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
out = []
for data in items:
sample_file = _select_sample(data, variant_file, work_dir)
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({
"variantcaller": "manta",
"vrn_file": effects_vcf or sample_file
})
out.append(data)
return out
def normalize(in_file,
data,
passonly=False,
normalize_indels=True,
split_biallelic=True,
rerun_effects=True,
remove_oldeffects=False,
nonrefonly=False,
work_dir=None):
"""Normalizes variants and reruns SnpEFF for resulting VCF
"""
if remove_oldeffects:
out_file = "%s-noeff-nomultiallelic%s" % utils.splitext_plus(in_file)
else:
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if work_dir:
out_file = os.path.join(work_dir, os.path.basename(out_file))
if not utils.file_exists(out_file):
if vcfutils.vcf_has_variants(in_file):
ready_ma_file = _normalize(in_file,
data,
passonly=passonly,
normalize_indels=normalize_indels,
split_biallelic=split_biallelic,
remove_oldeffects=remove_oldeffects,
nonrefonly=nonrefonly,
work_dir=work_dir)
if rerun_effects:
ann_ma_file, _ = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
utils.symlink_plus(ready_ma_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
def run(items):
"""Run MetaSV if we have enough supported callers, adding output to the set of calls.
"""
assert len(items) == 1, "Expect one input to MetaSV ensemble calling"
data = items[0]
work_dir = _sv_workdir(data)
out_file = os.path.join(work_dir, "variants.vcf.gz")
cmd = _get_cmd() + ["--sample", dd.get_sample_name(data), "--reference", dd.get_ref_file(data),
"--bam", dd.get_align_bam(data), "--outdir", work_dir]
methods = []
for call in data.get("sv", []):
if call["variantcaller"] in SUPPORTED and call["variantcaller"] not in methods:
methods.append(call["variantcaller"])
cmd += ["--%s_vcf" % call["variantcaller"], call.get("vcf_file", call["vrn_file"])]
if len(methods) >= MIN_CALLERS:
if not utils.file_exists(out_file):
tx_work_dir = utils.safe_makedir(os.path.join(work_dir, "raw"))
ins_stats = shared.calc_paired_insert_stats_save(dd.get_align_bam(data),
os.path.join(tx_work_dir, "insert-stats.yaml"))
cmd += ["--workdir", tx_work_dir, "--num_threads", str(dd.get_num_cores(data))]
cmd += ["--spades", utils.which("spades.py"), "--age", utils.which("age_align")]
cmd += ["--assembly_max_tools=1", "--assembly_pad=500"]
cmd += ["--boost_sc", "--isize_mean", ins_stats["mean"], "--isize_sd", ins_stats["std"]]
do.run(cmd, "Combine variant calls with MetaSV")
filters = ("(NUM_SVTOOLS = 1 && ABS(SVLEN)>50000) || "
"(NUM_SVTOOLS = 1 && ABS(SVLEN)<4000 && BA_FLANK_PERCENT>80) || "
"(NUM_SVTOOLS = 1 && ABS(SVLEN)<4000 && BA_NUM_GOOD_REC=0) || "
"(ABS(SVLEN)<4000 && BA_NUM_GOOD_REC>2)")
filter_file = vfilter.cutoff_w_expression(out_file, filters,
data, name="ReassemblyStats", limit_regions=None)
effects_vcf, _ = effects.add_to_vcf(filter_file, data, "snpeff")
data["sv"].append({"variantcaller": "metasv",
"vrn_file": effects_vcf or filter_file})
return [data]
def postprocess_variants(items):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
vrn_key = "vrn_file"
if not isinstance(items, dict):
items = [utils.to_single_data(x) for x in items]
if "vrn_file_joint" in items[0]:
vrn_key = "vrn_file_joint"
data, items = _get_batch_representative(items, vrn_key)
items = cwlutils.unpack_tarballs(items, data)
data = cwlutils.unpack_tarballs(data, data)
