def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif vcfutils.get_paired_phenotype(data):
out = []
for i, sub_data in enumerate(get_orig_items(data)):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
sub_data["vrn_file"] = data["vrn_file"]
else:
sub_data.pop("vrn_file", None)
out.append([sub_data])
return out
# joint calling, do not split back up due to potentially large sample sizes
elif tz.get_in(("config", "algorithm", "jointcaller"), data):
return [[data]]
# population or single sample
else:
out = []
for sub_data in get_orig_items(data):
sub_vrn_file = data["vrn_file"].replace(str(data["group"][0]) + "-", str(sub_data["name"][-1]) + "-")
if len(vcfutils.get_samples(data["vrn_file"])) > 1:
vcfutils.select_sample(data["vrn_file"], str(sub_data["name"][-1]), sub_vrn_file, data["config"])
elif not os.path.exists(sub_vrn_file):
utils.symlink_plus(data["vrn_file"], sub_vrn_file)
sub_data["vrn_file_batch"] = data["vrn_file"]
sub_data["vrn_file"] = sub_vrn_file
out.append([sub_data])
return out
def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif vcfutils.get_paired_phenotype(data):
out = []
for i, sub_data in enumerate(get_orig_items(data)):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
sub_data["vrn_file"] = data["vrn_file"]
else:
sub_data.pop("vrn_file", None)
out.append([sub_data])
return out
# joint calling, do not split back up due to potentially large sample sizes
elif tz.get_in(("config", "algorithm", "jointcaller"), data):
return [[data]]
# population or single sample
else:
out = []
for sub_data in get_orig_items(data):
sub_vrn_file = data["vrn_file"].replace(str(data["group"][0]) + "-", str(sub_data["name"][-1]) + "-")
if len(vcfutils.get_samples(data["vrn_file"])) > 1:
vcfutils.select_sample(data["vrn_file"], str(sub_data["name"][-1]), sub_vrn_file, data["config"])
elif not os.path.exists(sub_vrn_file):
utils.symlink_plus(data["vrn_file"], sub_vrn_file)
sub_data["vrn_file_batch"] = data["vrn_file"]
sub_data["vrn_file"] = sub_vrn_file
out.append([sub_data])
return out
def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif vcfutils.get_paired_phenotype(data):
out = []
for i, sub_data in enumerate(data["group_orig"]):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
sub_data["vrn_file"] = data["vrn_file"]
out.append([sub_data])
return out
# population or single sample
else:
out = []
for sub_data in data["group_orig"]:
sub_vrn_file = data["vrn_file"].replace(str(data["group"][0]) + "-", str(sub_data["name"][-1]) + "-")
if len(vcfutils.get_samples(data["vrn_file"])) > 1:
vcfutils.select_sample(data["vrn_file"], str(sub_data["name"][-1]), sub_vrn_file, data["config"])
elif not os.path.exists(sub_vrn_file):
utils.symlink_plus(data["vrn_file"], sub_vrn_file)
sub_data["vrn_file"] = sub_vrn_file
out.append([sub_data])
return out
def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif vcfutils.get_paired_phenotype(data):
out = []
for i, sub_data in enumerate(data["group_orig"]):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
if "combine" in data:
sub_data["combine"] = data["combine"]
sub_data["vrn_file"] = data["vrn_file"]
out.append([sub_data])
return out
# population or single sample
else:
out = []
for sub_data in data["group_orig"]:
sub_vrn_file = data["vrn_file"].replace(data["group"][0] + "-", sub_data["name"][-1] + "-")
if len(vcfutils.get_samples(data["vrn_file"])) > 1:
vcfutils.select_sample(data["vrn_file"], sub_data["name"][-1], sub_vrn_file, data["config"])
elif not os.path.exists(sub_vrn_file):
utils.symlink_plus(data["vrn_file"], sub_vrn_file)
if "combine" in data:
sub_data["combine"] = data["combine"]
sub_data["vrn_file"] = sub_vrn_file
out.append([sub_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)
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, background=None):
"""Detect copy number variations from batched set of samples using WHAM.
