id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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237,600 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _get_vqsr_training | def _get_vqsr_training(filter_type, vrn_files, gatk_type):
"""Return parameters for VQSR training, handling SNPs and Indels.
"""
params = []
for name, train_info, fname in _get_training_data(vrn_files)[filter_type]:
if gatk_type == "gatk4":
params.extend(["--resource:%s,%s" % (name, train_info), fname])
if filter_type == "INDEL":
params.extend(["--max-gaussians", "4"])
else:
params.extend(["-resource:%s,VCF,%s" % (name, train_info), fname])
if filter_type == "INDEL":
params.extend(["--maxGaussians", "4"])
return params | python | def _get_vqsr_training(filter_type, vrn_files, gatk_type):
params = []
for name, train_info, fname in _get_training_data(vrn_files)[filter_type]:
if gatk_type == "gatk4":
params.extend(["--resource:%s,%s" % (name, train_info), fname])
if filter_type == "INDEL":
params.extend(["--max-gaussians", "4"])
else:
params.extend(["-resource:%s,VCF,%s" % (name, train_info), fname])
if filter_type == "INDEL":
params.extend(["--maxGaussians", "4"])
return params | [
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237,601 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _get_vqsr_annotations | def _get_vqsr_annotations(filter_type, data):
"""Retrieve appropriate annotations to use for VQSR based on filter type.
Issues reported with MQ and bwa-mem quality distribution, results in intermittent
failures to use VQSR:
http://gatkforums.broadinstitute.org/discussion/4425/variant-recalibration-failing
http://gatkforums.broadinstitute.org/discussion/4248/variantrecalibrator-removing-all-snps-from-the-training-set
"""
if filter_type == "SNP":
# MQ, MQRankSum
anns = ["QD", "FS", "ReadPosRankSum", "SOR"]
else:
assert filter_type == "INDEL"
# MQRankSum
anns = ["QD", "FS", "ReadPosRankSum", "SOR"]
if dd.get_coverage_interval(data) == "genome":
anns += ["DP"]
return anns | python | def _get_vqsr_annotations(filter_type, data):
if filter_type == "SNP":
# MQ, MQRankSum
anns = ["QD", "FS", "ReadPosRankSum", "SOR"]
else:
assert filter_type == "INDEL"
# MQRankSum
anns = ["QD", "FS", "ReadPosRankSum", "SOR"]
if dd.get_coverage_interval(data) == "genome":
anns += ["DP"]
return anns | [
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237,602 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _run_vqsr | def _run_vqsr(in_file, ref_file, vrn_files, sensitivity_cutoff, filter_type, data):
"""Run variant quality score recalibration.
"""
cutoffs = ["100.0", "99.99", "99.98", "99.97", "99.96", "99.95", "99.94", "99.93", "99.92", "99.91",
"99.9", "99.8", "99.7", "99.6", "99.5", "99.0", "98.0", "90.0"]
if sensitivity_cutoff not in cutoffs:
cutoffs.append(sensitivity_cutoff)
cutoffs.sort()
broad_runner = broad.runner_from_config(data["config"])
gatk_type = broad_runner.gatk_type()
base = utils.splitext_plus(in_file)[0]
recal_file = ("%s-vqsrrecal.vcf.gz" % base) if gatk_type == "gatk4" else ("%s.recal" % base)
tranches_file = "%s.tranches" % base
plot_file = "%s-plots.R" % base
if not utils.file_exists(recal_file):
with file_transaction(data, recal_file, tranches_file, plot_file) as (tx_recal, tx_tranches, tx_plot_file):
params = ["-T", "VariantRecalibrator",
"-R", ref_file,
"--mode", filter_type]
if gatk_type == "gatk4":
params += ["--variant", in_file, "--output", tx_recal,
"--tranches-file", tx_tranches, "--rscript-file", tx_plot_file]
else:
params += ["--input", in_file, "--recal_file", tx_recal,
"--tranches_file", tx_tranches, "--rscript_file", tx_plot_file]
params += _get_vqsr_training(filter_type, vrn_files, gatk_type)
resources = config_utils.get_resources("gatk_variant_recalibrator", data["config"])
opts = resources.get("options", [])
if not opts:
for cutoff in cutoffs:
opts += ["-tranche", str(cutoff)]
for a in _get_vqsr_annotations(filter_type, data):
opts += ["-an", a]
params += opts
cores = dd.get_cores(data)
memscale = {"magnitude": 0.9 * cores, "direction": "increase"} if cores > 1 else None
try:
broad_runner.new_resources("gatk-vqsr")
broad_runner.run_gatk(params, log_error=False, memscale=memscale, parallel_gc=True)
except: # Can fail to run if not enough values are present to train.
return None, None
if gatk_type == "gatk4":
vcfutils.bgzip_and_index(recal_file, data["config"])
return recal_file, tranches_file | python | def _run_vqsr(in_file, ref_file, vrn_files, sensitivity_cutoff, filter_type, data):
cutoffs = ["100.0", "99.99", "99.98", "99.97", "99.96", "99.95", "99.94", "99.93", "99.92", "99.91",
"99.9", "99.8", "99.7", "99.6", "99.5", "99.0", "98.0", "90.0"]
if sensitivity_cutoff not in cutoffs:
cutoffs.append(sensitivity_cutoff)
cutoffs.sort()
broad_runner = broad.runner_from_config(data["config"])
gatk_type = broad_runner.gatk_type()
base = utils.splitext_plus(in_file)[0]
recal_file = ("%s-vqsrrecal.vcf.gz" % base) if gatk_type == "gatk4" else ("%s.recal" % base)
tranches_file = "%s.tranches" % base
plot_file = "%s-plots.R" % base
if not utils.file_exists(recal_file):
with file_transaction(data, recal_file, tranches_file, plot_file) as (tx_recal, tx_tranches, tx_plot_file):
params = ["-T", "VariantRecalibrator",
"-R", ref_file,
"--mode", filter_type]
if gatk_type == "gatk4":
params += ["--variant", in_file, "--output", tx_recal,
"--tranches-file", tx_tranches, "--rscript-file", tx_plot_file]
else:
params += ["--input", in_file, "--recal_file", tx_recal,
"--tranches_file", tx_tranches, "--rscript_file", tx_plot_file]
params += _get_vqsr_training(filter_type, vrn_files, gatk_type)
resources = config_utils.get_resources("gatk_variant_recalibrator", data["config"])
opts = resources.get("options", [])
if not opts:
for cutoff in cutoffs:
opts += ["-tranche", str(cutoff)]
for a in _get_vqsr_annotations(filter_type, data):
opts += ["-an", a]
params += opts
cores = dd.get_cores(data)
memscale = {"magnitude": 0.9 * cores, "direction": "increase"} if cores > 1 else None
try:
broad_runner.new_resources("gatk-vqsr")
broad_runner.run_gatk(params, log_error=False, memscale=memscale, parallel_gc=True)
except: # Can fail to run if not enough values are present to train.
return None, None
if gatk_type == "gatk4":
vcfutils.bgzip_and_index(recal_file, data["config"])
return recal_file, tranches_file | [
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237,603 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _already_cutoff_filtered | def _already_cutoff_filtered(in_file, filter_type):
"""Check if we have a pre-existing cutoff-based filter file from previous VQSR failure.
"""
filter_file = "%s-filter%s.vcf.gz" % (utils.splitext_plus(in_file)[0], filter_type)
return utils.file_exists(filter_file) | python | def _already_cutoff_filtered(in_file, filter_type):
filter_file = "%s-filter%s.vcf.gz" % (utils.splitext_plus(in_file)[0], filter_type)
return utils.file_exists(filter_file) | [
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237,604 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _variant_filtration | def _variant_filtration(in_file, ref_file, vrn_files, data, filter_type,
hard_filter_fn):
"""Filter SNP and indel variant calls using GATK best practice recommendations.
Use cutoff-based soft filters if configuration indicates too little data or
already finished a cutoff-based filtering step, otherwise try VQSR.
"""
# Algorithms multiplied by number of input files to check for large enough sample sizes
algs = [data["config"]["algorithm"]] * len(data.get("vrn_files", [1]))
if (not config_utils.use_vqsr(algs, in_file) or
_already_cutoff_filtered(in_file, filter_type)):
logger.info("Skipping VQSR, using cutoff-based filers: we don't have whole genome input data")
return hard_filter_fn(in_file, data)
elif not _have_training_data(vrn_files):
logger.info("Skipping VQSR, using cutoff-based filers: genome build does not have sufficient training data")
return hard_filter_fn(in_file, data)
else:
sensitivities = {"INDEL": "98.0", "SNP": "99.97"}
recal_file, tranches_file = _run_vqsr(in_file, ref_file, vrn_files,
sensitivities[filter_type], filter_type, data)
if recal_file is None: # VQSR failed
logger.info("VQSR failed due to lack of training data. Using cutoff-based soft filtering.")
return hard_filter_fn(in_file, data)
else:
return _apply_vqsr(in_file, ref_file, recal_file, tranches_file,
sensitivities[filter_type], filter_type, data) | python | def _variant_filtration(in_file, ref_file, vrn_files, data, filter_type,
hard_filter_fn):
# Algorithms multiplied by number of input files to check for large enough sample sizes
algs = [data["config"]["algorithm"]] * len(data.get("vrn_files", [1]))
if (not config_utils.use_vqsr(algs, in_file) or
_already_cutoff_filtered(in_file, filter_type)):
logger.info("Skipping VQSR, using cutoff-based filers: we don't have whole genome input data")
return hard_filter_fn(in_file, data)
elif not _have_training_data(vrn_files):
logger.info("Skipping VQSR, using cutoff-based filers: genome build does not have sufficient training data")
return hard_filter_fn(in_file, data)
else:
sensitivities = {"INDEL": "98.0", "SNP": "99.97"}
recal_file, tranches_file = _run_vqsr(in_file, ref_file, vrn_files,
sensitivities[filter_type], filter_type, data)
if recal_file is None: # VQSR failed
logger.info("VQSR failed due to lack of training data. Using cutoff-based soft filtering.")
return hard_filter_fn(in_file, data)
else:
return _apply_vqsr(in_file, ref_file, recal_file, tranches_file,
sensitivities[filter_type], filter_type, data) | [
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237,605 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | gatk_remove_missingalt | def gatk_remove_missingalt(in_file, data):
"""
GATK 4.1.0.0 outputs variants that have missing ALTs, which breaks downstream
tools, this filters those out.
"""
base = in_file.split('.vcf.gz')[0]
out_file = "%s-nomissingalt%s" % (base, '.vcf.gz')
if utils.file_exists(out_file):
return out_file
no_gzip_out = out_file.replace(".vcf.gz", ".vcf")
with file_transaction(no_gzip_out) as tx_out_file:
with utils.open_gzipsafe(in_file) as in_handle, open(tx_out_file, "w") as out_handle:
for line in in_handle:
line = remove_missingalt(line)
if line:
out_handle.write(line)
return vcfutils.bgzip_and_index(no_gzip_out, data["config"]) | python | def gatk_remove_missingalt(in_file, data):
base = in_file.split('.vcf.gz')[0]
out_file = "%s-nomissingalt%s" % (base, '.vcf.gz')
if utils.file_exists(out_file):
return out_file
no_gzip_out = out_file.replace(".vcf.gz", ".vcf")
with file_transaction(no_gzip_out) as tx_out_file:
with utils.open_gzipsafe(in_file) as in_handle, open(tx_out_file, "w") as out_handle:
for line in in_handle:
line = remove_missingalt(line)
if line:
out_handle.write(line)
return vcfutils.bgzip_and_index(no_gzip_out, data["config"]) | [
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237,606 | bcbio/bcbio-nextgen | bcbio/rnaseq/cufflinks.py | strand_unknown | def strand_unknown(db, transcript):
"""
for unstranded data with novel transcripts single exon genes
will have no strand information. single exon novel genes are also
a source of noise in the Cufflinks assembly so this removes them
"""
features = list(db.children(transcript))
strand = features[0].strand
if strand == ".":
return True
else:
return False | python | def strand_unknown(db, transcript):
features = list(db.children(transcript))
strand = features[0].strand
if strand == ".":
return True
else:
return False | [
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237,607 | bcbio/bcbio-nextgen | bcbio/rnaseq/cufflinks.py | fix_cufflinks_attributes | def fix_cufflinks_attributes(ref_gtf, merged_gtf, data, out_file=None):
"""
replace the cufflinks gene_id and transcript_id with the
gene_id and transcript_id from ref_gtf, where available
"""
base, ext = os.path.splitext(merged_gtf)
fixed = out_file if out_file else base + ".clean.fixed" + ext
if file_exists(fixed):
return fixed
ref_db = gtf.get_gtf_db(ref_gtf)
merged_db = gtf.get_gtf_db(merged_gtf, in_memory=True)
ref_tid_to_gid = {}
for gene in ref_db.features_of_type('gene'):
for transcript in ref_db.children(gene, level=1):
ref_tid_to_gid[transcript.id] = gene.id
ctid_to_cgid = {}
ctid_to_oid = {}
for gene in merged_db.features_of_type('gene'):
for transcript in merged_db.children(gene, level=1):
ctid_to_cgid[transcript.id] = gene.id
feature = list(merged_db.children(transcript))[0]
oid = feature.attributes.get("oId", [None])[0]
if oid:
ctid_to_oid[transcript.id] = oid
cgid_to_gid = {}
for ctid, oid in ctid_to_oid.items():
cgid = ctid_to_cgid.get(ctid, None)
oid = ctid_to_oid.get(ctid, None)
gid = ref_tid_to_gid.get(oid, None) if oid else None
if cgid and gid:
cgid_to_gid[cgid] = gid
with file_transaction(data, fixed) as tmp_fixed_file:
with open(tmp_fixed_file, "w") as out_handle:
for gene in merged_db.features_of_type('gene'):
for transcript in merged_db.children(gene, level=1):
for feature in merged_db.children(transcript):
cgid = feature.attributes.get("gene_id", [None])[0]
gid = cgid_to_gid.get(cgid, None)
ctid = None
if gid:
feature.attributes["gene_id"][0] = gid
ctid = feature.attributes.get("transcript_id",
[None])[0]
tid = ctid_to_oid.get(ctid, None)
if tid:
feature.attributes["transcript_id"][0] = tid
if "nearest_ref" in feature.attributes:
del feature.attributes["nearest_ref"]
if "oId" in feature.attributes:
del feature.attributes["oId"]
out_handle.write(str(feature) + "\n")
return fixed | python | def fix_cufflinks_attributes(ref_gtf, merged_gtf, data, out_file=None):
base, ext = os.path.splitext(merged_gtf)
fixed = out_file if out_file else base + ".clean.fixed" + ext
if file_exists(fixed):
return fixed
ref_db = gtf.get_gtf_db(ref_gtf)
merged_db = gtf.get_gtf_db(merged_gtf, in_memory=True)
ref_tid_to_gid = {}
for gene in ref_db.features_of_type('gene'):
for transcript in ref_db.children(gene, level=1):
ref_tid_to_gid[transcript.id] = gene.id
ctid_to_cgid = {}
ctid_to_oid = {}
for gene in merged_db.features_of_type('gene'):
for transcript in merged_db.children(gene, level=1):
ctid_to_cgid[transcript.id] = gene.id
feature = list(merged_db.children(transcript))[0]
oid = feature.attributes.get("oId", [None])[0]
if oid:
ctid_to_oid[transcript.id] = oid
cgid_to_gid = {}
for ctid, oid in ctid_to_oid.items():
cgid = ctid_to_cgid.get(ctid, None)
oid = ctid_to_oid.get(ctid, None)
gid = ref_tid_to_gid.get(oid, None) if oid else None
if cgid and gid:
cgid_to_gid[cgid] = gid
with file_transaction(data, fixed) as tmp_fixed_file:
with open(tmp_fixed_file, "w") as out_handle:
for gene in merged_db.features_of_type('gene'):
for transcript in merged_db.children(gene, level=1):
for feature in merged_db.children(transcript):
cgid = feature.attributes.get("gene_id", [None])[0]
gid = cgid_to_gid.get(cgid, None)
ctid = None
if gid:
feature.attributes["gene_id"][0] = gid
ctid = feature.attributes.get("transcript_id",
[None])[0]
tid = ctid_to_oid.get(ctid, None)
if tid:
feature.attributes["transcript_id"][0] = tid
if "nearest_ref" in feature.attributes:
del feature.attributes["nearest_ref"]
if "oId" in feature.attributes:
del feature.attributes["oId"]
out_handle.write(str(feature) + "\n")
return fixed | [
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237,608 | bcbio/bcbio-nextgen | bcbio/rnaseq/cufflinks.py | merge | def merge(assembled_gtfs, ref_file, gtf_file, num_cores, data):
"""
run cuffmerge on a set of assembled GTF files
"""
assembled_file = tempfile.NamedTemporaryFile(delete=False).name
with open(assembled_file, "w") as temp_handle:
for assembled in assembled_gtfs:
temp_handle.write(assembled + "\n")
out_dir = os.path.join("assembly", "cuffmerge")
merged_file = os.path.join(out_dir, "merged.gtf")
out_file = os.path.join(out_dir, "assembled.gtf")
if file_exists(out_file):
return out_file
if not file_exists(merged_file):
with file_transaction(data, out_dir) as tmp_out_dir:
cmd = ("cuffmerge -o {tmp_out_dir} --ref-gtf {gtf_file} "
"--num-threads {num_cores} --ref-sequence {ref_file} "
"{assembled_file}")
cmd = cmd.format(**locals())
message = ("Merging the following transcript assemblies with "
"Cuffmerge: %s" % ", ".join(assembled_gtfs))
do.run(cmd, message)
clean, _ = clean_assembly(merged_file)
fixed = fix_cufflinks_attributes(gtf_file, clean, data)
classified = annotate_gtf.annotate_novel_coding(fixed, gtf_file, ref_file,
data)
filtered = annotate_gtf.cleanup_transcripts(classified, gtf_file, ref_file)
shutil.move(filtered, out_file)
return out_file | python | def merge(assembled_gtfs, ref_file, gtf_file, num_cores, data):
assembled_file = tempfile.NamedTemporaryFile(delete=False).name
with open(assembled_file, "w") as temp_handle:
for assembled in assembled_gtfs:
temp_handle.write(assembled + "\n")
out_dir = os.path.join("assembly", "cuffmerge")
merged_file = os.path.join(out_dir, "merged.gtf")
out_file = os.path.join(out_dir, "assembled.gtf")
if file_exists(out_file):
return out_file
if not file_exists(merged_file):
with file_transaction(data, out_dir) as tmp_out_dir:
cmd = ("cuffmerge -o {tmp_out_dir} --ref-gtf {gtf_file} "
"--num-threads {num_cores} --ref-sequence {ref_file} "
"{assembled_file}")
cmd = cmd.format(**locals())
message = ("Merging the following transcript assemblies with "
"Cuffmerge: %s" % ", ".join(assembled_gtfs))
do.run(cmd, message)
clean, _ = clean_assembly(merged_file)
fixed = fix_cufflinks_attributes(gtf_file, clean, data)
classified = annotate_gtf.annotate_novel_coding(fixed, gtf_file, ref_file,
data)
filtered = annotate_gtf.cleanup_transcripts(classified, gtf_file, ref_file)
shutil.move(filtered, out_file)
return out_file | [
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237,609 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _vcf_info | def _vcf_info(start, end, mate_id, info=None):
"""Return breakend information line with mate and imprecise location.
