df['q_low']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(lowQuantile)
df['q_high']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(highQuantile)
df=df[(df.log2r>=df.q_low) & (df.log2r<=df.q_high)]
#df.to_csv('./train.csv')
#adjust copy number for small deletions (<1kb), no strong relationship b/w cn and size for dups evident so far
small_het_dels = df[(df.svtype=="DEL") & (df.GT=="0/1") & (df.svlen<1000) & (df.svlen>=50)]
small_hom_dels = df[(df.svtype=="DEL") & (df.GT=="1/1") & (df.svlen<1000) & (df.svlen>=50)]
het_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="0/1") & (df.svtype=="DEL")]['log2r'])
hom_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="1/1") & (df.svtype=="DEL")]['log2r'])
small_het_dels['offset']=small_het_dels['log2r']-het_del_mean
small_hom_dels['offset']=small_hom_dels['log2r']-hom_del_mean
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
hom_del_fit=smf.ols('offset~log_len',small_hom_dels).fit()
het_del_fit=smf.ols('offset~log_len',small_het_dels).fit()
#print hom_del_fit.summary()
#print het_del_fit.summary()
small_hom_dels['log2r_adj'] = small_hom_dels['log2r'] - hom_del_fit.predict(small_hom_dels)
small_het_dels['log2r_adj'] = small_het_dels['log2r'] - het_del_fit.predict(small_het_dels)
small_dels=small_hom_dels.append(small_het_dels)
small_dels=small_dels[['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN', 'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj']]
# dels of length<100 bp are excluded here
df1=df[(df.svtype!="DEL") | (df.GT=="0/0") | (df.svlen>=1000)]
df1['log2r_adj']=df1['log2r']
df1=df1.append(small_dels)
params=df1.groupby(['sample', 'svtype', 'GT'])['log2r_adj'].aggregate([np.mean,np.var, len]).reset_index()
params=pd.pivot_table(params, index=['sample', 'svtype'], columns='GT', values=['mean', 'var', 'len']).reset_index()
params.columns=['sample', 'svtype', 'mean0', 'mean1', 'mean2', 'var0', 'var1', 'var2', 'len0', 'len1', 'len2']
params['std_pooled']=np.sqrt((params['var0']*params['len0']+params['var1']*params['len1']+params['var2']*params['len2'])/(params['len0']+params['len1']+params['len2']))
#params.to_csv('./params.csv')
return (params, het_del_fit, hom_del_fit)
class TestVariant(TestCase):
def setUp(self):
header_lines = [
'##fileformat=VCFv4.2',
'##fileDate=20151202',
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
'#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878 NA0001' ]
self.vcf = Vcf()
self.vcf.add_header(header_lines)
self.variant_line = '1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG GT:SU 0/0:9 1/1:15'
self.variant = Variant(self.variant_line.split('\t'), self.vcf)
def test_parse_genotypes(self):
genotype_field_strings = ['0/1:20', '0/0:15']
parsed_dict = self.variant._parse_genotypes(genotype_field_strings)
na12878_gt = Genotype(self.variant, genotype_field_strings[0].split(':'))
na0001_gt = Genotype(self.variant, genotype_field_strings[1].split(':'))
expected_genotype_dict = { 'NA12878': na12878_gt, 'NA0001': na0001_gt }
self.assertEqual(parsed_dict, expected_genotype_dict)
def test_set_info(self):
self.variant.set_info('SVTYPE', 'INV')
self.assertEqual(self.variant.info['SVTYPE'], 'INV')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.info['IMAFLAG'], False)
with self.assertRaises(SystemExit) as cm:
self.variant.set_info('SUPER', True)
def test_get_info(self):
self.assertEqual(self.variant.get_info('IMAFLAG'), True)
self.assertEqual(self.variant.get_info('SVTYPE'), 'BND')
with self.assertRaises(KeyError) as cm:
self.variant.get_info('CALI')
def test_get_info_string(self):
self.assertEqual(self.variant.get_info_string(), 'SVTYPE=BND;STRANDS=-+:9;IMAFLAG')
self.variant.set_info('IMAFLAG', False)
self.assertEqual(self.variant.get_info_string(), 'SVTYPE=BND;STRANDS=-+:9')
def test_get_format_string(self):
self.assertEqual(self.variant.get_format_string(), 'GT:SU')
def test_get_gt_string(self):
self.assertEqual(self.variant.get_gt_string(), '0/0:9 1/1:15')
def test_genotype(self):
self.assertEqual(self.variant.genotype('NA12878').get_gt_string(), '0/0:9')
def test_genotypes(self):
self.assertEqual([ x.get_gt_string() for x in self.variant.genotypes() ], ['0/0:9', '1/1:15'])
def test_var_string(self):
self.assertEqual(self.variant.get_var_string(), self.variant_line)
self.variant.genotype('NA12878').set_format('GT', './.')