cur_name = "%s, %s" % (dd.get_sample_name(data), get_variantcaller(data))
logger.info("Finalizing variant calls: %s" % cur_name)
orig_vrn_file = data.get(vrn_key)
data = _symlink_to_workdir(data, [vrn_key])
data = _symlink_to_workdir(data,
["config", "algorithm", "variant_regions"])
if data.get(vrn_key):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file, vrn_stats = effects.add_to_vcf(data[vrn_key], data)
if ann_vrn_file:
data[vrn_key] = ann_vrn_file
if vrn_stats:
data["vrn_stats"] = vrn_stats
orig_items = _get_orig_items(items)
logger.info("Annotate VCF file: %s" % cur_name)
data[vrn_key] = annotation.finalize_vcf(data[vrn_key],
get_variantcaller(data),
orig_items)
if dd.get_analysis(data).lower().find("rna-seq") >= 0:
logger.info("Annotate RNA editing sites")
ann_file = vcfanno.run_vcfanno(dd.get_vrn_file(data), ["rnaedit"],
data)
if ann_file:
data[vrn_key] = ann_file
if cwlutils.is_cwl_run(data):
logger.info("Annotate with population level variation data")
ann_file = population.run_vcfanno(dd.get_vrn_file(data), data,
population.do_db_build([data]))
if ann_file:
data[vrn_key] = ann_file
logger.info("Filtering for %s" % cur_name)
data[vrn_key] = variant_filtration(
data[vrn_key], dd.get_ref_file(data),
tz.get_in(("genome_resources", "variation"), data, {}), data,
orig_items)
logger.info("Prioritization for %s" % cur_name)
prio_vrn_file = prioritize.handle_vcf_calls(data[vrn_key], data,
orig_items)
if prio_vrn_file != data[vrn_key]:
data[vrn_key] = prio_vrn_file
logger.info("Germline extraction for %s" % cur_name)
data = germline.extract(data, orig_items)
if dd.get_align_bam(data):
data = damage.run_filter(data[vrn_key], dd.get_align_bam(data),
dd.get_ref_file(data), data, orig_items)
if orig_vrn_file and os.path.samefile(data[vrn_key], orig_vrn_file):
data[vrn_key] = orig_vrn_file
return [[data]]
def postprocess_variants(items):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
data = _get_batch_representative(items, "vrn_file")
cur_name = "%s, %s" % (dd.get_sample_name(data), get_variantcaller(data))
logger.info("Finalizing variant calls: %s" % cur_name)
orig_vrn_file = data.get("vrn_file")
data = _symlink_to_workdir(data, ["vrn_file"])
data = _symlink_to_workdir(data,
["config", "algorithm", "variant_regions"])
if data.get("align_bam") and data.get("vrn_file"):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file, vrn_stats = effects.add_to_vcf(data["vrn_file"], data)
if ann_vrn_file:
data["vrn_file"] = ann_vrn_file
if vrn_stats:
data["vrn_stats"] = vrn_stats
orig_items = _get_orig_items(items)
logger.info("Annotate VCF file: %s" % cur_name)
data["vrn_file"] = annotation.finalize_vcf(data["vrn_file"],
get_variantcaller(data),
orig_items)
logger.info("Filtering for %s" % cur_name)
data["vrn_file"] = variant_filtration(
data["vrn_file"], dd.get_ref_file(data),
tz.get_in(("genome_resources", "variation"), data, {}), data,
orig_items)
logger.info("Prioritization for %s" % cur_name)
data["vrn_file"] = prioritize.handle_vcf_calls(data["vrn_file"], data,
orig_items)
logger.info("Germline extraction for %s" % cur_name)
data = germline.extract(data, orig_items)
if orig_vrn_file and os.path.samefile(data["vrn_file"], orig_vrn_file):
data["vrn_file"] = orig_vrn_file
return [[data]]
def _add_variantcalls_to_output(out, data):
"""Call ploidy and convert into VCF and BED representations.