"""
if not background: background = []
background_bams = []
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items)
if paired:
inputs = [paired.tumor_data]
if paired.normal_bam:
background = [paired.normal_data]
background_bams = [paired.normal_bam]
else:
assert not background
inputs, background = shared.find_case_control(items)
background_bams = [x["align_bam"] for x in background]
orig_vcf = _run_wham(inputs, background_bams)
out = []
for data in inputs:
if "sv" not in data:
data["sv"] = []
sample_vcf = "%s-%s.vcf.gz" % (utils.splitext_plus(orig_vcf)[0], dd.get_sample_name(data))
sample_vcf = vcfutils.select_sample(orig_vcf, dd.get_sample_name(data), sample_vcf, data["config"])
if background:
sample_vcf = filter_by_background(sample_vcf, orig_vcf, background, data)
data["sv"].append({"variantcaller": "wham",
"vrn_file": sample_vcf})
out.append(data)
return out
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(vrn_file)
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
ref_dir, ref_filebase = os.path.split(dd.get_ref_file(data))
rtg_ref = os.path.normpath(os.path.join(ref_dir, os.path.pardir, "rtg",
"%s.sdf" % (os.path.splitext(ref_filebase)[0])))
assert os.path.exists(rtg_ref), ("Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
cmd = ["rtg", "vcfeval", "-b", rm_file, "--bed-regions", interval_bed,
"-c", vrn_file, "-t", rtg_ref, "-o", out_dir]
do.run(cmd, "Validate calls using rtg vcfeval", data)
return {"tp": os.path.join(out_dir, "tp.vcf.gz"),
"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")}
def run(items, background=None):
"""Detect copy number variations from batched set of samples using WHAM.
"""
if not background: background = []
background_bams = []
paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items)
if paired:
inputs = [paired.tumor_data]
if paired.normal_bam:
background = [paired.normal_data]
background_bams = [paired.normal_bam]
else:
assert not background
inputs, background = shared.find_case_control(items)
background_bams = [x["align_bam"] for x in background]
orig_vcf = _run_wham(inputs, background_bams)
out = []
for data in inputs:
if "sv" not in data:
data["sv"] = []
sample_vcf = "%s-%s.vcf.gz" % (utils.splitext_plus(orig_vcf)[0], dd.get_sample_name(data))
sample_vcf = vcfutils.select_sample(orig_vcf, dd.get_sample_name(data), sample_vcf, data["config"])
if background:
sample_vcf = filter_by_background(sample_vcf, orig_vcf, background, data)
effects_vcf, _ = effects.add_to_vcf(sample_vcf, data, "snpeff")
data["sv"].append({"variantcaller": "wham",
"vrn_file": effects_vcf or sample_vcf})
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")
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 _organize_variants(samples, batch_id):
"""Retrieve variant calls for all samples, merging batched samples into single VCF.
"""
bam_files = set([])
caller_names = [x["variantcaller"] for x in samples[0]["variants"]]
calls = collections.defaultdict(list)
for data in samples:
if "work_bam" in data:
bam_files.add(data["work_bam"])
for vrn in data["variants"]:
# for somatic ensemble, discard normal samples and filtered
# variants from vcfs
vrn_file = vrn["vrn_file"]
if data.get("metadata", False) and data["metadata"].get(
"phenotype", "normal").lower().startswith("tumor"):
vrn_file_temp = vrn_file.replace(
".vcf", "_tumorOnly_noFilteredCalls.vcf"
) if ".vcf" in vrn_file else vrn_file_temp + "_tumorOnly_noFilteredCalls.vcf.gz"
# Select tumor sample and keep only PASS and . calls
vrn_file = vcfutils.select_sample(in_file=vrn_file,
sample=data["name"][1],
out_file=vrn_file_temp,
config=data["config"],
filters="PASS,.")
calls[vrn["variantcaller"]].append(vrn_file)
data = samples[0]
vrn_files = []
for caller in caller_names:
fnames = calls[caller]
if len(fnames) == 1:
vrn_files.append(fnames[0])
else:
vrn_files.append(
population.get_multisample_vcf(fnames, batch_id, caller, data))
return caller_names, vrn_files, list(bam_files)
def test_4_vcf_sample_select(self):
"""Select a sample from a VCF file.
"""
fname = os.path.join(self.var_dir, "S1-variants.vcf")
out_file = "%s-sampleselect%s.gz" % os.path.splitext(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def test_4_vcf_sample_select(self):
"""Select a sample from a VCF file.
"""
fname = os.path.join(self.var_dir, "S1-variants.vcf")
out_file = "%s-sampleselect%s.gz" % os.path.splitext(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def test_4_vcf_sample_select(self):
"""Select a sample from a VCF file.
"""
fname = os.path.join(self.var_dir, "S1_S2-combined.vcf.gz")
out_file = "%s-sampleselect%s" % utils.splitext_plus(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def test_4_vcf_sample_select(self):
"""Select a sample from a VCF file.
"""
fname = os.path.join(self.var_dir, "S1_S2-combined.vcf.gz")
out_file = "%s-sampleselect%s" % utils.splitext_plus(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def _organize_variants(samples, batch_id):
"""Retrieve variant calls for all samples, merging batched samples into single VCF.
