def __init__(self,
output_path,
header=None,
round_qualities=False,
excluded_info_fields=None,
excluded_format_fields=None):
"""Initializer for NativeVcfWriter.
Args:
output_path: str. The path to which to write the VCF file.
header: nucleus.genomics.v1.VcfHeader. The header that defines all
information germane to the constituent variants. This includes contigs,
FILTER fields, INFO fields, FORMAT fields, samples, and all other
structured and unstructured header lines.
round_qualities: bool. If True, the QUAL field is rounded to one point
past the decimal.
excluded_info_fields: list(str). A list of INFO field IDs that should not
be written to the output. If None, all INFO fields are included.
excluded_format_fields: list(str). A list of FORMAT field IDs that should
not be written to the output. If None, all FORMAT fields are included.
"""
super(NativeVcfWriter, self).__init__()
if header is None:
header = variants_pb2.VcfHeader()
writer_options = variants_pb2.VcfWriterOptions(
round_qual_values=round_qualities,
excluded_info_fields=excluded_info_fields,
excluded_format_fields=excluded_format_fields,
)
self._writer = vcf_writer.VcfWriter.to_file(output_path, header,
writer_options)
self.field_access_cache = VcfHeaderCache(header)
def setUp(self):
self.out_fname = test_utils.test_tmpfile('output.vcf')
self.header = variants_pb2.VcfHeader(
contigs=[
reference_pb2.ContigInfo(name='Chr1',
n_bases=50,
pos_in_fasta=0),
reference_pb2.ContigInfo(name='Chr2',
n_bases=25,
pos_in_fasta=1),
],
sample_names=['Fido', 'Spot'],
formats=[
variants_pb2.VcfFormatInfo(id='GT',
number='1',
type='String',
description='Genotype'),
variants_pb2.VcfFormatInfo(id='GQ',
number='1',
type='Float',
description='Genotype Quality')
],
)
self.options = variants_pb2.VcfWriterOptions()
self.writer = vcf_writer.VcfWriter.to_file(self.out_fname, self.header,
self.options)
self.variant = test_utils.make_variant(
chrom='Chr1',
start=10,
alleles=['A', 'C'],
)
self.variant.calls.extend([
variants_pb2.VariantCall(genotype=[0, 0], call_set_name='Fido'),
variants_pb2.VariantCall(genotype=[0, 1], call_set_name='Spot'),
])
def deepvariant_header(contigs, sample_names):
"""Returns a VcfHeader used for writing VCF output.
This function fills out the FILTER, INFO, FORMAT, and extra header information
created by the DeepVariant pipeline using consistent fields that DeepVariant
creates. The `contigs` and `sample_names` fields are unique depending on the
input data used, so are required inputs.
Args:
contigs: list(ContigInfo). The list of contigs on which variants were
called.
sample_names: list(str). The list of samples present in the run.
Returns:
A nucleus.genomics.v1.VcfHeader proto with known fixed headers and the given
samples and contigs populated.
"""
version = variants_pb2.VcfExtra(key='DeepVariant_version',
value=DEEP_VARIANT_VERSION)
return variants_pb2.VcfHeader(
fileformat='VCFv4.2',
filters=[
vcf_constants.reserved_filter_field(DEEP_VARIANT_PASS),
variants_pb2.VcfFilterInfo(
id=DEEP_VARIANT_REF_FILTER,
description='Genotyping model thinks this site is reference.'),
variants_pb2.VcfFilterInfo(
id=DEEP_VARIANT_QUAL_FILTER,
description='Confidence in this variant being real is below '
'calling threshold.'),
],
infos=[
vcf_constants.reserved_info_field('END'),
],
formats=[
vcf_constants.reserved_format_field('GT'),
vcf_constants.reserved_format_field('GQ'),
vcf_constants.reserved_format_field('DP'),
variants_pb2.VcfFormatInfo(
id=DEEP_VARIANT_MIN_DP_FORMAT,
number='1',
type='Integer',
description='Minimum DP observed within the GVCF block.'),
vcf_constants.reserved_format_field('AD'),
variants_pb2.VcfFormatInfo(
id=DEEP_VARIANT_VAF_FORMAT,
number='A',
type='Float',
description='Variant allele fractions.'),
vcf_constants.reserved_format_field('PL'),
],
contigs=contigs,
sample_names=sample_names,
extras=[version])
def __init__(self, header):
"""Initializer.
Args:
header: nucleus.genomics.v1.VcfHeader proto. Used to define the accessor
functions needed.