self.assertEqual(self.variant.get_var_string(use_cached_gt_string=True), self.variant_line)
self.assertNotEqual(self.variant.get_var_string(), self.variant_line)
def test_add_genotype(self):
header_lines = [
'##fileformat=VCFv4.2',
'##fileDate=20151202',
'##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">',
'##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">',
'##INFO=<ID=IMAFLAG,Number=.,Type=Flag,Description="Test Flag code">',
'##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">',
'##FORMAT=<ID=INACTIVE,Number=1,Type=Integer,Description="A format not in use">',
'#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA12878' ]
vcf = Vcf()
vcf.add_header(header_lines)
variant_line = '1 820915 5838_1 N ]GL000232.1:20940]N 0.00 . SVTYPE=BND;STRANDS=-+:9;IMAFLAG SU 9'
variant = Variant(variant_line.split('\t'), vcf)
self.assertEqual(variant.get_gt_string(), './.:9')
def sv_classify(vcf_in, vcf_out, gender_file, sex_chrom_names, exclude_file, ae_dict, f_overlap, slope_threshold, rsquared_threshold, p_cnv, het_del_fit, hom_del_fit, params, diag_outfile, method):
vcf = Vcf()
header = []
in_header = True
sex = {}
# read sample genders
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
if diag_outfile is not None:
outf=open(diag_outfile, 'w', 4096)
outf.write("varid\torig_svtype\tsvlen\tnum_pos_samps\tnb_support\tls_support\thybrid_support\thas_rd_support\n")
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL', 'DUP']:
vcf_out.write(line)
continue
var = Variant(v, vcf)
# check intersection with mobile elements
if ae_dict is not None and var.info['SVTYPE'] in ['DEL']:
ae = annotation_intersect(var, ae_dict, f_overlap)
if ae is not None:
if ae.startswith('SINE') or ae.startswith('LINE') or ae.split('|')[2].startswith('SVA'):
ae = 'ME:' + ae
var.alt = '<DEL:%s>' % ae
var.info['SVTYPE'] = 'MEI'
vcf_out.write(var.get_var_string(True) + '\n')
continue
#count positively genotyped samples
num_pos_samps = 0
num_total_samps=len(var.sample_list)
for s in var.sample_list:
if var.genotype(s).get_format('GT') not in ["./.", "0/0"]:
num_pos_samps += 1
nb_support = False
ls_support = False
hybrid_support = False
has_rd_support = False
if num_pos_samps == 0:
vcf_out.write(line)
else:
df = load_df(var, exclude, sex, sex_chrom_names)
if method == 'large_sample':
ls_support = has_rd_support_by_ls(df, slope_threshold, rsquared_threshold, num_pos_samps)
has_rd_support = ls_support
elif method == 'naive_bayes':
nb_support = has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params, p_cnv)
has_rd_support = nb_support
elif method == 'hybrid':
ls_support, nb_support, hybrid_support = has_rd_support_hybrid(
df,
het_del_fit,
hom_del_fit,
params, p_cnv,
slope_threshold,
rsquared_threshold,
num_pos_samps
)
has_rd_support=hybrid_support
if has_rd_support:
vcf_out.write(line)
else:
for m_var in to_bnd_strings(var, True):
vcf_out.write(m_var + '\n')
if diag_outfile is not None:
svlen=df['svlen'][0]
outf.write(
'\t'.join((
var.var_id,
svtype,
str(svlen),
str(num_pos_samps),
str(nb_support),
str(ls_support),
str(hybrid_support),
str(has_rd_support)
)) + "\n"
)
vcf_out.close()
if diag_outfile is not None:
outf.close()
vcf_in.close()
vcf_out.close()
gender_file.close()
if exclude_file is not None:
exclude_file.close()
return
def calc_params(vcf_path, sex_chrom_names):
tSet = list()
epsilon=0.1
header=[]
in_header = True
vcf = Vcf()
if vcf_path.endswith('.gz'):
vcf_file = gzip.open(vcf_path, 'rb')
else:
vcf_file = open(vcf_path, 'r')
