def setUp(self):
tfrecord_file = test_utils.genomics_core_testdata(
'test_features.gff.tfrecord')
self.records = list(
io_utils.read_tfrecords(tfrecord_file, proto=gff_pb2.GffRecord))
self.header = gff_pb2.GffHeader(
sequence_regions=[ranges.make_range('ctg123', 0, 1497228)])
def _transform_call_variants_output_to_variants(input_sorted_tfrecord_path,
qual_filter,
multi_allelic_qual_filter,
sample_name):
"""Yields Variant protos in sorted order from CallVariantsOutput protos.
Variants present in the input TFRecord are converted to Variant protos, with
the following filters applied: 1) variants are omitted if their quality is
lower than the `qual_filter` threshold. 2) multi-allelic variants omit
individual alleles whose qualities are lower than the
`multi_allelic_qual_filter` threshold.
Args:
input_sorted_tfrecord_path: str. TFRecord format file containing sorted
CallVariantsOutput protos.
qual_filter: double. The qual value below which to filter variants.
multi_allelic_qual_filter: double. The qual value below which to filter
multi-allelic variants.
sample_name: str. Sample name to write to VCF file.
Yields:
Variant protos in sorted order representing the CallVariantsOutput calls.
"""
for _, group in itertools.groupby(
io_utils.read_tfrecords(input_sorted_tfrecord_path,
proto=deepvariant_pb2.CallVariantsOutput),
lambda x: variant_utils.variant_range(x.variant)):
outputs = _sort_grouped_variants(group)
canonical_variant, predictions = merge_predictions(
outputs, multi_allelic_qual_filter)
variant = add_call_to_variant(canonical_variant,
predictions,
qual_filter=qual_filter,
sample_name=sample_name)
yield variant
def examples_to_variants(examples_path, max_records=None):
"""Yields Variant protos from the examples in examples_path.
This function reads in tf.Examples produced by DeepVariant from examples_path,
which may contain a sharded spec, sorts them, selects a representive example
when there are multiple versions representing different alt_alleles, and
yields the example_variant field from those examples.
Args:
examples_path: str. Path, or sharded spec, to labeled tf.Examples produced
by DeepVariant in training mode.
max_records: int or None. Maximum number of records to read, or None, to
read all of the records.
Yields:
nucleus.protos.Variant protos in coordinate-sorted order.
Raises:
ValueError: if we find a Variant in any example that doesn't have genotypes.
"""
examples = io_utils.read_tfrecords(examples_path, max_records=max_records)
variants = sorted(
(tf_utils.example_variant(example) for example in examples),
key=variant_utils.variant_range_tuple)
for _, group in itertools.groupby(variants,
variant_utils.variant_range_tuple):
variant = next(group)
if not variantcall_utils.has_genotypes(variant_utils.only_call(variant)):
raise ValueError((
'Variant {} does not have any genotypes. This tool only works with '
'variants that have been labeled.').format(
variant_utils.variant_key(variant)))
yield variant
def _transform_call_variants_output_to_variants(
input_sorted_tfrecord_path, qual_filter, multi_allelic_qual_filter,
sample_name):
"""Yields Variant protos in sorted order from CallVariantsOutput protos.
Variants present in the input TFRecord are converted to Variant protos, with
the following filters applied: 1) variants are omitted if their quality is
lower than the `qual_filter` threshold. 2) multi-allelic variants omit
individual alleles whose qualities are lower than the
`multi_allelic_qual_filter` threshold.
Args:
input_sorted_tfrecord_path: str. TFRecord format file containing sorted
CallVariantsOutput protos.
qual_filter: double. The qual value below which to filter variants.
multi_allelic_qual_filter: double. The qual value below which to filter
multi-allelic variants.
sample_name: str. Sample name to write to VCF file.
Yields:
Variant protos in sorted order representing the CallVariantsOutput calls.
"""
for _, group in itertools.groupby(
io_utils.read_tfrecords(
input_sorted_tfrecord_path, proto=deepvariant_pb2.CallVariantsOutput),
lambda x: variant_utils.variant_range(x.variant)):
outputs = list(group)
canonical_variant, predictions = merge_predictions(
outputs, multi_allelic_qual_filter)
variant = add_call_to_variant(
canonical_variant,
predictions,
qual_filter=qual_filter,
sample_name=sample_name)
yield variant
def assertDataSetExamplesMatchExpected(self, dataset, expected_dataset):
with tf.Session() as sess:
provider = slim.dataset_data_provider.DatasetDataProvider(
expected_dataset.get_slim_dataset(),
shuffle=False,
reader_kwargs={
'options':
io_utils.make_tfrecord_options(expected_dataset.source)
})
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
image, label, locus = provider.get(['image', 'label', 'locus'])
seen = [
sess.run([image, label, locus])[2]
for _ in range(expected_dataset.num_examples)
]
coord.request_stop()
coord.join(threads)
expected_loci = [
example.features.feature['locus'].bytes_list.value[0]
for example in io_utils.read_tfrecords(expected_dataset.source)
]
self.assertEqual(len(expected_loci), expected_dataset.num_examples)
self.assertEqual(expected_loci, seen)