"""
call_file = "%s-call%s" % os.path.splitext(out["cns"])
gender = dd.get_gender(data)
if not utils.file_exists(call_file):
with file_transaction(data, call_file) as tx_call_file:
cmd = [os.path.join(os.path.dirname(sys.executable), "cnvkit.py"), "call",
"--ploidy", str(dd.get_ploidy(data)),
"-o", tx_call_file, out["cns"]]
if gender:
cmd += ["--gender", gender]
if gender.lower() == "male":
cmd += ["--male-reference"]
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 utils.file_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(dd.get_ploidy(data)),
"-o", tx_out_file, call_file]
if gender and gender.lower() == "male":
cmd += ["--male-reference"]
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"]
return out
def postprocess_variants(items):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
data = _get_batch_representative(items, "vrn_file")
cur_name = "%s, %s" % (dd.get_sample_name(data), get_variantcaller(data))
logger.info("Finalizing variant calls: %s" % cur_name)
orig_vrn_file = data.get("vrn_file")
data = _symlink_to_workdir(data, ["vrn_file"])
data = _symlink_to_workdir(data, ["config", "algorithm", "variant_regions"])
if data.get("align_bam") and data.get("vrn_file"):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file, vrn_stats = effects.add_to_vcf(data["vrn_file"], data)
if ann_vrn_file:
data["vrn_file"] = ann_vrn_file
if vrn_stats:
data["vrn_stats"] = vrn_stats
logger.info("Filtering for %s" % cur_name)
orig_items = _get_orig_items(items)
data["vrn_file"] = variant_filtration(data["vrn_file"], dd.get_ref_file(data),
tz.get_in(("genome_resources", "variation"), data, {}),
data, orig_items)
logger.info("Prioritization for %s" % cur_name)
data["vrn_file"] = prioritize.handle_vcf_calls(data["vrn_file"], data, orig_items)
logger.info("Germline extraction for %s" % cur_name)
data = germline.extract(data, orig_items)
if orig_vrn_file and os.path.samefile(data["vrn_file"], orig_vrn_file):
data["vrn_file"] = orig_vrn_file
return [[data]]
def run(items):
"""Run MetaSV if we have enough supported callers, adding output to the set of calls.
"""
assert len(items) == 1, "Expect one input to MetaSV ensemble calling"
data = items[0]
work_dir = _sv_workdir(data)
out_file = os.path.join(work_dir, "variants.vcf.gz")
cmd = _get_cmd() + ["--sample", dd.get_sample_name(data), "--reference", dd.get_ref_file(data),
"--bam", dd.get_align_bam(data), "--outdir", work_dir]
methods = []
for call in data.get("sv", []):
if call["variantcaller"] in SUPPORTED and call["variantcaller"] not in methods:
methods.append(call["variantcaller"])
cmd += ["--%s_vcf" % call["variantcaller"], call.get("vcf_file", call["vrn_file"])]
if len(methods) >= MIN_CALLERS:
if not utils.file_exists(out_file):
tx_work_dir = utils.safe_makedir(os.path.join(work_dir, "raw"))
ins_stats = shared.calc_paired_insert_stats_save(dd.get_align_bam(data),
os.path.join(tx_work_dir, "insert-stats.yaml"))
cmd += ["--workdir", tx_work_dir, "--num_threads", str(dd.get_num_cores(data))]
cmd += ["--spades", utils.which("spades.py"), "--age", utils.which("age_align")]
cmd += ["--assembly_max_tools=1", "--assembly_pad=500"]
cmd += ["--boost_sc", "--isize_mean", ins_stats["mean"], "--isize_sd", ins_stats["std"]]
do.run(cmd, "Combine variant calls with MetaSV")
filters = ("(NUM_SVTOOLS = 1 && ABS(SVLEN)>50000) || "
"(NUM_SVTOOLS = 1 && ABS(SVLEN)<4000 && BA_FLANK_PERCENT>80) || "
"(NUM_SVTOOLS = 1 && ABS(SVLEN)<4000 && BA_NUM_GOOD_REC=0) || "
"(ABS(SVLEN)<4000 && BA_NUM_GOOD_REC>2)")
filter_file = vfilter.hard_w_expression(out_file, filters,
data, name="ReassemblyStats", limit_regions=None)
effects_vcf, _ = effects.add_to_vcf(filter_file, data, "snpeff")
data["sv"].append({"variantcaller": "metasv",
"vrn_file": effects_vcf or filter_file})
return [data]
def normalize(in_file, data, passonly=False, normalize_indels=True, split_biallelic=True,
rerun_effects=True, remove_oldeffects=False, nonrefonly=False, work_dir=None):
"""Normalizes variants and reruns SnpEFF for resulting VCF
"""
if remove_oldeffects:
out_file = "%s-noeff-nomultiallelic%s" % utils.splitext_plus(in_file)
else:
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if work_dir:
out_file = os.path.join(work_dir, os.path.basename(out_file))
if not utils.file_exists(out_file):
if vcfutils.vcf_has_variants(in_file):
ready_ma_file = _normalize(in_file, data, passonly=passonly,
normalize_indels=normalize_indels,
split_biallelic=split_biallelic,
remove_oldeffects=remove_oldeffects,
nonrefonly=nonrefonly,
work_dir=work_dir)
if rerun_effects:
ann_ma_file, _ = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
utils.symlink_plus(ready_ma_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
def to_vcf(in_file, caller, header_fn, vcf_fn, data, sep="\t"):
"""Convert output TitanCNA segs file into bgzipped VCF."""
