def step2_export_to_elasticsearch(hc, vds, args):
if args.start_with_step > 2 or args.stop_after_step < 2 or args.only_export_to_elasticsearch_at_the_end:
return hc, vds
logger.info(
"\n\n=============================== pipeline - step 2 - export to elasticsearch ==============================="
)
if vds is None or not args.skip_writing_intermediate_vds:
stop_hail_context(hc)
hc = create_hail_context()
vds = read_in_dataset(hc,
args.step1_output_vds,
dataset_type=args.dataset_type,
skip_summary=True,
num_partitions=args.cpu_limit)
export_to_elasticsearch(
vds,
args,
operation=ELASTICSEARCH_UPSERT,
delete_index_before_exporting=True,
export_genotypes=True,
disable_doc_values_for_fields=("sortedTranscriptConsequences", )
if not bool(args.use_nested_objects_for_vep) else (),
disable_index_for_fields=("sortedTranscriptConsequences", )
if not bool(args.use_nested_objects_for_vep) else (),
run_after_index_exists=(
lambda: route_index_to_temp_es_cluster(True, args))
if args.use_temp_loading_nodes else None,
)
args.start_with_step = 3 # step 2 finished, so, if an error occurs and it goes to retry, start with the next step
return hc, vds
"Elasticsearch index name. If specified, only this index will be updated.")
p.add_argument(
"--dataset-path",
help="(optional) Path of variant callset. If not specified, the original "
"vcf/vds path from which the data was loaded will be used.")
p.add_argument("--genome-version",
help="Genome build: 37 or 38",
choices=["37", "38"])
p.add_argument(
"--all",
help="Update all elasticsearch indices. This option is mutually-exclusive "
"with --index-name, --dataset-path, and --genome-version.",
action="store_true")
args = p.parse_args()
hc = create_hail_context()
if args.download_latest_clinvar_vcf:
for genome_version in ["37", "38"]:
vds = download_and_import_latest_clinvar_vcf(hc, genome_version)
write_vds(vds, CLINVAR_VDS_PATH.format(genome_version=genome_version))
if args.index_name and not args.all:
update_dataset(hc, args.index_name, args)
elif args.all:
update_all_datasets(hc, args)
else:
p.exit("ERROR: must specify either --index-name or --all")
def step3_add_reference_datasets(hc, vds, args):
if args.start_with_step > 3 or args.stop_after_step < 3:
return hc, vds
logger.info(
"\n\n=============================== pipeline - step 3 - add reference datasets ==============================="
)
if vds is None or not args.skip_writing_intermediate_vds:
stop_hail_context(hc)
hc = create_hail_context()
vds = read_in_dataset(hc,
args.step1_output_vds,
dataset_type=args.dataset_type,
skip_summary=True)
if not args.only_export_to_elasticsearch_at_the_end:
vds = compute_minimal_schema(vds, args.dataset_type)
if args.dataset_type == "VARIANTS":
# annotate with the combined reference data file which was generated using
# ../download_and_create_reference_datasets/v01/hail_scripts/combine_all_variant_level_reference_data.py
# and contains all these annotations in one .vds
if not (args.exclude_dbnsfp or args.exclude_cadd or args.exclude_1kg
or args.exclude_exac or args.exclude_topmed or args.exclude_mpc
or args.exclude_gnomad or args.exclude_eigen
or args.exclude_primate_ai or args.exclude_splice_ai):
logger.info("\n==> add combined variant-level reference data")
vds = add_combined_reference_data_to_vds(
hc, vds, args.genome_version, subset=args.filter_interval)
else:
# annotate with each reference data file - one-by-one
if not args.skip_annotations and not args.exclude_dbnsfp:
logger.info("\n==> add dbnsfp")
vds = add_dbnsfp_to_vds(hc,
vds,
args.genome_version,
root="va.dbnsfp",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_cadd:
logger.info("\n==> add cadd")
vds = add_cadd_to_vds(hc,
vds,
args.genome_version,
root="va.cadd",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_1kg:
logger.info("\n==> add 1kg")
vds = add_1kg_phase3_to_vds(hc,
vds,
args.genome_version,
root="va.g1k",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_exac:
logger.info("\n==> add exac")
vds = add_exac_to_vds(hc,
vds,
args.genome_version,
root="va.exac",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_topmed:
logger.info("\n==> add topmed")
vds = add_topmed_to_vds(hc,
vds,
args.genome_version,
root="va.topmed",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_mpc:
logger.info("\n==> add mpc")
vds = add_mpc_to_vds(hc,
vds,
args.genome_version,
root="va.mpc",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_gnomad:
logger.info("\n==> add gnomad exomes")
vds = add_gnomad_to_vds(hc,
vds,