"""
out = "SVTYPE=BND;MATEID={mate};IMPRECISE;CIPOS=0,{size}".format(
mate=mate_id, size=end-start)
if info is not None:
extra_info = ";".join("{0}={1}".format(k, v) for k, v in info.iteritems())
out = "{0};{1}".format(out, extra_info)
return out | python | def _vcf_info(start, end, mate_id, info=None):
out = "SVTYPE=BND;MATEID={mate};IMPRECISE;CIPOS=0,{size}".format(
mate=mate_id, size=end-start)
if info is not None:
extra_info = ";".join("{0}={1}".format(k, v) for k, v in info.iteritems())
out = "{0};{1}".format(out, extra_info)
return out | [
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237,610 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _vcf_alt | def _vcf_alt(base, other_chr, other_pos, isrc, is_first):
"""Create ALT allele line in VCF 4.1 format associating with other paired end.
"""
if is_first:
pipe = "[" if isrc else "]"
out_str = "{base}{pipe}{chr}:{pos}{pipe}"
else:
pipe = "]" if isrc else "["
out_str = "{pipe}{chr}:{pos}{pipe}{base}"
return out_str.format(pipe=pipe, chr=other_chr, pos=other_pos + 1,
base=base) | python | def _vcf_alt(base, other_chr, other_pos, isrc, is_first):
if is_first:
pipe = "[" if isrc else "]"
out_str = "{base}{pipe}{chr}:{pos}{pipe}"
else:
pipe = "]" if isrc else "["
out_str = "{pipe}{chr}:{pos}{pipe}{base}"
return out_str.format(pipe=pipe, chr=other_chr, pos=other_pos + 1,
base=base) | [
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237,611 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _breakend_orientation | def _breakend_orientation(strand1, strand2):
"""Convert BEDPE strand representation of breakpoints into VCF.
| strand1 | strand2 | VCF |
+----------+----------+--------------+
| + | - | t[p[ ]p]t |
| + | + | t]p] t]p] |
| - | - | [p[t [p[t |
| - | + | ]p]t t[p[ |
"""
EndOrientation = namedtuple("EndOrientation",
["is_first1", "is_rc1", "is_first2", "is_rc2"])
if strand1 == "+" and strand2 == "-":
return EndOrientation(True, True, False, True)
elif strand1 == "+" and strand2 == "+":
return EndOrientation(True, False, True, False)
elif strand1 == "-" and strand2 == "-":
return EndOrientation(False, False, False, False)
elif strand1 == "-" and strand2 == "+":
return EndOrientation(False, True, True, True)
else:
raise ValueError("Unexpected strand pairing: {0} {1}".format(
strand1, strand2)) | python | def _breakend_orientation(strand1, strand2):
EndOrientation = namedtuple("EndOrientation",
["is_first1", "is_rc1", "is_first2", "is_rc2"])
if strand1 == "+" and strand2 == "-":
return EndOrientation(True, True, False, True)
elif strand1 == "+" and strand2 == "+":
return EndOrientation(True, False, True, False)
elif strand1 == "-" and strand2 == "-":
return EndOrientation(False, False, False, False)
elif strand1 == "-" and strand2 == "+":
return EndOrientation(False, True, True, True)
else:
raise ValueError("Unexpected strand pairing: {0} {1}".format(
strand1, strand2)) | [
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237,612 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | build_vcf_parts | def build_vcf_parts(feature, genome_2bit, info=None):
"""Convert BedPe feature information into VCF part representation.
Each feature will have two VCF lines for each side of the breakpoint.
"""
base1 = genome_2bit[feature.chrom1].get(
feature.start1, feature.start1 + 1).upper()
id1 = "hydra{0}a".format(feature.name)
base2 = genome_2bit[feature.chrom2].get(
feature.start2, feature.start2 + 1).upper()
id2 = "hydra{0}b".format(feature.name)
orientation = _breakend_orientation(feature.strand1, feature.strand2)
return (VcfLine(feature.chrom1, feature.start1, id1, base1,
_vcf_alt(base1, feature.chrom2, feature.start2,
orientation.is_rc1, orientation.is_first1),
_vcf_info(feature.start1, feature.end1, id2, info)),
VcfLine(feature.chrom2, feature.start2, id2, base2,
_vcf_alt(base2, feature.chrom1, feature.start1,
orientation.is_rc2, orientation.is_first2),
_vcf_info(feature.start2, feature.end2, id1, info))) | python | def build_vcf_parts(feature, genome_2bit, info=None):
base1 = genome_2bit[feature.chrom1].get(
feature.start1, feature.start1 + 1).upper()
id1 = "hydra{0}a".format(feature.name)
base2 = genome_2bit[feature.chrom2].get(
feature.start2, feature.start2 + 1).upper()
id2 = "hydra{0}b".format(feature.name)
orientation = _breakend_orientation(feature.strand1, feature.strand2)
return (VcfLine(feature.chrom1, feature.start1, id1, base1,
_vcf_alt(base1, feature.chrom2, feature.start2,
orientation.is_rc1, orientation.is_first1),
_vcf_info(feature.start1, feature.end1, id2, info)),
VcfLine(feature.chrom2, feature.start2, id2, base2,
_vcf_alt(base2, feature.chrom1, feature.start1,
orientation.is_rc2, orientation.is_first2),
_vcf_info(feature.start2, feature.end2, id1, info))) | [
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237,613 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | build_vcf_deletion | def build_vcf_deletion(x, genome_2bit):
"""Provide representation of deletion from BedPE breakpoints.
"""
base1 = genome_2bit[x.chrom1].get(x.start1, x.start1 + 1).upper()
id1 = "hydra{0}".format(x.name)
return VcfLine(x.chrom1, x.start1, id1, base1, "<DEL>",
_vcf_single_end_info(x, "DEL", True)) | python | def build_vcf_deletion(x, genome_2bit):
base1 = genome_2bit[x.chrom1].get(x.start1, x.start1 + 1).upper()
id1 = "hydra{0}".format(x.name)
return VcfLine(x.chrom1, x.start1, id1, base1, "<DEL>",
_vcf_single_end_info(x, "DEL", True)) | [
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237,614 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | build_vcf_inversion | def build_vcf_inversion(x1, x2, genome_2bit):
"""Provide representation of inversion from BedPE breakpoints.
"""
id1 = "hydra{0}".format(x1.name)
start_coords = sorted([x1.start1, x1.end1, x2.start1, x2.end1])
end_coords = sorted([x1.start2, x1.end2, x2.start2, x2.start2])
start_pos = (start_coords[1] + start_coords[2]) // 2
end_pos = (end_coords[1] + end_coords[2]) // 2
base1 = genome_2bit[x1.chrom1].get(start_pos, start_pos + 1).upper()
info = "SVTYPE=INV;IMPRECISE;CIPOS={cip1},{cip2};CIEND={cie1},{cie2};" \
"END={end};SVLEN={length}".format(cip1=start_pos - start_coords[0],
cip2=start_coords[-1] - start_pos,
cie1=end_pos - end_coords[0],
cie2=end_coords[-1] - end_pos,
end=end_pos,
length=end_pos-start_pos)
return VcfLine(x1.chrom1, start_pos, id1, base1, "<INV>", info) | python | def build_vcf_inversion(x1, x2, genome_2bit):
id1 = "hydra{0}".format(x1.name)
start_coords = sorted([x1.start1, x1.end1, x2.start1, x2.end1])
end_coords = sorted([x1.start2, x1.end2, x2.start2, x2.start2])
start_pos = (start_coords[1] + start_coords[2]) // 2
end_pos = (end_coords[1] + end_coords[2]) // 2
base1 = genome_2bit[x1.chrom1].get(start_pos, start_pos + 1).upper()
info = "SVTYPE=INV;IMPRECISE;CIPOS={cip1},{cip2};CIEND={cie1},{cie2};" \
"END={end};SVLEN={length}".format(cip1=start_pos - start_coords[0],
cip2=start_coords[-1] - start_pos,
cie1=end_pos - end_coords[0],
cie2=end_coords[-1] - end_pos,
end=end_pos,
length=end_pos-start_pos)
return VcfLine(x1.chrom1, start_pos, id1, base1, "<INV>", info) | [
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237,615 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | hydra_parser | def hydra_parser(in_file, options=None):
"""Parse hydra input file into namedtuple of values.
"""
if options is None: options = {}
BedPe = namedtuple('BedPe', ["chrom1", "start1", "end1",
"chrom2", "start2", "end2",
"name", "strand1", "strand2",
"support"])
with open(in_file) as in_handle:
reader = csv.reader(in_handle, dialect="excel-tab")
for line in reader:
cur = BedPe(line[0], int(line[1]), int(line[2]),
line[3], int(line[4]), int(line[5]),
line[6], line[8], line[9],
float(line[18]))
if cur.support >= options.get("min_support", 0):
yield cur | python | def hydra_parser(in_file, options=None):
if options is None: options = {}
BedPe = namedtuple('BedPe', ["chrom1", "start1", "end1",
"chrom2", "start2", "end2",
"name", "strand1", "strand2",
"support"])
with open(in_file) as in_handle:
reader = csv.reader(in_handle, dialect="excel-tab")
for line in reader:
cur = BedPe(line[0], int(line[1]), int(line[2]),
line[3], int(line[4]), int(line[5]),
line[6], line[8], line[9],
float(line[18]))
if cur.support >= options.get("min_support", 0):
yield cur | [
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237,616 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _cluster_by | def _cluster_by(end_iter, attr1, attr2, cluster_distance):
"""Cluster breakends by specified attributes.
"""
ClusterInfo = namedtuple("ClusterInfo", ["chroms", "clusters", "lookup"])
chr_clusters = {}
chroms = []
brends_by_id = {}
for brend in end_iter:
if not chr_clusters.has_key(brend.chrom1):
chroms.append(brend.chrom1)
chr_clusters[brend.chrom1] = ClusterTree(cluster_distance, 1)
brends_by_id[int(brend.name)] = brend
chr_clusters[brend.chrom1].insert(getattr(brend, attr1),
getattr(brend, attr2),
int(brend.name))
return ClusterInfo(chroms, chr_clusters, brends_by_id) | python | def _cluster_by(end_iter, attr1, attr2, cluster_distance):
ClusterInfo = namedtuple("ClusterInfo", ["chroms", "clusters", "lookup"])
chr_clusters = {}
chroms = []
brends_by_id = {}
for brend in end_iter:
if not chr_clusters.has_key(brend.chrom1):
chroms.append(brend.chrom1)
chr_clusters[brend.chrom1] = ClusterTree(cluster_distance, 1)
brends_by_id[int(brend.name)] = brend
chr_clusters[brend.chrom1].insert(getattr(brend, attr1),
getattr(brend, attr2),
int(brend.name))
return ClusterInfo(chroms, chr_clusters, brends_by_id) | [
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237,617 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _calculate_cluster_distance | def _calculate_cluster_distance(end_iter):
"""Compute allowed distance for clustering based on end confidence intervals.
"""
out = []
sizes = []
for x in end_iter:
out.append(x)
sizes.append(x.end1 - x.start1)
sizes.append(x.end2 - x.start2)
distance = sum(sizes) // len(sizes)
return distance, out | python | def _calculate_cluster_distance(end_iter):
out = []
sizes = []
for x in end_iter:
out.append(x)
sizes.append(x.end1 - x.start1)
sizes.append(x.end2 - x.start2)
distance = sum(sizes) // len(sizes)
return distance, out | [
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237,618 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | group_hydra_breakends | def group_hydra_breakends(end_iter):
"""Group together hydra breakends with overlapping ends.
This provides a way to identify inversions, translocations
and insertions present in hydra break point ends. We cluster together the
endpoints and return together any items with closely oriented pairs.
This helps in describing more complex rearrangement events.
"""
cluster_distance, all_ends = _calculate_cluster_distance(end_iter)
first_cluster = _cluster_by(all_ends, "start1", "end1", cluster_distance)
for chrom in first_cluster.chroms:
for _, _, brends in first_cluster.clusters[chrom].getregions():
if len(brends) == 1:
yield [first_cluster.lookup[brends[0]]]
else:
second_cluster = _cluster_by([first_cluster.lookup[x] for x in brends],
"start2", "end2", cluster_distance)
for chrom2 in second_cluster.chroms:
for _, _, brends in second_cluster.clusters[chrom].getregions():
yield [second_cluster.lookup[x] for x in brends] | python | def group_hydra_breakends(end_iter):
cluster_distance, all_ends = _calculate_cluster_distance(end_iter)
first_cluster = _cluster_by(all_ends, "start1", "end1", cluster_distance)
for chrom in first_cluster.chroms:
for _, _, brends in first_cluster.clusters[chrom].getregions():
if len(brends) == 1:
yield [first_cluster.lookup[brends[0]]]
else:
second_cluster = _cluster_by([first_cluster.lookup[x] for x in brends],
"start2", "end2", cluster_distance)
for chrom2 in second_cluster.chroms:
for _, _, brends in second_cluster.clusters[chrom].getregions():
yield [second_cluster.lookup[x] for x in brends] | [
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237,619 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _write_vcf_header | def _write_vcf_header(out_handle):
"""Write VCF header information for Hydra structural variant.
"""
def w(line):
out_handle.write("{0}\n".format(line))
w('##fileformat=VCFv4.1')
w('##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">')
w('##INFO=<ID=END,Number=1,Type=Integer,'
'Description="End position of the variant described in this record">')
w('##INFO=<ID=CIPOS,Number=2,Type=Integer,'
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w('##INFO=<ID=CIEND,Number=2,Type=Integer,'
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w('##INFO=<ID=SVLEN,Number=.,Type=Integer,'
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w('##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">')
w('##INFO=<ID=MATEID,Number=.,Type=String,Description="ID of mate breakends">')
w('##INFO=<ID=EVENT,Number=1,Type=String,Description="ID of event associated to breakend">')
w('##ALT=<ID=DEL,Description="Deletion">')
w('##ALT=<ID=INV,Description="Inversion">')
w('##ALT=<ID=DUP,Description="Duplication">')
w('##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">')
w('##source=hydra')
w("#" + "\t".join(["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"])) | python | def _write_vcf_header(out_handle):
def w(line):
out_handle.write("{0}\n".format(line))
w('##fileformat=VCFv4.1')
w('##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">')
w('##INFO=<ID=END,Number=1,Type=Integer,'
'Description="End position of the variant described in this record">')
w('##INFO=<ID=CIPOS,Number=2,Type=Integer,'
'Description="Confidence interval around POS for imprecise variants">')
w('##INFO=<ID=CIEND,Number=2,Type=Integer,'
'Description="Confidence interval around END for imprecise variants">')
w('##INFO=<ID=SVLEN,Number=.,Type=Integer,'
'Description="Difference in length between REF and ALT alleles">')
w('##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">')
w('##INFO=<ID=MATEID,Number=.,Type=String,Description="ID of mate breakends">')
w('##INFO=<ID=EVENT,Number=1,Type=String,Description="ID of event associated to breakend">')
w('##ALT=<ID=DEL,Description="Deletion">')
w('##ALT=<ID=INV,Description="Inversion">')
w('##ALT=<ID=DUP,Description="Duplication">')
w('##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">')
w('##source=hydra')
w("#" + "\t".join(["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"])) | [
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237,620 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _write_vcf_breakend | def _write_vcf_breakend(brend, out_handle):
"""Write out a single VCF line with breakpoint information.
"""
out_handle.write("{0}\n".format("\t".join(str(x) for x in
[brend.chrom, brend.pos + 1, brend.id, brend.ref, brend.alt,
".", "PASS", brend.info]))) | python | def _write_vcf_breakend(brend, out_handle):
out_handle.write("{0}\n".format("\t".join(str(x) for x in
[brend.chrom, brend.pos + 1, brend.id, brend.ref, brend.alt,
".", "PASS", brend.info]))) | [
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237,621 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | _get_vcf_breakends | def _get_vcf_breakends(hydra_file, genome_2bit, options=None):
"""Parse BEDPE input, yielding VCF ready breakends.
"""
if options is None: options = {}
for features in group_hydra_breakends(hydra_parser(hydra_file, options)):
if len(features) == 1 and is_deletion(features[0], options):
yield build_vcf_deletion(features[0], genome_2bit)
elif len(features) == 1 and is_tandem_dup(features[0], options):
yield build_tandem_deletion(features[0], genome_2bit)
elif len(features) == 2 and is_inversion(*features):
yield build_vcf_inversion(features[0], features[1], genome_2bit)
elif len(features) == 2 and is_translocation(*features):
info = get_translocation_info(features[0], features[1])
for feature in features:
for brend in build_vcf_parts(feature, genome_2bit, info):
yield brend
else:
for feature in features:
for brend in build_vcf_parts(feature, genome_2bit):
yield brend | python | def _get_vcf_breakends(hydra_file, genome_2bit, options=None):
if options is None: options = {}
for features in group_hydra_breakends(hydra_parser(hydra_file, options)):
if len(features) == 1 and is_deletion(features[0], options):
yield build_vcf_deletion(features[0], genome_2bit)
elif len(features) == 1 and is_tandem_dup(features[0], options):
yield build_tandem_deletion(features[0], genome_2bit)
elif len(features) == 2 and is_inversion(*features):
yield build_vcf_inversion(features[0], features[1], genome_2bit)
elif len(features) == 2 and is_translocation(*features):
info = get_translocation_info(features[0], features[1])
for feature in features:
for brend in build_vcf_parts(feature, genome_2bit, info):
yield brend
else:
for feature in features:
for brend in build_vcf_parts(feature, genome_2bit):
yield brend | [
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237,622 | bcbio/bcbio-nextgen | scripts/utils/hydra_to_vcf.py | hydra_to_vcf_writer | def hydra_to_vcf_writer(hydra_file, genome_2bit, options, out_handle):
"""Write hydra output as sorted VCF file.
Requires loading the hydra file into memory to perform sorting
on output VCF. Could generalize this to no sorting or by-chromosome
approach if this proves too memory intensive.