for line in vcf_file:
if in_header:
if line[0] == '#':
header.append(line)
if line[1] != '#':
vcf_samples = line.rstrip().split('\t')[9:]
in_header = False
vcf.add_header(header)
continue
else:
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
if svtype not in ['DEL', 'DUP'] or v[0] in sex_chrom_names:
continue
var = Variant(v, vcf)
for sample in vcf_samples:
sample_genotype = var.genotype(sample)
if sample_genotype.get_format('GT') != './.':
log2r = math.log((float(sample_genotype.get_format('CN'))+ epsilon)/2,2) #to avoid log(0)
tSet.append(
CN_rec(
var.var_id,
sample,
var.info['SVTYPE'],
abs(float(var.info['SVLEN'])),
var.info['AF'],
sample_genotype.get_format('GT'),
sample_genotype.get_format('CN'),
sample_genotype.get_format('AB'),
math.log(abs(float(var.info['SVLEN']))), log2r
)
)
df=pd.DataFrame(tSet, columns=CN_rec._fields)
#exclude from training data, DELs and DUPs with CN in the tails of the distribution
df.loc[:,'q_low']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(lowQuantile)
df.loc[:,'q_high']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(highQuantile)
df=df[(df.log2r>=df.q_low) & (df.log2r<=df.q_high)]
#df.to_csv('./train.csv')
#adjust copy number for small deletions (<1kb), no strong relationship b/w cn and size for dups evident so far
small_het_dels = df[(df.svtype=="DEL") & (df.GT=="0/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
small_hom_dels = df[(df.svtype=="DEL") & (df.GT=="1/1") & (df.svlen<1000) & (df.svlen>=50)].copy()
het_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="0/1") & (df.svtype=="DEL")]['log2r'])
hom_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="1/1") & (df.svtype=="DEL")]['log2r'])
small_het_dels.loc[:,'offset']=small_het_dels.loc[:,'log2r']-het_del_mean
small_hom_dels.loc[:,'offset']=small_hom_dels.loc[:,'log2r']-hom_del_mean
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
hom_del_fit=smf.ols('offset~log_len',small_hom_dels).fit()
het_del_fit=smf.ols('offset~log_len',small_het_dels).fit()
#print hom_del_fit.summary()
#print het_del_fit.summary()
small_hom_dels.loc[:,'log2r_adj'] = small_hom_dels.loc[:,'log2r'] - hom_del_fit.predict(small_hom_dels)
small_het_dels.loc[:,'log2r_adj'] = small_het_dels.loc[:,'log2r'] - het_del_fit.predict(small_het_dels)
small_dels=small_hom_dels.append(small_het_dels)
small_dels=small_dels[['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN', 'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj']]
# dels of length<100 bp are excluded here
df1=df.loc[(df.svtype!="DEL") | (df.GT=="0/0") | (df.svlen>=1000), :].copy()
df1.loc[:,'log2r_adj']=df1.loc[:,'log2r']
df1=df1.append(small_dels)
params=df1.groupby(['sample', 'svtype', 'GT'])['log2r_adj'].aggregate([np.mean,np.var, len]).reset_index()
params=pd.pivot_table(params, index=['sample', 'svtype'], columns='GT', values=['mean', 'var', 'len']).reset_index()
params.columns=['sample', 'svtype', 'mean0', 'mean1', 'mean2', 'var0', 'var1', 'var2', 'len0', 'len1', 'len2']
params['std_pooled'] = np.sqrt((params['var0']*params['len0']+params['var1']*params['len1']+params['var2']*params['len2'])/(params['len0']+params['len1']+params['len2']))
#params.to_csv('./params.csv')
return (params, het_del_fit, hom_del_fit)
def execute(self, output_handle=sys.stdout):
in_header = True
header = []
vcf = Vcf()
vcf_out = output_handle
# read input VCF
for line in self.vcf_stream:
if in_header:
if line.startswith('##'):
header.append(line)
continue