# Note that this expected shape comes from the golden dataset. If the data
# is remade in the future, the values might need to be modified accordingly.
self.assertEqual([100, 221, pileup_image.DEFAULT_NUM_CHANNEL],
expected_dataset.tensor_shape)
def assertDataSetExamplesMatchExpected(self, dataset, expected_dataset):
with tf.Session() as sess:
provider = slim.dataset_data_provider.DatasetDataProvider(
expected_dataset.get_slim_dataset(),
shuffle=False,
reader_kwargs={
'options': io_utils.make_tfrecord_options(expected_dataset.source)
})
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
image, label, locus = provider.get(['image', 'label', 'locus'])
seen = [
sess.run([image, label, locus])[2]
for _ in range(expected_dataset.num_examples)
]
coord.request_stop()
coord.join(threads)
expected_loci = [
example.features.feature['locus'].bytes_list.value[0]
for example in io_utils.read_tfrecords(expected_dataset.source)
]
self.assertEqual(len(expected_loci), expected_dataset.num_examples)
self.assertEqual(expected_loci, seen)
# Note that this expected shape comes from the golden dataset. If the data
# is remade in the future, the values might need to be modified accordingly.
self.assertEqual([100, 221, pileup_image.DEFAULT_NUM_CHANNEL],
expected_dataset.tensor_shape)
def _get_one_example_from_examples_path(source):
"""Reads one record from source."""
# redacted
# io_utils.read_tfrecord can read wildcard file patterns.
# The source can be a comma-separated list.
source_paths = source.split(',')
for source_path in source_paths:
files = tf.gfile.Glob(
io_utils.NormalizeToShardedFilePattern(source_path))
if not files:
if len(source_paths) > 1:
raise ValueError(
'Cannot find matching files with the pattern "{}" in "{}"'.
format(source_path, ','.join(source_paths)))
else:
raise ValueError(
'Cannot find matching files with the pattern "{}"'.format(
source_path))
for f in files:
try:
return io_utils.read_tfrecords(f).next()
except StopIteration:
# Getting a StopIteration from one next() means source_path is empty.
# Move on to the next one to try to get one example.
pass
return None
def examples_to_variants(examples_path, max_records=None):
"""Yields Variant protos from the examples in examples_path.
This function reads in tf.Examples produced by DeepVariant from examples_path,
which may contain a sharded spec, sorts them, selects a representive example
when there are multiple versions representing different alt_alleles, and
yields the example_variant field from those examples.
Args:
examples_path: str. Path, or sharded spec, to labeled tf.Examples produced
by DeepVariant in training mode.
max_records: int or None. Maximum number of records to read, or None, to
read all of the records.
Yields:
nucleus.protos.Variant protos in coordinate-sorted order.
Raises:
ValueError: if we find a Variant in any example that doesn't have genotypes.
"""
examples = io_utils.read_tfrecords(examples_path, max_records=max_records)
variants = sorted(
(tf_utils.example_variant(example) for example in examples),
key=variant_utils.variant_range_tuple)
for _, group in itertools.groupby(variants,
variant_utils.variant_range_tuple):
variant = next(group)
if not variantcall_utils.has_genotypes(
variant_utils.only_call(variant)):
raise ValueError((
'Variant {} does not have any genotypes. This tool only works with '
'variants that have been labeled.').format(
variant_utils.variant_key(variant)))
yield variant
def test_realigner_diagnostics(self, enabled, emit_reads):
# Make sure that by default we aren't emitting any diagnostic outputs.
dx_dir = test_utils.test_tmpfile('dx')
region_str = 'chr20:10046178-10046188'
region = ranges.parse_literal(region_str)
assembled_region_str = 'chr20:10046099-10046267'
reads = _get_reads(region)
self.config = realigner.realigner_config(FLAGS)
self.config.diagnostics.enabled = enabled
self.config.diagnostics.output_root = dx_dir
self.config.diagnostics.emit_realigned_reads = emit_reads
self.reads_realigner = realigner.Realigner(self.config, self.ref_reader)
_, realigned_reads = self.reads_realigner.realign_reads(reads, region)
self.reads_realigner.diagnostic_logger.close() # Force close all resources.
if not enabled:
# Make sure our diagnostic output isn't emitted.
self.assertFalse(tf.gfile.Exists(dx_dir))
else:
# Our root directory exists.
self.assertTrue(tf.gfile.IsDirectory(dx_dir))
# We expect a realigner_metrics.csv in our rootdir with 1 entry in it.
metrics_file = os.path.join(
dx_dir, self.reads_realigner.diagnostic_logger.metrics_filename)
self.assertTrue(tf.gfile.Exists(metrics_file))
with tf.gfile.FastGFile(metrics_file) as fin:
rows = list(csv.DictReader(fin))
self.assertEqual(len(rows), 1)
self.assertEqual(
set(rows[0].keys()), {'window', 'k', 'n_haplotypes', 'time'})
self.assertEqual(rows[0]['window'], assembled_region_str)
self.assertEqual(int(rows[0]['k']), 25)
self.assertTrue(int(rows[0]['n_haplotypes']), 2)
# Check that our runtime is reasonable (greater than 0, less than 10 s).
self.assertTrue(0.0 < float(rows[0]['time']) < 10.0)