"""
_write_vcf_header(out_handle)
brends = list(_get_vcf_breakends(hydra_file, genome_2bit, options))
brends.sort(key=attrgetter("chrom", "pos"))
for brend in brends:
_write_vcf_breakend(brend, out_handle) | python | def hydra_to_vcf_writer(hydra_file, genome_2bit, options, out_handle):
_write_vcf_header(out_handle)
brends = list(_get_vcf_breakends(hydra_file, genome_2bit, options))
brends.sort(key=attrgetter("chrom", "pos"))
for brend in brends:
_write_vcf_breakend(brend, out_handle) | [
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237,623 | bcbio/bcbio-nextgen | bcbio/rnaseq/kallisto.py | kallisto_table | def kallisto_table(kallisto_dir, index):
"""
convert kallisto output to a count table where the rows are
equivalence classes and the columns are cells
"""
quant_dir = os.path.join(kallisto_dir, "quant")
out_file = os.path.join(quant_dir, "matrix.csv")
if file_exists(out_file):
return out_file
tsvfile = os.path.join(quant_dir, "matrix.tsv")
ecfile = os.path.join(quant_dir, "matrix.ec")
cellsfile = os.path.join(quant_dir, "matrix.cells")
fastafile = os.path.splitext(index)[0] + ".fa"
fasta_names = fasta.sequence_names(fastafile)
ec_names = get_ec_names(ecfile, fasta_names)
df = pd.read_table(tsvfile, header=None, names=["ec", "cell", "count"])
df["ec"] = [ec_names[x] for x in df["ec"]]
df = df.pivot(index='ec', columns='cell', values='count')
cellnames = get_cell_names(cellsfile)
colnames = [cellnames[x] for x in df.columns]
df.columns = colnames
df.to_csv(out_file)
return out_file | python | def kallisto_table(kallisto_dir, index):
quant_dir = os.path.join(kallisto_dir, "quant")
out_file = os.path.join(quant_dir, "matrix.csv")
if file_exists(out_file):
return out_file
tsvfile = os.path.join(quant_dir, "matrix.tsv")
ecfile = os.path.join(quant_dir, "matrix.ec")
cellsfile = os.path.join(quant_dir, "matrix.cells")
fastafile = os.path.splitext(index)[0] + ".fa"
fasta_names = fasta.sequence_names(fastafile)
ec_names = get_ec_names(ecfile, fasta_names)
df = pd.read_table(tsvfile, header=None, names=["ec", "cell", "count"])
df["ec"] = [ec_names[x] for x in df["ec"]]
df = df.pivot(index='ec', columns='cell', values='count')
cellnames = get_cell_names(cellsfile)
colnames = [cellnames[x] for x in df.columns]
df.columns = colnames
df.to_csv(out_file)
return out_file | [
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237,624 | bcbio/bcbio-nextgen | bcbio/rnaseq/kallisto.py | get_ec_names | def get_ec_names(ecfile, fasta_names):
"""
convert equivalence classes to their set of transcripts
"""
df = pd.read_table(ecfile, header=None, names=["ec", "transcripts"])
transcript_groups = [x.split(",") for x in df["transcripts"]]
transcripts = []
for group in transcript_groups:
transcripts.append(":".join([fasta_names[int(x)] for x in group]))
return transcripts | python | def get_ec_names(ecfile, fasta_names):
df = pd.read_table(ecfile, header=None, names=["ec", "transcripts"])
transcript_groups = [x.split(",") for x in df["transcripts"]]
transcripts = []
for group in transcript_groups:
transcripts.append(":".join([fasta_names[int(x)] for x in group]))
return transcripts | [
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237,625 | bcbio/bcbio-nextgen | bcbio/illumina/flowcell.py | parse_dirname | def parse_dirname(fc_dir):
"""Parse the flow cell ID and date from a flow cell directory.
"""
(_, fc_dir) = os.path.split(fc_dir)
parts = fc_dir.split("_")
name = None
date = None
for p in parts:
if p.endswith(("XX", "xx", "XY", "X2")):
name = p
elif len(p) == 6:
try:
int(p)
date = p
except ValueError:
pass
if name is None or date is None:
raise ValueError("Did not find flowcell name: %s" % fc_dir)
return name, date | python | def parse_dirname(fc_dir):
(_, fc_dir) = os.path.split(fc_dir)
parts = fc_dir.split("_")
name = None
date = None
for p in parts:
if p.endswith(("XX", "xx", "XY", "X2")):
name = p
elif len(p) == 6:
try:
int(p)
date = p
except ValueError:
pass
if name is None or date is None:
raise ValueError("Did not find flowcell name: %s" % fc_dir)
return name, date | [
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237,626 | bcbio/bcbio-nextgen | bcbio/illumina/flowcell.py | get_qseq_dir | def get_qseq_dir(fc_dir):
"""Retrieve the qseq directory within Solexa flowcell output.
"""
machine_bc = os.path.join(fc_dir, "Data", "Intensities", "BaseCalls")
if os.path.exists(machine_bc):
return machine_bc
# otherwise assume we are in the qseq directory
# XXX What other cases can we end up with here?
else:
return fc_dir | python | def get_qseq_dir(fc_dir):
machine_bc = os.path.join(fc_dir, "Data", "Intensities", "BaseCalls")
if os.path.exists(machine_bc):
return machine_bc
# otherwise assume we are in the qseq directory
# XXX What other cases can we end up with here?
else:
return fc_dir | [
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237,627 | bcbio/bcbio-nextgen | bcbio/illumina/flowcell.py | get_fastq_dir | def get_fastq_dir(fc_dir):
"""Retrieve the fastq directory within Solexa flowcell output.
"""
full_goat_bc = glob.glob(os.path.join(fc_dir, "Data", "*Firecrest*", "Bustard*"))
bustard_bc = glob.glob(os.path.join(fc_dir, "Data", "Intensities", "*Bustard*"))
machine_bc = os.path.join(fc_dir, "Data", "Intensities", "BaseCalls")
if os.path.exists(machine_bc):
return os.path.join(machine_bc, "fastq")
elif len(full_goat_bc) > 0:
return os.path.join(full_goat_bc[0], "fastq")
elif len(bustard_bc) > 0:
return os.path.join(bustard_bc[0], "fastq")
# otherwise assume we are in the fastq directory
# XXX What other cases can we end up with here?
else:
return fc_dir | python | def get_fastq_dir(fc_dir):
full_goat_bc = glob.glob(os.path.join(fc_dir, "Data", "*Firecrest*", "Bustard*"))
bustard_bc = glob.glob(os.path.join(fc_dir, "Data", "Intensities", "*Bustard*"))
machine_bc = os.path.join(fc_dir, "Data", "Intensities", "BaseCalls")
if os.path.exists(machine_bc):
return os.path.join(machine_bc, "fastq")
elif len(full_goat_bc) > 0:
return os.path.join(full_goat_bc[0], "fastq")
elif len(bustard_bc) > 0:
return os.path.join(bustard_bc[0], "fastq")
# otherwise assume we are in the fastq directory
# XXX What other cases can we end up with here?
else:
return fc_dir | [
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237,628 | bcbio/bcbio-nextgen | bcbio/illumina/flowcell.py | GalaxySqnLimsApi.run_details | def run_details(self, run):
"""Retrieve sequencing run details as a dictionary.
"""
run_data = dict(run=run)
req = urllib.request.Request("%s/nglims/api_run_details" % self._base_url,
urllib.parse.urlencode(run_data))
response = urllib.request.urlopen(req)
info = json.loads(response.read())
if "error" in info:
raise ValueError("Problem retrieving info: %s" % info["error"])
else:
return info["details"] | python | def run_details(self, run):
run_data = dict(run=run)
req = urllib.request.Request("%s/nglims/api_run_details" % self._base_url,
urllib.parse.urlencode(run_data))
response = urllib.request.urlopen(req)
info = json.loads(response.read())
if "error" in info:
raise ValueError("Problem retrieving info: %s" % info["error"])
else:
return info["details"] | [
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237,629 | bcbio/bcbio-nextgen | bcbio/ngsalign/mosaik.py | _mosaik_args_from_config | def _mosaik_args_from_config(config):
"""Configurable high level options for mosaik.
"""
multi_mappers = config["algorithm"].get("multiple_mappers", True)
multi_flags = ["-m", "all"] if multi_mappers else ["-m", "unique"]
error_flags = ["-mm", "2"]
num_cores = config["algorithm"].get("num_cores", 1)
core_flags = ["-p", str(num_cores)] if num_cores > 1 else []
return core_flags + multi_flags + error_flags | python | def _mosaik_args_from_config(config):
multi_mappers = config["algorithm"].get("multiple_mappers", True)
multi_flags = ["-m", "all"] if multi_mappers else ["-m", "unique"]
error_flags = ["-mm", "2"]
num_cores = config["algorithm"].get("num_cores", 1)
core_flags = ["-p", str(num_cores)] if num_cores > 1 else []
return core_flags + multi_flags + error_flags | [
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237,630 | bcbio/bcbio-nextgen | bcbio/ngsalign/mosaik.py | _convert_fastq | def _convert_fastq(fastq_file, pair_file, rg_name, out_file, config):
"""Convert fastq inputs into internal Mosaik representation.
"""
out_file = "{0}-fq.mkb".format(os.path.splitext(out_file)[0])
if not file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
cl = [config_utils.get_program("mosaik", config,
default="MosaikAligner").replace("Aligner", "Build")]
cl += ["-q", fastq_file,
"-out", tx_out_file,
"-st", config["algorithm"].get("platform", "illumina").lower()]
if pair_file:
cl += ["-q2", pair_file]
if rg_name:
cl += ["-id", rg_name]
env_set = "export MOSAIK_TMP={0}".format(os.path.dirname(tx_out_file))
subprocess.check_call(env_set + " && " + " ".join(cl), shell=True)
return out_file | python | def _convert_fastq(fastq_file, pair_file, rg_name, out_file, config):
out_file = "{0}-fq.mkb".format(os.path.splitext(out_file)[0])
if not file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
cl = [config_utils.get_program("mosaik", config,
default="MosaikAligner").replace("Aligner", "Build")]
cl += ["-q", fastq_file,
"-out", tx_out_file,
"-st", config["algorithm"].get("platform", "illumina").lower()]
if pair_file:
cl += ["-q2", pair_file]
if rg_name:
cl += ["-id", rg_name]
env_set = "export MOSAIK_TMP={0}".format(os.path.dirname(tx_out_file))
subprocess.check_call(env_set + " && " + " ".join(cl), shell=True)
return out_file | [
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237,631 | bcbio/bcbio-nextgen | bcbio/ngsalign/mosaik.py | _get_mosaik_nn_args | def _get_mosaik_nn_args(out_file):
"""Retrieve default neural network files from GitHub to pass to Mosaik.
"""
base_nn_url = "https://raw.github.com/wanpinglee/MOSAIK/master/src/networkFile/"
out = []
for arg, fname in [("-annse", "2.1.26.se.100.005.ann"),
("-annpe", "2.1.26.pe.100.0065.ann")]:
arg_fname = os.path.join(os.path.dirname(out_file), fname)
if not file_exists(arg_fname):
subprocess.check_call(["wget", "-O", arg_fname, base_nn_url + fname])
out += [arg, arg_fname]
return out | python | def _get_mosaik_nn_args(out_file):
base_nn_url = "https://raw.github.com/wanpinglee/MOSAIK/master/src/networkFile/"
out = []
for arg, fname in [("-annse", "2.1.26.se.100.005.ann"),
("-annpe", "2.1.26.pe.100.0065.ann")]:
arg_fname = os.path.join(os.path.dirname(out_file), fname)
if not file_exists(arg_fname):
subprocess.check_call(["wget", "-O", arg_fname, base_nn_url + fname])
out += [arg, arg_fname]
return out | [
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237,632 | bcbio/bcbio-nextgen | bcbio/ngsalign/mosaik.py | align | def align(fastq_file, pair_file, ref_file, names, align_dir, data,
extra_args=None):
"""Alignment with MosaikAligner.
"""
config = data["config"]
rg_name = names.get("rg", None) if names else None
out_file = os.path.join(align_dir, "%s-align.bam" % names["lane"])
if not file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
built_fastq = _convert_fastq(fastq_file, pair_file, rg_name,
out_file, config)
cl = [config_utils.get_program("mosaik", config, default="MosaikAligner")]
cl += _mosaik_args_from_config(config)
cl += extra_args if extra_args is not None else []
cl += ["-ia", ref_file,
"-in", built_fastq,
"-out", os.path.splitext(tx_out_file)[0]]
jump_base = os.path.splitext(ref_file)[0]
key_file = "{0}_keys.jmp".format(jump_base)
if file_exists(key_file):
cl += ["-j", jump_base]
# XXX hacky way to guess key size which needs to match
# Can I get hash size directly
jump_size_gb = os.path.getsize(key_file) / 1073741824.0
if jump_size_gb < 1.0:
cl += ["-hs", "13"]
cl += _get_mosaik_nn_args(out_file)
env_set = "export MOSAIK_TMP={0}".format(os.path.dirname(tx_out_file))
subprocess.check_call(env_set + " && "+
" ".join([str(x) for x in cl]), shell=True)
os.remove(built_fastq)
return out_file | python | def align(fastq_file, pair_file, ref_file, names, align_dir, data,
extra_args=None):
config = data["config"]
rg_name = names.get("rg", None) if names else None
out_file = os.path.join(align_dir, "%s-align.bam" % names["lane"])
if not file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
built_fastq = _convert_fastq(fastq_file, pair_file, rg_name,
out_file, config)
cl = [config_utils.get_program("mosaik", config, default="MosaikAligner")]
cl += _mosaik_args_from_config(config)
cl += extra_args if extra_args is not None else []
cl += ["-ia", ref_file,
"-in", built_fastq,
"-out", os.path.splitext(tx_out_file)[0]]
jump_base = os.path.splitext(ref_file)[0]
key_file = "{0}_keys.jmp".format(jump_base)
if file_exists(key_file):
cl += ["-j", jump_base]
# XXX hacky way to guess key size which needs to match
# Can I get hash size directly
jump_size_gb = os.path.getsize(key_file) / 1073741824.0
if jump_size_gb < 1.0:
cl += ["-hs", "13"]
cl += _get_mosaik_nn_args(out_file)
env_set = "export MOSAIK_TMP={0}".format(os.path.dirname(tx_out_file))
subprocess.check_call(env_set + " && "+
" ".join([str(x) for x in cl]), shell=True)
os.remove(built_fastq)
return out_file | [
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237,633 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | get_bcbio_timings | def get_bcbio_timings(path):
"""Fetch timing information from a bcbio log file."""
with open(path, 'r') as file_handle:
steps = {}
for line in file_handle:
matches = re.search(r'^\[([^\]]+)\] ([^:]+: .*)', line)
if not matches:
continue
tstamp = matches.group(1)
msg = matches.group(2)
# XXX: new special logs do not have this
#if not msg.find('Timing: ') >= 0:
# continue
when = datetime.strptime(tstamp, '%Y-%m-%dT%H:%MZ').replace(
tzinfo=pytz.timezone('UTC'))
step = msg.split(":")[-1].strip()
steps[when] = step
return steps | python | def get_bcbio_timings(path):
with open(path, 'r') as file_handle:
steps = {}
for line in file_handle:
matches = re.search(r'^\[([^\]]+)\] ([^:]+: .*)', line)
if not matches:
continue
tstamp = matches.group(1)
msg = matches.group(2)
# XXX: new special logs do not have this
#if not msg.find('Timing: ') >= 0:
# continue
when = datetime.strptime(tstamp, '%Y-%m-%dT%H:%MZ').replace(
tzinfo=pytz.timezone('UTC'))
step = msg.split(":")[-1].strip()
steps[when] = step
return steps | [
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237,634 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | this_and_prev | def this_and_prev(iterable):
"""Walk an iterable, returning the current and previous items
as a two-tuple."""
try:
item = next(iterable)
while True:
next_item = next(iterable)
yield item, next_item
item = next_item
except StopIteration:
return | python | def this_and_prev(iterable):
try:
item = next(iterable)
while True:
next_item = next(iterable)
yield item, next_item
item = next_item
except StopIteration:
return | [
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237,635 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | remove_outliers | def remove_outliers(series, stddev):
"""Remove the outliers from a series."""
return series[(series - series.mean()).abs() < stddev * series.std()] | python | def remove_outliers(series, stddev):
return series[(series - series.mean()).abs() < stddev * series.std()] | [
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237,636 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | prep_for_graph | def prep_for_graph(data_frame, series=None, delta_series=None, smoothing=None,
outlier_stddev=None):
"""Prepare a dataframe for graphing by calculating deltas for
series that need them, resampling, and removing outliers.
"""
series = series or []
delta_series = delta_series or []
graph = calc_deltas(data_frame, delta_series)
for s in series + delta_series:
if smoothing:
graph[s] = graph[s].resample(smoothing)
if outlier_stddev:
graph[s] = remove_outliers(graph[s], outlier_stddev)
return graph[series + delta_series] | python | def prep_for_graph(data_frame, series=None, delta_series=None, smoothing=None,
outlier_stddev=None):
series = series or []
delta_series = delta_series or []
graph = calc_deltas(data_frame, delta_series)
for s in series + delta_series:
if smoothing:
graph[s] = graph[s].resample(smoothing)
if outlier_stddev:
graph[s] = remove_outliers(graph[s], outlier_stddev)
return graph[series + delta_series] | [
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237,637 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | add_common_plot_features | def add_common_plot_features(plot, steps):
"""Add plot features common to all plots, such as bcbio step
information.
"""
_setup_matplotlib()
plot.yaxis.set_tick_params(labelright=True)
plot.set_xlabel('')
ymax = plot.get_ylim()[1]
ticks = {}
for tstamp, step in steps.items():
if step == 'finished':
continue
plot.vlines(tstamp, 0, ymax, linestyles='dashed')
tstamp = mpl.dates.num2epoch(mpl.dates.date2num(tstamp))
ticks[tstamp] = step
tick_kvs = sorted(ticks.items())
top_axis = plot.twiny()
top_axis.set_xlim(*plot.get_xlim())
top_axis.set_xticks([k for k, v in tick_kvs])
top_axis.set_xticklabels([v for k, v in tick_kvs],
rotation=45, ha='left', size=pylab.rcParams['font.size'])
plot.set_ylim(0)
return plot | python | def add_common_plot_features(plot, steps):
_setup_matplotlib()
plot.yaxis.set_tick_params(labelright=True)
plot.set_xlabel('')
ymax = plot.get_ylim()[1]
ticks = {}
for tstamp, step in steps.items():
if step == 'finished':
continue
plot.vlines(tstamp, 0, ymax, linestyles='dashed')
tstamp = mpl.dates.num2epoch(mpl.dates.date2num(tstamp))
ticks[tstamp] = step
tick_kvs = sorted(ticks.items())
top_axis = plot.twiny()
top_axis.set_xlim(*plot.get_xlim())
top_axis.set_xticks([k for k, v in tick_kvs])
top_axis.set_xticklabels([v for k, v in tick_kvs],
rotation=45, ha='left', size=pylab.rcParams['font.size'])
plot.set_ylim(0)
return plot | [
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237,638 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | log_time_frame | def log_time_frame(bcbio_log):
"""The bcbio running time frame.