"""
if header is None:
header = variants_pb2.VcfHeader()
self._info_get_cache = _create_get_fn_cache(header.infos)
self._info_set_cache = _create_set_fn_cache(header.infos)
self._format_get_cache = _create_get_fn_cache(header.formats)
self._format_set_cache = _create_set_fn_cache(header.formats)
def _add_class_to_variant(variant, class_status):
if class_status is None:
return variant
header = variants_pb2.VcfHeader(
infos=[
variants_pb2.VcfInfo(
id=CUSTOMIZED_INFO_FIELD_NAME,
number='A',
type=vcf_constants.STRING_TYPE,
description='Customized class label for the variant.')])
my_cache = vcf.VcfHeaderCache(header)
vcf_object = FakeVCFObject(field_access_cache=my_cache)
variant_utils.set_info(variant, CUSTOMIZED_INFO_FIELD_NAME, class_status,
vcf_object=vcf_object)
return variant
def setUp(self):
self.variants = [
test_utils.make_variant(chrom='1', start=10),
test_utils.make_variant(chrom='1', start=20),
test_utils.make_variant(chrom='1', start=30),
test_utils.make_variant(chrom='2', start=25),
test_utils.make_variant(chrom='2', start=55),
test_utils.make_variant(chrom='3', start=10),
]
self.header = variants_pb2.VcfHeader(contigs=[
reference_pb2.ContigInfo(name='1', n_bases=100),
reference_pb2.ContigInfo(name='2', n_bases=100),
reference_pb2.ContigInfo(name='3', n_bases=100),
reference_pb2.ContigInfo(name='4', n_bases=100),
],
filters=[],
sample_names=['NA12878'])
self.reader = vcf.InMemoryVcfReader(self.variants, self.header)
def write_variant_to_tempfile(self, variant):
output_path = test_utils.test_tmpfile('test.vcf')
header = variants_pb2.VcfHeader(
contigs=[reference_pb2.ContigInfo(name='20')],
sample_names=[call.call_set_name for call in variant.calls],
formats=[
variants_pb2.VcfFormatInfo(
id='DP', number='1', type='Integer', description='Read depth'),
variants_pb2.VcfFormatInfo(
id='AD',
number='R',
type='Integer',
description='Read depth for each allele')
])
writer = vcf.VcfWriter(output_path, header=header)
with writer:
writer.write(variant)
return output_path
def test_writing_canned_variants(self):
"""Tests writing all the variants that are 'canned' in our tfrecord file."""
# This file is in the TF record format
tfrecord_file = test_utils.genomics_core_testdata(
'test_samples.vcf.golden.tfrecord')
writer_options = variants_pb2.VcfWriterOptions()
header = variants_pb2.VcfHeader(
contigs=[
reference_pb2.ContigInfo(name='chr1', n_bases=248956422),
reference_pb2.ContigInfo(name='chr2', n_bases=242193529),
reference_pb2.ContigInfo(name='chr3', n_bases=198295559),
reference_pb2.ContigInfo(name='chrX', n_bases=156040895)
],
sample_names=['NA12878_18_99'],
filters=[
variants_pb2.VcfFilterInfo(id='PASS',
description='All filters passed'),
variants_pb2.VcfFilterInfo(id='LowQual', description=''),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL95.00to96.00'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL96.00to97.00'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL97.00to99.00'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL99.00to99.50'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL99.50to99.90'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL99.90to99.95'),
variants_pb2.VcfFilterInfo(
id='VQSRTrancheINDEL99.95to100.00+'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheINDEL99.95to100.00'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.50to99.60'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.60to99.80'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.80to99.90'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.90to99.95'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.95to100.00+'),
variants_pb2.VcfFilterInfo(id='VQSRTrancheSNP99.95to100.00'),
],
infos=[
variants_pb2.VcfInfo(
id='END',
number='1',
type='Integer',
description='Stop position of the interval')
],
formats=[
variants_pb2.VcfFormatInfo(id='GT',
number='1',
type='String',
description='Genotype'),
variants_pb2.VcfFormatInfo(id='GQ',
number='1',
type='Integer',
description='Genotype Quality'),
variants_pb2.VcfFormatInfo(
id='DP',
number='1',
type='Integer',
description='Read depth of all passing filters reads.'),
variants_pb2.VcfFormatInfo(
id='MIN_DP',
number='1',
type='Integer',
description='Minimum DP observed within the GVCF block.'),
variants_pb2.VcfFormatInfo(
id='AD',
number='R',
type='Integer',
description=
'Read depth of all passing filters reads for each allele.'
),
variants_pb2.VcfFormatInfo(
id='VAF',
number='A',
type='Float',
description='Variant allele fractions.'),
variants_pb2.VcfFormatInfo(
id='PL',
number='G',
type='Integer',
description='Genotype likelihoods, Phred encoded'),
],
)
variant_records = list(
io_utils.read_tfrecords(tfrecord_file, proto=variants_pb2.Variant))
out_fname = test_utils.test_tmpfile('output.vcf')
with vcf_writer.VcfWriter.to_file(out_fname, header,
writer_options) as writer:
for record in variant_records[:5]:
writer.write(record)