out_file = "%s.vcf" % utils.splitext_plus(in_file)[0]
out_file_gz = out_file + ".gz"
if not utils.file_exists(out_file +
".gz") and not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
with open(in_file) as in_handle:
with open(tx_out_file, "w") as out_handle:
out_handle.write(_vcf_header.format(caller=caller))
out_handle.write("\t".join([
"#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER",
"INFO", "FORMAT",
dd.get_sample_name(data)
]) + "\n")
header, in_handle = header_fn(in_handle)
for line in in_handle:
out = vcf_fn(dict(zip(header,
line.strip().split(sep))))
if out:
out_handle.write("\t".join(out) + "\n")
# also does bgzip and index
out_file_prep_vcf_gz = vcfutils.sort_by_ref(out_file, data)
shutil.move(out_file_prep_vcf_gz, out_file_gz)
shutil.move(out_file_prep_vcf_gz + ".tbi", out_file_gz + ".tbi")
effects_vcf, _ = effects.add_to_vcf(out_file_gz, data, "snpeff")
return effects_vcf or out_file_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.
"""
if not all(
utils.get_in(data, ("config", "algorithm", "aligner")) in
["bwa", "sentieon-bwa", "minimap2", False, None] for data in items):
raise ValueError(
"Require bwa or minimap2 alignment input for lumpy structural variation detection"
)
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)
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 = []
for data in items:
if "sv" not in data:
data["sv"] = []
vcf_file = gt_vcfs.get(dd.get_sample_name(data))
if vcf_file:
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)
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", 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.
"""
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 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 = [dd.get_align_bam(d) for d in items]
ref_file = dd.get_ref_file(items[0])
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)
effects_vcf, _ = effects.add_to_vcf(delly_vcf, data, "snpeff")
data["sv"].append({
"variantcaller": "delly",
"vrn_file": effects_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", "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 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 to_single(in_file, data, passonly=False):
"""Convert multi-allelic inputs in the original VCF file into single alleles.
"""
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
if vcfutils.vcf_has_variants(in_file):
ready_ma_file = _decompose(in_file, data, passonly=passonly)
ann_ma_file, _ = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
out_file = ready_ma_file
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
def to_single(in_file, data, passonly=False):
"""Convert multi-allelic inputs in the original VCF file into single alleles.
