def run(items):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
data = paired.tumor_data if paired else items[0]
work_dir = _sv_workdir(data)
variant_file = _get_out_file(work_dir, paired)
if not utils.file_exists(variant_file):
with file_transaction(data, work_dir) as tx_work_dir:
utils.safe_makedir(tx_work_dir)
tx_workflow_file = _prep_config(items, paired, tx_work_dir)
_run_workflow(items, paired, tx_workflow_file, tx_work_dir)
assert utils.file_exists(variant_file), "Manta finished without output file %s" % variant_file
variant_file = shared.annotate_with_depth(variant_file, items)
out = []
upload_counts = collections.defaultdict(int)
for data in items:
if "break-point-inspector" in dd.get_tools_on(data):
if paired and paired.normal_bam and paired.tumor_name == dd.get_sample_name(data):
variant_file = _run_break_point_inspector(data, variant_file, paired, work_dir)
if "sv" not in data:
data["sv"] = []
final_vcf = shared.finalize_sv(variant_file, data, items)
vc = {"variantcaller": "manta",
"do_upload": upload_counts[final_vcf] == 0, # only upload a single file per batch
"vrn_file": final_vcf}
evidence_bam = _get_evidence_bam(work_dir, data)
if evidence_bam:
vc["read_evidence"] = evidence_bam
data["sv"].append(vc)
upload_counts[final_vcf] += 1
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
data = paired.tumor_data if paired else items[0]
work_dir = _sv_workdir(data)
variant_file = _get_out_file(work_dir, paired)
if not utils.file_exists(variant_file):
with file_transaction(data, work_dir) as tx_work_dir:
utils.safe_makedir(tx_work_dir)
tx_workflow_file = _prep_config(items, paired, tx_work_dir)
_run_workflow(items, paired, tx_workflow_file, tx_work_dir)
assert utils.file_exists(
variant_file), "Manta finished without output file %s" % variant_file
variant_file = shared.annotate_with_depth(variant_file, items)
out = []
for data in items:
if paired and paired.normal_bam and "break-point-inspector" in dd.get_tools_on(
data):
variant_file = _run_break_point_inspector(data, variant_file,
paired)
if "sv" not in data:
data["sv"] = []
final_vcf = shared.finalize_sv(variant_file, data, items)
data["sv"].append({"variantcaller": "manta", "vrn_file": final_vcf})
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with lumpy.
"""
paired = vcfutils.get_paired(items)
work_dir = _sv_workdir(
paired.tumor_data if paired and paired.tumor_data else items[0])
previous_evidence = {}
full_bams, sr_bams, disc_bams = [], [], []
for data in items:
full_bams.append(dd.get_align_bam(data))
sr_bam, disc_bam = sshared.find_existing_split_discordants(data)
sr_bams.append(sr_bam)
disc_bams.append(disc_bam)
cur_dels, cur_dups = _bedpes_from_cnv_caller(data, work_dir)
previous_evidence[dd.get_sample_name(data)] = {}
if cur_dels and utils.file_exists(cur_dels):
previous_evidence[dd.get_sample_name(data)]["dels"] = cur_dels
if cur_dups and utils.file_exists(cur_dups):
previous_evidence[dd.get_sample_name(data)]["dups"] = cur_dups
lumpy_vcf, exclude_file = _run_smoove(full_bams, sr_bams, disc_bams,
work_dir, items)
lumpy_vcf = sshared.annotate_with_depth(lumpy_vcf, items)
gt_vcfs = {}
# Retain paired samples with tumor/normal genotyped in one file
if paired and paired.normal_name:
batches = [[paired.tumor_data, paired.normal_data]]
else:
batches = [[x] for x in items]
for batch_items in batches:
for data in batch_items:
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(
lumpy_vcf, data)
if paired and paired.normal_name:
gt_vcfs = _filter_by_background(paired.tumor_name,
[paired.normal_name], gt_vcfs,
paired.tumor_data)
out = []
upload_counts = collections.defaultdict(int)
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs.get(dd.get_sample_name(data))
if vcf_file:
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
data["sv"].append({
"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"do_upload": upload_counts[vcf_file] ==
0, # only upload a single file per batch
"exclude_file": exclude_file
})
upload_counts[vcf_file] += 1
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with lumpy.
"""
paired = vcfutils.get_paired(items)
work_dir = _sv_workdir(paired.tumor_data if paired and paired.tumor_data else items[0])
previous_evidence = {}
full_bams, sr_bams, disc_bams = [], [], []
for data in items:
full_bams.append(dd.get_align_bam(data))
sr_bam, disc_bam = sshared.find_existing_split_discordants(data)
sr_bams.append(sr_bam)
disc_bams.append(disc_bam)
cur_dels, cur_dups = _bedpes_from_cnv_caller(data, work_dir)
previous_evidence[dd.get_sample_name(data)] = {}
if cur_dels and utils.file_exists(cur_dels):
previous_evidence[dd.get_sample_name(data)]["dels"] = cur_dels
if cur_dups and utils.file_exists(cur_dups):
previous_evidence[dd.get_sample_name(data)]["dups"] = cur_dups
lumpy_vcf, exclude_file = _run_smoove(full_bams, sr_bams, disc_bams, work_dir, items)
lumpy_vcf = sshared.annotate_with_depth(lumpy_vcf, items)
gt_vcfs = {}
# Retain paired samples with tumor/normal genotyped in one file
if paired and paired.normal_name:
batches = [[paired.tumor_data, paired.normal_data]]
else:
batches = [[x] for x in items]
for batch_items in batches:
for data in batch_items:
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(lumpy_vcf, data)
if paired and paired.normal_name:
gt_vcfs = _filter_by_background(paired.tumor_name, [paired.normal_name], gt_vcfs, paired.tumor_data)
out = []
upload_counts = collections.defaultdict(int)
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs.get(dd.get_sample_name(data))
if vcf_file:
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"do_upload": upload_counts[vcf_file] == 0, # only upload a single file per batch
"exclude_file": exclude_file})
upload_counts[vcf_file] += 1
out.append(data)
return out
def _add_variantcalls_to_output(out, data, items, is_somatic=False):
"""Call ploidy and convert into VCF and BED representations.