args.genome_version,
exomes_or_genomes="exomes",
root="va.gnomad_exomes",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_gnomad:
logger.info("\n==> add gnomad genomes")
vds = add_gnomad_to_vds(hc,
vds,
args.genome_version,
exomes_or_genomes="genomes",
root="va.gnomad_genomes",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_eigen:
logger.info("\n==> add eigen")
vds = add_eigen_to_vds(hc,
vds,
args.genome_version,
root="va.eigen",
subset=args.filter_interval)
if not args.exclude_primate_ai:
logger.info("\n==> add primate_ai")
vds = add_primate_ai_to_vds(hc,
vds,
args.genome_version,
root="va.primate_ai",
subset=args.filter_interval)
if not args.exclude_splice_ai:
logger.info("\n==> add splice_ai")
vds = add_splice_ai_to_vds(hc,
vds,
args.genome_version,
root="va.splice_ai",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_clinvar:
logger.info("\n==> add clinvar")
vds = add_clinvar_to_vds(hc,
vds,
args.genome_version,
root="va.clinvar",
subset=args.filter_interval)
if not args.skip_annotations and not args.exclude_hgmd:
logger.info("\n==> add hgmd")
vds = add_hgmd_to_vds(hc,
vds,
args.genome_version,
root="va.hgmd",
subset=args.filter_interval)
if not args.is_running_locally and not args.skip_writing_intermediate_vds:
write_vds(vds, args.step3_output_vds)
args.start_with_step = 4 # step 3 finished, so, if an error occurs and it goes to retry, start with the next step
return hc, vds
def step1_compute_derived_fields(hc, vds, args):
if args.start_with_step > 1 or args.stop_after_step < 1:
return hc, vds
logger.info(
"\n\n=============================== pipeline - step 1 - compute derived fields ==============================="
)
if vds is None or not args.skip_writing_intermediate_vds:
stop_hail_context(hc)
hc = create_hail_context()
vds = read_in_dataset(hc,
args.step0_output_vds,
dataset_type=args.dataset_type,
skip_summary=True,
num_partitions=args.cpu_limit)
parallel_computed_annotation_exprs = [
"va.docId = %s" % get_expr_for_variant_id(512),
"va.variantId = %s" % get_expr_for_variant_id(),
"va.variantType= %s" % get_expr_for_variant_type(),
"va.contig = %s" % get_expr_for_contig(),
"va.pos = %s" % get_expr_for_start_pos(),
"va.start = %s" % get_expr_for_start_pos(),
"va.end = %s" % get_expr_for_end_pos(),
"va.ref = %s" % get_expr_for_ref_allele(),
"va.alt = %s" % get_expr_for_alt_allele(),
"va.xpos = %s" % get_expr_for_xpos(pos_field="start"),
"va.xstart = %s" % get_expr_for_xpos(pos_field="start"),
"va.sortedTranscriptConsequences = %s" %
get_expr_for_vep_sorted_transcript_consequences_array(
vep_root="va.vep",
include_coding_annotations=True,
add_transcript_rank=bool(args.use_nested_objects_for_vep)),
]
if args.dataset_type == "VARIANTS":
FAF_CONFIDENCE_INTERVAL = 0.95 # based on https://macarthurlab.slack.com/archives/C027LHMPP/p1528132141000430
parallel_computed_annotation_exprs += [
"va.FAF = %s" % get_expr_for_filtering_allele_frequency(
"va.info.AC[va.aIndex - 1]", "va.info.AN",
FAF_CONFIDENCE_INTERVAL),
]
serial_computed_annotation_exprs = [
"va.xstop = %s" %
get_expr_for_xpos(field_prefix="va.", pos_field="end"),
"va.transcriptIds = %s" % get_expr_for_vep_transcript_ids_set(
vep_transcript_consequences_root="va.sortedTranscriptConsequences"
),
"va.domains = %s" % get_expr_for_vep_protein_domains_set(
vep_transcript_consequences_root="va.sortedTranscriptConsequences"
),
"va.transcriptConsequenceTerms = %s" %
get_expr_for_vep_consequence_terms_set(
vep_transcript_consequences_root="va.sortedTranscriptConsequences"
),
"va.mainTranscript = %s" %
get_expr_for_worst_transcript_consequence_annotations_struct(
"va.sortedTranscriptConsequences"),
"va.geneIds = %s" % get_expr_for_vep_gene_ids_set(
vep_transcript_consequences_root="va.sortedTranscriptConsequences"
),
"va.codingGeneIds = %s" % get_expr_for_vep_gene_ids_set(
vep_transcript_consequences_root="va.sortedTranscriptConsequences",
only_coding_genes=True),
]
# serial_computed_annotation_exprs += [
# "va.sortedTranscriptConsequences = va.sortedTranscriptConsequences.map(c => drop(c, amino_acids, biotype))"
#]
if not bool(args.use_nested_objects_for_vep):
serial_computed_annotation_exprs += [
"va.sortedTranscriptConsequences = json(va.sortedTranscriptConsequences)"
]
vds = vds.annotate_variants_expr(parallel_computed_annotation_exprs)
for expr in serial_computed_annotation_exprs:
vds = vds.annotate_variants_expr(expr)
pprint(vds.variant_schema)
INPUT_SCHEMA = {}
if args.dataset_type == "VARIANTS":
INPUT_SCHEMA["top_level_fields"] = """
