def get_analysis_intervals(data, vrn_file, base_dir):
"""Retrieve analysis regions for the current variant calling pipeline.
"""
if vrn_file and "gvcf" in dd.get_tools_on(data):
callable_bed = _callable_from_gvcf(data, vrn_file, base_dir)
if callable_bed:
return callable_bed
if data.get("ensemble_bed"):
return data["ensemble_bed"]
elif dd.get_callable_regions(data):
return dd.get_callable_regions(data)
elif data.get("align_bam"):
return callable.sample_callable_bed(data["align_bam"],
dd.get_ref_file(data), data)[0]
elif data.get("work_bam"):
return callable.sample_callable_bed(data["work_bam"],
dd.get_ref_file(data), data)[0]
elif data.get("work_bam_callable"):
return callable.sample_callable_bed(data["work_bam_callable"],
dd.get_ref_file(data), data)[0]
elif tz.get_in(["config", "algorithm", "callable_regions"], data):
return tz.get_in(["config", "algorithm", "callable_regions"], data)
elif tz.get_in(["config", "algorithm", "variant_regions"], data):
return tz.get_in(["config", "algorithm", "variant_regions"], data)
def get_analysis_intervals(data):
"""Retrieve analysis regions for the current variant calling pipeline.
"""
if data.get("ensemble_bed"):
return data["ensemble_bed"]
elif dd.get_callable_regions(data):
return dd.get_callable_regions(data)
elif data.get("align_bam"):
return callable.sample_callable_bed(data["align_bam"], dd.get_ref_file(data), data)
elif data.get("work_bam"):
return callable.sample_callable_bed(data["work_bam"], dd.get_ref_file(data), data)
elif data.get("work_bam_callable"):
return callable.sample_callable_bed(data["work_bam_callable"], dd.get_ref_file(data), data)
elif tz.get_in(["config", "algorithm", "callable_regions"], data):
return tz.get_in(["config", "algorithm", "callable_regions"], data)
elif tz.get_in(["config", "algorithm", "variant_regions"], data):
return tz.get_in(["config", "algorithm", "variant_regions"], data)
def get_analysis_intervals(data):
"""Retrieve analysis regions for the current variant calling pipeline.
"""
if data.get("ensemble_bed"):
return data["ensemble_bed"]
elif dd.get_callable_regions(data):
return dd.get_callable_regions(data)
elif data.get("align_bam"):
return callable.sample_callable_bed(data["align_bam"],
dd.get_ref_file(data), data)
elif data.get("work_bam"):
return callable.sample_callable_bed(data["work_bam"],
dd.get_ref_file(data), data)
elif data.get("work_bam_callable"):
return callable.sample_callable_bed(data["work_bam_callable"],
dd.get_ref_file(data), data)
elif tz.get_in(["config", "algorithm", "callable_regions"], data):
return tz.get_in(["config", "algorithm", "callable_regions"], data)
elif tz.get_in(["config", "algorithm", "variant_regions"], data):
return tz.get_in(["config", "algorithm", "variant_regions"], data)
def _evaluate_vcf(calls, truth_vcf, work_dir, data):
out_file = os.path.join(work_dir, os.path.join("%s-sv-validate.csv" % dd.get_sample_name(data)))
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
writer = csv.writer(out_handle)
writer.writerow(["sample", "caller", "vtype", "metric", "value"])
for call in calls:
for stats in _validate_caller_vcf(call["vrn_file"], truth_vcf, dd.get_callable_regions(data),
call["variantcaller"], data):
writer.writerow(stats)
return out_file
def get_analysis_intervals(data, vrn_file, base_dir):
"""Retrieve analysis regions for the current variant calling pipeline.
"""
if vrn_file and "gvcf" in dd.get_tools_on(data):
callable_bed = _callable_from_gvcf(data, vrn_file, base_dir)
if callable_bed:
return callable_bed
if data.get("ensemble_bed"):
return data["ensemble_bed"]
elif dd.get_callable_regions(data):
return dd.get_callable_regions(data)
elif data.get("align_bam"):
return callable.sample_callable_bed(data["align_bam"], dd.get_ref_file(data), data)
elif data.get("work_bam"):
return callable.sample_callable_bed(data["work_bam"], dd.get_ref_file(data), data)
elif data.get("work_bam_callable"):
return callable.sample_callable_bed(data["work_bam_callable"], dd.get_ref_file(data), data)
elif tz.get_in(["config", "algorithm", "callable_regions"], data):
return tz.get_in(["config", "algorithm", "callable_regions"], data)
elif tz.get_in(["config", "algorithm", "variant_regions"], data):
return tz.get_in(["config", "algorithm", "variant_regions"], data)
def _evaluate_vcf(calls, truth_vcf, work_dir, data):
out_file = os.path.join(
work_dir,
os.path.join("%s-sv-validate.csv" % dd.get_sample_name(data)))
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
writer = csv.writer(out_handle)
writer.writerow(
["sample", "caller", "vtype", "metric", "value"])
for call in calls:
for stats in _validate_caller_vcf(
call["vrn_file"], truth_vcf,
dd.get_callable_regions(data),
call["variantcaller"], data):
writer.writerow(stats)
return out_file
def _run_purecn_dx(out, paired):
"""Extract copy number and mutational metrics from PureCN rds file.
"""
out_base, out, all_files = _get_purecn_dx_files(paired, out)
if not utils.file_uptodate(out["mutation_burden"], out["rds"]):
with file_transaction(paired.tumor_data, out_base) as tx_out_base:
cmd = [
"PureCN_Dx.R", "--rds", out["rds"], "--callable",
dd.get_callable_regions(paired.tumor_data), "--signatures",
"--out", tx_out_base
]
do.run(cmd, "PureCN Dx mutational burden and signatures")
for f in all_files:
if os.path.exists(os.path.join(os.path.dirname(tx_out_base),
f)):
shutil.move(os.path.join(os.path.dirname(tx_out_base), f),
os.path.join(os.path.dirname(out_base), f))
return out