# As does the subdirectory for this region.
region_subdir = os.path.join(dx_dir, assembled_region_str)
self.assertTrue(tf.gfile.IsDirectory(region_subdir))
# We always have a graph.dot
self.assertTrue(
tf.gfile.Exists(
os.path.join(
region_subdir,
self.reads_realigner.diagnostic_logger.graph_filename)))
reads_file = os.path.join(
dx_dir, region_str,
self.reads_realigner.diagnostic_logger.realigned_reads_filename)
if emit_reads:
self.assertTrue(tf.gfile.Exists(reads_file))
reads_from_dx = io_utils.read_tfrecords(reads_file, reads_pb2.Read)
self.assertCountEqual(reads_from_dx, realigned_reads)
else:
self.assertFalse(tf.gfile.Exists(reads_file))
def test_call_end2end_with_empty_shards(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(
testdata.GOLDEN_CALLING_EXAMPLES, max_records=10))
# Write to 15 shards, which means there will be multiple empty shards.
source_path = test_utils.test_tmpfile('sharded@{}'.format(15))
io_utils.write_tfrecords(examples, source_path)
self.assertCallVariantsEmitsNRecordsForRandomGuess(source_path,
len(examples))
def test_call_end2end_with_empty_shards(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES,
max_records=10))
# Write to 15 shards, which means there will be multiple empty shards.
source_path = test_utils.test_tmpfile('sharded@{}'.format(15))
io_utils.write_tfrecords(examples, source_path)
self.assertCallVariantsEmitsNRecordsForRandomGuess(
source_path, len(examples))
def test_call_end2end_empty_first_shard(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(
testdata.GOLDEN_CALLING_EXAMPLES, max_records=10))
empty_first_file = test_utils.test_tmpfile('empty_1st_shard-00000-of-00002')
io_utils.write_tfrecords([], empty_first_file)
second_file = test_utils.test_tmpfile('empty_1st_shard-00001-of-00002')
io_utils.write_tfrecords(examples, second_file)
self.assertCallVariantsEmitsNRecordsForRandomGuess(
test_utils.test_tmpfile('empty_1st_shard@2'), len(examples))
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile('make_golden_dataset.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(testdata.GOLDEN_TRAINING_EXAMPLES), source_path)
else:
source_path = testdata.GOLDEN_TRAINING_EXAMPLES
return data_providers.DeepVariantDataSet(
name='labeled_golden',
source=source_path,
num_examples=testdata.N_GOLDEN_TRAINING_EXAMPLES)
def test_call_end2end_empty_first_shard(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(
testdata.GOLDEN_CALLING_EXAMPLES, max_records=10))
empty_first_file = test_utils.test_tmpfile('empty_1st_shard-00000-of-00002')
io_utils.write_tfrecords([], empty_first_file)
second_file = test_utils.test_tmpfile('empty_1st_shard-00001-of-00002')
io_utils.write_tfrecords(examples, second_file)
self.assertCallVariantsEmitsNRecordsForRandomGuess(
test_utils.test_tmpfile('empty_1st_shard@2'), len(examples))
def test_read_tfrecords_max_records(self, filename, max_records):
protos, path = self.write_test_protos(filename)
# Create our generator of records from read_tfrecords.
if max_records is None:
expected_n = len(protos)
else:
expected_n = min(max_records, len(protos))
actual = io.read_tfrecords(
path, reference_pb2.ContigInfo, max_records=max_records)
self.assertLen(list(actual), expected_n)
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'golden.postprocess_single_site_input.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(
testdata.GOLDEN_POSTPROCESS_INPUT,
proto=deepvariant_pb2.CallVariantsOutput), source_path)
else:
source_path = testdata.GOLDEN_POSTPROCESS_INPUT
return source_path
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'golden.postprocess_single_site_input.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(
testdata.GOLDEN_POSTPROCESS_INPUT,
proto=deepvariant_pb2.CallVariantsOutput), source_path)
else:
source_path = testdata.GOLDEN_POSTPROCESS_INPUT
return source_path
def test_call_variants_with_no_shape(self, model):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
# Remove image/shape.
del example.features.feature['image/shape']
source_path = test_utils.test_tmpfile('make_examples_out_noshape.tfrecord')
io_utils.write_tfrecords([example], source_path)
with self.assertRaisesRegexp(
ValueError, 'Invalid image/shape: we expect to find an image/shape '
'field with length 3.'):
call_variants.prepare_inputs(source_path, model, batch_size=1)
def _call_end2end_helper(self, examples_path, model, shard_inputs):
examples = list(io_utils.read_tfrecords(examples_path))
if shard_inputs:
# Create a sharded version of our golden examples.
source_path = test_utils.test_tmpfile('sharded@{}'.format(3))
io_utils.write_tfrecords(examples, source_path)
else:
source_path = examples_path
# If we point the test at a headless server, it will often be 2x2,
# which has 8 replicas. Otherwise a smaller batch size is fine.
if FLAGS.use_tpu:
batch_size = 8
else:
batch_size = 4
if model.name == 'random_guess':
# For the random guess model we can run everything.
max_batches = None
else:
# For all other models we only run a single batch for inference.
max_batches = 1
outfile = test_utils.test_tmpfile('call_variants.tfrecord')
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=_LEAVE_MODEL_UNINITIALIZED,
model=model,
output_file=outfile,
batch_size=batch_size,
max_batches=max_batches,
master='',
use_tpu=FLAGS.use_tpu,
)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile,
deepvariant_pb2.CallVariantsOutput))
return call_variants_outputs, examples, batch_size, max_batches
def test_call_variants_with_no_shape(self, model):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