"""
bam_files = set([])
caller_names = [x["variantcaller"] for x in samples[0]["variants"]]
calls = collections.defaultdict(list)
for data in samples:
if "work_bam" in data:
bam_files.add(data["work_bam"])
for vrn in data["variants"]:
# for somatic ensemble, discard normal samples and filtered
# variants from vcfs
vrn_file = vrn["vrn_file"]
if data.get("metadata", False) and data["metadata"].get("phenotype", "normal").lower().startswith("tumor"):
vrn_file_temp = vrn_file.replace(".vcf", "_tumorOnly_noFilteredCalls.vcf") if ".vcf" in vrn_file else vrn_file_temp + "_tumorOnly_noFilteredCalls.vcf.gz"
# Select tumor sample and keep only PASS and . calls
vrn_file = vcfutils.select_sample(in_file=vrn_file, sample=data["name"][1],
out_file=vrn_file_temp,
config=data["config"], filters="PASS,.")
calls[vrn["variantcaller"]].append(vrn_file)
data = samples[0]
vrn_files = []
for caller in caller_names:
fnames = calls[caller]
if len(fnames) == 1:
vrn_files.append(fnames[0])
else:
vrn_files.append(population.get_multisample_vcf(fnames, batch_id, caller, data))
return caller_names, vrn_files, list(bam_files)
def test_4_vcf_sample_select(self, install_test_files, data_dir):
"""Select a sample from a VCF file.
"""
from bcbio.variation import vcfutils
fname = os.path.join(self.var_dir, "S1_S2-combined.vcf.gz")
out_file = "%s-sampleselect%s" % utils.splitext_plus(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def test_4_vcf_sample_select(self, install_test_files, data_dir):
"""Select a sample from a VCF file.
"""
from bcbio.variation import vcfutils
fname = os.path.join(self.var_dir, "S1_S2-combined.vcf.gz")
out_file = "%s-sampleselect%s" % utils.splitext_plus(fname)
out_file = vcfutils.select_sample(fname, "S2", out_file, {})
self._remove_vcf(out_file)
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file +
".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file,
data["config"],
out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_file = os.path.join(
base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file,
dd.get_sample_name(data),
sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file +
".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file,
data["config"],
out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
rtg_ref = tz.get_in(["reference", "rtg"], data)
assert rtg_ref and os.path.exists(rtg_ref), (
"Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
# handle CWL where we have a reference to a single file in the RTG directory
if os.path.isfile(rtg_ref):
rtg_ref = os.path.dirname(rtg_ref)
threads = min(dd.get_num_cores(data), 6)
mem = "%sg" % threads
cmd = [
"rtg", "vcfeval", "--threads",
str(threads), "-b", rm_file, "--bed-regions", interval_bed, "-c",
vrn_file, "-t", rtg_ref, "-o", out_dir
]
cmd += [
"--vcf-score-field='%s'" % (_pick_best_quality_score(vrn_file))
]
mem_export = "export RTG_JAVA_OPTS='-Xms1g' && export RTG_MEM=%s" % mem
cmd = mem_export + " && " + " ".join(cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
out = {
"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")
}
tp_calls = os.path.join(out_dir, "tp.vcf.gz")
tp_baseline = os.path.join(out_dir, "tp-baseline.vcf.gz")
if os.path.exists(tp_baseline):
out["tp"] = tp_baseline
out["tp-calls"] = tp_calls
else:
out["tp"] = tp_calls
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(items):
"""Perform detection of structural variations with delly.
Performs post-call filtering with a custom filter tuned based
on NA12878 Moleculo and PacBio data, using calls prepared by
@ryanlayer and @cc2qe
Filters using the high quality variant pairs (DV) compared with
high quality reference pairs (DR).
"""
work_dir = utils.safe_makedir(
os.path.join(items[0]["dirs"]["work"], "structural",
items[0]["name"][-1], "delly"))
work_bams = [data["align_bam"] for data in items]
ref_file = utils.get_in(items[0], ("reference", "fasta", "base"))
# Add core request for delly
config = copy.deepcopy(items[0]["config"])
delly_config = utils.get_in(config, ("resources", "delly"), {})
delly_config["cores"] = len(items)
config["resources"]["delly"] = delly_config
parallel = {
"type": "local",
"cores": config["algorithm"].get("num_cores", 1),
"progs": ["delly"]
}
sv_types = [
"DEL", "DUP", "INV"
] # "TRA" has invalid VCF END specifications that GATK doesn't like
with closing(pysam.Samfile(work_bams[0], "rb")) as pysam_work_bam:
bytype_vcfs = run_multicore(
_run_delly,
[(work_bams, chrom, sv_type, ref_file, work_dir, items)
for (chrom, sv_type
) in itertools.product(pysam_work_bam.references, sv_types)],
config, parallel)
out_file = "%s.vcf.gz" % os.path.commonprefix(bytype_vcfs)
combo_vcf = vcfutils.combine_variant_files(bytype_vcfs, out_file, ref_file,
items[0]["config"])
delly_vcf = vfilter.genotype_filter(combo_vcf,
'DV / (DV + DR) > 0.35 && DV > 4',
data, "DVSupport")
out = []
for data in items:
if "sv" not in data:
data["sv"] = []
base, ext = utils.splitext_plus(delly_vcf)
sample = tz.get_in(["rgnames", "sample"], data)
delly_sample_vcf = "%s-%s%s" % (base, sample, ext)
data["sv"].append({
"variantcaller":
"delly",
"vrn_file":
vcfutils.select_sample(delly_vcf, sample, delly_sample_vcf,
data["config"])
})
out.append(data)
return out
def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, assign the combined file to the
tumor sample instead of splitting, and remove variant files from the normal.