"""
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
if vcfutils.vcf_has_variants(in_file):
ready_ma_file = _decompose(in_file, data, passonly=passonly)
ann_ma_file, _ = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
out_file = ready_ma_file
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
def postprocess_variants(items):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
vrn_key = "vrn_file"
if not isinstance(items, dict):
items = [utils.to_single_data(x) for x in items]
if "vrn_file_joint" in items[0]:
vrn_key = "vrn_file_joint"
data, items = _get_batch_representative(items, vrn_key)
items = cwlutils.unpack_tarballs(items, data)
data = cwlutils.unpack_tarballs(data, data)
cur_name = "%s, %s" % (dd.get_sample_name(data), get_variantcaller(data, require_bam=False))
logger.info("Finalizing variant calls: %s" % cur_name)
orig_vrn_file = data.get(vrn_key)
data = _symlink_to_workdir(data, [vrn_key])
data = _symlink_to_workdir(data, ["config", "algorithm", "variant_regions"])
if data.get(vrn_key):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file, vrn_stats = effects.add_to_vcf(data[vrn_key], data)
if ann_vrn_file:
data[vrn_key] = ann_vrn_file
if vrn_stats:
data["vrn_stats"] = vrn_stats
orig_items = _get_orig_items(items)
logger.info("Annotate VCF file: %s" % cur_name)
data[vrn_key] = annotation.finalize_vcf(data[vrn_key], get_variantcaller(data, require_bam=False),
orig_items)
if cwlutils.is_cwl_run(data):
logger.info("Annotate with population level variation data")
ann_file = population.run_vcfanno(data[vrn_key], data)
if ann_file:
data[vrn_key] = ann_file
logger.info("Filtering for %s" % cur_name)
data[vrn_key] = variant_filtration(data[vrn_key], dd.get_ref_file(data),
tz.get_in(("genome_resources", "variation"), data, {}),
data, orig_items)
logger.info("Prioritization for %s" % cur_name)
prio_vrn_file = prioritize.handle_vcf_calls(data[vrn_key], data, orig_items)
if prio_vrn_file != data[vrn_key]:
data[vrn_key] = prio_vrn_file
logger.info("Germline extraction for %s" % cur_name)
data = germline.extract(data, orig_items)
if dd.get_align_bam(data):
data = damage.run_filter(data[vrn_key], dd.get_align_bam(data), dd.get_ref_file(data),
data, orig_items)
if orig_vrn_file and os.path.samefile(data[vrn_key], orig_vrn_file):
data[vrn_key] = orig_vrn_file
return [[data]]
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 to_single(in_file, data):
"""Convert multi-allelic inputs in the original VCF file into single alleles.
"""
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
ba_file, ma_file = _split_mulitallelic(in_file, data)
if vcfutils.vcf_has_variants(ma_file):
ready_ma_file = _decompose(ma_file, data)
ann_ma_file = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
out_file = vcfutils.merge_sorted([ready_ma_file, ba_file], out_file, data)
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
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 = population.get_gender(data)
if gender and gender.lower() != "unknown":
cmd += ["--gender", gender]
if gender.lower() == "male":
cmd += ["--male-reference"]
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
]
if gender and gender.lower() == "male":
cmd += ["--male-reference"]
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"]
return out
def run_rnaseq_ann_filter(data):
"""Run RNA-seq annotation and filtering.
"""
data = to_single_data(data)
if dd.get_vrn_file(data):
eff_file = effects.add_to_vcf(dd.get_vrn_file(data), data)[0]
if eff_file:
data = dd.set_vrn_file(data, eff_file)
ann_file = population.run_vcfanno(dd.get_vrn_file(data), data)
if ann_file:
data = dd.set_vrn_file(data, ann_file)
variantcaller = dd.get_variantcaller(data)
if variantcaller and ("gatk-haplotype" in variantcaller):
filter_file = variation.gatk_filter_rnaseq(dd.get_vrn_file(data), data)
data = dd.set_vrn_file(data, filter_file)
return [[data]]
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 postprocess_variants(data):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
cur_name = "%s, %s" % (data["name"][-1], get_variantcaller(data))
logger.info("Finalizing variant calls: %s" % cur_name)
if data.get("align_bam") and data.get("vrn_file"):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file = effects.add_to_vcf(data["vrn_file"], data)
if ann_vrn_file:
data["vrn_file"] = ann_vrn_file
logger.info("Filtering for %s" % cur_name)
data["vrn_file"] = variant_filtration(data["vrn_file"], data["sam_ref"],
tz.get_in(("genome_resources", "variation"), data, {}),
data)
logger.info("Prioritization for %s" % cur_name)
data["vrn_file"] = prioritize.handle_vcf_calls(data["vrn_file"], data)
return [[data]]
def run(items):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
work_dir = _sv_workdir(paired.tumor_data if paired else items[0])
workflow_file = _prep_config(items, paired, work_dir)
variant_file = _run_workflow(items, paired, workflow_file, work_dir)
out = []
for data in items:
sample_file = _select_sample(data, variant_file, work_dir)
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({"variantcaller": "manta",
"vrn_file": effects_vcf or sample_file})
out.append(data)
return out
def run(items):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
work_dir = _sv_workdir(paired.tumor_data if paired else items[0])
workflow_file = _prep_config(items, paired, work_dir)
variant_file = _run_workflow(items, paired, workflow_file, work_dir)
out = []
for data in items:
sample_file = _select_sample(data, variant_file, work_dir)
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({"variantcaller": "manta",
"vrn_file": effects_vcf or sample_file})
out.append(data)
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"))
# 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 = [dd.get_align_bam(d) for d in items]
ref_file = dd.get_ref_file(items[0])
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)
effects_vcf, _ = effects.add_to_vcf(delly_vcf, data, "snpeff")
data["sv"].append({"variantcaller": "delly", "vrn_file": effects_vcf,
"exclude": exclude_file})
out.append(data)
return out
def postprocess_variants(data):
"""Provide post-processing of variant calls: filtering and effects annotation.