# Remove image/shape.
del example.features.feature['image/shape']
source_path = test_utils.test_tmpfile('make_examples_out_noshape.tfrecord')
io_utils.write_tfrecords([example], source_path)
with self.assertRaisesRegexp(
ValueError, 'Invalid image/shape: we expect to find an image/shape '
'field with length 3.'):
ds = call_variants.prepare_inputs(source_path)
_ = list(data_providers.get_infer_batches(ds, model=model, batch_size=1))
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'make_golden_dataset.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(testdata.GOLDEN_TRAINING_EXAMPLES),
source_path)
else:
source_path = testdata.GOLDEN_TRAINING_EXAMPLES
return data_providers.DeepVariantDataSet(
name='labeled_golden',
source=source_path,
num_examples=testdata.N_GOLDEN_TRAINING_EXAMPLES)
def test_make_read_writer_tfrecords(self):
outfile = test_utils.test_tmpfile('test.tfrecord')
writer = sam.SamWriter(outfile, header=self.header)
# Test that the writer is a context manager and that we can write a read to
# it.
with writer:
writer.write(self.read1)
writer.write(self.read2)
# Our output should have exactly one read in it.
self.assertEqual([self.read1, self.read2],
list(
io_utils.read_tfrecords(outfile,
proto=reads_pb2.Read)))
def test_reading_sharded_input_with_empty_shards_does_not_crash(self):
valid_variants = io_utils.read_tfrecords(
testdata.GOLDEN_POSTPROCESS_INPUT,
proto=deepvariant_pb2.CallVariantsOutput)
empty_shard_one = test_utils.test_tmpfile(
'reading_empty_shard.tfrecord-00000-of-00002')
none_empty_shard_two = test_utils.test_tmpfile(
'reading_empty_shard.tfrecord-00001-of-00002')
io_utils.write_tfrecords([], empty_shard_one)
io_utils.write_tfrecords(valid_variants, none_empty_shard_two)
FLAGS.infile = test_utils.test_tmpfile('reading_empty_shard.tfrecord@2')
FLAGS.ref = testdata.CHR20_FASTA
FLAGS.outfile = test_utils.test_tmpfile('calls_reading_empty_shard.vcf')
postprocess_variants.main(['postprocess_variants.py'])
def test_read_write_tfrecords(self, filename):
protos, path = self.write_test_protos(filename)
# Create our generator of records from read_tfrecords.
reader = io.read_tfrecords(path, reference_pb2.ContigInfo)
# Make sure it's actually a generator.
self.assertEqual(type(reader), types.GeneratorType)
# Check the round-trip contents.
if '@' in filename:
# Sharded outputs are striped across shards, so order isn't preserved.
self.assertCountEqual(protos, reader)
else:
self.assertEqual(protos, list(reader))
def test_make_read_writer_tfrecords(self):
outfile = test_utils.test_tmpfile('test.tfrecord')
writer = sam.SamWriter(outfile, header=self.header)
# Test that the writer is a context manager and that we can write a read to
# it.
with writer:
writer.write(self.read1)
writer.write(self.read2)
# Our output should have exactly one read in it.
self.assertEqual([self.read1, self.read2],
list(
io_utils.read_tfrecords(outfile,
proto=reads_pb2.Read)))
def assertCallVariantsEmitsNRecordsForRandomGuess(self, filename,
num_examples):
outfile = test_utils.test_tmpfile('call_variants.tfrecord')
model = modeling.get_model('random_guess')
call_variants.call_variants(
examples_filename=filename,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=4,
max_batches=None)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile, deepvariant_pb2.CallVariantsOutput))
# Check that we have the right number of output protos.
self.assertEqual(len(call_variants_outputs), num_examples)
def test_reading_sharded_dataset(self, compressed_inputs):
golden_dataset = make_golden_dataset(compressed_inputs)
n_shards = 3
sharded_path = test_utils.test_tmpfile('sharded@{}'.format(n_shards))
io_utils.write_tfrecords(
io_utils.read_tfrecords(golden_dataset.source), sharded_path)
config_file = _test_dataset_config(
'test_sharded.pbtxt',
name='sharded_test',
tfrecord_path=sharded_path,
num_examples=golden_dataset.num_examples)
self.assertDataSetExamplesMatchExpected(
data_providers.get_dataset(config_file).get_slim_dataset(),
golden_dataset)
def test_writing_canned_records(self):
"""Tests writing all the records that are 'canned' in our tfrecord file."""
# This file is in TFRecord format.
tfrecord_file = test_utils.genomics_core_testdata(
'test_features.gff.tfrecord')
writer_options = gff_pb2.GffWriterOptions()
gff_records = list(
io_utils.read_tfrecords(tfrecord_file, proto=gff_pb2.GffRecord))
out_fname = test_utils.test_tmpfile('output.gff')
with gff_writer.GffWriter.to_file(out_fname, self.header,
writer_options) as writer:
for record in gff_records:
writer.write(record)
with open(out_fname) as f:
self.assertEqual(f.readlines(), self.expected_gff_content)
def test_writing_canned_records(self):
"""Tests writing all the variants that are 'canned' in our tfrecord file."""