"""
config = data["config"]
vrn_file = data["vrn_file"]
out = []
# cancer tumor/normal
if vcfutils.get_paired_phenotype(data):
# handle trailing normals, which we don't need to process
if len(data["group_orig"]) == 1 and vcfutils.get_paired_phenotype(data["group_orig"][0][0]) == "normal":
sub_data, sub_vrn_file = data["group_orig"][0]
sub_data.pop("vrn_file", None)
sub_data["vrn_file-shared"] = sub_vrn_file
out.append(sub_data)
else:
has_tumor = False
for sub_data, sub_vrn_file in data["group_orig"]:
paired_phenotype = vcfutils.get_paired_phenotype(sub_data)
if paired_phenotype == "tumor":
has_tumor = True
if not os.path.exists(sub_vrn_file):
utils.symlink_plus(vrn_file, sub_vrn_file)
sub_data["vrn_file"] = sub_vrn_file
out.append(sub_data)
else:
sub_data.pop("vrn_file", None)
sub_data["vrn_file-shared"] = sub_vrn_file
out.append(sub_data)
if not has_tumor:
raise ValueError("Did not find tumor sample in paired analysis")
# population or single sample
else:
for sub_data, sub_vrn_file in data["group_orig"]:
if len(vcfutils.get_samples(vrn_file)) > 1:
vcfutils.select_sample(vrn_file, sub_data["name"][-1], sub_vrn_file, config)
elif not os.path.exists(sub_vrn_file):
utils.symlink_plus(vrn_file, sub_vrn_file)
if sub_vrn_file:
sub_data["vrn_file"] = sub_vrn_file
out.append(sub_data)
return out
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file +
".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file,
data["config"],
out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(vrn_file)
sample_file = os.path.join(
base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file,
dd.get_sample_name(data),
sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file +
".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file,
data["config"],
out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
ref_dir, ref_filebase = os.path.split(dd.get_ref_file(data))
rtg_ref = os.path.normpath(
os.path.join(ref_dir, os.path.pardir, "rtg",
"%s.sdf" % (os.path.splitext(ref_filebase)[0])))
assert os.path.exists(rtg_ref), (
"Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
cmd = [
"rtg", "vcfeval", "--threads", "6", "-b", rm_file, "--bed-regions",
interval_bed, "-c", vrn_file, "-t", rtg_ref, "-o", out_dir
]
caller = _get_caller(data)
# flexible quality scores for building ROC curves, handle multiple cases
# MuTect has no quality scores
# not clear how to get t_lod_fstar into VCF cleanly
if caller == "mutect":
cmd += ["--vcf-score-field=BQ"]
# otherwise use quality score as a standard
# Discussion point: is it worth using caller specific annotations or settling
# on a single metric for comparison
else:
cmd += ["--vcf-score-field=QUAL"]
cmd = "export RTG_JAVA_OPTS='-Xms1g' export RTG_MEM=5g && " + " ".join(
cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
return {
"tp": os.path.join(out_dir, "tp.vcf.gz"),
"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")
}
def run(items):
"""Perform detection of structural variations with delly.
Performs post-call filtering with a custom filter tuned based
on NA12878 Moleculo and PacBio data, using calls prepared by
@ryanlayer and @cc2qe
Filters using the high quality variant pairs (DV) compared with
high quality reference pairs (DR).