elif line.startswith('#CHROM'):
v = line.rstrip().split('\t')
header.append('\t'.join(v))
in_header = False
vcf.add_header(header)
vcf.add_info('AF', 'A', 'Float', 'Allele Frequency, for each ALT allele, in the same order as listed')
vcf.add_info('NSAMP', '1', 'Integer', 'Number of samples with non-reference genotypes')
vcf.add_info('MSQ', '1', 'Float', 'Mean sample quality of positively genotyped samples')
# write header
vcf_out.write(vcf.get_header() + '\n')
#vcf_out.write('\t' + '\t'.join(v[8:]) + '\n')
continue
v = line.rstrip().split('\t')
var = Variant(v, vcf, fixed_genotypes=True)
# extract genotypes from VCF
num_alt = len(var.alt.split(','))
alleles = [0] * (num_alt + 1)
num_samp = 0
gt = [var.genotype(s).get_format('GT') for s in var.sample_list]
for gt_string in gt:
if '.' in gt_string:
continue
gt = gt_string.split('/')
if len(gt) == 1:
gt = gt_string.split('|')
gt = map(int, gt)
for i in xrange(len(gt)):
alleles[gt[i]] += 1
# iterate the number of non-reference samples
if sum(gt) > 0:
num_samp += 1
allele_sum = float(sum(alleles))
allele_freq = ['.'] * len(alleles)
# populate AF
if allele_sum > 0:
for i in xrange(len(alleles)):
allele_freq[i] = alleles[i] / allele_sum
var.info['AF'] = ','.join(map(str, ['%.4g' % a for a in allele_freq[1:]]))
else:
var.info['AF'] = ','.join(map(str, allele_freq[1:]))
# populate NSAMP
var.info['NSAMP'] = num_samp
var.info['MSQ'] = self.calc_msq(var)
# after all samples have been processed, write
vcf_out.write(var.get_var_string(use_cached_gt_string=True) + '\n')
vcf_out.close()
def sv_classify(vcf_in, gender_file, exclude_file, ae_dict, f_overlap, slope_threshold, rsquared_threshold):
vcf_out = sys.stdout
vcf = Vcf()
header = []
in_header = True
min_pos_samps_for_regression = 10
gender = {}
# read sample genders
for line in gender_file:
v = line.rstrip().split('\t')
gender[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
for line in vcf_in:
if in_header:
if line[0] == '#':
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
# write the output header
vcf_out.write(vcf.get_header() + '\n')
# split variant line, quick pre-check if the SVTYPE is BND, and skip if so
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL or DUP prior to reclassification
if svtype not in ['DEL', 'DUP']:
vcf_out.write(line)
continue
# parse the VCF line
var = Variant(v, vcf, True)
# check intersection with mobile elements
if ae_dict is not None and var.info['SVTYPE'] in ['DEL']:
ae = annotation_intersect(var, ae_dict, f_overlap)
if ae is not None:
if ae.startswith('SINE') or ae.startswith('LINE') or ae.split('|')[2].startswith('SVA'):
ae = 'ME:' + ae
var.alt = '<DEL:%s>' % ae
var.info['SVTYPE'] = 'MEI'
vcf_out.write(var.get_var_string(True) + '\n')
continue
# # write to directory
# writedir = 'data/r11.100kb.dup'
# annotate based on read depth
if var.info['SVTYPE'] in ['DEL', 'DUP']:
# count the number of positively genotyped samples
num_pos_samps = 0;
for s in var.sample_list:
if s in exclude:
continue
if var.genotype(s).get_format('GT') not in ["./.", "0/0"]:
num_pos_samps += 1
if num_pos_samps < min_pos_samps_for_regression:
if has_low_freq_depth_support(var, gender, exclude):
# has_low_freq_depth_support(var, gender, exclude, writedir + '/low_freq_rd')
# has_high_freq_depth_support(var, gender, exclude, slope_threshold, rsquared_threshold, writedir + '/low_freq_rd')
# write variant
#vcf_out.write(var.get_var_string(True) + '\n')
vcf_out.write(line)
else:
