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)
out = []
for i, data in enumerate(items):
if "sv" not in data:
data["sv"] = []
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"])
gt_vcf = _run_svtyper(sample_vcf, dedup_bam, sr_bam, data)
filter_vcf = _filter_by_support(gt_vcf, data)
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": filter_vcf,
"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.combine_variant_files(
orig_files=[std_gt_vcf, bnd_vcf],
out_file="%s-combined.vcf.gz" % utils.splitext_plus(std_gt_vcf)[0],
ref_file=dd.get_ref_file(data),
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])
previous_evidence = {}
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)
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)
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"])
if "bnd-genotype" in dd.get_tools_on(data):
gt_vcf = _run_svtyper(sample_vcf, dedup_bam, sr_bam, exclude_file, data)
else:
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)]
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 _prep_subsampled_bams(data, work_dir):
"""Prepare a subsampled BAM file with discordants from samblaster and minimal correct pairs.
This attempts to minimize run times by pre-extracting useful reads mixed
with subsampled normal pairs to estimate paired end distributions:
https://groups.google.com/d/msg/delly-users/xmia4lwOd1Q/uaajoBkahAIJ
Subsamples correctly aligned reads to 100 million based on speedseq defaults and
evaluations on NA12878 whole genome data:
https://github.com/cc2qe/speedseq/blob/ca624ba9affb0bd0fb88834ca896e9122639ec94/bin/speedseq#L1102
XXX Currently not used as new versions of delly do not get good sensitivity
with downsampled BAMs.
"""
sr_bam, disc_bam = sshared.get_split_discordants(data, work_dir)
ds_bam = bam.downsample(
dd.get_align_bam(data),
data,
1e8,
read_filter="-F 'not secondary_alignment and proper_pair'",
always_run=True,
work_dir=work_dir)
out_bam = "%s-final%s" % utils.splitext_plus(ds_bam)
if not utils.file_exists(out_bam):
bam.merge([ds_bam, sr_bam, disc_bam], out_bam, data["config"])
bam.index(out_bam, data["config"])
return [out_bam]
def _prep_subsampled_bams(data, work_dir):
"""Prepare a subsampled BAM file with discordants from samblaster and minimal correct pairs.
This attempts to minimize run times by pre-extracting useful reads mixed
with subsampled normal pairs to estimate paired end distributions:
https://groups.google.com/d/msg/delly-users/xmia4lwOd1Q/uaajoBkahAIJ
Subsamples correctly aligned reads to 100 million based on speedseq defaults and
evaluations on NA12878 whole genome data:
https://github.com/cc2qe/speedseq/blob/ca624ba9affb0bd0fb88834ca896e9122639ec94/bin/speedseq#L1102
XXX Currently does not downsample as new versions do not get good sensitivity with
downsampled BAMs.
"""
full_bam, sr_bam, disc_bam = sshared.get_split_discordants(data, work_dir)
return [full_bam]
ds_bam = bam.downsample(full_bam, data, 1e8, read_filter="-F 'not secondary_alignment and proper_pair'",
always_run=True, work_dir=work_dir)
out_bam = "%s-final%s" % utils.splitext_plus(ds_bam)
if not utils.file_exists(out_bam):
bam.merge([ds_bam, sr_bam, disc_bam], out_bam, data["config"])
bam.index(out_bam, data["config"])
return [out_bam]
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")
work_dir = utils.safe_makedir(os.path.join(items[0]["dirs"]["work"], "structural", items[0]["name"][-1],
"lumpy"))
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)
pebed_file, exclude_file = _run_lumpy(full_bams, sr_bams, disc_bams, work_dir, items)
out = []
sample_config_file = _write_samples_to_ids(pebed_file, items)
lumpy_vcf = _bedpe_to_vcf(pebed_file, sample_config_file, items)
for i, data in enumerate(items):
if "sv" not in data:
data["sv"] = []
sample = tz.get_in(["rgnames", "sample"], data)
sample_bedpe = _filter_by_support(_subset_to_sample(pebed_file, i, data), i, data)
if lumpy_vcf:
sample_vcf = utils.append_stem(lumpy_vcf, "-%s" % sample)
sample_vcf = _filter_by_bedpe(vcfutils.select_sample(lumpy_vcf, sample, sample_vcf, data["config"]),
sample_bedpe, data)
else:
sample_vcf = None
data["sv"].append({"variantcaller": "lumpy",
"vrn_file": sample_vcf,
"exclude_file": exclude_file,
"bedpe_file": sample_bedpe,
"sample_bed": sample_config_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)
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(in_file, work_dir, data):
dedup_bam, sr_bam, _ = sshared.get_split_discordants(data, work_dir)
return _run_svtyper(in_file, dedup_bam, sr_bam,
call.get("exclude_file"), data)
def _run(in_file, work_dir, data):
dedup_bam, sr_bam, _ = sshared.get_split_discordants(data, work_dir)
return _run_svtyper(in_file, dedup_bam, sr_bam, call.get("exclude_file"), data)