"""
work_dir = utils.safe_makedir(
os.path.join(items[0]["dirs"]["work"], "structural",
items[0]["name"][-1], "delly"))
# Add core request for delly
config = copy.deepcopy(items[0]["config"])
delly_config = utils.get_in(config, ("resources", "delly"), {})
delly_config["cores"] = 1
config["resources"]["delly"] = delly_config
parallel = {
"type": "local",
"cores": config["algorithm"].get("num_cores", 1),
"progs": ["delly"]
}
work_bams = run_multicore(_prep_subsampled_bams,
[(data, work_dir) for data in items], config,
parallel)
ref_file = utils.get_in(items[0], ("reference", "fasta", "base"))
sv_types = [
"DEL", "DUP"
] # "TRA" has invalid VCF END specifications that GATK doesn't like, "INV" very slow
exclude_file = _get_full_exclude_file(items, work_dir)
bytype_vcfs = run_multicore(
_run_delly,
[(work_bams, chrom, sv_type, ref_file, work_dir, items)
for (chrom, sv_type) in itertools.product(
sshared.get_sv_chroms(items, exclude_file), sv_types)], config,
parallel)
out_file = "%s.vcf.gz" % sshared.outname_from_inputs(bytype_vcfs)
combo_vcf = vcfutils.combine_variant_files(bytype_vcfs, out_file, ref_file,
config)
out = []
for data in items:
if "sv" not in data:
data["sv"] = []
base, ext = utils.splitext_plus(combo_vcf)
sample = tz.get_in(["rgnames", "sample"], data)
delly_sample_vcf = vcfutils.select_sample(
combo_vcf, sample, "%s-%s%s" % (base, sample, ext), data["config"])
delly_vcf = _delly_count_evidence_filter(delly_sample_vcf, data)
data["sv"].append({
"variantcaller": "delly",
"vrn_file": delly_vcf,
"exclude": 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", "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_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file +
".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file,
data["config"],
out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(vrn_file)
sample_file = os.path.join(
base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file,
dd.get_sample_name(data),
sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file +
".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file,
data["config"],
out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
ref_dir, ref_filebase = os.path.split(dd.get_ref_file(data))
rtg_ref = os.path.normpath(
os.path.join(ref_dir, os.path.pardir, "rtg",
"%s.sdf" % (os.path.splitext(ref_filebase)[0])))
assert os.path.exists(rtg_ref), (
"Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
cmd = [
"rtg", "vcfeval", "--threads", "6", "-b", rm_file, "--bed-regions",
interval_bed, "-c", vrn_file, "-t", rtg_ref, "-o", out_dir
]
cmd += [
"--vcf-score-field='%s'" % (_pick_best_quality_score(vrn_file))
]
cmd = "export RTG_JAVA_OPTS='-Xms1g' && export RTG_MEM=5g && " + " ".join(
cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
out = {
"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")
}
tp_calls = os.path.join(out_dir, "tp.vcf.gz")
tp_baseline = os.path.join(out_dir, "tp-baseline.vcf.gz")
if os.path.exists(tp_baseline):
out["tp"] = tp_baseline
out["tp-calls"] = tp_calls
else:
out["tp"] = tp_calls
return out
def _handle_somatic_ensemble(vrn_file, data):
"""For somatic ensemble, discard normal samples and filtered variants from vcfs.
Only needed for bcbio.variation based ensemble calling.
"""
if tz.get_in(["metadata", "phenotype"], data, "").lower().startswith("tumor"):
vrn_file_temp = vrn_file.replace(".vcf", "_tumorOnly_noFilteredCalls.vcf")
# Select tumor sample and keep only PASS and . calls
vrn_file = vcfutils.select_sample(in_file=vrn_file, sample=data["name"][1],
out_file=vrn_file_temp,
config=data["config"], filters="PASS,.")
return vrn_file
def _handle_somatic_ensemble(vrn_file, data):
"""For somatic ensemble, discard normal samples and filtered variants from vcfs.
Only needed for bcbio.variation based ensemble calling.
"""
if tz.get_in(["metadata", "phenotype"], data, "").lower().startswith("tumor"):
vrn_file_temp = vrn_file.replace(".vcf", "_tumorOnly_noFilteredCalls.vcf")
# Select tumor sample and keep only PASS and . calls
vrn_file = vcfutils.select_sample(in_file=vrn_file, sample=data["name"][1],
out_file=vrn_file_temp,
config=data["config"], filters="PASS,.")
return vrn_file
def _prepare_inputs(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Prepare input VCF and BED files for validation.
"""
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, vrn_file, base_dir, data)
return vrn_file, rm_file, interval_bed
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
rtg_ref = tz.get_in(["reference", "rtg"], data)
assert rtg_ref and os.path.exists(rtg_ref), ("Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
# handle CWL where we have a reference to a single file in the RTG directory
if os.path.isfile(rtg_ref):
rtg_ref = os.path.dirname(rtg_ref)
# get core and memory usage from standard configuration
threads = min(dd.get_num_cores(data), 6)
resources = config_utils.get_resources("rtg", data["config"])
memory = config_utils.adjust_opts(resources.get("jvm_opts", ["-Xms500m", "-Xmx1500m"]),
{"algorithm": {"memory_adjust": {"magnitude": threads,
"direction": "increase"}}})
jvm_stack = [x for x in memory if x.startswith("-Xms")]
jvm_mem = [x for x in memory if x.startswith("-Xmx")]
jvm_stack = jvm_stack[0] if len(jvm_stack) > 0 else "-Xms500m"
jvm_mem = jvm_mem[0].replace("-Xmx", "") if len(jvm_mem) > 0 else "3g"
cmd = ["rtg", "vcfeval", "--threads", str(threads),
"-b", rm_file, "--bed-regions", interval_bed,
"-c", vrn_file, "-t", rtg_ref, "-o", out_dir]
cmd += ["--vcf-score-field='%s'" % (_pick_best_quality_score(vrn_file))]
mem_export = "export RTG_JAVA_OPTS='%s' && export RTG_MEM=%s" % (jvm_stack, jvm_mem)
cmd = mem_export + " && " + " ".join(cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
out = {"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")}
tp_calls = os.path.join(out_dir, "tp.vcf.gz")
tp_baseline = os.path.join(out_dir, "tp-baseline.vcf.gz")
if os.path.exists(tp_baseline):
out["tp"] = tp_baseline
out["tp-calls"] = tp_calls
else:
out["tp"] = tp_calls
return out
def _prepare_inputs(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Prepare input VCF and BED files for validation.
