def _has_ensemble(data):
# for tumour-normal calling, a sample may have "ensemble" for the normal
# sample configured but there won't be any variant files per se
variants_to_process = (len(data["variants"]) > 1
and any([x.get('vrn_file', None) is not None or x.get('vrn_file_batch', None) is not None
for x in data["variants"]]))
return variants_to_process and dd.get_ensemble(data)
def combine_calls(*args):
"""Combine multiple callsets into a final set of merged calls.
"""
if len(args) == 3:
is_cwl = False
batch_id, samples, data = args
caller_names, vrn_files = _organize_variants(samples, batch_id)
else:
is_cwl = True
samples = [utils.to_single_data(x) for x in args]
samples = [cwlutils.unpack_tarballs(x, x) for x in samples]
data = samples[0]
batch_id = data["batch_id"]
caller_names = data["variants"]["variantcallers"]
vrn_files = data["variants"]["calls"]
logger.info("Ensemble consensus calls for {0}: {1}".format(
batch_id, ",".join(caller_names)))
edata = copy.deepcopy(data)
base_dir = utils.safe_makedir(os.path.join(edata["dirs"]["work"], "ensemble", batch_id))
if any([vcfutils.vcf_has_variants(f) for f in vrn_files]):
# Decompose multiallelic variants and normalize
passonly = not tz.get_in(["config", "algorithm", "ensemble", "use_filtered"], edata, False)
vrn_files = [normalize.normalize(f, data, passonly=passonly, rerun_effects=False, remove_oldeffects=True,
nonrefonly=True,
work_dir=utils.safe_makedir(os.path.join(base_dir, c)))
for c, f in zip(caller_names, vrn_files)]
if "classifiers" not in (dd.get_ensemble(edata) or {}):
callinfo = _run_ensemble_intersection(batch_id, vrn_files, caller_names, base_dir, edata)
else:
config_file = _write_config_file(batch_id, caller_names, base_dir, edata)
callinfo = _run_ensemble(batch_id, vrn_files, config_file, base_dir,
dd.get_ref_file(edata), edata)
callinfo["vrn_file"] = vcfutils.bgzip_and_index(callinfo["vrn_file"], data["config"])
# After decomposing multiallelic variants and normalizing, re-evaluate effects
ann_ma_file, _ = effects.add_to_vcf(callinfo["vrn_file"], data)
if ann_ma_file:
callinfo["vrn_file"] = ann_ma_file
edata["config"]["algorithm"]["variantcaller"] = "ensemble"
edata["vrn_file"] = callinfo["vrn_file"]
edata["ensemble_bed"] = callinfo["bed_file"]
callinfo["validate"] = validate.compare_to_rm(edata)[0][0].get("validate")
else:
out_vcf_file = os.path.join(base_dir, "{0}-ensemble.vcf".format(batch_id))
vcfutils.write_empty_vcf(out_vcf_file, samples=[dd.get_sample_name(d) for d in samples])
callinfo = {"variantcaller": "ensemble",
"vrn_file": vcfutils.bgzip_and_index(out_vcf_file, data["config"]),
"bed_file": None}
if is_cwl:
callinfo["batch_samples"] = data["batch_samples"]
callinfo["batch_id"] = batch_id
return [{"ensemble": callinfo}]
else:
return [[batch_id, callinfo]]
def _variant_checkpoints(samples):
"""Check sample configuration to identify required steps in analysis.
"""
checkpoints = {}
checkpoints["vc"] = any([dd.get_variantcaller(d) or d.get("vrn_file") for d in samples])
checkpoints["sv"] = any([dd.get_svcaller(d) for d in samples])
checkpoints["jointvc"] = any([(dd.get_jointcaller(d) or "gvcf" in dd.get_tools_on(d))
for d in samples])
checkpoints["hla"] = any([dd.get_hlacaller(d) for d in samples])
checkpoints["align"] = any([(dd.get_aligner(d) or dd.get_bam_clean(d)) for d in samples])
checkpoints["align_split"] = not all([(dd.get_align_split_size(d) is False or
not dd.get_aligner(d))
for d in samples])
checkpoints["archive"] = any([dd.get_archive(d) for d in samples])
checkpoints["umi"] = any([dd.get_umi_consensus(d) for d in samples])
checkpoints["ensemble"] = any([dd.get_ensemble(d) for d in samples])
checkpoints["cancer"] = any(dd.get_phenotype(d) in ["tumor"] for d in samples)
return checkpoints
def _variant_checkpoints(samples):
"""Check sample configuration to identify required steps in analysis.