"""
call_file = "%s-call%s" % os.path.splitext(out["cns"])
if not utils.file_exists(call_file):
with file_transaction(data, call_file) as tx_call_file:
filters = ["--filter", "cn"]
cmd = [os.path.join(os.path.dirname(sys.executable), "cnvkit.py"), "call"] + \
filters + \
["--ploidy", str(ploidy.get_ploidy([data])),
"-o", tx_call_file, out["cns"]]
small_vrn_files = _compatible_small_variants(data, items)
if len(small_vrn_files) > 0 and _cna_has_values(out["cns"]):
cmd += [
"--vcf", small_vrn_files[0].name, "--sample-id",
small_vrn_files[0].sample
]
if small_vrn_files[0].normal:
cmd += ["--normal-id", small_vrn_files[0].normal]
if not is_somatic:
cmd += ["-m", "clonal"]
gender = _get_batch_gender(items)
if gender:
cmd += ["--sample-sex", gender]
do.run(cmd, "CNVkit call ploidy")
calls = {}
for outformat in ["bed", "vcf"]:
out_file = "%s.%s" % (os.path.splitext(call_file)[0], outformat)
calls[outformat] = out_file
if not os.path.exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
cmd = [
os.path.join(os.path.dirname(sys.executable), "cnvkit.py"),
"export", outformat, "--sample-id",
dd.get_sample_name(data), "--ploidy",
str(ploidy.get_ploidy([data])), "-o", tx_out_file,
call_file
]
do.run(cmd, "CNVkit export %s" % outformat)
out["call_file"] = call_file
out["vrn_bed"] = annotate.add_genes(calls["bed"], data)
effects_vcf, _ = effects.add_to_vcf(calls["vcf"], data, "snpeff")
out["vrn_file"] = effects_vcf or calls["vcf"]
out["vrn_file"] = shared.annotate_with_depth(out["vrn_file"], items)
return out
def run(items):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
data = paired.tumor_data if paired else items[0]
work_dir = _sv_workdir(data)
variant_file = _get_out_file(work_dir, paired)
if not utils.file_exists(variant_file):
with file_transaction(data, work_dir) as tx_work_dir:
utils.safe_makedir(tx_work_dir)
tx_workflow_file = _prep_config(items, paired, tx_work_dir)
_run_workflow(items, paired, tx_workflow_file, tx_work_dir)
assert utils.file_exists(
variant_file), "Manta finished without output file %s" % variant_file
variant_file = shared.annotate_with_depth(variant_file, items)
out = []
upload_counts = collections.defaultdict(int)
for data in items:
if "break-point-inspector" in dd.get_tools_on(data):
if paired and paired.normal_bam and paired.tumor_name == dd.get_sample_name(
data):
variant_file = _run_break_point_inspector(
data, variant_file, paired, work_dir)
if "sv" not in data:
data["sv"] = []
final_vcf = shared.finalize_sv(variant_file, data, items)
vc = {
"variantcaller": "manta",
"do_upload": upload_counts[final_vcf] ==
0, # only upload a single file per batch
"vrn_file": final_vcf
}
evidence_bam = _get_evidence_bam(work_dir, data)
if evidence_bam:
vc["read_evidence"] = evidence_bam
data["sv"].append(vc)
upload_counts[final_vcf] += 1
out.append(data)
return out
def _add_variantcalls_to_output(out, data, items, is_somatic=False):
"""Call ploidy and convert into VCF and BED representations.
"""
call_file = "%s-call%s" % os.path.splitext(out["cns"])
if not utils.file_exists(call_file):
with file_transaction(data, call_file) as tx_call_file:
filters = ["--filter", "cn"]
cmd = [os.path.join(os.path.dirname(sys.executable), "cnvkit.py"), "call"] + \
filters + \
["--ploidy", str(ploidy.get_ploidy([data])),
"-o", tx_call_file, out["cns"]]
small_vrn_files = _compatible_small_variants(data, items)
if len(small_vrn_files) > 0 and _cna_has_values(out["cns"]):
cmd += ["--vcf", small_vrn_files[0].name, "--sample-id", small_vrn_files[0].sample]
if small_vrn_files[0].normal:
cmd += ["--normal-id", small_vrn_files[0].normal]
if not is_somatic:
cmd += ["-m", "clonal"]
gender = _get_batch_gender(items)
if gender:
cmd += ["--sample-sex", gender]
do.run(cmd, "CNVkit call ploidy")
calls = {}
for outformat in ["bed", "vcf"]:
out_file = "%s.%s" % (os.path.splitext(call_file)[0], outformat)
calls[outformat] = out_file
if not os.path.exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
cmd = [os.path.join(os.path.dirname(sys.executable), "cnvkit.py"), "export",
outformat, "--sample-id", dd.get_sample_name(data),
"--ploidy", str(ploidy.get_ploidy([data])),
"-o", tx_out_file, call_file]
do.run(cmd, "CNVkit export %s" % outformat)
out["call_file"] = call_file
out["vrn_bed"] = annotate.add_genes(calls["bed"], data)
effects_vcf, _ = effects.add_to_vcf(calls["vcf"], data, "snpeff")
out["vrn_file"] = effects_vcf or calls["vcf"]
out["vrn_file"] = shared.annotate_with_depth(out["vrn_file"], items)
return out