:return: an instance of :class collections.namedtuple:
with the following fields: start and end
"""
output = collections.namedtuple("Time", ["start", "end", "steps"])
bcbio_timings = get_bcbio_timings(bcbio_log)
return output(min(bcbio_timings), max(bcbio_timings), bcbio_timings) | python | def log_time_frame(bcbio_log):
output = collections.namedtuple("Time", ["start", "end", "steps"])
bcbio_timings = get_bcbio_timings(bcbio_log)
return output(min(bcbio_timings), max(bcbio_timings), bcbio_timings) | [
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237,639 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | resource_usage | def resource_usage(bcbio_log, cluster, rawdir, verbose):
"""Generate system statistics from bcbio runs.
Parse the obtained files and put the information in
a :class pandas.DataFrame:.
:param bcbio_log: local path to bcbio log file written by the run
:param cluster:
:param rawdir: directory to put raw data files
:param verbose: increase verbosity
:return: a tuple with three dictionaries, the first one contains
an instance of :pandas.DataFrame: for each host, the second one
contains information regarding the hardware configuration and
the last one contains information regarding timing.
:type return: tuple
"""
data_frames = {}
hardware_info = {}
time_frame = log_time_frame(bcbio_log)
for collectl_file in sorted(os.listdir(rawdir)):
if not collectl_file.endswith('.raw.gz'):
continue
# Only load filenames within sampling timerange (gathered from bcbio_log time_frame)
if rawfile_within_timeframe(collectl_file, time_frame):
collectl_path = os.path.join(rawdir, collectl_file)
data, hardware = load_collectl(
collectl_path, time_frame.start, time_frame.end)
if len(data) == 0:
#raise ValueError("No data present in collectl file %s, mismatch in timestamps between raw collectl and log file?", collectl_path)
continue
host = re.sub(r'-\d{8}-\d{6}\.raw\.gz$', '', collectl_file)
hardware_info[host] = hardware
if host not in data_frames:
data_frames[host] = data
else:
data_frames[host] = pd.concat([data_frames[host], data])
return (data_frames, hardware_info, time_frame.steps) | python | def resource_usage(bcbio_log, cluster, rawdir, verbose):
data_frames = {}
hardware_info = {}
time_frame = log_time_frame(bcbio_log)
for collectl_file in sorted(os.listdir(rawdir)):
if not collectl_file.endswith('.raw.gz'):
continue
# Only load filenames within sampling timerange (gathered from bcbio_log time_frame)
if rawfile_within_timeframe(collectl_file, time_frame):
collectl_path = os.path.join(rawdir, collectl_file)
data, hardware = load_collectl(
collectl_path, time_frame.start, time_frame.end)
if len(data) == 0:
#raise ValueError("No data present in collectl file %s, mismatch in timestamps between raw collectl and log file?", collectl_path)
continue
host = re.sub(r'-\d{8}-\d{6}\.raw\.gz$', '', collectl_file)
hardware_info[host] = hardware
if host not in data_frames:
data_frames[host] = data
else:
data_frames[host] = pd.concat([data_frames[host], data])
return (data_frames, hardware_info, time_frame.steps) | [
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:return: a tuple with three dictionaries, the first one contains
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237,640 | bcbio/bcbio-nextgen | bcbio/graph/graph.py | generate_graphs | def generate_graphs(data_frames, hardware_info, steps, outdir,
verbose=False):
"""Generate all graphs for a bcbio run."""
_setup_matplotlib()
# Hash of hosts containing (data, hardware, steps) tuple
collectl_info = collections.defaultdict(dict)
for host, data_frame in data_frames.items():
if verbose:
print('Generating CPU graph for {}...'.format(host))
graph, data_cpu = graph_cpu(data_frame, steps, hardware_info[host]['num_cpus'])
graph.get_figure().savefig(
os.path.join(outdir, '{}_cpu.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
ifaces = set([series.split('_')[0]
for series in data_frame.keys()
if series.startswith(('eth', 'ib'))])
if verbose:
print('Generating network graphs for {}...'.format(host))
graph, data_net_bytes = graph_net_bytes(data_frame, steps, ifaces)
graph.get_figure().savefig(
os.path.join(outdir, '{}_net_bytes.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
graph, data_net_pkts = graph_net_pkts(data_frame, steps, ifaces)
graph.get_figure().savefig(
os.path.join(outdir, '{}_net_pkts.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
if verbose:
print('Generating memory graph for {}...'.format(host))
graph, data_mem = graph_memory(data_frame, steps, hardware_info[host]["memory"])
graph.get_figure().savefig(
os.path.join(outdir, '{}_memory.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
if verbose:
print('Generating storage I/O graph for {}...'.format(host))
drives = set([
series.split('_')[0]
for series in data_frame.keys()
if series.startswith(('sd', 'vd', 'hd', 'xvd'))
])
graph, data_disk = graph_disk_io(data_frame, steps, drives)
graph.get_figure().savefig(
os.path.join(outdir, '{}_disk_io.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
print('Serializing output to pickle object for node {}...'.format(host))
# "Clean" dataframes ready to be plotted
collectl_info[host] = { "hardware": hardware_info,
"steps": steps, "cpu": data_cpu, "mem": data_mem,
"disk": data_disk, "net_bytes": data_net_bytes,
"net_pkts": data_net_pkts
}
return collectl_info | python | def generate_graphs(data_frames, hardware_info, steps, outdir,
verbose=False):
_setup_matplotlib()
# Hash of hosts containing (data, hardware, steps) tuple
collectl_info = collections.defaultdict(dict)
for host, data_frame in data_frames.items():
if verbose:
print('Generating CPU graph for {}...'.format(host))
graph, data_cpu = graph_cpu(data_frame, steps, hardware_info[host]['num_cpus'])
graph.get_figure().savefig(
os.path.join(outdir, '{}_cpu.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
ifaces = set([series.split('_')[0]
for series in data_frame.keys()
if series.startswith(('eth', 'ib'))])
if verbose:
print('Generating network graphs for {}...'.format(host))
graph, data_net_bytes = graph_net_bytes(data_frame, steps, ifaces)
graph.get_figure().savefig(
os.path.join(outdir, '{}_net_bytes.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
graph, data_net_pkts = graph_net_pkts(data_frame, steps, ifaces)
graph.get_figure().savefig(
os.path.join(outdir, '{}_net_pkts.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
if verbose:
print('Generating memory graph for {}...'.format(host))
graph, data_mem = graph_memory(data_frame, steps, hardware_info[host]["memory"])
graph.get_figure().savefig(
os.path.join(outdir, '{}_memory.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
if verbose:
print('Generating storage I/O graph for {}...'.format(host))
drives = set([
series.split('_')[0]
for series in data_frame.keys()
if series.startswith(('sd', 'vd', 'hd', 'xvd'))
])
graph, data_disk = graph_disk_io(data_frame, steps, drives)
graph.get_figure().savefig(
os.path.join(outdir, '{}_disk_io.png'.format(host)),
bbox_inches='tight', pad_inches=0.25)
pylab.close()
print('Serializing output to pickle object for node {}...'.format(host))
# "Clean" dataframes ready to be plotted
collectl_info[host] = { "hardware": hardware_info,
"steps": steps, "cpu": data_cpu, "mem": data_mem,
"disk": data_disk, "net_bytes": data_net_bytes,
"net_pkts": data_net_pkts
}
return collectl_info | [
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237,641 | bcbio/bcbio-nextgen | bcbio/variation/ploidy.py | get_ploidy | def get_ploidy(items, region=None):
"""Retrieve ploidy of a region, handling special cases.
"""
chrom = chromosome_special_cases(region[0] if isinstance(region, (list, tuple))
else None)
ploidy = _configured_ploidy(items)
sexes = _configured_genders(items)
if chrom == "mitochondrial":
# For now, do haploid calling. Could also do pooled calling
# but not entirely clear what the best default would be.
return ploidy.get("mitochondrial", 1)
elif chrom == "X":
# Do standard diploid calling if we have any females or unspecified.
if "female" in sexes or "f" in sexes:
return ploidy.get("female", ploidy["default"])
elif "male" in sexes or "m" in sexes:
return ploidy.get("male", 1)
else:
return ploidy.get("female", ploidy["default"])
elif chrom == "Y":
# Always call Y single. If female, filter_vcf_by_sex removes Y regions.
return 1
else:
return ploidy["default"] | python | def get_ploidy(items, region=None):
chrom = chromosome_special_cases(region[0] if isinstance(region, (list, tuple))
else None)
ploidy = _configured_ploidy(items)
sexes = _configured_genders(items)
if chrom == "mitochondrial":
# For now, do haploid calling. Could also do pooled calling
# but not entirely clear what the best default would be.
return ploidy.get("mitochondrial", 1)
elif chrom == "X":
# Do standard diploid calling if we have any females or unspecified.
if "female" in sexes or "f" in sexes:
return ploidy.get("female", ploidy["default"])
elif "male" in sexes or "m" in sexes:
return ploidy.get("male", 1)
else:
return ploidy.get("female", ploidy["default"])
elif chrom == "Y":
# Always call Y single. If female, filter_vcf_by_sex removes Y regions.
return 1
else:
return ploidy["default"] | [
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237,642 | bcbio/bcbio-nextgen | bcbio/variation/ploidy.py | filter_vcf_by_sex | def filter_vcf_by_sex(vcf_file, items):
"""Post-filter a single sample VCF, handling sex chromosomes.
Removes Y chromosomes from batches with all female samples.
"""
out_file = "%s-ploidyfix%s" % utils.splitext_plus(vcf_file)
if not utils.file_exists(out_file):
genders = list(_configured_genders(items))
is_female = len(genders) == 1 and genders[0] and genders[0] in ["female", "f"]
if is_female:
orig_out_file = out_file
out_file = orig_out_file.replace(".vcf.gz", ".vcf")
with file_transaction(items[0], out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
with utils.open_gzipsafe(vcf_file) as in_handle:
for line in in_handle:
if line.startswith("#"):
out_handle.write(line)
else:
chrom = chromosome_special_cases(line.split("\t"))
if chrom != "Y":
out_handle.write(line)
if orig_out_file.endswith(".gz"):
out_file = vcfutils.bgzip_and_index(out_file, items[0]["config"])
else:
out_file = vcf_file
return out_file | python | def filter_vcf_by_sex(vcf_file, items):
out_file = "%s-ploidyfix%s" % utils.splitext_plus(vcf_file)
if not utils.file_exists(out_file):
genders = list(_configured_genders(items))
is_female = len(genders) == 1 and genders[0] and genders[0] in ["female", "f"]
if is_female:
orig_out_file = out_file
out_file = orig_out_file.replace(".vcf.gz", ".vcf")
with file_transaction(items[0], out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
with utils.open_gzipsafe(vcf_file) as in_handle:
for line in in_handle:
if line.startswith("#"):
out_handle.write(line)
else:
chrom = chromosome_special_cases(line.split("\t"))
if chrom != "Y":
out_handle.write(line)
if orig_out_file.endswith(".gz"):
out_file = vcfutils.bgzip_and_index(out_file, items[0]["config"])
else:
out_file = vcf_file
return out_file | [
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237,643 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | variant_filtration | def variant_filtration(call_file, ref_file, vrn_files, data, items):
"""Filter variant calls using Variant Quality Score Recalibration.
Newer GATK with Haplotype calling has combined SNP/indel filtering.
"""
caller = data["config"]["algorithm"].get("variantcaller")
if "gvcf" not in dd.get_tools_on(data):
call_file = ploidy.filter_vcf_by_sex(call_file, items)
if caller in ["freebayes"]:
return vfilter.freebayes(call_file, ref_file, vrn_files, data)
elif caller in ["platypus"]:
return vfilter.platypus(call_file, data)
elif caller in ["samtools"]:
return vfilter.samtools(call_file, data)
elif caller in ["gatk", "gatk-haplotype", "haplotyper"]:
if dd.get_analysis(data).lower().find("rna-seq") >= 0:
from bcbio.rnaseq import variation as rnaseq_variation
return rnaseq_variation.gatk_filter_rnaseq(call_file, data)
else:
return gatkfilter.run(call_file, ref_file, vrn_files, data)
# no additional filtration for callers that filter as part of call process
else:
return call_file | python | def variant_filtration(call_file, ref_file, vrn_files, data, items):
caller = data["config"]["algorithm"].get("variantcaller")
if "gvcf" not in dd.get_tools_on(data):
call_file = ploidy.filter_vcf_by_sex(call_file, items)
if caller in ["freebayes"]:
return vfilter.freebayes(call_file, ref_file, vrn_files, data)
elif caller in ["platypus"]:
return vfilter.platypus(call_file, data)
elif caller in ["samtools"]:
return vfilter.samtools(call_file, data)
elif caller in ["gatk", "gatk-haplotype", "haplotyper"]:
if dd.get_analysis(data).lower().find("rna-seq") >= 0:
from bcbio.rnaseq import variation as rnaseq_variation
return rnaseq_variation.gatk_filter_rnaseq(call_file, data)
else:
return gatkfilter.run(call_file, ref_file, vrn_files, data)
# no additional filtration for callers that filter as part of call process
else:
return call_file | [
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237,644 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _split_by_ready_regions | def _split_by_ready_regions(ext, file_key, dir_ext_fn):
"""Organize splits based on regions generated by parallel_prep_region.
Sort splits so largest regions analyzed first, avoiding potentially lagging runs
at end.
"""
def _sort_by_size(region_w_bams):
region, _ = region_w_bams
_, start, end = region
return end - start
def _assign_bams_to_regions(data):
"""Ensure BAMs aligned with input regions, either global or individual.
"""
for i, region in enumerate(data["region"]):
work_bams = []
for xs in data["region_bams"]:
if len(xs) == 1:
work_bams.append(xs[0])
else:
work_bams.append(xs[i])
for work_bam in work_bams:
assert os.path.exists(work_bam), work_bam
yield region, work_bams
def _do_work(data):
if "region" in data:
name = data["group"][0] if "group" in data else data["description"]
out_dir = os.path.join(data["dirs"]["work"], dir_ext_fn(data))
out_file = os.path.join(out_dir, "%s%s" % (name, ext))
assert isinstance(data["region"], (list, tuple))
out_parts = []
for r, work_bams in sorted(_assign_bams_to_regions(data), key=_sort_by_size, reverse=True):
out_region_dir = os.path.join(out_dir, r[0])
out_region_file = os.path.join(out_region_dir,
"%s-%s%s" % (name, pregion.to_safestr(r), ext))
out_parts.append((r, work_bams, out_region_file))
return out_file, out_parts
else:
return None, []
return _do_work | python | def _split_by_ready_regions(ext, file_key, dir_ext_fn):
def _sort_by_size(region_w_bams):
region, _ = region_w_bams
_, start, end = region
return end - start
def _assign_bams_to_regions(data):
"""Ensure BAMs aligned with input regions, either global or individual.
"""
for i, region in enumerate(data["region"]):
work_bams = []
for xs in data["region_bams"]:
if len(xs) == 1:
work_bams.append(xs[0])
else:
work_bams.append(xs[i])
for work_bam in work_bams:
assert os.path.exists(work_bam), work_bam
yield region, work_bams
def _do_work(data):
if "region" in data:
name = data["group"][0] if "group" in data else data["description"]
out_dir = os.path.join(data["dirs"]["work"], dir_ext_fn(data))
out_file = os.path.join(out_dir, "%s%s" % (name, ext))
assert isinstance(data["region"], (list, tuple))
out_parts = []
for r, work_bams in sorted(_assign_bams_to_regions(data), key=_sort_by_size, reverse=True):
out_region_dir = os.path.join(out_dir, r[0])
out_region_file = os.path.join(out_region_dir,
"%s-%s%s" % (name, pregion.to_safestr(r), ext))
out_parts.append((r, work_bams, out_region_file))
return out_file, out_parts
else:
return None, []
return _do_work | [
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237,645 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _collapse_by_bam_variantcaller | def _collapse_by_bam_variantcaller(samples):
"""Collapse regions to a single representative by BAM input, variant caller and batch.
"""
by_bam = collections.OrderedDict()
for data in (x[0] for x in samples):
work_bam = utils.get_in(data, ("combine", "work_bam", "out"), data.get("align_bam"))
variantcaller = get_variantcaller(data)
if isinstance(work_bam, list):
work_bam = tuple(work_bam)
key = (multi.get_batch_for_key(data), work_bam, variantcaller)
try:
by_bam[key].append(data)
except KeyError:
by_bam[key] = [data]
out = []
for grouped_data in by_bam.values():
cur = grouped_data[0]
cur.pop("region", None)
region_bams = cur.pop("region_bams", None)
if region_bams and len(region_bams[0]) > 1:
cur.pop("work_bam", None)
out.append([cur])
return out | python | def _collapse_by_bam_variantcaller(samples):
by_bam = collections.OrderedDict()
for data in (x[0] for x in samples):
work_bam = utils.get_in(data, ("combine", "work_bam", "out"), data.get("align_bam"))
variantcaller = get_variantcaller(data)
if isinstance(work_bam, list):
work_bam = tuple(work_bam)
key = (multi.get_batch_for_key(data), work_bam, variantcaller)
try:
by_bam[key].append(data)
except KeyError:
by_bam[key] = [data]
out = []
for grouped_data in by_bam.values():
cur = grouped_data[0]
cur.pop("region", None)
region_bams = cur.pop("region_bams", None)
if region_bams and len(region_bams[0]) > 1:
cur.pop("work_bam", None)
out.append([cur])
return out | [
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237,646 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _dup_samples_by_variantcaller | def _dup_samples_by_variantcaller(samples, require_bam=True):
"""Prepare samples by variant callers, duplicating any with multiple callers.
"""
samples = [utils.to_single_data(x) for x in samples]
samples = germline.split_somatic(samples)
to_process = []
extras = []
for data in samples:
added = False
for i, add in enumerate(handle_multiple_callers(data, "variantcaller", require_bam=require_bam)):
added = True
add = dd.set_variantcaller_order(add, i)
to_process.append([add])
if not added:
data = _handle_precalled(data)
data = dd.set_variantcaller_order(data, 0)
extras.append([data])
return to_process, extras | python | def _dup_samples_by_variantcaller(samples, require_bam=True):
samples = [utils.to_single_data(x) for x in samples]
samples = germline.split_somatic(samples)
to_process = []
extras = []
for data in samples:
added = False
for i, add in enumerate(handle_multiple_callers(data, "variantcaller", require_bam=require_bam)):
added = True
add = dd.set_variantcaller_order(add, i)
to_process.append([add])
if not added:
data = _handle_precalled(data)
data = dd.set_variantcaller_order(data, 0)
extras.append([data])
return to_process, extras | [
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237,647 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | parallel_variantcall_region | def parallel_variantcall_region(samples, run_parallel):
"""Perform variant calling and post-analysis on samples by region.