"""
checkpoints = {}
checkpoints["vc"] = any(
[dd.get_variantcaller(d) or d.get("vrn_file") for d in samples])
checkpoints["sv"] = any([dd.get_svcaller(d) for d in samples])
checkpoints["jointvc"] = any([(dd.get_jointcaller(d)
or "gvcf" in dd.get_tools_on(d))
for d in samples])
checkpoints["hla"] = any([dd.get_hlacaller(d) for d in samples])
checkpoints["align"] = any([(dd.get_aligner(d) or dd.get_bam_clean(d))
for d in samples])
checkpoints["align_split"] = not all([(dd.get_align_split_size(d) is False
or not dd.get_aligner(d))
for d in samples])
checkpoints["umi"] = any([dd.get_umi_consensus(d) for d in samples])
checkpoints["ensemble"] = any([dd.get_ensemble(d) for d in samples])
return checkpoints
def combine_calls(*args):
"""Combine multiple callsets into a final set of merged calls.
"""
if len(args) == 3:
is_cwl = False
batch_id, samples, data = args
caller_names, vrn_files = _organize_variants(samples, batch_id)
else:
is_cwl = True
samples = [utils.to_single_data(x) for x in args]
samples = [cwlutils.unpack_tarballs(x, x) for x in samples]
data = samples[0]
batch_id = data["batch_id"]
caller_names = data["variants"]["variantcallers"]
vrn_files = data["variants"]["calls"]
logger.info("Ensemble consensus calls for {0}: {1}".format(
batch_id, ",".join(caller_names)))
edata = copy.deepcopy(data)
base_dir = utils.safe_makedir(
os.path.join(edata["dirs"]["work"], "ensemble", batch_id))
if any([vcfutils.vcf_has_variants(f) for f in vrn_files]):
# Decompose multiallelic variants and normalize
passonly = not tz.get_in(
["config", "algorithm", "ensemble", "use_filtered"], edata, False)
vrn_files = [
normalize.normalize(f,
data,
passonly=passonly,
rerun_effects=False,
remove_oldeffects=True,
nonrefonly=True,
work_dir=utils.safe_makedir(
os.path.join(base_dir, c)))
for c, f in zip(caller_names, vrn_files)
]
if "classifiers" not in (dd.get_ensemble(edata) or {}):
callinfo = _run_ensemble_intersection(batch_id, vrn_files,
caller_names, base_dir,
edata)
else:
config_file = _write_config_file(batch_id, caller_names, base_dir,
edata)
callinfo = _run_ensemble(batch_id, vrn_files, config_file,
base_dir, dd.get_ref_file(edata), edata)
callinfo["vrn_file"] = vcfutils.bgzip_and_index(
callinfo["vrn_file"], data["config"])
# After decomposing multiallelic variants and normalizing, re-evaluate effects
ann_ma_file, _ = effects.add_to_vcf(callinfo["vrn_file"], data)
if ann_ma_file:
callinfo["vrn_file"] = ann_ma_file
edata["config"]["algorithm"]["variantcaller"] = "ensemble"
edata["vrn_file"] = callinfo["vrn_file"]
edata["ensemble_bed"] = callinfo["bed_file"]
callinfo["validate"] = validate.compare_to_rm(edata)[0][0].get(
"validate")
else:
out_vcf_file = os.path.join(base_dir,
"{0}-ensemble.vcf".format(batch_id))
vcfutils.write_empty_vcf(
out_vcf_file, samples=[dd.get_sample_name(d) for d in samples])
callinfo = {
"variantcaller": "ensemble",
"vrn_file": vcfutils.bgzip_and_index(out_vcf_file, data["config"]),
"bed_file": None
}
if is_cwl:
callinfo["batch_samples"] = data["batch_samples"]
callinfo["batch_id"] = batch_id
return [{"ensemble": callinfo}]
else:
return [[batch_id, callinfo]]