# Check: are the variants written as expected?
# pylint: disable=line-too-long
expected_vcf_content = [
'##fileformat=VCFv4.2\n',
'##FILTER=<ID=PASS,Description="All filters passed">\n',
'##FILTER=<ID=LowQual,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL95.00to96.00,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL96.00to97.00,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL97.00to99.00,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL99.00to99.50,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL99.50to99.90,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL99.90to99.95,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL99.95to100.00+,Description="">\n',
'##FILTER=<ID=VQSRTrancheINDEL99.95to100.00,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.50to99.60,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.60to99.80,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.80to99.90,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.90to99.95,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.95to100.00+,Description="">\n',
'##FILTER=<ID=VQSRTrancheSNP99.95to100.00,Description="">\n',
'##INFO=<ID=END,Number=1,Type=Integer,Description="Stop position of '
'the interval">\n',
'##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">\n',
'##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">\n',
'##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read depth of all '
'passing filters reads.">\n',
'##FORMAT=<ID=MIN_DP,Number=1,Type=Integer,Description="Minimum DP '
'observed within the GVCF block.">\n',
'##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Read depth of all '
'passing filters reads for each allele.">\n',
'##FORMAT=<ID=VAF,Number=A,Type=Float,Description=\"Variant allele '
'fractions.">\n',
'##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Genotype '
'likelihoods, Phred encoded">\n',
'##contig=<ID=chr1,length=248956422>\n',
'##contig=<ID=chr2,length=242193529>\n',
'##contig=<ID=chr3,length=198295559>\n',
'##contig=<ID=chrX,length=156040895>\n',
'#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tNA12878_18_99\n',
'chr1\t13613\t.\tT\tA\t39.88\tVQSRTrancheSNP99.90to99.95\t.\tGT:GQ:DP:AD:PL\t0/1:16:4:1,3:68,0,16\n',
'chr1\t13813\t.\tT\tG\t90.28\tPASS\t.\tGT:GQ:DP:AD:PL\t1/1:9:3:0,3:118,9,0\n',
'chr1\t13838\trs28428499\tC\tT\t62.74\tPASS\t.\tGT:GQ:DP:AD:PL\t1/1:6:2:0,2:90,6,0\n',
'chr1\t14397\trs756427959\tCTGT\tC\t37.73\tPASS\t.\tGT:GQ:DP:AD:PL\t0/1:75:5:3,2:75,0,152\n',
'chr1\t14522\t.\tG\tA\t49.77\tVQSRTrancheSNP99.60to99.80\t.\tGT:GQ:DP:AD:PL\t0/1:78:10:6,4:78,0,118\n'
]
# pylint: enable=line-too-long
with gfile.GFile(out_fname, 'r') as f:
self.assertEqual(f.readlines(), expected_vcf_content)
def deepvariant_header(contigs,
sample_names,
add_info_candidates=False,
include_med_dp=True):
"""Returns a VcfHeader used for writing VCF output.
This function fills out the FILTER, INFO, FORMAT, and extra header information
created by the DeepVariant pipeline using consistent fields that DeepVariant
creates. The `contigs` and `sample_names` fields are unique depending on the
input data used, so are required inputs.
Args:
contigs: list(ContigInfo). The list of contigs on which variants were
called.
sample_names: list(str). The list of samples present in the run.
add_info_candidates: Adds the 'CANDIDATES' info field for
debugging purposes.
include_med_dp: boolean. If True, we will include MED_DP.
Returns:
A nucleus.genomics.v1.VcfHeader proto with known fixed headers and the given
samples and contigs populated.
"""
version = variants_pb2.VcfExtra(
key='DeepVariant_version', value=DEEP_VARIANT_VERSION)
info_fields = [
vcf_constants.reserved_info_field('END'),
]
formats = [
vcf_constants.reserved_format_field('GT'),
vcf_constants.reserved_format_field('GQ'),
vcf_constants.reserved_format_field('DP'),
variants_pb2.VcfFormatInfo(
id=DEEP_VARIANT_MIN_DP_FORMAT,
number='1',
type='Integer',
description='Minimum DP observed within the GVCF block.'),
vcf_constants.reserved_format_field('AD'),
variants_pb2.VcfFormatInfo(
id=DEEP_VARIANT_VAF_FORMAT,
number='A',
type='Float',
description='Variant allele fractions.'),
vcf_constants.reserved_format_field('PL'),
]
if add_info_candidates:
info_fields.append(
variants_pb2.VcfInfo(
id='CANDIDATES',
number='1',
type=vcf_constants.STRING_TYPE,
description='pipe-delimited candidate alleles.'))
if include_med_dp:
formats.append(
variants_pb2.VcfFormatInfo(
id=DEEP_VARIANT_MED_DP_FORMAT,
number='1',
type='Integer',
description='Median DP observed within the GVCF block '
'rounded to the nearest integer.'))
return variants_pb2.VcfHeader(
fileformat='VCFv4.2',
filters=[
vcf_constants.reserved_filter_field(DEEP_VARIANT_PASS),
variants_pb2.VcfFilterInfo(
id=DEEP_VARIANT_REF_FILTER,
description='Genotyping model thinks this site is reference.'),
variants_pb2.VcfFilterInfo(
id=DEEP_VARIANT_QUAL_FILTER,
description='Confidence in this variant being real is below '
'calling threshold.'),
variants_pb2.VcfFilterInfo(
id=DEEP_VARIANT_NO_CALL,
description='Site has depth=0 resulting in no call.'),
],
infos=info_fields,
formats=formats,
contigs=contigs,
sample_names=sample_names,
extras=[version])