# has_low_freq_depth_support(var, gender, exclude, writedir + '/low_freq_no_rd')
# has_high_freq_depth_support(var, gender, exclude, slope_threshold, rsquared_threshold, writedir + '/low_freq_no_rd')
for m_var in to_bnd_strings(var):
vcf_out.write(m_var + '\n')
else:
if has_high_freq_depth_support(var, gender, exclude, slope_threshold, rsquared_threshold):
# has_high_freq_depth_support(var, gender, exclude, slope_threshold, rsquared_threshold, writedir + '/high_freq_rd')
# has_low_freq_depth_support(var, gender, exclude, writedir + '/high_freq_rd')
# write variant
#vcf_out.write(var.get_var_string(True) + '\n')
vcf_out.write(line)
else:
# has_high_freq_depth_support(var, gender, exclude, slope_threshold, rsquared_threshold, writedir + '/high_freq_no_rd')
# has_low_freq_depth_support(var, gender, exclude, writedir + '/high_freq_no_rd')
for m_var in to_bnd_strings(var):
vcf_out.write(m_var + '\n')
vcf_out.close()
return
def run_gt_refine(vcf_in, vcf_out, diag_outfile, gender_file, exclude_file, batch_file):
vcf = Vcf()
header = []
in_header = True
sex = {}
for line in gender_file:
v = line.rstrip().split('\t')
sex[v[0]] = int(v[1])
exclude = []
if exclude_file is not None:
for line in exclude_file:
exclude.append(line.rstrip())
batch = dict()
if batch_file is not None:
for line in batch_file:
fields = line.rstrip().split('\t')
if fields[1] == 'None':
raise RuntimeError('Batch file contains a batch label of None. This label is reserved.')
batch[fields[0]] = fields[1]
outf = open(diag_outfile, 'w', 4096)
ct = 1
for line in vcf_in:
if in_header:
if line[0] == "#":
header.append(line)
continue
else:
in_header = False
vcf.add_header(header)
vcf.add_info('MEDGQR', '1', 'Float', 'Median quality for refined GT')
vcf.add_info('Q10GQR', '1', 'Float', 'Q10 quality for refined GT')
vcf.add_format('GQO', 1, 'Integer', 'Quality of original genotype')
vcf.add_format('GTO', 1, 'String', 'Genotype before refinement')
vcf_out.write(vcf.get_header() + '\n')
v = line.rstrip().split('\t')
info = v[7].split(';')
svtype = None
for x in info:
if x.startswith('SVTYPE='):
svtype = x.split('=')[1]
break
# bail if not DEL prior to reclassification
# DUPs can be quite complicated in their allelic structure
# and thus less amenable to refinement by clustering in many cases
# INV and BNDs are also unclear.
# See earlier commits for code of previous attempts to refine these.
if svtype not in ['DEL', 'MEI']:
vcf_out.write(line)
continue
var = Variant(v, vcf)
sys.stderr.write("%s\n" % var.var_id)
sys.stderr.write("%f\n" % float(var.get_info('AF')))
if float(var.get_info('AF')) < 0.01:
vcf_out.write(line)
else:
df = load_df(var, exclude, sex, batch)
recdf = recluster(df)
if ct == 1:
recdf.to_csv(outf, header=True)
ct += 1
else:
recdf.to_csv(outf, header=False)
var.set_info("MEDGQR", '{:.2f}'.format(recdf.iloc[0, :].loc['med_gq_re']))
var.set_info("Q10GQR", '{:.2f}'.format(recdf.iloc[0, :].loc['q10_gq_re']))
recdf.set_index('sample', inplace=True)
for s in var.sample_list:
g = var.genotype(s)
g.set_format("GTO", g.get_format("GT"))
g.set_format("GQO", g.get_format("GQ"))
if s in recdf.index:
var.genotype(s).set_format("GT", recdf.loc[s, 'GTR'])
var.genotype(s).set_format("GQ", '{:.0f}'.format(recdf.loc[s, 'gq_re']))
else:
var.genotype(s).set_format("GT", "./.")
var.genotype(s).set_format("GQ", 0)
vcf_out.write(var.get_var_string(use_cached_gt_string=False) + '\n')
vcf_out.close()
vcf_in.close()
gender_file.close()
outf.close()
if exclude_file is not None:
exclude_file.close()
return