# This file is in TFRecord format.
tfrecord_file = test_utils.genomics_core_testdata(
'test_reads.fastq.tfrecord')
writer_options = fastq_pb2.FastqWriterOptions()
fastq_records = list(
io_utils.read_tfrecords(tfrecord_file, proto=fastq_pb2.FastqRecord))
out_fname = test_utils.test_tmpfile('output.fastq')
with fastq_writer.FastqWriter.to_file(out_fname, writer_options) as writer:
for record in fastq_records:
writer.write(record)
with gfile.GFile(out_fname, 'r') as f:
self.assertEqual(f.readlines(), self.expected_fastq_content)
def test_reading_sharded_dataset(self, compressed_inputs):
golden_dataset = make_golden_dataset(compressed_inputs)
n_shards = 3
sharded_path = test_utils.test_tmpfile('sharded@{}'.format(n_shards))
io_utils.write_tfrecords(
io_utils.read_tfrecords(golden_dataset.source), sharded_path)
config_file = _test_dataset_config(
'test_sharded.pbtxt',
name='sharded_test',
tfrecord_path=sharded_path,
num_examples=golden_dataset.num_examples)
self.assertDataSetExamplesMatchExpected(
data_providers.get_dataset(config_file).get_slim_dataset(),
golden_dataset)
def assertCallVariantsEmitsNRecordsForRandomGuess(self, filename,
num_examples):
outfile = test_utils.test_tmpfile('call_variants.tfrecord')
model = modeling.get_model('random_guess')
call_variants.call_variants(
examples_filename=filename,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=4,
max_batches=None)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile,
deepvariant_pb2.CallVariantsOutput))
# Check that we have the right number of output protos.
self.assertEqual(len(call_variants_outputs), num_examples)
def assertTfDataSetExamplesMatchExpected(self,
input_fn,
expected_dataset,
use_tpu=False,
workaround_list_files=False):
# Note that we use input_fn to get an iterator, while we use
# expected_dataset to get a filename, even though they are the same
# type (DeepVariantInput), and may even be the same object.
with tf.Session() as sess:
params = {'batch_size': 1}
batch_feed = input_fn(params).make_one_shot_iterator().get_next()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
seen = []
while True:
try:
features, _ = sess.run(batch_feed)
except tf.errors.OutOfRangeError:
break
locus = features['locus'][0]
if use_tpu:
locus = tf_utils.int_tensor_to_string(locus)
# NB, this looks like: array(['chr20:10001019-10001019'], dtype=object)
seen.append(locus)
if workaround_list_files:
# This really only works for loci, because those are string valued and
# are expected to show up in sorted order. For arbitrary data that's
# not true. In prod we have the version of tf that lets us turn off
# shuffling so this path is skipped, but kokoro hits this.
seen = sorted(seen)
expected_loci = [
example.features.feature['locus'].bytes_list.value[0] for example
in io_utils.read_tfrecords(expected_dataset.input_file_spec)
]
self.assertEqual(len(expected_loci), expected_dataset.num_examples)
if seen != expected_loci:
print('\n\nlen expected seen', len(expected_loci), len(seen))
print('\n\nexpected=', expected_loci)
print('\n\nseen=', seen)
self.assertEqual(expected_loci, seen)
# Note that this expected shape comes from the golden dataset. If the data
# is remade in the future, the values might need to be modified accordingly.
self.assertEqual(dv_constants.PILEUP_DEFAULT_DIMS,
expected_dataset.tensor_shape)
def assertCallVariantsEmitsNRecordsForInceptionV3(self, filename,
num_examples):
outfile = test_utils.test_tmpfile('inception_v3.call_variants.tfrecord')
model = modeling.get_model('inception_v3')
checkpoint_path = _LEAVE_MODEL_UNINITIALIZED
call_variants.call_variants(
examples_filename=filename,
checkpoint_path=checkpoint_path,
model=model,
output_file=outfile,
batch_size=4,
max_batches=None)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile, deepvariant_pb2.CallVariantsOutput))
# Check that we have the right number of output protos.
self.assertEqual(len(call_variants_outputs), num_examples)
def test_writing_canned_records(self):
"""Tests writing all the records that are 'canned' in our tfrecord file."""
# This file is in TFRecord format.
tfrecord_file = test_utils.genomics_core_testdata(
'test_regions.bed.tfrecord')
header = bed_pb2.BedHeader(num_fields=12)
writer_options = bed_pb2.BedWriterOptions()
bed_records = list(
io_utils.read_tfrecords(tfrecord_file, proto=bed_pb2.BedRecord))
out_fname = test_utils.test_tmpfile('output.bed')
with bed_writer.BedWriter.to_file(out_fname, header,
writer_options) as writer:
for record in bed_records:
writer.write(record)
with gfile.GFile(out_fname, 'r') as f:
self.assertEqual(f.readlines(), self.expected_bed_content)
def test_call_variants_with_invalid_format(self, model, bad_format):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
# Overwrite the image/format field to be an invalid value
# (anything but 'raw').
example.features.feature['image/format'].bytes_list.value[0] = bad_format
source_path = test_utils.test_tmpfile('make_examples_output.tfrecord')
io_utils.write_tfrecords([example], source_path)
outfile = test_utils.test_tmpfile('call_variants_invalid_format.tfrecord')
with self.assertRaises(ValueError):
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=1,
max_batches=1)
def make_golden_dataset(compressed_inputs=False,
mode=tf.estimator.ModeKeys.EVAL,
use_tpu=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'make_golden_dataset.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(testdata.GOLDEN_TRAINING_EXAMPLES),
source_path)
else:
source_path = testdata.GOLDEN_TRAINING_EXAMPLES
return data_providers.get_input_fn_from_filespec(
input_file_spec=source_path,
num_examples=testdata.N_GOLDEN_TRAINING_EXAMPLES,
name='labeled_golden',
mode=mode,
tensor_shape=None,
use_tpu=use_tpu)
def test_call_variants_with_invalid_format(self, model, bad_format):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