"""
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, vrn_file, base_dir, data)
return vrn_file, rm_file, interval_bed
def finalize_sv(orig_vcf, data, items):
"""Finalize structural variants, adding effects and splitting if needed.
"""
paired = vcfutils.get_paired(items)
# For paired/somatic, attach combined calls to tumor sample
if paired:
sample_vcf = orig_vcf if paired.tumor_name == dd.get_sample_name(data) else None
else:
sample_vcf = "%s-%s.vcf.gz" % (utils.splitext_plus(orig_vcf)[0], dd.get_sample_name(data))
sample_vcf = vcfutils.select_sample(orig_vcf, dd.get_sample_name(data), sample_vcf, data["config"])
if sample_vcf:
effects_vcf, _ = effects.add_to_vcf(sample_vcf, data, "snpeff")
else:
effects_vcf = None
return effects_vcf or sample_vcf
def _prepare_inputs(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Prepare input VCF and BED files for validation.
"""
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base = utils.splitext_plus(os.path.basename(vrn_file))[0]
sample_file = os.path.join(base_dir, "%s-%s.vcf.gz" % (base, dd.get_sample_name(data)))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
# rtg fails on bgzipped VCFs produced by GatherVcfs so we re-prep them
else:
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, vrn_file, base_dir, data)
return vrn_file, rm_file, interval_bed
def _compatible_small_variants(data):
"""Retrieve small variant (SNP, indel) VCFs compatible with CNVkit.
"""
supported = set(["vardict", "freebayes", "gatk-haplotype", "mutect2", "vardict"])
out = []
for v in data.get("variants", []):
vrn_file = v.get("vrn_file")
if vrn_file and v.get("variantcaller") in supported:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_vrn_file = os.path.join(dd.get_work_dir(data), v["variantcaller"],
"%s-%s%s" % (base, dd.get_sample_name(data), ext))
sample_vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_vrn_file,
data["config"])
out.append(sample_vrn_file)
return out
def _prepare_inputs(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Prepare input VCF and BED files for validation.
"""
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base = utils.splitext_plus(os.path.basename(vrn_file))[0]
sample_file = os.path.join(base_dir, "%s-%s.vcf.gz" % (base, dd.get_sample_name(data)))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
# rtg fails on bgzipped VCFs produced by GatherVcfs so we re-prep them
else:
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, vrn_file, base_dir, data)
return vrn_file, rm_file, interval_bed
def run(items):
"""Perform detection of structural variations with delly.
Performs post-call filtering with a custom filter tuned based
on NA12878 Moleculo and PacBio data, using calls prepared by
@ryanlayer and @cc2qe
Filters using the high quality variant pairs (DV) compared with
high quality reference pairs (DR).
"""
work_dir = utils.safe_makedir(os.path.join(items[0]["dirs"]["work"], "structural",
items[0]["name"][-1], "delly"))
# Add core request for delly
config = copy.deepcopy(items[0]["config"])
delly_config = utils.get_in(config, ("resources", "delly"), {})
delly_config["cores"] = 1
config["resources"]["delly"] = delly_config
parallel = {"type": "local", "cores": config["algorithm"].get("num_cores", 1),
"progs": ["delly"]}
work_bams = run_multicore(_prep_subsampled_bams,
[(data, work_dir) for data in items],
config, parallel)
ref_file = utils.get_in(items[0], ("reference", "fasta", "base"))
sv_types = ["DEL", "DUP"] # "TRA" has invalid VCF END specifications that GATK doesn't like, "INV" very slow
exclude_file = _get_full_exclude_file(items, work_dir)
bytype_vcfs = run_multicore(_run_delly,
[(work_bams, chrom, sv_type, ref_file, work_dir, items)
for (chrom, sv_type)
in itertools.product(sshared.get_sv_chroms(items, exclude_file), sv_types)],
config, parallel)
out_file = "%s.vcf.gz" % sshared.outname_from_inputs(bytype_vcfs)
combo_vcf = vcfutils.combine_variant_files(bytype_vcfs, out_file, ref_file, config)
out = []
for data in items:
if "sv" not in data:
data["sv"] = []
base, ext = utils.splitext_plus(combo_vcf)
sample = tz.get_in(["rgnames", "sample"], data)
delly_sample_vcf = vcfutils.select_sample(combo_vcf, sample,
"%s-%s%s" % (base, sample, ext), data["config"])
delly_vcf = _delly_count_evidence_filter(delly_sample_vcf, data)
data["sv"].append({"variantcaller": "delly", "vrn_file": delly_vcf,
"exclude": exclude_file})
out.append(data)
return out
def _compatible_small_variants(data):
"""Retrieve small variant (SNP, indel) VCFs compatible with CNVkit.