"""
to_process, extras = _dup_samples_by_variantcaller(samples)
split_fn = _split_by_ready_regions(".vcf.gz", "work_bam", get_variantcaller)
samples = _collapse_by_bam_variantcaller(
grouped_parallel_split_combine(to_process, split_fn,
multi.group_batches, run_parallel,
"variantcall_sample", "concat_variant_files",
"vrn_file", ["region", "sam_ref", "config"]))
return extras + samples | python | def parallel_variantcall_region(samples, run_parallel):
to_process, extras = _dup_samples_by_variantcaller(samples)
split_fn = _split_by_ready_regions(".vcf.gz", "work_bam", get_variantcaller)
samples = _collapse_by_bam_variantcaller(
grouped_parallel_split_combine(to_process, split_fn,
multi.group_batches, run_parallel,
"variantcall_sample", "concat_variant_files",
"vrn_file", ["region", "sam_ref", "config"]))
return extras + samples | [
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237,648 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | vc_output_record | def vc_output_record(samples):
"""Prepare output record from variant calling to feed into downstream analysis.
Prep work handles reformatting so we return generated dictionaries.
For any shared keys that are calculated only once for a batch, like variant calls
for the batch, we assign to every sample.
"""
shared_keys = [["vrn_file"], ["validate", "summary"],
["validate", "tp"], ["validate", "fp"], ["validate", "fn"]]
raw = cwlutils.samples_to_records([utils.to_single_data(x) for x in samples])
shared = {}
for key in shared_keys:
cur = list(set([x for x in [tz.get_in(key, d) for d in raw] if x]))
if len(cur) > 0:
assert len(cur) == 1, (key, cur)
shared[tuple(key)] = cur[0]
else:
shared[tuple(key)] = None
out = []
for d in raw:
for key, val in shared.items():
d = tz.update_in(d, key, lambda x: val)
out.append([d])
return out | python | def vc_output_record(samples):
shared_keys = [["vrn_file"], ["validate", "summary"],
["validate", "tp"], ["validate", "fp"], ["validate", "fn"]]
raw = cwlutils.samples_to_records([utils.to_single_data(x) for x in samples])
shared = {}
for key in shared_keys:
cur = list(set([x for x in [tz.get_in(key, d) for d in raw] if x]))
if len(cur) > 0:
assert len(cur) == 1, (key, cur)
shared[tuple(key)] = cur[0]
else:
shared[tuple(key)] = None
out = []
for d in raw:
for key, val in shared.items():
d = tz.update_in(d, key, lambda x: val)
out.append([d])
return out | [
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237,649 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | batch_for_variantcall | def batch_for_variantcall(samples):
"""Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch.
"""
sample_order = [dd.get_sample_name(utils.to_single_data(x)) for x in samples]
to_process, extras = _dup_samples_by_variantcaller(samples, require_bam=False)
batch_groups = collections.defaultdict(list)
to_process = [utils.to_single_data(x) for x in to_process]
for data in cwlutils.samples_to_records(to_process):
vc = get_variantcaller(data, require_bam=False)
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_groups[(b, vc)].append(utils.deepish_copy(data))
batches = []
for cur_group in batch_groups.values():
joint_calling = any([is_joint(d) for d in cur_group])
if joint_calling:
for d in cur_group:
batches.append([d])
else:
batches.append(cur_group)
def by_original_order(xs):
return (min([sample_order.index(dd.get_sample_name(x)) for x in xs]),
min([dd.get_variantcaller_order(x) for x in xs]))
return sorted(batches + extras, key=by_original_order) | python | def batch_for_variantcall(samples):
sample_order = [dd.get_sample_name(utils.to_single_data(x)) for x in samples]
to_process, extras = _dup_samples_by_variantcaller(samples, require_bam=False)
batch_groups = collections.defaultdict(list)
to_process = [utils.to_single_data(x) for x in to_process]
for data in cwlutils.samples_to_records(to_process):
vc = get_variantcaller(data, require_bam=False)
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_groups[(b, vc)].append(utils.deepish_copy(data))
batches = []
for cur_group in batch_groups.values():
joint_calling = any([is_joint(d) for d in cur_group])
if joint_calling:
for d in cur_group:
batches.append([d])
else:
batches.append(cur_group)
def by_original_order(xs):
return (min([sample_order.index(dd.get_sample_name(x)) for x in xs]),
min([dd.get_variantcaller_order(x) for x in xs]))
return sorted(batches + extras, key=by_original_order) | [
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237,650 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _handle_precalled | def _handle_precalled(data):
"""Copy in external pre-called variants fed into analysis.
Symlinks for non-CWL runs where we want to ensure VCF present
in a local directory.
"""
if data.get("vrn_file") and not cwlutils.is_cwl_run(data):
vrn_file = data["vrn_file"]
if isinstance(vrn_file, (list, tuple)):
assert len(vrn_file) == 1
vrn_file = vrn_file[0]
precalled_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "precalled"))
ext = utils.splitext_plus(vrn_file)[-1]
orig_file = os.path.abspath(vrn_file)
our_vrn_file = os.path.join(precalled_dir, "%s-precalled%s" % (dd.get_sample_name(data), ext))
utils.copy_plus(orig_file, our_vrn_file)
data["vrn_file"] = our_vrn_file
return data | python | def _handle_precalled(data):
if data.get("vrn_file") and not cwlutils.is_cwl_run(data):
vrn_file = data["vrn_file"]
if isinstance(vrn_file, (list, tuple)):
assert len(vrn_file) == 1
vrn_file = vrn_file[0]
precalled_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "precalled"))
ext = utils.splitext_plus(vrn_file)[-1]
orig_file = os.path.abspath(vrn_file)
our_vrn_file = os.path.join(precalled_dir, "%s-precalled%s" % (dd.get_sample_name(data), ext))
utils.copy_plus(orig_file, our_vrn_file)
data["vrn_file"] = our_vrn_file
return data | [
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237,651 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | handle_multiple_callers | def handle_multiple_callers(data, key, default=None, require_bam=True):
"""Split samples that potentially require multiple variant calling approaches.
"""
callers = get_variantcaller(data, key, default, require_bam=require_bam)
if isinstance(callers, six.string_types):
return [data]
elif not callers:
return []
else:
out = []
for caller in callers:
base = copy.deepcopy(data)
if not base["config"]["algorithm"].get("orig_%s" % key):
base["config"]["algorithm"]["orig_%s" % key] = \
base["config"]["algorithm"][key]
base["config"]["algorithm"][key] = caller
# if splitting by variant caller, also split by jointcaller
if key == "variantcaller":
jcallers = get_variantcaller(data, "jointcaller", [])
if isinstance(jcallers, six.string_types):
jcallers = [jcallers]
if jcallers:
base["config"]["algorithm"]["orig_jointcaller"] = jcallers
jcallers = [x for x in jcallers if x.startswith(caller)]
if jcallers:
base["config"]["algorithm"]["jointcaller"] = jcallers[0]
else:
base["config"]["algorithm"]["jointcaller"] = False
out.append(base)
return out | python | def handle_multiple_callers(data, key, default=None, require_bam=True):
callers = get_variantcaller(data, key, default, require_bam=require_bam)
if isinstance(callers, six.string_types):
return [data]
elif not callers:
return []
else:
out = []
for caller in callers:
base = copy.deepcopy(data)
if not base["config"]["algorithm"].get("orig_%s" % key):
base["config"]["algorithm"]["orig_%s" % key] = \
base["config"]["algorithm"][key]
base["config"]["algorithm"][key] = caller
# if splitting by variant caller, also split by jointcaller
if key == "variantcaller":
jcallers = get_variantcaller(data, "jointcaller", [])
if isinstance(jcallers, six.string_types):
jcallers = [jcallers]
if jcallers:
base["config"]["algorithm"]["orig_jointcaller"] = jcallers
jcallers = [x for x in jcallers if x.startswith(caller)]
if jcallers:
base["config"]["algorithm"]["jointcaller"] = jcallers[0]
else:
base["config"]["algorithm"]["jointcaller"] = False
out.append(base)
return out | [
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237,652 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | variantcall_sample | def variantcall_sample(data, region=None, align_bams=None, out_file=None):
"""Parallel entry point for doing genotyping of a region of a sample.
"""
if out_file is None or not os.path.exists(out_file) or not os.path.lexists(out_file):
utils.safe_makedir(os.path.dirname(out_file))
ref_file = dd.get_ref_file(data)
config = data["config"]
caller_fns = get_variantcallers()
caller_fn = caller_fns[config["algorithm"].get("variantcaller")]
if len(align_bams) == 1:
items = [data]
else:
items = multi.get_orig_items(data)
assert len(items) == len(align_bams)
assoc_files = tz.get_in(("genome_resources", "variation"), data, {})
if not assoc_files: assoc_files = {}
for bam_file in align_bams:
bam.index(bam_file, data["config"], check_timestamp=False)
out_file = caller_fn(align_bams, items, ref_file, assoc_files, region, out_file)
if region:
data["region"] = region
data["vrn_file"] = out_file
return [data] | python | def variantcall_sample(data, region=None, align_bams=None, out_file=None):
if out_file is None or not os.path.exists(out_file) or not os.path.lexists(out_file):
utils.safe_makedir(os.path.dirname(out_file))
ref_file = dd.get_ref_file(data)
config = data["config"]
caller_fns = get_variantcallers()
caller_fn = caller_fns[config["algorithm"].get("variantcaller")]
if len(align_bams) == 1:
items = [data]
else:
items = multi.get_orig_items(data)
assert len(items) == len(align_bams)
assoc_files = tz.get_in(("genome_resources", "variation"), data, {})
if not assoc_files: assoc_files = {}
for bam_file in align_bams:
bam.index(bam_file, data["config"], check_timestamp=False)
out_file = caller_fn(align_bams, items, ref_file, assoc_files, region, out_file)
if region:
data["region"] = region
data["vrn_file"] = out_file
return [data] | [
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237,653 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _get_batch_name | def _get_batch_name(items, skip_jointcheck=False):
"""Retrieve the shared batch name for a group of items.
"""
batch_names = collections.defaultdict(int)
has_joint = any([is_joint(d) for d in items])
for data in items:
if has_joint and not skip_jointcheck:
batches = dd.get_sample_name(data)
else:
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_names[b] += 1
return sorted(batch_names.items(), key=lambda x: x[-1], reverse=True)[0][0] | python | def _get_batch_name(items, skip_jointcheck=False):
batch_names = collections.defaultdict(int)
has_joint = any([is_joint(d) for d in items])
for data in items:
if has_joint and not skip_jointcheck:
batches = dd.get_sample_name(data)
else:
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_names[b] += 1
return sorted(batch_names.items(), key=lambda x: x[-1], reverse=True)[0][0] | [
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237,654 | bcbio/bcbio-nextgen | bcbio/variation/genotype.py | _run_variantcall_batch_multicore | def _run_variantcall_batch_multicore(items, regions, final_file):
"""Run variant calling on a batch of items using multiple cores.
"""
batch_name = _get_batch_name(items)
variantcaller = _get_batch_variantcaller(items)
work_bams = [dd.get_work_bam(d) or dd.get_align_bam(d) for d in items]
def split_fn(data):
out = []
for region in regions:
region = _region_to_coords(region)
chrom, start, end = region
region_str = "_".join(str(x) for x in region)
out_file = os.path.join(dd.get_work_dir(items[0]), variantcaller, chrom,
"%s-%s.vcf.gz" % (batch_name, region_str))
out.append((region, work_bams, out_file))
return final_file, out
parallel = {"type": "local", "num_jobs": dd.get_num_cores(items[0]), "cores_per_job": 1}
run_parallel = dmulti.runner(parallel, items[0]["config"])
to_run = copy.deepcopy(items[0])
to_run["sam_ref"] = dd.get_ref_file(to_run)
to_run["group_orig"] = items
parallel_split_combine([[to_run]], split_fn, run_parallel,
"variantcall_sample", "concat_variant_files",
"vrn_file", ["region", "sam_ref", "config"])
return final_file | python | def _run_variantcall_batch_multicore(items, regions, final_file):
batch_name = _get_batch_name(items)
variantcaller = _get_batch_variantcaller(items)
work_bams = [dd.get_work_bam(d) or dd.get_align_bam(d) for d in items]
def split_fn(data):
out = []
for region in regions:
region = _region_to_coords(region)
chrom, start, end = region
region_str = "_".join(str(x) for x in region)
out_file = os.path.join(dd.get_work_dir(items[0]), variantcaller, chrom,
"%s-%s.vcf.gz" % (batch_name, region_str))
out.append((region, work_bams, out_file))
return final_file, out
parallel = {"type": "local", "num_jobs": dd.get_num_cores(items[0]), "cores_per_job": 1}
run_parallel = dmulti.runner(parallel, items[0]["config"])
to_run = copy.deepcopy(items[0])
to_run["sam_ref"] = dd.get_ref_file(to_run)
to_run["group_orig"] = items
parallel_split_combine([[to_run]], split_fn, run_parallel,
"variantcall_sample", "concat_variant_files",
"vrn_file", ["region", "sam_ref", "config"])
return final_file | [
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237,655 | bcbio/bcbio-nextgen | bcbio/distributed/ipython.py | create | def create(parallel, dirs, config):
"""Create a cluster based on the provided parallel arguments.
Returns an IPython view on the cluster, enabling processing on jobs.
Adds a mincores specification if he have machines with a larger
number of cores to allow jobs to be batched together for shared
memory usage.
"""
profile_dir = utils.safe_makedir(os.path.join(dirs["work"], get_log_dir(config), "ipython"))
has_mincores = any(x.startswith("mincores=") for x in parallel["resources"])
cores = min(_get_common_cores(config["resources"]), parallel["system_cores"])
if cores > 1 and not has_mincores:
adj_cores = max(1, int(math.floor(cores * float(parallel.get("mem_pct", 1.0)))))
# if we have less scheduled cores than per machine, use the scheduled count
if cores > parallel["cores"]:
cores = parallel["cores"]
# if we have less total cores required for the entire process, use that
elif adj_cores > parallel["num_jobs"] * parallel["cores_per_job"]:
cores = parallel["num_jobs"] * parallel["cores_per_job"]
else:
cores = adj_cores
cores = per_machine_target_cores(cores, parallel["num_jobs"])
parallel["resources"].append("mincores=%s" % cores)
return ipython_cluster.cluster_view(parallel["scheduler"].lower(), parallel["queue"],
parallel["num_jobs"], parallel["cores_per_job"],
profile=profile_dir, start_wait=parallel["timeout"],
extra_params={"resources": parallel["resources"],
"mem": parallel["mem"],
"tag": parallel.get("tag"),
"run_local": parallel.get("run_local"),
"local_controller": parallel.get("local_controller")},
retries=parallel.get("retries")) | python | def create(parallel, dirs, config):
profile_dir = utils.safe_makedir(os.path.join(dirs["work"], get_log_dir(config), "ipython"))
has_mincores = any(x.startswith("mincores=") for x in parallel["resources"])
cores = min(_get_common_cores(config["resources"]), parallel["system_cores"])
if cores > 1 and not has_mincores:
adj_cores = max(1, int(math.floor(cores * float(parallel.get("mem_pct", 1.0)))))
# if we have less scheduled cores than per machine, use the scheduled count
if cores > parallel["cores"]:
cores = parallel["cores"]
# if we have less total cores required for the entire process, use that
elif adj_cores > parallel["num_jobs"] * parallel["cores_per_job"]:
cores = parallel["num_jobs"] * parallel["cores_per_job"]
else:
cores = adj_cores
cores = per_machine_target_cores(cores, parallel["num_jobs"])
parallel["resources"].append("mincores=%s" % cores)
return ipython_cluster.cluster_view(parallel["scheduler"].lower(), parallel["queue"],
parallel["num_jobs"], parallel["cores_per_job"],
profile=profile_dir, start_wait=parallel["timeout"],
extra_params={"resources": parallel["resources"],
"mem": parallel["mem"],
"tag": parallel.get("tag"),
"run_local": parallel.get("run_local"),
"local_controller": parallel.get("local_controller")},
retries=parallel.get("retries")) | [
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237,656 | bcbio/bcbio-nextgen | bcbio/distributed/ipython.py | per_machine_target_cores | def per_machine_target_cores(cores, num_jobs):
"""Select target cores on larger machines to leave room for batch script and controller.
On resource constrained environments, we want to pack all bcbio submissions onto a specific
number of machines. This gives up some cores to enable sharing cores with the controller
and batch script on larger machines.
"""
if cores >= 32 and num_jobs == 1:
cores = cores - 2
elif cores >= 16 and num_jobs in [1, 2]:
cores = cores - 1
return cores | python | def per_machine_target_cores(cores, num_jobs):
if cores >= 32 and num_jobs == 1:
cores = cores - 2
elif cores >= 16 and num_jobs in [1, 2]:
cores = cores - 1
return cores | [
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237,657 | bcbio/bcbio-nextgen | bcbio/distributed/ipython.py | _get_common_cores | def _get_common_cores(resources):
"""Retrieve the most common configured number of cores in the input file.
"""
all_cores = []
for vs in resources.values():
cores = vs.get("cores")
if cores:
all_cores.append(int(vs["cores"]))
return collections.Counter(all_cores).most_common(1)[0][0] | python | def _get_common_cores(resources):
all_cores = []
for vs in resources.values():
cores = vs.get("cores")
if cores:
all_cores.append(int(vs["cores"]))
return collections.Counter(all_cores).most_common(1)[0][0] | [
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237,658 | bcbio/bcbio-nextgen | bcbio/distributed/ipython.py | zip_args | def zip_args(args, config=None):
"""Compress arguments using msgpack.
"""
if msgpack:
return [msgpack.packb(x, use_single_float=True, use_bin_type=True) for x in args]
else:
return args | python | def zip_args(args, config=None):
if msgpack:
return [msgpack.packb(x, use_single_float=True, use_bin_type=True) for x in args]
else:
return args | [
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237,659 | bcbio/bcbio-nextgen | bcbio/distributed/ipython.py | runner | def runner(view, parallel, dirs, config):
"""Run a task on an ipython parallel cluster, allowing alternative queue types.
view provides map-style access to an existing Ipython cluster.