"""
cur_name = "%s, %s" % (data["name"][-1], get_variantcaller(data))
logger.info("Finalizing variant calls: %s" % cur_name)
if data.get("align_bam") and data.get("vrn_file"):
logger.info("Calculating variation effects for %s" % cur_name)
ann_vrn_file, vrn_stats = effects.add_to_vcf(data["vrn_file"], data)
if ann_vrn_file:
data["vrn_file"] = ann_vrn_file
if vrn_stats:
data["vrn_stats"] = vrn_stats
logger.info("Filtering for %s" % cur_name)
data["vrn_file"] = variant_filtration(
data["vrn_file"], data["sam_ref"],
tz.get_in(("genome_resources", "variation"), data, {}), data)
logger.info("Prioritization for %s" % cur_name)
data["vrn_file"] = prioritize.handle_vcf_calls(data["vrn_file"], data)
return [[data]]
def run_rnaseq_ann_filter(data):
"""Run RNA-seq annotation and filtering.
"""
data = to_single_data(data)
if dd.get_vrn_file(data):
eff_file = effects.add_to_vcf(dd.get_vrn_file(data), data)[0]
if eff_file:
data = dd.set_vrn_file(data, eff_file)
ann_file = population.run_vcfanno(dd.get_vrn_file(data), data)
if ann_file:
data = dd.set_vrn_file(data, ann_file)
variantcaller = dd.get_variantcaller(data)
if variantcaller and ("gatk-haplotype" in variantcaller):
filter_file = variation.gatk_filter_rnaseq(dd.get_vrn_file(data), data)
data = dd.set_vrn_file(data, filter_file)
# remove variants close to splice junctions
vrn_file = dd.get_vrn_file(data)
vrn_file = variation.filter_junction_variants(vrn_file, data)
data = dd.set_vrn_file(data, vrn_file)
return [[data]]
def run_rnaseq_ann_filter(data):
"""Run RNA-seq annotation and filtering.
"""
data = to_single_data(data)
if dd.get_vrn_file(data):
eff_file = effects.add_to_vcf(dd.get_vrn_file(data), data)[0]
if eff_file:
data = dd.set_vrn_file(data, eff_file)
ann_file = population.run_vcfanno(dd.get_vrn_file(data), data)
if ann_file:
data = dd.set_vrn_file(data, ann_file)
jointcaller = dd.get_jointcaller(data)
if jointcaller and 'gatk-haplotype-joint' in jointcaller:
filter_file = variation.gatk_filter_rnaseq(dd.get_vrn_file(data), data)
data = dd.set_vrn_file(data, filter_file)
# remove variants close to splice junctions
vrn_file = dd.get_vrn_file(data)
vrn_file = variation.filter_junction_variants(vrn_file, data)
data = dd.set_vrn_file(data, vrn_file)
return [[data]]
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 combine_calls(batch_id, samples, data):
"""Combine multiple callsets into a final set of merged calls.