# Overwrite the image/format field to be an invalid value
# (anything but 'raw').
example.features.feature['image/format'].bytes_list.value[0] = bad_format
source_path = test_utils.test_tmpfile('make_examples_output.tfrecord')
io_utils.write_tfrecords([example], source_path)
outfile = test_utils.test_tmpfile('call_variants_invalid_format.tfrecord')
with self.assertRaises(ValueError):
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=_LEAVE_MODEL_UNINITIALIZED,
model=model,
output_file=outfile,
batch_size=1,
max_batches=1,
use_tpu=FLAGS.use_tpu)
def verify_examples(self, examples_filename, region, options, verify_labels):
# Do some simple structural checks on the tf.Examples in the file.
expected_features = [
'variant/encoded', 'locus', 'image/format', 'image/encoded',
'alt_allele_indices/encoded'
]
if verify_labels:
expected_features += ['label']
examples = list(io_utils.read_tfrecords(examples_filename))
for example in examples:
for label_feature in expected_features:
self.assertIn(label_feature, example.features.feature)
# pylint: disable=g-explicit-length-test
self.assertGreater(len(tf_utils.example_alt_alleles_indices(example)), 0)
# Check that the variants in the examples are good.
variants = [tf_utils.example_variant(x) for x in examples]
self.verify_variants(variants, region, options, is_gvcf=False)
return examples
def test_reading_sharded_dataset(self, compressed_inputs, use_tpu):
golden_dataset = make_golden_dataset(compressed_inputs,
use_tpu=use_tpu)
n_shards = 3
sharded_path = test_utils.test_tmpfile('sharded@{}'.format(n_shards))
io_utils.write_tfrecords(
io_utils.read_tfrecords(golden_dataset.input_file_spec),
sharded_path)
config_file = _test_dataset_config(
'test_sharded.pbtxt',
name='sharded_test',
tfrecord_path=sharded_path,
num_examples=golden_dataset.num_examples)
self.assertTfDataSetExamplesMatchExpected(
data_providers.get_input_fn_from_dataset(
config_file, mode=tf.estimator.ModeKeys.EVAL),
golden_dataset,
# workaround_list_files is needed because wildcards, and so sharded
# files, are nondeterministicly ordered (for now).
workaround_list_files=True,
)
def get_one_example_from_examples_path(source, proto=None):
"""Get the first record from `source`.
Args:
source: str. A pattern or a comma-separated list of patterns that represent
file names.
proto: A proto class. proto.FromString() will be called on each serialized
record in path to parse it.
Returns:
The first record, or None.
"""
files = io_utils.GlobListShardedFilePatterns(source)
if not files:
raise ValueError(
'Cannot find matching files with the pattern "{}"'.format(source))
for f in files:
try:
return io_utils.read_tfrecords(f, proto=proto).next()
except StopIteration:
# Getting a StopIteration from one next() means source_path is empty.
# Move on to the next one to try to get one example.
pass
return None
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='GL',
number='G',
type='Float',
description='Genotype likelihoods, log10 encoded'),
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=GL,Number=G,Type=Float,Description="Genotype '
'likelihoods, log10 encoded">\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 tf.gfile.GFile(out_fname, 'r') as f:
self.assertEqual(f.readlines(), expected_vcf_content)
def testGoldenCallingExamples(self, use_tpu):
# Read the golden calling examples, and read the batch_feed instantiated
# from the golden calling examples, and ensure that we get the same
# parsed records in both cases.
# Read and parse the canonical data.
expected_decoded_records = list(
io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES,
proto=example_pb2.Example))
# Read and parse the data using tf. This is the function under test,
# although we indirectly check parse_tfexample as well.
batch_feed = self.get_batch_feed(batch_size=1, use_tpu=use_tpu)
with self.test_session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
n = 0
while True:
# Read from batch.
try:
features = sess.run(batch_feed)
except tf.errors.OutOfRangeError:
break
# Get the corresponding parsed golden example.
example = expected_decoded_records[n]
expected_alt_allele_indices_encoded = example.features.feature[
'alt_allele_indices/encoded'].bytes_list.value[0]
expected_variant_encoded = example.features.feature[
'variant/encoded'].bytes_list.value[0]