"""
def run(fn_name, items):
setpath.prepend_bcbiopath()
out = []
fn, fn_name = (fn_name, fn_name.__name__) if callable(fn_name) else (_get_ipython_fn(fn_name, parallel), fn_name)
items = [x for x in items if x is not None]
items = diagnostics.track_parallel(items, fn_name)
logger.info("ipython: %s" % fn_name)
if len(items) > 0:
items = [config_utils.add_cores_to_config(x, parallel["cores_per_job"], parallel) for x in items]
if "wrapper" in parallel:
wrap_parallel = {k: v for k, v in parallel.items() if k in set(["fresources"])}
items = [[fn_name] + parallel.get("wrapper_args", []) + [wrap_parallel] + list(x) for x in items]
items = zip_args([args for args in items])
for data in view.map_sync(fn, items, track=False):
if data:
out.extend(unzip_args(data))
return out
return run | python | def runner(view, parallel, dirs, config):
def run(fn_name, items):
setpath.prepend_bcbiopath()
out = []
fn, fn_name = (fn_name, fn_name.__name__) if callable(fn_name) else (_get_ipython_fn(fn_name, parallel), fn_name)
items = [x for x in items if x is not None]
items = diagnostics.track_parallel(items, fn_name)
logger.info("ipython: %s" % fn_name)
if len(items) > 0:
items = [config_utils.add_cores_to_config(x, parallel["cores_per_job"], parallel) for x in items]
if "wrapper" in parallel:
wrap_parallel = {k: v for k, v in parallel.items() if k in set(["fresources"])}
items = [[fn_name] + parallel.get("wrapper_args", []) + [wrap_parallel] + list(x) for x in items]
items = zip_args([args for args in items])
for data in view.map_sync(fn, items, track=False):
if data:
out.extend(unzip_args(data))
return out
return run | [
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237,660 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | peakcall_prepare | def peakcall_prepare(data, run_parallel):
"""Entry point for doing peak calling"""
caller_fns = get_callers()
to_process = []
for sample in data:
mimic = copy.copy(sample[0])
callers = dd.get_peakcaller(sample[0])
if not isinstance(callers, list):
callers = [callers]
for caller in callers:
if caller in caller_fns:
mimic["peak_fn"] = caller
name = dd.get_sample_name(mimic)
mimic = _check(mimic, data)
if mimic:
to_process.append(mimic)
else:
logger.info("Skipping peak calling. No input sample for %s" % name)
if to_process:
after_process = run_parallel("peakcalling", to_process)
data = _sync(data, after_process)
return data | python | def peakcall_prepare(data, run_parallel):
caller_fns = get_callers()
to_process = []
for sample in data:
mimic = copy.copy(sample[0])
callers = dd.get_peakcaller(sample[0])
if not isinstance(callers, list):
callers = [callers]
for caller in callers:
if caller in caller_fns:
mimic["peak_fn"] = caller
name = dd.get_sample_name(mimic)
mimic = _check(mimic, data)
if mimic:
to_process.append(mimic)
else:
logger.info("Skipping peak calling. No input sample for %s" % name)
if to_process:
after_process = run_parallel("peakcalling", to_process)
data = _sync(data, after_process)
return data | [
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237,661 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | calling | def calling(data):
"""Main function to parallelize peak calling."""
chip_bam = data.get("work_bam")
input_bam = data.get("work_bam_input", None)
caller_fn = get_callers()[data["peak_fn"]]
name = dd.get_sample_name(data)
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), data["peak_fn"], name))
out_files = caller_fn(name, chip_bam, input_bam, dd.get_genome_build(data), out_dir,
dd.get_chip_method(data), data["resources"], data)
greylistdir = greylisting(data)
data.update({"peaks_files": out_files})
# data["input_bam_filter"] = input_bam
if greylistdir:
data["greylist"] = greylistdir
return [[data]] | python | def calling(data):
chip_bam = data.get("work_bam")
input_bam = data.get("work_bam_input", None)
caller_fn = get_callers()[data["peak_fn"]]
name = dd.get_sample_name(data)
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), data["peak_fn"], name))
out_files = caller_fn(name, chip_bam, input_bam, dd.get_genome_build(data), out_dir,
dd.get_chip_method(data), data["resources"], data)
greylistdir = greylisting(data)
data.update({"peaks_files": out_files})
# data["input_bam_filter"] = input_bam
if greylistdir:
data["greylist"] = greylistdir
return [[data]] | [
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237,662 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | _sync | def _sync(original, processed):
"""
Add output to data if run sucessfully.
For now only macs2 is available, so no need
to consider multiple callers.
"""
for original_sample in original:
original_sample[0]["peaks_files"] = {}
for process_sample in processed:
if dd.get_sample_name(original_sample[0]) == dd.get_sample_name(process_sample[0]):
for key in ["peaks_files"]:
if process_sample[0].get(key):
original_sample[0][key] = process_sample[0][key]
return original | python | def _sync(original, processed):
for original_sample in original:
original_sample[0]["peaks_files"] = {}
for process_sample in processed:
if dd.get_sample_name(original_sample[0]) == dd.get_sample_name(process_sample[0]):
for key in ["peaks_files"]:
if process_sample[0].get(key):
original_sample[0][key] = process_sample[0][key]
return original | [
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237,663 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | _check | def _check(sample, data):
"""Get input sample for each chip bam file."""
if dd.get_chip_method(sample).lower() == "atac":
return [sample]
if dd.get_phenotype(sample) == "input":
return None
for origin in data:
if dd.get_batch(sample) in (dd.get_batches(origin[0]) or []) and dd.get_phenotype(origin[0]) == "input":
sample["work_bam_input"] = origin[0].get("work_bam")
return [sample]
return [sample] | python | def _check(sample, data):
if dd.get_chip_method(sample).lower() == "atac":
return [sample]
if dd.get_phenotype(sample) == "input":
return None
for origin in data:
if dd.get_batch(sample) in (dd.get_batches(origin[0]) or []) and dd.get_phenotype(origin[0]) == "input":
sample["work_bam_input"] = origin[0].get("work_bam")
return [sample]
return [sample] | [
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237,664 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | _get_multiplier | def _get_multiplier(samples):
"""Get multiplier to get jobs
only for samples that have input
"""
to_process = 1.0
to_skip = 0
for sample in samples:
if dd.get_phenotype(sample[0]) == "chip":
to_process += 1.0
elif dd.get_chip_method(sample[0]).lower() == "atac":
to_process += 1.0
else:
to_skip += 1.0
mult = (to_process - to_skip) / len(samples)
if mult <= 0:
mult = 1 / len(samples)
return max(mult, 1) | python | def _get_multiplier(samples):
to_process = 1.0
to_skip = 0
for sample in samples:
if dd.get_phenotype(sample[0]) == "chip":
to_process += 1.0
elif dd.get_chip_method(sample[0]).lower() == "atac":
to_process += 1.0
else:
to_skip += 1.0
mult = (to_process - to_skip) / len(samples)
if mult <= 0:
mult = 1 / len(samples)
return max(mult, 1) | [
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237,665 | bcbio/bcbio-nextgen | bcbio/chipseq/peaks.py | greylisting | def greylisting(data):
"""
Run ChIP-seq greylisting
"""
input_bam = data.get("work_bam_input", None)
if not input_bam:
logger.info("No input BAM file detected, skipping greylisting.")
return None
try:
greylister = config_utils.get_program("chipseq-greylist", data)
except config_utils.CmdNotFound:
logger.info("No greylister found, skipping greylisting.")
return None
greylistdir = os.path.join(os.path.dirname(input_bam), "greylist")
if os.path.exists(greylistdir):
return greylistdir
cmd = "{greylister} --outdir {txgreylistdir} {input_bam}"
message = "Running greylisting on %s." % input_bam
with file_transaction(greylistdir) as txgreylistdir:
utils.safe_makedir(txgreylistdir)
try:
do.run(cmd.format(**locals()), message)
except subprocess.CalledProcessError as msg:
if str(msg).find("Cannot take a larger sample than population when 'replace=False'") >= 0:
logger.info("Skipping chipseq greylisting because of small sample size: %s"
% dd.get_sample_name(data))
return None
return greylistdir | python | def greylisting(data):
input_bam = data.get("work_bam_input", None)
if not input_bam:
logger.info("No input BAM file detected, skipping greylisting.")
return None
try:
greylister = config_utils.get_program("chipseq-greylist", data)
except config_utils.CmdNotFound:
logger.info("No greylister found, skipping greylisting.")
return None
greylistdir = os.path.join(os.path.dirname(input_bam), "greylist")
if os.path.exists(greylistdir):
return greylistdir
cmd = "{greylister} --outdir {txgreylistdir} {input_bam}"
message = "Running greylisting on %s." % input_bam
with file_transaction(greylistdir) as txgreylistdir:
utils.safe_makedir(txgreylistdir)
try:
do.run(cmd.format(**locals()), message)
except subprocess.CalledProcessError as msg:
if str(msg).find("Cannot take a larger sample than population when 'replace=False'") >= 0:
logger.info("Skipping chipseq greylisting because of small sample size: %s"
% dd.get_sample_name(data))
return None
return greylistdir | [
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237,666 | bcbio/bcbio-nextgen | bcbio/distributed/clargs.py | to_parallel | def to_parallel(args, module="bcbio.distributed"):
"""Convert input arguments into a parallel dictionary for passing to processing.
"""
ptype, cores = _get_cores_and_type(args.numcores,
getattr(args, "paralleltype", None),
args.scheduler)
local_controller = getattr(args, "local_controller", False)
parallel = {"type": ptype, "cores": cores,
"scheduler": args.scheduler, "queue": args.queue,
"tag": args.tag, "module": module,
"resources": args.resources, "timeout": args.timeout,
"retries": args.retries,
"run_local": args.queue == "localrun",
"local_controller": local_controller}
return parallel | python | def to_parallel(args, module="bcbio.distributed"):
ptype, cores = _get_cores_and_type(args.numcores,
getattr(args, "paralleltype", None),
args.scheduler)
local_controller = getattr(args, "local_controller", False)
parallel = {"type": ptype, "cores": cores,
"scheduler": args.scheduler, "queue": args.queue,
"tag": args.tag, "module": module,
"resources": args.resources, "timeout": args.timeout,
"retries": args.retries,
"run_local": args.queue == "localrun",
"local_controller": local_controller}
return parallel | [
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237,667 | bcbio/bcbio-nextgen | bcbio/distributed/clargs.py | _get_cores_and_type | def _get_cores_and_type(numcores, paralleltype, scheduler):
"""Return core and parallelization approach from command line providing sane defaults.
"""
if scheduler is not None:
paralleltype = "ipython"
if paralleltype is None:
paralleltype = "local"
if not numcores or int(numcores) < 1:
numcores = 1
return paralleltype, int(numcores) | python | def _get_cores_and_type(numcores, paralleltype, scheduler):
if scheduler is not None:
paralleltype = "ipython"
if paralleltype is None:
paralleltype = "local"
if not numcores or int(numcores) < 1:
numcores = 1
return paralleltype, int(numcores) | [
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237,668 | bcbio/bcbio-nextgen | bcbio/ngsalign/tophat.py | _fix_mates | def _fix_mates(orig_file, out_file, ref_file, config):
"""Fix problematic unmapped mate pairs in TopHat output.
TopHat 2.0.9 appears to have issues with secondary reads:
https://groups.google.com/forum/#!topic/tuxedo-tools-users/puLfDNbN9bo
This cleans the input file to only keep properly mapped pairs,
providing a general fix that will handle correctly mapped secondary
reads as well.
"""
if not file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
samtools = config_utils.get_program("samtools", config)
cmd = "{samtools} view -bS -h -t {ref_file}.fai -F 8 {orig_file} > {tx_out_file}"
do.run(cmd.format(**locals()), "Fix mate pairs in TopHat output", {})
return out_file | python | def _fix_mates(orig_file, out_file, ref_file, config):
if not file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
samtools = config_utils.get_program("samtools", config)
cmd = "{samtools} view -bS -h -t {ref_file}.fai -F 8 {orig_file} > {tx_out_file}"
do.run(cmd.format(**locals()), "Fix mate pairs in TopHat output", {})
return out_file | [
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237,669 | bcbio/bcbio-nextgen | bcbio/ngsalign/tophat.py | _add_rg | def _add_rg(unmapped_file, config, names):
"""Add the missing RG header."""
picard = broad.runner_from_path("picard", config)
rg_fixed = picard.run_fn("picard_fix_rgs", unmapped_file, names)
return rg_fixed | python | def _add_rg(unmapped_file, config, names):
picard = broad.runner_from_path("picard", config)
rg_fixed = picard.run_fn("picard_fix_rgs", unmapped_file, names)
return rg_fixed | [
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237,670 | bcbio/bcbio-nextgen | bcbio/ngsalign/tophat.py | _estimate_paired_innerdist | def _estimate_paired_innerdist(fastq_file, pair_file, ref_file, out_base,
out_dir, data):
"""Use Bowtie to estimate the inner distance of paired reads.
"""
mean, stdev = _bowtie_for_innerdist("100000", fastq_file, pair_file, ref_file,
out_base, out_dir, data, True)
if not mean or not stdev:
mean, stdev = _bowtie_for_innerdist("1", fastq_file, pair_file, ref_file,
out_base, out_dir, data, True)
# No reads aligning so no data to process, set some default values
if not mean or not stdev:
mean, stdev = 200, 50
return mean, stdev | python | def _estimate_paired_innerdist(fastq_file, pair_file, ref_file, out_base,
out_dir, data):
mean, stdev = _bowtie_for_innerdist("100000", fastq_file, pair_file, ref_file,
out_base, out_dir, data, True)
if not mean or not stdev:
mean, stdev = _bowtie_for_innerdist("1", fastq_file, pair_file, ref_file,
out_base, out_dir, data, True)
# No reads aligning so no data to process, set some default values
if not mean or not stdev:
mean, stdev = 200, 50
return mean, stdev | [
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237,671 | bcbio/bcbio-nextgen | bcbio/ngsalign/tophat.py | fix_insert_size | def fix_insert_size(in_bam, config):
"""
Tophat sets PI in the RG to be the inner distance size, but the SAM spec
states should be the insert size. This fixes the RG in the alignment
file generated by Tophat header to match the spec
"""
fixed_file = os.path.splitext(in_bam)[0] + ".pi_fixed.bam"
if file_exists(fixed_file):
return fixed_file
header_file = os.path.splitext(in_bam)[0] + ".header.sam"
read_length = bam.estimate_read_length(in_bam)
bam_handle= bam.open_samfile(in_bam)
header = bam_handle.header.copy()
rg_dict = header['RG'][0]
if 'PI' not in rg_dict:
return in_bam
PI = int(rg_dict.get('PI'))
PI = PI + 2*read_length
rg_dict['PI'] = PI
header['RG'][0] = rg_dict
with pysam.Samfile(header_file, "wb", header=header) as out_handle:
with bam.open_samfile(in_bam) as in_handle:
for record in in_handle:
out_handle.write(record)
shutil.move(header_file, fixed_file)
return fixed_file | python | def fix_insert_size(in_bam, config):
fixed_file = os.path.splitext(in_bam)[0] + ".pi_fixed.bam"
if file_exists(fixed_file):
return fixed_file
header_file = os.path.splitext(in_bam)[0] + ".header.sam"
read_length = bam.estimate_read_length(in_bam)
bam_handle= bam.open_samfile(in_bam)
header = bam_handle.header.copy()
rg_dict = header['RG'][0]
if 'PI' not in rg_dict:
return in_bam
PI = int(rg_dict.get('PI'))
PI = PI + 2*read_length
rg_dict['PI'] = PI
header['RG'][0] = rg_dict
with pysam.Samfile(header_file, "wb", header=header) as out_handle:
with bam.open_samfile(in_bam) as in_handle:
for record in in_handle:
out_handle.write(record)
shutil.move(header_file, fixed_file)
return fixed_file | [
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237,672 | bcbio/bcbio-nextgen | bcbio/variation/damage.py | _filter_to_info | def _filter_to_info(in_file, data):
"""Move DKFZ filter information into INFO field.
"""
header = ("""##INFO=<ID=DKFZBias,Number=.,Type=String,"""
"""Description="Bias estimation based on unequal read support from DKFZBiasFilterVariant Depth">\n""")
out_file = "%s-ann.vcf" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file):
with file_transaction(data, out_file) as tx_out_file:
with utils.open_gzipsafe(in_file) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#CHROM"):
out_handle.write(header + line)
elif line.startswith("#"):
out_handle.write(line)
else:
out_handle.write(_rec_filter_to_info(line))
return vcfutils.bgzip_and_index(out_file, data["config"]) | python | def _filter_to_info(in_file, data):
header = ("""##INFO=<ID=DKFZBias,Number=.,Type=String,"""
"""Description="Bias estimation based on unequal read support from DKFZBiasFilterVariant Depth">\n""")
out_file = "%s-ann.vcf" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file):
with file_transaction(data, out_file) as tx_out_file:
with utils.open_gzipsafe(in_file) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#CHROM"):
out_handle.write(header + line)
elif line.startswith("#"):
out_handle.write(line)
else:
out_handle.write(_rec_filter_to_info(line))
return vcfutils.bgzip_and_index(out_file, data["config"]) | [
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237,673 | bcbio/bcbio-nextgen | bcbio/variation/damage.py | _rec_filter_to_info | def _rec_filter_to_info(line):
"""Move a DKFZBias filter to the INFO field, for a record.
"""
parts = line.rstrip().split("\t")
move_filters = {"bSeq": "strand", "bPcr": "damage"}
new_filters = []
bias_info = []
for f in parts[6].split(";"):
if f in move_filters:
bias_info.append(move_filters[f])
elif f not in ["."]:
new_filters.append(f)
if bias_info:
parts[7] += ";DKFZBias=%s" % ",".join(bias_info)
parts[6] = ";".join(new_filters or ["PASS"])
return "\t".join(parts) + "\n" | python | def _rec_filter_to_info(line):
parts = line.rstrip().split("\t")
move_filters = {"bSeq": "strand", "bPcr": "damage"}
new_filters = []
bias_info = []
for f in parts[6].split(";"):
if f in move_filters:
bias_info.append(move_filters[f])
elif f not in ["."]:
new_filters.append(f)
if bias_info:
parts[7] += ";DKFZBias=%s" % ",".join(bias_info)
parts[6] = ";".join(new_filters or ["PASS"])
return "\t".join(parts) + "\n" | [
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237,674 | bcbio/bcbio-nextgen | bcbio/variation/damage.py | should_filter | def should_filter(items):
"""Check if we should do damage filtering on somatic calling with low frequency events.
"""
return (vcfutils.get_paired(items) is not None and
any("damage_filter" in dd.get_tools_on(d) for d in items)) | python | def should_filter(items):
return (vcfutils.get_paired(items) is not None and
any("damage_filter" in dd.get_tools_on(d) for d in items)) | [
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237,675 | bcbio/bcbio-nextgen | bcbio/provenance/diagnostics.py | start_cmd | def start_cmd(cmd, descr, data):
"""Retain details about starting a command, returning a command identifier.
"""
if data and "provenance" in data:
entity_id = tz.get_in(["provenance", "entity"], data) | python | def start_cmd(cmd, descr, data):
if data and "provenance" in data:
entity_id = tz.get_in(["provenance", "entity"], data) | [
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237,676 | bcbio/bcbio-nextgen | bcbio/provenance/diagnostics.py | initialize | def initialize(dirs):
"""Initialize the biolite database to load provenance information.