"""
supported = set(["vardict", "freebayes", "gatk-haplotype", "mutect2", "vardict"])
out = []
for v in data.get("variants", []):
vrn_file = v.get("vrn_file")
if vrn_file and v.get("variantcaller") in supported:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
if vcfutils.get_paired_phenotype(data):
out.append(vrn_file)
else:
sample_vrn_file = os.path.join(dd.get_work_dir(data), v["variantcaller"],
"%s-%s%s" % (base, dd.get_sample_name(data), ext))
sample_vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_vrn_file,
data["config"])
out.append(sample_vrn_file)
return out
def finalize_sv(orig_vcf, data, items):
"""Finalize structural variants, adding effects and splitting if needed.
"""
paired = vcfutils.get_paired(items)
# For paired/somatic, attach combined calls to tumor sample
if paired:
sample_vcf = orig_vcf if paired.tumor_name == dd.get_sample_name(
data) else None
else:
sample_vcf = "%s-%s.vcf.gz" % (utils.splitext_plus(orig_vcf)[0],
dd.get_sample_name(data))
sample_vcf = vcfutils.select_sample(orig_vcf, dd.get_sample_name(data),
sample_vcf, data["config"])
if sample_vcf:
effects_vcf, _ = effects.add_to_vcf(sample_vcf, data, "snpeff")
else:
effects_vcf = None
return effects_vcf or sample_vcf
def run(items):
"""Perform detection of structural variations with delly.
Performs post-call filtering with a custom filter tuned based
on NA12878 Moleculo and PacBio data, using calls prepared by
@ryanlayer and @cc2qe
Filters using the high quality variant pairs (DV) compared with
high quality reference pairs (DR).
"""
work_dir = utils.safe_makedir(os.path.join(items[0]["dirs"]["work"], "structural",
items[0]["name"][-1], "delly"))
work_bams = [data["align_bam"] for data in items]
ref_file = utils.get_in(items[0], ("reference", "fasta", "base"))
# Add core request for delly
config = copy.deepcopy(items[0]["config"])
delly_config = utils.get_in(config, ("resources", "delly"), {})
delly_config["cores"] = len(items)
config["resources"]["delly"] = delly_config
parallel = {"type": "local", "cores": config["algorithm"].get("num_cores", 1),
"progs": ["delly"]}
sv_types = ["DEL", "DUP", "INV"] # "TRA" has invalid VCF END specifications that GATK doesn't like
with closing(pysam.Samfile(work_bams[0], "rb")) as pysam_work_bam:
bytype_vcfs = run_multicore(_run_delly, [(work_bams, chrom, sv_type, ref_file, work_dir, items)
for (chrom, sv_type)
in itertools.product(pysam_work_bam.references, sv_types)],
config, parallel)
out_file = "%s.vcf.gz" % os.path.commonprefix(bytype_vcfs)
combo_vcf = vcfutils.combine_variant_files(bytype_vcfs, out_file, ref_file, items[0]["config"])
out = []
for data in items:
if "sv" not in data:
data["sv"] = []
base, ext = utils.splitext_plus(combo_vcf)
sample = tz.get_in(["rgnames", "sample"], data)
delly_sample_vcf = vcfutils.select_sample(combo_vcf, sample,
"%s-%s%s" % (base, sample, ext), data["config"])
delly_vcf = vfilter.hard_w_expression(delly_sample_vcf,
"FMT/DV < 4 || (FMT/DV / (FMT/DV + FMT/DR)) < 0.2", data,
name="DVSupport")
data["sv"].append({"variantcaller": "delly", "vrn_file": delly_vcf})
out.append(data)
return out
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(os.path.basename(vrn_file))
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
rtg_ref = tz.get_in(["reference", "rtg"], data)
assert rtg_ref and os.path.exists(rtg_ref), ("Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
# handle CWL where we have a reference to a single file in the RTG directory
if os.path.isfile(rtg_ref):
rtg_ref = os.path.dirname(rtg_ref)
threads = min(dd.get_num_cores(data), 6)
mem = "%sg" % threads
cmd = ["rtg", "vcfeval", "--threads", str(threads),
"-b", rm_file, "--bed-regions", interval_bed,
"-c", vrn_file, "-t", rtg_ref, "-o", out_dir]