# Compare against the parsed batch feed.
a = features['image'][0] # np.ndarray
self.assertEqual(list(a.shape),
dv_constants.PILEUP_DEFAULT_DIMS)
self.assertIsNotNone(a)
if use_tpu:
self.assertEqual(a.dtype, np.dtype('int32'))
else:
self.assertEqual(a.dtype, np.dtype('uint8'))
a = features['alt_allele_indices'][0]
if use_tpu:
self.assertEqual(a.dtype, np.dtype('int32'))
self.assertEqual(a.shape,
(tf_utils.STRING_TO_INT_BUFFER_LENGTH, ))
actual_alt_allele_indices_encoded = tf_utils.int_tensor_to_string(
a)
else:
self.assertIsInstance(a, six.string_types)
actual_alt_allele_indices_encoded = a
self.assertEqual(expected_alt_allele_indices_encoded,
actual_alt_allele_indices_encoded)
a = features['variant'][0]
if use_tpu:
self.assertEqual(a.dtype, np.dtype('int32'))
self.assertEqual(a.shape,
(tf_utils.STRING_TO_INT_BUFFER_LENGTH, ))
actual_variant_encoded = tf_utils.int_tensor_to_string(a)
else:
self.assertIsInstance(a, six.string_types)
actual_variant_encoded = a
self.assertEqual(expected_variant_encoded,
actual_variant_encoded)
n += 1
self.assertEqual(n, testdata.N_GOLDEN_CALLING_EXAMPLES)
def main(argv=()):
with errors.clean_commandline_error_exit():
if len(argv) > 1:
errors.log_and_raise(
'Command line parsing failure: postprocess_variants does not accept '
'positional arguments but some are present on the command line: '
'"{}".'.format(str(argv)), errors.CommandLineError)
del argv # Unused.
if (not FLAGS.nonvariant_site_tfrecord_path) != (not FLAGS.gvcf_outfile):
errors.log_and_raise(
'gVCF creation requires both nonvariant_site_tfrecord_path and '
'gvcf_outfile flags to be set.', errors.CommandLineError)
proto_utils.uses_fast_cpp_protos_or_die()
logging_level.set_from_flag()
fasta_reader = fasta.RefFastaReader(FLAGS.ref, cache_size=_FASTA_CACHE_SIZE)
contigs = fasta_reader.header.contigs
paths = io_utils.maybe_generate_sharded_filenames(FLAGS.infile)
# Read one CallVariantsOutput record and extract the sample name from it.
# Note that this assumes that all CallVariantsOutput protos in the infile
# contain a single VariantCall within their constituent Variant proto, and
# that the call_set_name is identical in each of the records.
record = next(
io_utils.read_tfrecords(
paths[0], proto=deepvariant_pb2.CallVariantsOutput, max_records=1))
sample_name = _extract_single_sample_name(record)
header = dv_vcf_constants.deepvariant_header(
contigs=contigs, sample_names=[sample_name])
with tempfile.NamedTemporaryFile() as temp:
postprocess_variants_lib.process_single_sites_tfrecords(
contigs, paths, temp.name)
independent_variants = _transform_call_variants_output_to_variants(
input_sorted_tfrecord_path=temp.name,
qual_filter=FLAGS.qual_filter,
multi_allelic_qual_filter=FLAGS.multi_allelic_qual_filter,
sample_name=sample_name)
variant_generator = haplotypes.maybe_resolve_conflicting_variants(
independent_variants)
write_variants_to_vcf(
variant_generator=variant_generator,
output_vcf_path=FLAGS.outfile,
header=header)
# Also write out the gVCF file if it was provided.
if FLAGS.nonvariant_site_tfrecord_path:
nonvariant_generator = io_utils.read_shard_sorted_tfrecords(
FLAGS.nonvariant_site_tfrecord_path,
key=_get_contig_based_variant_sort_keyfn(contigs),
proto=variants_pb2.Variant)
with vcf.VcfReader(FLAGS.outfile, use_index=False) as variant_reader:
lessthanfn = _get_contig_based_lessthan(contigs)
gvcf_variants = (
_transform_to_gvcf_record(variant)
for variant in variant_reader.iterate())
merged_variants = merge_variants_and_nonvariants(
gvcf_variants, nonvariant_generator, lessthanfn, fasta_reader)
write_variants_to_vcf(
variant_generator=merged_variants,
output_vcf_path=FLAGS.gvcf_outfile,
header=header)
def setUpClass(cls):
cls.examples = list(
io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
cls.variants = [tf_utils.example_variant(ex) for ex in cls.examples]
cls.model = modeling.get_model('random_guess')
def test_call_end2end(self, model, shard_inputs, include_debug_info):
FLAGS.include_debug_info = include_debug_info
examples = list(io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
if shard_inputs:
# Create a sharded version of our golden examples.
source_path = test_utils.test_tmpfile('sharded@{}'.format(3))
io_utils.write_tfrecords(examples, source_path)
else:
source_path = testdata.GOLDEN_CALLING_EXAMPLES
batch_size = 4
if model.name == 'random_guess':
# For the random guess model we can run everything.
max_batches = None
else:
# For all other models we only run a single batch for inference.
max_batches = 1
outfile = test_utils.test_tmpfile('call_variants.tfrecord')
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=batch_size,
max_batches=max_batches)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile, deepvariant_pb2.CallVariantsOutput))
# Check that we have the right number of output protos.
self.assertEqual(
len(call_variants_outputs), batch_size * max_batches
if max_batches else len(examples))