"""
if biolite and dirs.get("work"):
base_dir = utils.safe_makedir(os.path.join(dirs["work"], "provenance"))
p_db = os.path.join(base_dir, "biolite.db")
biolite.config.resources["database"] = p_db
biolite.database.connect() | python | def initialize(dirs):
if biolite and dirs.get("work"):
base_dir = utils.safe_makedir(os.path.join(dirs["work"], "provenance"))
p_db = os.path.join(base_dir, "biolite.db")
biolite.config.resources["database"] = p_db
biolite.database.connect() | [
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237,677 | bcbio/bcbio-nextgen | bcbio/provenance/diagnostics.py | track_parallel | def track_parallel(items, sub_type):
"""Create entity identifiers to trace the given items in sub-commands.
Helps handle nesting in parallel program execution:
run id => sub-section id => parallel ids
"""
out = []
for i, args in enumerate(items):
item_i, item = _get_provitem_from_args(args)
if item:
sub_entity = "%s.%s.%s" % (item["provenance"]["entity"], sub_type, i)
item["provenance"]["entity"] = sub_entity
args = list(args)
args[item_i] = item
out.append(args)
# TODO: store mapping of entity to sub identifiers
return out | python | def track_parallel(items, sub_type):
out = []
for i, args in enumerate(items):
item_i, item = _get_provitem_from_args(args)
if item:
sub_entity = "%s.%s.%s" % (item["provenance"]["entity"], sub_type, i)
item["provenance"]["entity"] = sub_entity
args = list(args)
args[item_i] = item
out.append(args)
# TODO: store mapping of entity to sub identifiers
return out | [
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237,678 | bcbio/bcbio-nextgen | bcbio/provenance/diagnostics.py | _get_provitem_from_args | def _get_provitem_from_args(xs):
"""Retrieve processed item from list of input arguments.
"""
for i, x in enumerate(xs):
if _has_provenance(x):
return i, x
return -1, None | python | def _get_provitem_from_args(xs):
for i, x in enumerate(xs):
if _has_provenance(x):
return i, x
return -1, None | [
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237,679 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | handle_vcf_calls | def handle_vcf_calls(vcf_file, data, orig_items):
"""Prioritize VCF calls based on external annotations supplied through GEMINI.
"""
if not _do_prioritize(orig_items):
return vcf_file
else:
ann_vcf = population.run_vcfanno(vcf_file, data)
if ann_vcf:
priority_file = _prep_priority_filter_vcfanno(ann_vcf, data)
return _apply_priority_filter(ann_vcf, priority_file, data)
# No data available for filtering, return original file
else:
return vcf_file | python | def handle_vcf_calls(vcf_file, data, orig_items):
if not _do_prioritize(orig_items):
return vcf_file
else:
ann_vcf = population.run_vcfanno(vcf_file, data)
if ann_vcf:
priority_file = _prep_priority_filter_vcfanno(ann_vcf, data)
return _apply_priority_filter(ann_vcf, priority_file, data)
# No data available for filtering, return original file
else:
return vcf_file | [
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237,680 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _apply_priority_filter | def _apply_priority_filter(in_file, priority_file, data):
"""Annotate variants with priority information and use to apply filters.
"""
out_file = "%s-priority%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
header = ('##INFO=<ID=EPR,Number=.,Type=String,'
'Description="Somatic prioritization based on external annotations, '
'identify as likely germline">')
header_file = "%s-repeatheader.txt" % utils.splitext_plus(tx_out_file)[0]
with open(header_file, "w") as out_handle:
out_handle.write(header)
if "tumoronly_germline_filter" in dd.get_tools_on(data):
filter_cmd = ("bcftools filter -m '+' -s 'LowPriority' "
"""-e "EPR[0] != 'pass'" |""")
else:
filter_cmd = ""
cmd = ("bcftools annotate -a {priority_file} -h {header_file} "
"-c CHROM,FROM,TO,REF,ALT,INFO/EPR {in_file} | "
"{filter_cmd} bgzip -c > {tx_out_file}")
do.run(cmd.format(**locals()), "Run external annotation based prioritization filtering")
vcfutils.bgzip_and_index(out_file, data["config"])
return out_file | python | def _apply_priority_filter(in_file, priority_file, data):
out_file = "%s-priority%s" % utils.splitext_plus(in_file)
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
header = ('##INFO=<ID=EPR,Number=.,Type=String,'
'Description="Somatic prioritization based on external annotations, '
'identify as likely germline">')
header_file = "%s-repeatheader.txt" % utils.splitext_plus(tx_out_file)[0]
with open(header_file, "w") as out_handle:
out_handle.write(header)
if "tumoronly_germline_filter" in dd.get_tools_on(data):
filter_cmd = ("bcftools filter -m '+' -s 'LowPriority' "
"""-e "EPR[0] != 'pass'" |""")
else:
filter_cmd = ""
cmd = ("bcftools annotate -a {priority_file} -h {header_file} "
"-c CHROM,FROM,TO,REF,ALT,INFO/EPR {in_file} | "
"{filter_cmd} bgzip -c > {tx_out_file}")
do.run(cmd.format(**locals()), "Run external annotation based prioritization filtering")
vcfutils.bgzip_and_index(out_file, data["config"])
return out_file | [
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237,681 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _prep_priority_filter_vcfanno | def _prep_priority_filter_vcfanno(in_vcf, data):
"""Prepare tabix file with priority filters based on vcfanno annotations.
"""
pops = ['af_adj_exac_afr', 'af_adj_exac_amr', 'af_adj_exac_eas',
'af_adj_exac_fin', 'af_adj_exac_nfe', 'af_adj_exac_oth', 'af_adj_exac_sas',
'af_exac_all', 'max_aaf_all',
"af_esp_ea", "af_esp_aa", "af_esp_all", "af_1kg_amr", "af_1kg_eas",
"af_1kg_sas", "af_1kg_afr", "af_1kg_eur", "af_1kg_all"]
known = ["cosmic_ids", "cosmic_id", "clinvar_sig"]
out_file = "%s-priority.tsv" % utils.splitext_plus(in_vcf)[0]
if not utils.file_exists(out_file) and not utils.file_exists(out_file + ".gz"):
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
writer = csv.writer(out_handle, dialect="excel-tab")
header = ["#chrom", "start", "end", "ref", "alt", "filter"]
writer.writerow(header)
vcf_reader = cyvcf2.VCF(in_vcf)
impact_info = _get_impact_info(vcf_reader)
for rec in vcf_reader:
row = _prepare_vcf_rec(rec, pops, known, impact_info)
cur_filter = _calc_priority_filter(row, pops)
writer.writerow([rec.CHROM, rec.start, rec.end, rec.REF, ",".join(rec.ALT), cur_filter])
return vcfutils.bgzip_and_index(out_file, data["config"],
tabix_args="-0 -c '#' -s 1 -b 2 -e 3") | python | def _prep_priority_filter_vcfanno(in_vcf, data):
pops = ['af_adj_exac_afr', 'af_adj_exac_amr', 'af_adj_exac_eas',
'af_adj_exac_fin', 'af_adj_exac_nfe', 'af_adj_exac_oth', 'af_adj_exac_sas',
'af_exac_all', 'max_aaf_all',
"af_esp_ea", "af_esp_aa", "af_esp_all", "af_1kg_amr", "af_1kg_eas",
"af_1kg_sas", "af_1kg_afr", "af_1kg_eur", "af_1kg_all"]
known = ["cosmic_ids", "cosmic_id", "clinvar_sig"]
out_file = "%s-priority.tsv" % utils.splitext_plus(in_vcf)[0]
if not utils.file_exists(out_file) and not utils.file_exists(out_file + ".gz"):
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
writer = csv.writer(out_handle, dialect="excel-tab")
header = ["#chrom", "start", "end", "ref", "alt", "filter"]
writer.writerow(header)
vcf_reader = cyvcf2.VCF(in_vcf)
impact_info = _get_impact_info(vcf_reader)
for rec in vcf_reader:
row = _prepare_vcf_rec(rec, pops, known, impact_info)
cur_filter = _calc_priority_filter(row, pops)
writer.writerow([rec.CHROM, rec.start, rec.end, rec.REF, ",".join(rec.ALT), cur_filter])
return vcfutils.bgzip_and_index(out_file, data["config"],
tabix_args="-0 -c '#' -s 1 -b 2 -e 3") | [
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237,682 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _get_impact_info | def _get_impact_info(vcf_reader):
"""Retrieve impact parsing information from INFO header.
"""
ImpactInfo = collections.namedtuple("ImpactInfo", "header, gclass, id")
KEY_2_CLASS = {
'CSQ': geneimpacts.VEP,
'ANN': geneimpacts.SnpEff,
'BCSQ': geneimpacts.BCFT}
for l in (x.strip() for x in _from_bytes(vcf_reader.raw_header).split("\n")):
if l.startswith("##INFO"):
patt = re.compile("(\w+)=(\"[^\"]+\"|[^,]+)")
stub = l.split("=<")[1].rstrip(">")
d = dict(patt.findall(_from_bytes(stub)))
if d["ID"] in KEY_2_CLASS:
return ImpactInfo(_parse_impact_header(d), KEY_2_CLASS[d["ID"]], d["ID"]) | python | def _get_impact_info(vcf_reader):
ImpactInfo = collections.namedtuple("ImpactInfo", "header, gclass, id")
KEY_2_CLASS = {
'CSQ': geneimpacts.VEP,
'ANN': geneimpacts.SnpEff,
'BCSQ': geneimpacts.BCFT}
for l in (x.strip() for x in _from_bytes(vcf_reader.raw_header).split("\n")):
if l.startswith("##INFO"):
patt = re.compile("(\w+)=(\"[^\"]+\"|[^,]+)")
stub = l.split("=<")[1].rstrip(">")
d = dict(patt.findall(_from_bytes(stub)))
if d["ID"] in KEY_2_CLASS:
return ImpactInfo(_parse_impact_header(d), KEY_2_CLASS[d["ID"]], d["ID"]) | [
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237,683 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _parse_impact_header | def _parse_impact_header(hdr_dict):
"""Parse fields for impact, taken from vcf2db
"""
desc = hdr_dict["Description"]
if hdr_dict["ID"] == "ANN":
parts = [x.strip("\"'") for x in re.split("\s*\|\s*", desc.split(":", 1)[1].strip('" '))]
elif hdr_dict["ID"] == "EFF":
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elif hdr_dict["ID"] == "CSQ":
parts = [x.strip(" [])'(\"") for x in re.split("\||\(", desc.split(":", 1)[1].strip())]
elif hdr_dict["ID"] == "BCSQ":
parts = desc.split(']', 1)[1].split(']')[0].replace('[','').split("|")
else:
raise Exception("don't know how to use %s as annotation" % hdr_dict["ID"])
return parts | python | def _parse_impact_header(hdr_dict):
desc = hdr_dict["Description"]
if hdr_dict["ID"] == "ANN":
parts = [x.strip("\"'") for x in re.split("\s*\|\s*", desc.split(":", 1)[1].strip('" '))]
elif hdr_dict["ID"] == "EFF":
parts = [x.strip(" [])'(\"") for x in re.split("\||\(", desc.split(":", 1)[1].strip())]
elif hdr_dict["ID"] == "CSQ":
parts = [x.strip(" [])'(\"") for x in re.split("\||\(", desc.split(":", 1)[1].strip())]
elif hdr_dict["ID"] == "BCSQ":
parts = desc.split(']', 1)[1].split(']')[0].replace('[','').split("|")
else:
raise Exception("don't know how to use %s as annotation" % hdr_dict["ID"])
return parts | [
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237,684 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _prepare_vcf_rec | def _prepare_vcf_rec(rec, pops, known, impact_info):
"""Parse a vcfanno output into a dictionary of useful attributes.
"""
out = {}
for k in pops + known:
out[k] = rec.INFO.get(k)
if impact_info:
cur_info = rec.INFO.get(impact_info.id)
if cur_info:
cur_impacts = [impact_info.gclass(e, impact_info.header) for e in _from_bytes(cur_info).split(",")]
top = geneimpacts.Effect.top_severity(cur_impacts)
if isinstance(top, list):
top = top[0]
out["impact_severity"] = top.effect_severity
return out | python | def _prepare_vcf_rec(rec, pops, known, impact_info):
out = {}
for k in pops + known:
out[k] = rec.INFO.get(k)
if impact_info:
cur_info = rec.INFO.get(impact_info.id)
if cur_info:
cur_impacts = [impact_info.gclass(e, impact_info.header) for e in _from_bytes(cur_info).split(",")]
top = geneimpacts.Effect.top_severity(cur_impacts)
if isinstance(top, list):
top = top[0]
out["impact_severity"] = top.effect_severity
return out | [
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237,685 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _calc_priority_filter | def _calc_priority_filter(row, pops):
"""Calculate the priority filter based on external associated data.
- Pass high/medium impact variants not found in population databases
- Pass variants found in COSMIC or Clinvar provided they don't have two
additional reasons to filter (found in multiple external populations)
"""
filters = []
passes = []
passes.extend(_find_known(row))
filters.extend(_known_populations(row, pops))
if len(filters) == 0 or (len(passes) > 0 and len(filters) < 2):
passes.insert(0, "pass")
return ",".join(passes + filters) | python | def _calc_priority_filter(row, pops):
filters = []
passes = []
passes.extend(_find_known(row))
filters.extend(_known_populations(row, pops))
if len(filters) == 0 or (len(passes) > 0 and len(filters) < 2):
passes.insert(0, "pass")
return ",".join(passes + filters) | [
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237,686 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _known_populations | def _known_populations(row, pops):
"""Find variants present in substantial frequency in population databases.
"""
cutoff = 0.01
out = set([])
for pop, base in [("esp", "af_esp_all"), ("1000g", "af_1kg_all"),
("exac", "af_exac_all"), ("anypop", "max_aaf_all")]:
for key in [x for x in pops if x.startswith(base)]:
val = row[key]
if val and val > cutoff:
out.add(pop)
return sorted(list(out)) | python | def _known_populations(row, pops):
cutoff = 0.01
out = set([])
for pop, base in [("esp", "af_esp_all"), ("1000g", "af_1kg_all"),
("exac", "af_exac_all"), ("anypop", "max_aaf_all")]:
for key in [x for x in pops if x.startswith(base)]:
val = row[key]
if val and val > cutoff:
out.add(pop)
return sorted(list(out)) | [
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237,687 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _find_known | def _find_known(row):
"""Find variant present in known pathogenic databases.
"""
out = []
clinvar_no = set(["unknown", "untested", "non-pathogenic", "probable-non-pathogenic",
"uncertain_significance", "uncertain_significance", "not_provided",
"benign", "likely_benign"])
if row["cosmic_ids"] or row["cosmic_id"]:
out.append("cosmic")
if row["clinvar_sig"] and not row["clinvar_sig"].lower() in clinvar_no:
out.append("clinvar")
return out | python | def _find_known(row):
out = []
clinvar_no = set(["unknown", "untested", "non-pathogenic", "probable-non-pathogenic",
"uncertain_significance", "uncertain_significance", "not_provided",
"benign", "likely_benign"])
if row["cosmic_ids"] or row["cosmic_id"]:
out.append("cosmic")
if row["clinvar_sig"] and not row["clinvar_sig"].lower() in clinvar_no:
out.append("clinvar")
return out | [
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237,688 | bcbio/bcbio-nextgen | bcbio/variation/prioritize.py | _do_prioritize | def _do_prioritize(items):
"""Determine if we should perform prioritization.
Currently done on tumor-only input samples and feeding into PureCN
which needs the germline annotations.
"""
if not any("tumoronly-prioritization" in dd.get_tools_off(d) for d in items):
if vcfutils.get_paired_phenotype(items[0]):
has_tumor = False
has_normal = False
for sub_data in items:
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
has_tumor = True
elif vcfutils.get_paired_phenotype(sub_data) == "normal":
has_normal = True
return has_tumor and not has_normal | python | def _do_prioritize(items):
if not any("tumoronly-prioritization" in dd.get_tools_off(d) for d in items):
if vcfutils.get_paired_phenotype(items[0]):
has_tumor = False
has_normal = False
for sub_data in items:
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
has_tumor = True
elif vcfutils.get_paired_phenotype(sub_data) == "normal":
has_normal = True
return has_tumor and not has_normal | [
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237,689 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | run_cortex | def run_cortex(align_bams, items, ref_file, assoc_files, region=None,
out_file=None):
"""Top level entry to regional de-novo based variant calling with cortex_var.
"""
raise NotImplementedError("Cortex currently out of date and needs reworking.")
if len(align_bams) == 1:
align_bam = align_bams[0]
config = items[0]["config"]
else:
raise NotImplementedError("Need to add multisample calling for cortex_var")
if out_file is None:
out_file = "%s-cortex.vcf" % os.path.splitext(align_bam)[0]
if region is not None:
work_dir = safe_makedir(os.path.join(os.path.dirname(out_file),
region.replace(".", "_")))
else:
work_dir = os.path.dirname(out_file)
if not file_exists(out_file):
bam.index(align_bam, config)
variant_regions = config["algorithm"].get("variant_regions", None)
if not variant_regions:
raise ValueError("Only support regional variant calling with cortex_var: set variant_regions")
target_regions = subset_variant_regions(variant_regions, region, out_file)
if os.path.isfile(target_regions):
with open(target_regions) as in_handle:
regional_vcfs = [_run_cortex_on_region(x.strip().split("\t")[:3], align_bam,
ref_file, work_dir, out_file, config)
for x in in_handle]
combine_file = "{0}-raw{1}".format(*os.path.splitext(out_file))
_combine_variants(regional_vcfs, combine_file, ref_file, config)
_select_final_variants(combine_file, out_file, config)
else:
vcfutils.write_empty_vcf(out_file)
return out_file | python | def run_cortex(align_bams, items, ref_file, assoc_files, region=None,
out_file=None):
raise NotImplementedError("Cortex currently out of date and needs reworking.")
if len(align_bams) == 1:
align_bam = align_bams[0]
config = items[0]["config"]
else:
raise NotImplementedError("Need to add multisample calling for cortex_var")
if out_file is None:
out_file = "%s-cortex.vcf" % os.path.splitext(align_bam)[0]
if region is not None:
work_dir = safe_makedir(os.path.join(os.path.dirname(out_file),
region.replace(".", "_")))
else:
work_dir = os.path.dirname(out_file)
if not file_exists(out_file):
bam.index(align_bam, config)
variant_regions = config["algorithm"].get("variant_regions", None)
if not variant_regions:
raise ValueError("Only support regional variant calling with cortex_var: set variant_regions")
target_regions = subset_variant_regions(variant_regions, region, out_file)
if os.path.isfile(target_regions):
with open(target_regions) as in_handle:
regional_vcfs = [_run_cortex_on_region(x.strip().split("\t")[:3], align_bam,
ref_file, work_dir, out_file, config)
for x in in_handle]
combine_file = "{0}-raw{1}".format(*os.path.splitext(out_file))
_combine_variants(regional_vcfs, combine_file, ref_file, config)
_select_final_variants(combine_file, out_file, config)
else:
vcfutils.write_empty_vcf(out_file)
return out_file | [
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237,690 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _passes_cortex_depth | def _passes_cortex_depth(line, min_depth):
"""Do any genotypes in the cortex_var VCF line passes the minimum depth requirement?