cmd += ["--vcf-score-field='%s'" % (_pick_best_quality_score(vrn_file))]
mem_export = "export RTG_JAVA_OPTS='-Xms1g' && export RTG_MEM=%s" % mem
cmd = mem_export + " && " + " ".join(cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
out = {"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")}
tp_calls = os.path.join(out_dir, "tp.vcf.gz")
tp_baseline = os.path.join(out_dir, "tp-baseline.vcf.gz")
if os.path.exists(tp_baseline):
out["tp"] = tp_baseline
out["tp-calls"] = tp_calls
else:
out["tp"] = tp_calls
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_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(vrn_file)
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
ref_dir, ref_filebase = os.path.split(dd.get_ref_file(data))
rtg_ref = os.path.normpath(os.path.join(ref_dir, os.path.pardir, "rtg",
"%s.sdf" % (os.path.splitext(ref_filebase)[0])))
assert os.path.exists(rtg_ref), ("Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
cmd = ["rtg", "vcfeval", "--threads", "6",
"-b", rm_file, "--bed-regions", interval_bed,
"-c", vrn_file, "-t", rtg_ref, "-o", out_dir]
caller = _get_caller(data)
# flexible quality scores for building ROC curves, handle multiple cases
# MuTect has no quality scores
# not clear how to get t_lod_fstar into VCF cleanly
if caller == "mutect":
cmd += ["--vcf-score-field=BQ"]
# otherwise use quality score as a standard
# Discussion point: is it worth using caller specific annotations or settling
# on a single metric for comparison
else:
cmd += ["--vcf-score-field=QUAL"]
cmd = "export RTG_JAVA_OPTS='-Xms1g' export RTG_MEM=5g && " + " ".join(cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
return {"tp": os.path.join(out_dir, "tp.vcf.gz"),
"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")}
def _run_rtg_eval(vrn_file, rm_file, rm_interval_file, base_dir, data):
"""Run evaluation of a caller against the truth set using rtg vcfeval.
"""
out_dir = os.path.join(base_dir, "rtg")
if not utils.file_exists(os.path.join(out_dir, "done")):
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
if not rm_file.endswith(".vcf.gz") or not os.path.exists(rm_file + ".tbi"):
rm_file = vcfutils.bgzip_and_index(rm_file, data["config"], out_dir=base_dir)
if len(vcfutils.get_samples(vrn_file)) > 1:
base, ext = utils.splitext_plus(vrn_file)
sample_file = os.path.join(base_dir, "%s-%s%s" % (base, dd.get_sample_name(data), ext))
vrn_file = vcfutils.select_sample(vrn_file, dd.get_sample_name(data), sample_file, data["config"])
if not vrn_file.endswith(".vcf.gz") or not os.path.exists(vrn_file + ".tbi"):
vrn_file = vcfutils.bgzip_and_index(vrn_file, data["config"], out_dir=base_dir)
interval_bed = _get_merged_intervals(rm_interval_file, base_dir, data)
ref_dir, ref_filebase = os.path.split(dd.get_ref_file(data))
rtg_ref = os.path.normpath(os.path.join(ref_dir, os.path.pardir, "rtg",
"%s.sdf" % (os.path.splitext(ref_filebase)[0])))
assert os.path.exists(rtg_ref), ("Did not find rtg indexed reference file for validation:\n%s\n"
"Run bcbio_nextgen.py upgrade --data --aligners rtg" % rtg_ref)
cmd = ["rtg", "vcfeval", "--threads", "6",
"-b", rm_file, "--bed-regions", interval_bed,
"-c", vrn_file, "-t", rtg_ref, "-o", out_dir]
cmd += ["--vcf-score-field='%s'" % (_pick_best_quality_score(vrn_file))]
cmd = "export RTG_JAVA_OPTS='-Xms1g' && export RTG_MEM=5g && " + " ".join(cmd)
do.run(cmd, "Validate calls using rtg vcfeval", data)
out = {"fp": os.path.join(out_dir, "fp.vcf.gz"),
"fn": os.path.join(out_dir, "fn.vcf.gz")}
tp_calls = os.path.join(out_dir, "tp.vcf.gz")
tp_baseline = os.path.join(out_dir, "tp-baseline.vcf.gz")
if os.path.exists(tp_baseline):
out["tp"] = tp_baseline
out["tp-calls"] = tp_calls
else:
out["tp"] = tp_calls
return out