# Check that our CallVariantsOutput (CVO) have the following critical
# properties:
# - we have one CVO for each example we processed.
# - the variant in the CVO is exactly what was in the example.
# - the alt_allele_indices of the CVO match those of its corresponding
# example.
# - there are 3 genotype probabilities and these are between 0.0 and 1.0.
# We can only do this test when processing all of the variants (max_batches
# is None), since we processed all of the examples with that model.
if max_batches is None:
self.assertItemsEqual([cvo.variant for cvo in call_variants_outputs],
[tf_utils.example_variant(ex) for ex in examples])
# Check the CVO debug_info: not filled if include_debug_info is False;
# else, filled by logic based on CVO.
if not include_debug_info:
for cvo in call_variants_outputs:
self.assertEqual(cvo.debug_info,
deepvariant_pb2.CallVariantsOutput.DebugInfo())
else:
for cvo in call_variants_outputs:
self.assertEqual(cvo.debug_info.has_insertion,
variant_utils.has_insertion(cvo.variant))
self.assertEqual(cvo.debug_info.has_deletion,
variant_utils.has_deletion(cvo.variant))
self.assertEqual(cvo.debug_info.is_snp, variant_utils.is_snp(
cvo.variant))
self.assertEqual(cvo.debug_info.predicted_label,
np.argmax(cvo.genotype_probabilities))
def example_matches_call_variants_output(example, call_variants_output):
return (tf_utils.example_variant(example) == call_variants_output.variant
and tf_utils.example_alt_alleles_indices(
example) == call_variants_output.alt_allele_indices.indices)
for call_variants_output in call_variants_outputs:
# Find all matching examples.
matches = [
ex for ex in examples
if example_matches_call_variants_output(ex, call_variants_output)
]
# We should have exactly one match.
self.assertEqual(len(matches), 1)
example = matches[0]
# Check that we've faithfully copied in the alt alleles (though currently
# as implemented we find our example using this information so it cannot
# fail). Included here in case that changes in the future.
self.assertEqual(
list(tf_utils.example_alt_alleles_indices(example)),
list(call_variants_output.alt_allele_indices.indices))
# We should have exactly three genotype probabilities (assuming our
# ploidy == 2).
self.assertEqual(len(call_variants_output.genotype_probabilities), 3)
# These are probabilities so they should be between 0 and 1.
self.assertTrue(
0 <= gp <= 1 for gp in call_variants_output.genotype_probabilities)
def setUpClass(cls):
cls.examples = list(
io_utils.read_tfrecords(testdata.GOLDEN_CALLING_EXAMPLES))
cls.variants = [tf_utils.example_variant(ex) for ex in cls.examples]
cls.model = modeling.get_model('random_guess')
def test_make_examples_end2end(self, mode, num_shards,
labeler_algorithm=None):
self.maxDiff = None
self.assertIn(mode, {'calling', 'training'})
region = ranges.parse_literal('chr20:10,000,000-10,010,000')
FLAGS.ref = testdata.CHR20_FASTA
FLAGS.reads = testdata.CHR20_BAM
FLAGS.candidates = test_utils.test_tmpfile(
_sharded('vsc.tfrecord', num_shards))
FLAGS.examples = test_utils.test_tmpfile(
_sharded('examples.tfrecord', num_shards))
FLAGS.regions = [ranges.to_literal(region)]
FLAGS.partition_size = 1000
FLAGS.mode = mode
FLAGS.gvcf_gq_binsize = 5
if labeler_algorithm is not None:
FLAGS.labeler_algorithm = labeler_algorithm
if mode == 'calling':
FLAGS.gvcf = test_utils.test_tmpfile(
_sharded('gvcf.tfrecord', num_shards))
else:
FLAGS.truth_variants = testdata.TRUTH_VARIANTS_VCF
FLAGS.confident_regions = testdata.CONFIDENT_REGIONS_BED
for task_id in range(max(num_shards, 1)):
FLAGS.task = task_id
options = make_examples.default_options(add_flags=True)
make_examples.make_examples_runner(options)
# Test that our candidates are reasonable, calling specific helper functions
# to check lots of properties of the output.
candidates = sorted(
io_utils.read_tfrecords(
FLAGS.candidates, proto=deepvariant_pb2.DeepVariantCall),
key=lambda c: variant_utils.variant_range_tuple(c.variant))
self.verify_deepvariant_calls(candidates, options)
self.verify_variants(
[call.variant for call in candidates], region, options, is_gvcf=False)
# Verify that the variants in the examples are all good.
examples = self.verify_examples(
FLAGS.examples, region, options, verify_labels=mode == 'training')
example_variants = [tf_utils.example_variant(ex) for ex in examples]
self.verify_variants(example_variants, region, options, is_gvcf=False)
# Verify the integrity of the examples and then check that they match our
# golden labeled examples. Note we expect the order for both training and
# calling modes to produce deterministic order because we fix the random
# seed.
if mode == 'calling':
golden_file = _sharded(testdata.GOLDEN_CALLING_EXAMPLES, num_shards)
else:
golden_file = _sharded(testdata.GOLDEN_TRAINING_EXAMPLES, num_shards)
self.assertDeepVariantExamplesEqual(
examples, list(io_utils.read_tfrecords(golden_file)))
if mode == 'calling':
nist_reader = vcf.VcfReader(testdata.TRUTH_VARIANTS_VCF)
nist_variants = list(nist_reader.query(region))
self.verify_nist_concordance(example_variants, nist_variants)
# Check the quality of our generated gvcf file.
gvcfs = variant_utils.sorted_variants(
io_utils.read_tfrecords(FLAGS.gvcf, proto=variants_pb2.Variant))
self.verify_variants(gvcfs, region, options, is_gvcf=True)
self.verify_contiguity(gvcfs, region)
gvcf_golden_file = _sharded(testdata.GOLDEN_POSTPROCESS_GVCF_INPUT,
num_shards)
expected_gvcfs = list(
io_utils.read_tfrecords(gvcf_golden_file, proto=variants_pb2.Variant))
self.assertItemsEqual(gvcfs, expected_gvcfs)
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)