"""
parts = line.split("\t")
cov_index = parts[8].split(":").index("COV")
passes_depth = False
for gt in parts[9:]:
cur_cov = gt.split(":")[cov_index]
cur_depth = sum(int(x) for x in cur_cov.split(","))
if cur_depth >= min_depth:
passes_depth = True
return passes_depth | python | def _passes_cortex_depth(line, min_depth):
parts = line.split("\t")
cov_index = parts[8].split(":").index("COV")
passes_depth = False
for gt in parts[9:]:
cur_cov = gt.split(":")[cov_index]
cur_depth = sum(int(x) for x in cur_cov.split(","))
if cur_depth >= min_depth:
passes_depth = True
return passes_depth | [
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237,691 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _select_final_variants | def _select_final_variants(base_vcf, out_vcf, config):
"""Filter input file, removing items with low depth of support.
cortex_var calls are tricky to filter by depth. Count information is in
the COV FORMAT field grouped by alleles, so we need to sum up values and
compare.
"""
min_depth = int(config["algorithm"].get("min_depth", 4))
with file_transaction(out_vcf) as tx_out_file:
with open(base_vcf) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#"):
passes = True
else:
passes = _passes_cortex_depth(line, min_depth)
if passes:
out_handle.write(line)
return out_vcf | python | def _select_final_variants(base_vcf, out_vcf, config):
min_depth = int(config["algorithm"].get("min_depth", 4))
with file_transaction(out_vcf) as tx_out_file:
with open(base_vcf) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#"):
passes = True
else:
passes = _passes_cortex_depth(line, min_depth)
if passes:
out_handle.write(line)
return out_vcf | [
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237,692 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _combine_variants | def _combine_variants(in_vcfs, out_file, ref_file, config):
"""Combine variant files, writing the header from the first non-empty input.
in_vcfs is a list with each item starting with the chromosome regions,
and ending with the input file.
We sort by these regions to ensure the output file is in the expected order.
"""
in_vcfs.sort()
wrote_header = False
with open(out_file, "w") as out_handle:
for in_vcf in (x[-1] for x in in_vcfs):
with open(in_vcf) as in_handle:
header = list(itertools.takewhile(lambda x: x.startswith("#"),
in_handle))
if not header[0].startswith("##fileformat=VCFv4"):
raise ValueError("Unexpected VCF file: %s" % in_vcf)
for line in in_handle:
if not wrote_header:
wrote_header = True
out_handle.write("".join(header))
out_handle.write(line)
if not wrote_header:
out_handle.write("".join(header))
return out_file | python | def _combine_variants(in_vcfs, out_file, ref_file, config):
in_vcfs.sort()
wrote_header = False
with open(out_file, "w") as out_handle:
for in_vcf in (x[-1] for x in in_vcfs):
with open(in_vcf) as in_handle:
header = list(itertools.takewhile(lambda x: x.startswith("#"),
in_handle))
if not header[0].startswith("##fileformat=VCFv4"):
raise ValueError("Unexpected VCF file: %s" % in_vcf)
for line in in_handle:
if not wrote_header:
wrote_header = True
out_handle.write("".join(header))
out_handle.write(line)
if not wrote_header:
out_handle.write("".join(header))
return out_file | [
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237,693 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _remap_cortex_out | def _remap_cortex_out(cortex_out, region, out_file):
"""Remap coordinates in local cortex variant calls to the original global region.
"""
def _remap_vcf_line(line, contig, start):
parts = line.split("\t")
if parts[0] == "" or parts[1] == "":
return None
parts[0] = contig
try:
parts[1] = str(int(parts[1]) + start)
except ValueError:
raise ValueError("Problem in {0} with \n{1}".format(
cortex_out, parts))
return "\t".join(parts)
def _not_filtered(line):
parts = line.split("\t")
return parts[6] == "PASS"
contig, start, _ = region
start = int(start)
with open(cortex_out) as in_handle:
with open(out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("##fileDate"):
pass
elif line.startswith("#"):
out_handle.write(line)
elif _not_filtered(line):
update_line = _remap_vcf_line(line, contig, start)
if update_line:
out_handle.write(update_line) | python | def _remap_cortex_out(cortex_out, region, out_file):
def _remap_vcf_line(line, contig, start):
parts = line.split("\t")
if parts[0] == "" or parts[1] == "":
return None
parts[0] = contig
try:
parts[1] = str(int(parts[1]) + start)
except ValueError:
raise ValueError("Problem in {0} with \n{1}".format(
cortex_out, parts))
return "\t".join(parts)
def _not_filtered(line):
parts = line.split("\t")
return parts[6] == "PASS"
contig, start, _ = region
start = int(start)
with open(cortex_out) as in_handle:
with open(out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("##fileDate"):
pass
elif line.startswith("#"):
out_handle.write(line)
elif _not_filtered(line):
update_line = _remap_vcf_line(line, contig, start)
if update_line:
out_handle.write(update_line) | [
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237,694 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _run_cortex | def _run_cortex(fastq, indexes, params, out_base, dirs, config):
"""Run cortex_var run_calls.pl, producing a VCF variant file.
"""
print(out_base)
fastaq_index = "{0}.fastaq_index".format(out_base)
se_fastq_index = "{0}.se_fastq".format(out_base)
pe_fastq_index = "{0}.pe_fastq".format(out_base)
reffasta_index = "{0}.list_ref_fasta".format(out_base)
with open(se_fastq_index, "w") as out_handle:
out_handle.write(fastq + "\n")
with open(pe_fastq_index, "w") as out_handle:
out_handle.write("")
with open(fastaq_index, "w") as out_handle:
out_handle.write("{0}\t{1}\t{2}\t{2}\n".format(params["sample"], se_fastq_index,
pe_fastq_index))
with open(reffasta_index, "w") as out_handle:
for x in indexes["fasta"]:
out_handle.write(x + "\n")
os.environ["PERL5LIB"] = "{0}:{1}:{2}".format(
os.path.join(dirs["cortex"], "scripts/calling"),
os.path.join(dirs["cortex"], "scripts/analyse_variants/bioinf-perl/lib"),
os.environ.get("PERL5LIB", ""))
kmers = sorted(params["kmers"])
kmer_info = ["--first_kmer", str(kmers[0])]
if len(kmers) > 1:
kmer_info += ["--last_kmer", str(kmers[-1]),
"--kmer_step", str(kmers[1] - kmers[0])]
subprocess.check_call(["perl", os.path.join(dirs["cortex"], "scripts", "calling", "run_calls.pl"),
"--fastaq_index", fastaq_index,
"--auto_cleaning", "yes", "--bc", "yes", "--pd", "yes",
"--outdir", os.path.dirname(out_base), "--outvcf", os.path.basename(out_base),
"--ploidy", str(config["algorithm"].get("ploidy", 2)),
"--stampy_hash", indexes["stampy"],
"--stampy_bin", os.path.join(dirs["stampy"], "stampy.py"),
"--refbindir", os.path.dirname(indexes["cortex"][0]),
"--list_ref_fasta", reffasta_index,
"--genome_size", str(params["genome_size"]),
"--max_read_len", "30000",
#"--max_var_len", "4000",
"--format", "FASTQ", "--qthresh", "5", "--do_union", "yes",
"--mem_height", "17", "--mem_width", "100",
"--ref", "CoordinatesAndInCalling", "--workflow", "independent",
"--vcftools_dir", dirs["vcftools"],
"--logfile", "{0}.logfile,f".format(out_base)]
+ kmer_info)
final = glob.glob(os.path.join(os.path.dirname(out_base), "vcfs",
"{0}*FINALcombined_BC*decomp.vcf".format(os.path.basename(out_base))))
# No calls, need to setup an empty file
if len(final) != 1:
print("Did not find output VCF file for {0}".format(out_base))
return None
else:
return final[0] | python | def _run_cortex(fastq, indexes, params, out_base, dirs, config):
print(out_base)
fastaq_index = "{0}.fastaq_index".format(out_base)
se_fastq_index = "{0}.se_fastq".format(out_base)
pe_fastq_index = "{0}.pe_fastq".format(out_base)
reffasta_index = "{0}.list_ref_fasta".format(out_base)
with open(se_fastq_index, "w") as out_handle:
out_handle.write(fastq + "\n")
with open(pe_fastq_index, "w") as out_handle:
out_handle.write("")
with open(fastaq_index, "w") as out_handle:
out_handle.write("{0}\t{1}\t{2}\t{2}\n".format(params["sample"], se_fastq_index,
pe_fastq_index))
with open(reffasta_index, "w") as out_handle:
for x in indexes["fasta"]:
out_handle.write(x + "\n")
os.environ["PERL5LIB"] = "{0}:{1}:{2}".format(
os.path.join(dirs["cortex"], "scripts/calling"),
os.path.join(dirs["cortex"], "scripts/analyse_variants/bioinf-perl/lib"),
os.environ.get("PERL5LIB", ""))
kmers = sorted(params["kmers"])
kmer_info = ["--first_kmer", str(kmers[0])]
if len(kmers) > 1:
kmer_info += ["--last_kmer", str(kmers[-1]),
"--kmer_step", str(kmers[1] - kmers[0])]
subprocess.check_call(["perl", os.path.join(dirs["cortex"], "scripts", "calling", "run_calls.pl"),
"--fastaq_index", fastaq_index,
"--auto_cleaning", "yes", "--bc", "yes", "--pd", "yes",
"--outdir", os.path.dirname(out_base), "--outvcf", os.path.basename(out_base),
"--ploidy", str(config["algorithm"].get("ploidy", 2)),
"--stampy_hash", indexes["stampy"],
"--stampy_bin", os.path.join(dirs["stampy"], "stampy.py"),
"--refbindir", os.path.dirname(indexes["cortex"][0]),
"--list_ref_fasta", reffasta_index,
"--genome_size", str(params["genome_size"]),
"--max_read_len", "30000",
#"--max_var_len", "4000",
"--format", "FASTQ", "--qthresh", "5", "--do_union", "yes",
"--mem_height", "17", "--mem_width", "100",
"--ref", "CoordinatesAndInCalling", "--workflow", "independent",
"--vcftools_dir", dirs["vcftools"],
"--logfile", "{0}.logfile,f".format(out_base)]
+ kmer_info)
final = glob.glob(os.path.join(os.path.dirname(out_base), "vcfs",
"{0}*FINALcombined_BC*decomp.vcf".format(os.path.basename(out_base))))
# No calls, need to setup an empty file
if len(final) != 1:
print("Did not find output VCF file for {0}".format(out_base))
return None
else:
return final[0] | [
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237,695 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _index_local_ref | def _index_local_ref(fasta_file, cortex_dir, stampy_dir, kmers):
"""Pre-index a generated local reference sequence with cortex_var and stampy.
"""
base_out = os.path.splitext(fasta_file)[0]
cindexes = []
for kmer in kmers:
out_file = "{0}.k{1}.ctx".format(base_out, kmer)
if not file_exists(out_file):
file_list = "{0}.se_list".format(base_out)
with open(file_list, "w") as out_handle:
out_handle.write(fasta_file + "\n")
subprocess.check_call([_get_cortex_binary(kmer, cortex_dir),
"--kmer_size", str(kmer), "--mem_height", "17",
"--se_list", file_list, "--format", "FASTA",
"--max_read_len", "30000",
"--sample_id", base_out,
"--dump_binary", out_file])
cindexes.append(out_file)
if not file_exists("{0}.stidx".format(base_out)):
subprocess.check_call([os.path.join(stampy_dir, "stampy.py"), "-G",
base_out, fasta_file])
subprocess.check_call([os.path.join(stampy_dir, "stampy.py"), "-g",
base_out, "-H", base_out])
return {"stampy": base_out,
"cortex": cindexes,
"fasta": [fasta_file]} | python | def _index_local_ref(fasta_file, cortex_dir, stampy_dir, kmers):
base_out = os.path.splitext(fasta_file)[0]
cindexes = []
for kmer in kmers:
out_file = "{0}.k{1}.ctx".format(base_out, kmer)
if not file_exists(out_file):
file_list = "{0}.se_list".format(base_out)
with open(file_list, "w") as out_handle:
out_handle.write(fasta_file + "\n")
subprocess.check_call([_get_cortex_binary(kmer, cortex_dir),
"--kmer_size", str(kmer), "--mem_height", "17",
"--se_list", file_list, "--format", "FASTA",
"--max_read_len", "30000",
"--sample_id", base_out,
"--dump_binary", out_file])
cindexes.append(out_file)
if not file_exists("{0}.stidx".format(base_out)):
subprocess.check_call([os.path.join(stampy_dir, "stampy.py"), "-G",
base_out, fasta_file])
subprocess.check_call([os.path.join(stampy_dir, "stampy.py"), "-g",
base_out, "-H", base_out])
return {"stampy": base_out,
"cortex": cindexes,
"fasta": [fasta_file]} | [
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237,696 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _get_local_ref | def _get_local_ref(region, ref_file, out_vcf_base):
"""Retrieve a local FASTA file corresponding to the specified region.
"""
out_file = "{0}.fa".format(out_vcf_base)
if not file_exists(out_file):
with pysam.Fastafile(ref_file) as in_pysam:
contig, start, end = region
seq = in_pysam.fetch(contig, int(start), int(end))
with open(out_file, "w") as out_handle:
out_handle.write(">{0}-{1}-{2}\n{3}".format(contig, start, end,
str(seq)))
with open(out_file) as in_handle:
in_handle.readline()
size = len(in_handle.readline().strip())
return out_file, size | python | def _get_local_ref(region, ref_file, out_vcf_base):
out_file = "{0}.fa".format(out_vcf_base)
if not file_exists(out_file):
with pysam.Fastafile(ref_file) as in_pysam:
contig, start, end = region
seq = in_pysam.fetch(contig, int(start), int(end))
with open(out_file, "w") as out_handle:
out_handle.write(">{0}-{1}-{2}\n{3}".format(contig, start, end,
str(seq)))
with open(out_file) as in_handle:
in_handle.readline()
size = len(in_handle.readline().strip())
return out_file, size | [
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237,697 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _get_fastq_in_region | def _get_fastq_in_region(region, align_bam, out_base):
"""Retrieve fastq files in region as single end.
Paired end is more complicated since pairs can map off the region, so focus
on local only assembly since we've previously used paired information for mapping.
"""
out_file = "{0}.fastq".format(out_base)
if not file_exists(out_file):
with pysam.Samfile(align_bam, "rb") as in_pysam:
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
contig, start, end = region
for read in in_pysam.fetch(contig, int(start), int(end)):
seq = Seq.Seq(read.seq)
qual = list(read.qual)
if read.is_reverse:
seq = seq.reverse_complement()
qual.reverse()
out_handle.write("@{name}\n{seq}\n+\n{qual}\n".format(
name=read.qname, seq=str(seq), qual="".join(qual)))
return out_file | python | def _get_fastq_in_region(region, align_bam, out_base):
out_file = "{0}.fastq".format(out_base)
if not file_exists(out_file):
with pysam.Samfile(align_bam, "rb") as in_pysam:
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
contig, start, end = region
for read in in_pysam.fetch(contig, int(start), int(end)):
seq = Seq.Seq(read.seq)
qual = list(read.qual)
if read.is_reverse:
seq = seq.reverse_complement()
qual.reverse()
out_handle.write("@{name}\n{seq}\n+\n{qual}\n".format(
name=read.qname, seq=str(seq), qual="".join(qual)))
return out_file | [
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237,698 | bcbio/bcbio-nextgen | bcbio/variation/cortex.py | _count_fastq_reads | def _count_fastq_reads(in_fastq, min_reads):
"""Count the number of fastq reads in a file, stopping after reaching min_reads.
"""
with open(in_fastq) as in_handle:
items = list(itertools.takewhile(lambda i : i <= min_reads,
(i for i, _ in enumerate(FastqGeneralIterator(in_handle)))))
return len(items) | python | def _count_fastq_reads(in_fastq, min_reads):
with open(in_fastq) as in_handle:
items = list(itertools.takewhile(lambda i : i <= min_reads,
(i for i, _ in enumerate(FastqGeneralIterator(in_handle)))))
return len(items) | [
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237,699 | bcbio/bcbio-nextgen | bcbio/distributed/transaction.py | _move_file_with_sizecheck | def _move_file_with_sizecheck(tx_file, final_file):
"""Move transaction file to final location,
with size checks avoiding failed transfers.
Creates an empty file with '.bcbiotmp' extention in the destination
location, which serves as a flag. If a file like that is present,
it means that transaction didn't finish successfully.
"""
#logger.debug("Moving %s to %s" % (tx_file, final_file))
tmp_file = final_file + ".bcbiotmp"
open(tmp_file, 'wb').close()
want_size = utils.get_size(tx_file)
shutil.move(tx_file, final_file)
transfer_size = utils.get_size(final_file)
assert want_size == transfer_size, (
'distributed.transaction.file_transaction: File copy error: '
'file or directory on temporary storage ({}) size {} bytes '
'does not equal size of file or directory after transfer to '
'shared storage ({}) size {} bytes'.format(
tx_file, want_size, final_file, transfer_size)
)
utils.remove_safe(tmp_file) | python | def _move_file_with_sizecheck(tx_file, final_file):
#logger.debug("Moving %s to %s" % (tx_file, final_file))
tmp_file = final_file + ".bcbiotmp"
open(tmp_file, 'wb').close()
want_size = utils.get_size(tx_file)
shutil.move(tx_file, final_file)
transfer_size = utils.get_size(final_file)
assert want_size == transfer_size, (
'distributed.transaction.file_transaction: File copy error: '
'file or directory on temporary storage ({}) size {} bytes '
'does not equal size of file or directory after transfer to '
'shared storage ({}) size {} bytes'.format(
tx_file, want_size, final_file, transfer_size)
)
utils.remove_safe(tmp_file) | [
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"want... | Move transaction file to final location,
with size checks avoiding failed transfers.
Creates an empty file with '.bcbiotmp' extention in the destination
location, which serves as a flag. If a file like that is present,
it means that transaction didn't finish successfully. | [
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"transaction",
"file",
"to",
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"location",
"with",
"size",
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"avoiding",
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"transfers",
"."
] | 6a9348c0054ccd5baffd22f1bb7d0422f6978b20 | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/distributed/transaction.py#L102-L127 |
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