def run(items):
"""Perform detection of structural variations with lumpy, using bwa-mem alignment.
"""
if not all(utils.get_in(data, ("config", "algorithm", "aligner"))
in ["bwa", "sentieon-bwa", False, None] for data in items):
raise ValueError("Require bwa-mem alignment input for lumpy structural variation detection")
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], 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:
sr_bam, disc_bam = sshared.get_split_discordants(data, work_dir)
full_bams.append(dd.get_align_bam(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_lumpy(full_bams, sr_bams, disc_bams, previous_evidence,
work_dir, items)
gt_vcfs = {}
for data in items:
sample = dd.get_sample_name(data)
sample_vcf = vcfutils.select_sample(lumpy_vcf, sample,
utils.append_stem(lumpy_vcf, "-%s" % sample),
data["config"])
if "bnd-genotype" in dd.get_tools_on(data):
gt_vcf = _run_svtyper(sample_vcf, dd.get_align_bam(data), exclude_file, data)
elif "lumpy-genotype" in dd.get_tools_off(data):
gt_vcf = sample_vcf
else:
std_vcf, bnd_vcf = _split_breakends(sample_vcf, data)
std_gt_vcf = _run_svtyper(std_vcf, dd.get_align_bam(data), exclude_file, data)
gt_vcf = vcfutils.concat_variant_files_bcftools(
orig_files=[std_gt_vcf, bnd_vcf],
out_file="%s-combined.vcf.gz" % utils.splitext_plus(std_gt_vcf)[0],
config=data["config"])
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(gt_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 = []
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs[dd.get_sample_name(data)]
if dd.get_svprioritize(data):
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
else:
effects_vcf = None
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"exclude_file": exclude_file})
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with lumpy, using bwa-mem alignment.
"""
if not all(utils.get_in(data, ("config", "algorithm", "aligner"))
in ["bwa", "sentieon-bwa", False, None] for data in items):
raise ValueError("Require bwa-mem alignment input for lumpy structural variation detection")
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], 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:
sr_bam, disc_bam = sshared.get_split_discordants(data, work_dir)
full_bams.append(dd.get_align_bam(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_lumpy(full_bams, sr_bams, disc_bams, previous_evidence,
work_dir, items)
gt_vcfs = {}
for data in items:
sample = dd.get_sample_name(data)
sample_vcf = vcfutils.select_sample(lumpy_vcf, sample,
utils.append_stem(lumpy_vcf, "-%s" % sample),
data["config"])
if "bnd-genotype" in dd.get_tools_on(data):
gt_vcf = _run_svtyper(sample_vcf, dd.get_align_bam(data), exclude_file, data)
else:
std_vcf, bnd_vcf = _split_breakends(sample_vcf, data)
std_gt_vcf = _run_svtyper(std_vcf, dd.get_align_bam(data), exclude_file, data)
gt_vcf = vcfutils.concat_variant_files_bcftools(
orig_files=[std_gt_vcf, bnd_vcf],
out_file="%s-combined.vcf.gz" % utils.splitext_plus(std_gt_vcf)[0],
config=data["config"])
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(gt_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 = []
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs[dd.get_sample_name(data)]
if dd.get_svprioritize(data):
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
else:
effects_vcf = None
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"exclude_file": exclude_file})
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with lumpy, using bwa-mem alignment.
"""
if not all(
utils.get_in(data, ("config", "algorithm",
"aligner")) in ["bwa", False, None]
for data in items):
raise ValueError(
"Require bwa-mem alignment input for lumpy structural variation detection"
)
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items)
work_dir = _sv_workdir(
paired.tumor_data if paired and paired.tumor_data else items[0])
full_bams, sr_bams, disc_bams = [], [], []
for data in items:
dedup_bam, sr_bam, disc_bam = sshared.get_split_discordants(
data, work_dir)
full_bams.append(dedup_bam)
sr_bams.append(sr_bam)
disc_bams.append(disc_bam)
lumpy_vcf, exclude_file = _run_lumpy(full_bams, sr_bams, disc_bams,
work_dir, items)
gt_vcfs = {}
for data in items:
sample = dd.get_sample_name(data)
dedup_bam, sr_bam, _ = sshared.get_split_discordants(data, work_dir)
sample_vcf = vcfutils.select_sample(
lumpy_vcf, sample, utils.append_stem(lumpy_vcf, "-%s" % sample),
data["config"])
std_vcf, bnd_vcf = _split_breakends(sample_vcf, data)
std_gt_vcf = _run_svtyper(std_vcf, dedup_bam, sr_bam, exclude_file,
data)
gt_vcf = vcfutils.concat_variant_files_bcftools(
orig_files=[std_gt_vcf, bnd_vcf],
out_file="%s-combined.vcf.gz" % utils.splitext_plus(std_gt_vcf)[0],
config=data["config"])
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(gt_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 = []
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs[dd.get_sample_name(data)]
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
data["sv"].append({
"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"exclude_file": exclude_file
})
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with lumpy, using bwa-mem alignment.
"""
if not all(utils.get_in(data, ("config", "algorithm", "aligner")) in ["bwa", False, None] for data in items):
raise ValueError("Require bwa-mem alignment input for lumpy structural variation detection")
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items)
work_dir = _sv_workdir(paired.tumor_data if paired and paired.tumor_data else items[0])
full_bams, sr_bams, disc_bams = [], [], []
for data in items:
dedup_bam, sr_bam, disc_bam = sshared.get_split_discordants(data, work_dir)
full_bams.append(dedup_bam)
sr_bams.append(sr_bam)
disc_bams.append(disc_bam)
lumpy_vcf, exclude_file = _run_lumpy(full_bams, sr_bams, disc_bams, work_dir, items)
gt_vcfs = {}
for data in items:
sample = dd.get_sample_name(data)
dedup_bam, sr_bam, _ = sshared.get_split_discordants(data, work_dir)
sample_vcf = vcfutils.select_sample(lumpy_vcf, sample,
utils.append_stem(lumpy_vcf, "-%s" % sample),
data["config"])
std_vcf, bnd_vcf = _split_breakends(sample_vcf, data)
std_gt_vcf = _run_svtyper(std_vcf, dedup_bam, sr_bam, exclude_file, data)
gt_vcf = vcfutils.concat_variant_files_bcftools(
orig_files=[std_gt_vcf, bnd_vcf],
out_file="%s-combined.vcf.gz" % utils.splitext_plus(std_gt_vcf)[0],
config=data["config"])
gt_vcfs[dd.get_sample_name(data)] = _filter_by_support(gt_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 = []
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs[dd.get_sample_name(data)]
effects_vcf, _ = effects.add_to_vcf(vcf_file, data, "snpeff")
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": effects_vcf or vcf_file,
"exclude_file": exclude_file})
out.append(data)
return out