def _shard_variants(known_args, pipeline_args, pipeline_mode):
# type: (argparse.Namespace, List[str], int) -> List[str]
"""Reads the variants and writes them to VCF shards.
Returns:
The VCF shards directory.
"""
options = pipeline_options.PipelineOptions(pipeline_args)
google_cloud_options = options.view_as(pipeline_options.GoogleCloudOptions)
shard_files_job_name = pipeline_common.generate_unique_name(
_SHARD_VCF_FILES_JOB_NAME)
_update_google_cloud_job_name(google_cloud_options, shard_files_job_name)
vcf_shards_output_dir = filesystems.FileSystems.join(
known_args.annotation_output_dir, _SHARDS_FOLDER)
with beam.Pipeline(options=options) as p:
variants = _read_variants(
known_args.all_patterns, p, known_args, pipeline_mode)
call_names = (variants
| 'CombineCallNames' >>
combine_call_names.CallNamesCombiner())
_ = (variants
| 'DensifyVariants' >> densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(call_names))
| 'WriteToShards' >> write_variants_to_shards.WriteToShards(
vcf_shards_output_dir,
beam.pvalue.AsSingleton(call_names),
known_args.number_of_variants_per_shard))
return [vep_runner_util.format_dir_path(vcf_shards_output_dir) +
_GCS_RECURSIVE_WILDCARD]
def test_call_names_combiner_pipeline_duplicate_call_names(self):
variant_call = vcfio.VariantCall(name='sample1')
variants = [vcfio.Variant(calls=[variant_call, variant_call])]
pipeline = TestPipeline()
_ = (pipeline
| transforms.Create(variants)
| 'CombineCallNames' >> combine_call_names.CallNamesCombiner())
with self.assertRaises(ValueError):
pipeline.run()
def _bigquery_to_vcf_shards(
known_args, # type: argparse.Namespace
beam_pipeline_options, # type: pipeline_options.PipelineOptions
vcf_data_temp_folder, # type: str
header_file_path, # type: str
):
# type: (...) -> None
"""Runs BigQuery to VCF shards pipelines.
It reads the variants from BigQuery table, groups a collection of variants
within a contiguous region of the genome (the size of the collection is
adjustable through flag `--number_of_bases_per_shard`), sorts them, and then
writes to one VCF file. All VCF data files are saved in
`vcf_data_temp_folder`.
Also, it writes the meta info and data header with the call names to
`vcf_header_file_path`.
"""
schema = _get_schema(known_args.input_table)
# TODO(allieychen): Modify the SQL query with the specified call_names.
query = _get_bigquery_query(known_args, schema)
logging.info('Processing BigQuery query %s:', query)
bq_source = bigquery.BigQuerySource(query=query,
validate=True,
use_standard_sql=True)
annotation_names = _extract_annotation_names(schema)
with beam.Pipeline(options=beam_pipeline_options) as p:
variants = (p
| 'ReadFromBigQuery ' >> beam.io.Read(bq_source)
| bigquery_to_variant.BigQueryToVariant(annotation_names))
if known_args.call_names:
call_names = (p
| transforms.Create(known_args.call_names)
| beam.combiners.ToList())
else:
call_names = (
variants
| 'CombineCallNames' >> combine_call_names.CallNamesCombiner(
known_args.preserve_call_names_order))
_ = (call_names
| 'GenerateVcfDataHeader' >> beam.ParDo(
_write_vcf_header_with_call_names, _VCF_FIXED_COLUMNS,
known_args.representative_header_file, header_file_path))
_ = (variants
| densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(call_names))
| 'PairVariantWithKey' >> beam.Map(
_pair_variant_with_key, known_args.number_of_bases_per_shard)
| 'GroupVariantsByKey' >> beam.GroupByKey()
| beam.ParDo(_get_file_path_and_sorted_variants,
vcf_data_temp_folder)
| vcfio.WriteVcfDataLines())
def _bigquery_to_vcf_shards(
known_args, # type: argparse.Namespace
beam_pipeline_options, # type: pipeline_options.PipelineOptions
vcf_data_temp_folder, # type: str
vcf_data_header_file_path, # type: str
):
# type: (...) -> None
"""Runs BigQuery to VCF shards pipelines.
It reads the variants from BigQuery table, groups a collection of variants
within a contiguous region of the genome (the size of the collection is
adjustable through flag `--number_of_bases_per_shard`), sorts them, and then
writes to one VCF file. All VCF data files are saved in
`vcf_data_temp_folder`.
Also, it writes the data header to `vcf_data_header_file_path`.
TODO(allieychen): Eventually, it also generates the meta information file.
"""
bq_source = bigquery.BigQuerySource(
query=_BASE_QUERY_TEMPLATE.format(INPUT_TABLE='.'.join(
bigquery_util.parse_table_reference(known_args.input_table))),
validate=True,
use_standard_sql=True)
with beam.Pipeline(options=beam_pipeline_options) as p:
variants = (p
| 'ReadFromBigQuery ' >> beam.io.Read(bq_source)
| bigquery_to_variant.BigQueryToVariant())
call_names = (
variants
| 'CombineCallNames' >> combine_call_names.CallNamesCombiner())
_ = (call_names
| 'GenerateVcfDataHeader' >> beam.ParDo(
_write_vcf_data_header, _VCF_FIXED_COLUMNS,
vcf_data_header_file_path))
_ = (variants
| densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(call_names))
| 'PairVariantWithKey' >> beam.Map(
_pair_variant_with_key, known_args.number_of_bases_per_shard)
| 'GroupVariantsByKey' >> beam.GroupByKey()
| beam.ParDo(_get_file_path_and_sorted_variants,
vcf_data_temp_folder)
| vcfio.WriteVcfDataLines())
def test_call_names_combiner_pipeline(self):
call_names = ['sample1', 'sample2', 'sample3']
variant_calls = [
vcfio.VariantCall(name=call_names[0]),
vcfio.VariantCall(name=call_names[1]),
vcfio.VariantCall(name=call_names[2])
]
variants = [
vcfio.Variant(calls=[variant_calls[0], variant_calls[1]]),
vcfio.Variant(calls=[variant_calls[1], variant_calls[2]])
]
pipeline = TestPipeline()
combined_call_names = (
pipeline
| transforms.Create(variants)
| 'CombineCallNames' >> combine_call_names.CallNamesCombiner())
assert_that(combined_call_names, equal_to([call_names]))
pipeline.run()
def test_call_names_combiner_pipeline_preserve_call_names_order_error(
self):
call_names = ['sample1', 'sample2', 'sample3']
variant_calls = [
vcfio.VariantCall(name=call_names[0]),
vcfio.VariantCall(name=call_names[1]),
vcfio.VariantCall(name=call_names[2])
]
variants = [
vcfio.Variant(calls=[variant_calls[0], variant_calls[1]]),
vcfio.Variant(calls=[variant_calls[1], variant_calls[2]])
]
pipeline = TestPipeline()
_ = (pipeline
| transforms.Create(variants)
| 'CombineCallNames' >> combine_call_names.CallNamesCombiner(
preserve_call_names_order=True))
with self.assertRaises(ValueError):
pipeline.run()