docId: String,
variantId: String,
originalAltAlleles: Set[String],
contig: String,
start: Int,
pos: Int,
end: Int,
ref: String,
alt: String,
xpos: Long,
xstart: Long,
xstop: Long,
rsid: String,
--- qual: Double,
filters: Set[String],
aIndex: Int,
geneIds: Set[String],
transcriptIds: Set[String],
codingGeneIds: Set[String],
domains: Set[String],
transcriptConsequenceTerms: Set[String],
sortedTranscriptConsequences: String,
mainTranscript: Struct,
"""
if args.not_gatk_genotypes:
INPUT_SCHEMA["info_fields"] = """
AC: Array[Int],
AF: Array[Double],
AN: Int,
--- BaseQRankSum: Double,
--- ClippingRankSum: Double,
--- DP: Int,
--- FS: Double,
--- InbreedingCoeff: Double,
--- MQ: Double,
--- MQRankSum: Double,
--- QD: Double,
--- ReadPosRankSum: Double,
--- VQSLOD: Double,
--- culprit: String,
"""
else:
INPUT_SCHEMA["info_fields"] = """
AC: Array[Int],
AF: Array[Double],
AN: Int,
--- BaseQRankSum: Double,
--- ClippingRankSum: Double,
--- DP: Int,
--- FS: Double,
--- InbreedingCoeff: Double,
--- MQ: Double,
--- MQRankSum: Double,
--- QD: Double,
--- ReadPosRankSum: Double,
--- VQSLOD: Double,
--- culprit: String,
"""
elif args.dataset_type == "SV":
INPUT_SCHEMA["top_level_fields"] = """
docId: String,
variantId: String,
contig: String,
start: Int,
pos: Int,
end: Int,
ref: String,
alt: String,
xpos: Long,
xstart: Long,
xstop: Long,
rsid: String,
--- qual: Double,
filters: Set[String],
aIndex: Int,
geneIds: Set[String],
transcriptIds: Set[String],
codingGeneIds: Set[String],
domains: Set[String],
transcriptConsequenceTerms: Set[String],
sortedTranscriptConsequences: String,
mainTranscript: Struct,
"""
# END=100371979;SVTYPE=DEL;SVLEN=-70;CIGAR=1M70D GT:FT:GQ:PL:PR:SR
INPUT_SCHEMA["info_fields"] = """
IMPRECISE: Boolean,
SVTYPE: String,
SVLEN: Int,
END: Int,
--- OCC: Int,
--- FRQ: Double,
"""
else:
raise ValueError("Unexpected dataset_type: %s" % args.dataset_type)
if args.exclude_vcf_info_field:
INPUT_SCHEMA["info_fields"] = ""
expr = convert_vds_schema_string_to_annotate_variants_expr(root="va.clean",
**INPUT_SCHEMA)
vds = vds.annotate_variants_expr(expr=expr)
vds = vds.annotate_variants_expr("va = va.clean")
if not args.skip_writing_intermediate_vds:
write_vds(vds, args.step1_output_vds)
args.start_with_step = 2 # step 1 finished, so, if an error occurs and it goes to retry, start with the next step
return hc, vds
def run_pipeline():
args = init_command_line_args()
# compute additional derived params and add them to the args object for convenience
args.output_vds_prefix = compute_output_vds_prefix(args)
args.step0_output_vcf = args.output_vds_prefix + (
".vep.vcf.bgz" if ".vep" not in args.output_vds_prefix
and not args.skip_vep else ".vcf.bgz")
args.step0_output_vds = args.output_vds_prefix + (
".vep.vds" if ".vep" not in args.output_vds_prefix
and not args.skip_vep else ".vds")
args.step1_output_vds = args.output_vds_prefix + ".vep_and_computed_annotations.vds"
args.step3_output_vds = args.output_vds_prefix + ".vep_and_all_annotations.vds"
hc = create_hail_context()
args.is_running_locally = hc.sc.master.startswith(
"local") # is the pipeline is running locally or on dataproc
logger.info("is_running_locally = %s", args.is_running_locally)
# pipeline steps
vds = None
hc, vds = step0_init_and_run_vep(hc, vds, args)
hc, vds = step1_compute_derived_fields(hc, vds, args)
hc, vds = step2_export_to_elasticsearch(hc, vds, args)
hc, vds = step3_add_reference_datasets(hc, vds, args)
hc, vds = step4_export_to_elasticsearch(hc, vds, args)
if args.stop_after_step > 4:
update_operations_log(args)
cleanup_steps(args)
if args.use_temp_loading_nodes:
# move data off of the loading nodes
route_index_to_temp_es_cluster(False, args)
logger.info("==> Pipeline completed")
logger.info("")
logger.info("")
logger.info("")
logger.info(
"========================================================================================================"
)
logger.info("")
logger.info(
"==> To add this dataset to a seqr project, click 'Edit Datasets' on the seqr project page "
)
logger.info(
" (https://seqr.broadinstitute.org/project/{}/project_page) and enter: "
.format(args.project_guid))
logger.info("")
logger.info(" Elasticsearch Index: {} ".format(args.index))
logger.info(" Sample Type: {} ".format(args.sample_type))
logger.info(" Dataset Path: {} ".format(args.input_dataset))
logger.info("")
logger.info(
"========================================================================================================"
)
logger.info("")
logger.info("")