"""
logger.info("Ensemble consensus calls for {0}: {1}".format(
batch_id, ",".join(x["variantcaller"] for x in samples[0]["variants"])))
edata = copy.deepcopy(data)
base_dir = utils.safe_makedir(os.path.join(edata["dirs"]["work"], "ensemble", batch_id))
caller_names, vrn_files, bam_files = _organize_variants(samples, batch_id)
exist_variants = False
for tmp_vrn_file in vrn_files:
if vcfutils.vcf_has_variants(tmp_vrn_file):
exist_variants = True
break
if exist_variants:
# 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)
for f in vrn_files]
if "classifiers" not in edata["config"]["algorithm"]["ensemble"]:
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,
edata["sam_ref"], 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}
return [[batch_id, callinfo]]
def _add_variantcalls_to_output(out, data, is_somatic=False):
"""Call ploidy and convert into VCF and BED representations.
"""
call_file = "%s-call%s" % os.path.splitext(out["cns"])
gender = population.get_gender(data)
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)
if len(small_vrn_files) > 0 and _cna_has_values(out["cns"]):
cmd += ["-v", small_vrn_files[0]]
if not is_somatic:
cmd += ["-m", "clonal"]
if gender and gender.lower() != "unknown":
cmd += ["--gender", gender]
if gender.lower() == "male":
cmd += ["--male-reference"]
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]
if gender and gender.lower() == "male":
cmd += ["--male-reference"]
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"]
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, background=None):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
if paired:
inputs = [paired.tumor_data]
background = [paired.normal_data] if paired.normal_bam else []
else:
assert not background
inputs, background = sshared.find_case_control(items)
work_dir = _sv_workdir(inputs[0])
variant_file = _run_gridss(inputs, background, work_dir)
out = []
for data in items:
sample_file = variant_file
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({"variantcaller": "gridss",
"vrn_file": effects_vcf or sample_file})
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
out = []
for data in items:
sample_file = _select_sample(data, variant_file, work_dir)
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({"variantcaller": "manta",
"vrn_file": effects_vcf or sample_file})
out.append(data)
return out
def _add_variantcalls_to_output(out, data):
"""Call ploidy and convert into VCF and BED representations.
"""
call_file = "%s-call%s" % os.path.splitext(out["cns"])
gender = dd.get_gender(data)
if not utils.file_exists(call_file):
with file_transaction(data, call_file) as tx_call_file:
cmd = [
os.path.join(os.path.dirname(sys.executable), "cnvkit.py"),
"call", "--ploidy",
str(dd.get_ploidy(data)), "-o", tx_call_file, out["cns"]
]
if gender:
cmd += ["--gender", gender]
if gender.lower() == "male":
cmd += ["--male-reference"]
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 utils.file_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(dd.get_ploidy(data)), "-o", tx_out_file, call_file
]
if gender and gender.lower() == "male":
cmd += ["--male-reference"]
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"]
return out
def normalize(in_file,
data,
passonly=False,
normalize_indels=True,
split_biallelic=True,
rerun_effects=True):
"""Normalizes variants and reruns SnpEFF for resulting VCF
"""
out_file = "%s-nomultiallelic%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
if vcfutils.vcf_has_variants(in_file):
ready_ma_file = _normalize(in_file,
data,
passonly=passonly,
normalize_indels=normalize_indels,
split_biallelic=split_biallelic)
if rerun_effects:
ann_ma_file, _ = effects.add_to_vcf(ready_ma_file, data)
if ann_ma_file:
ready_ma_file = ann_ma_file
utils.symlink_plus(ready_ma_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return vcfutils.bgzip_and_index(out_file, data["config"])
def run(items, background=None):
"""Perform detection of structural variations with Manta.
"""
paired = vcfutils.get_paired(items)
if paired:
inputs = [paired.tumor_data]
background = [paired.normal_data] if paired.normal_bam else []
else:
assert not background
inputs, background = sshared.find_case_control(items)
work_dir = _sv_workdir(inputs[0])
variant_file = _run_gridss(inputs, background, work_dir)
out = []
for data in items:
sample_file = variant_file
if "sv" not in data:
data["sv"] = []
effects_vcf, _ = effects.add_to_vcf(sample_file, data, "snpeff")
data["sv"].append({
"variantcaller": "gridss",
"vrn_file": effects_vcf or sample_file
})
out.append(data)
return out
