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 75 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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223,700 | bcbio/bcbio-nextgen | bcbio/utils.py | reservoir_sample | def reservoir_sample(stream, num_items, item_parser=lambda x: x):
"""
samples num_items from the stream keeping each with equal probability
"""
kept = []
for index, item in enumerate(stream):
if index < num_items:
kept.append(item_parser(item))
else:
r = random.randint(0, index)
if r < num_items:
kept[r] = item_parser(item)
return kept | python | def reservoir_sample(stream, num_items, item_parser=lambda x: x):
"""
samples num_items from the stream keeping each with equal probability
"""
kept = []
for index, item in enumerate(stream):
if index < num_items:
kept.append(item_parser(item))
else:
r = random.randint(0, index)
if r < num_items:
kept[r] = item_parser(item)
return kept | [
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223,701 | bcbio/bcbio-nextgen | bcbio/utils.py | dictapply | def dictapply(d, fn):
"""
apply a function to all non-dict values in a dictionary
"""
for k, v in d.items():
if isinstance(v, dict):
v = dictapply(v, fn)
else:
d[k] = fn(v)
return d | python | def dictapply(d, fn):
"""
apply a function to all non-dict values in a dictionary
"""
for k, v in d.items():
if isinstance(v, dict):
v = dictapply(v, fn)
else:
d[k] = fn(v)
return d | [
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223,702 | bcbio/bcbio-nextgen | bcbio/utils.py | Rscript_cmd | def Rscript_cmd():
"""Retrieve path to locally installed Rscript or first in PATH.
Prefers Rscript version installed via conda to a system version.
"""
rscript = which(os.path.join(get_bcbio_bin(), "Rscript"))
if rscript:
return rscript
else:
return which("Rscript") | python | def Rscript_cmd():
"""Retrieve path to locally installed Rscript or first in PATH.
Prefers Rscript version installed via conda to a system version.
"""
rscript = which(os.path.join(get_bcbio_bin(), "Rscript"))
if rscript:
return rscript
else:
return which("Rscript") | [
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223,703 | bcbio/bcbio-nextgen | bcbio/utils.py | R_package_resource | def R_package_resource(package, resource):
"""
return a path to an R package resource, if it is available
"""
package_path = R_package_path(package)
if not package_path:
return None
package_resource = os.path.join(package_path, resource)
if not file_exists(package_resource):
return None
else:
return package_resource | python | def R_package_resource(package, resource):
"""
return a path to an R package resource, if it is available
"""
package_path = R_package_path(package)
if not package_path:
return None
package_resource = os.path.join(package_path, resource)
if not file_exists(package_resource):
return None
else:
return package_resource | [
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223,704 | bcbio/bcbio-nextgen | bcbio/utils.py | get_java_binpath | def get_java_binpath(cmd=None):
"""Retrieve path for java to use, handling custom BCBIO_JAVA_HOME
Defaults to the dirname of cmd, or local anaconda directory
"""
if os.environ.get("BCBIO_JAVA_HOME"):
test_cmd = os.path.join(os.environ["BCBIO_JAVA_HOME"], "bin", "java")
if os.path.exists(test_cmd):
cmd = test_cmd
if not cmd:
cmd = Rscript_cmd()
return os.path.dirname(cmd) | python | def get_java_binpath(cmd=None):
"""Retrieve path for java to use, handling custom BCBIO_JAVA_HOME
Defaults to the dirname of cmd, or local anaconda directory
"""
if os.environ.get("BCBIO_JAVA_HOME"):
test_cmd = os.path.join(os.environ["BCBIO_JAVA_HOME"], "bin", "java")
if os.path.exists(test_cmd):
cmd = test_cmd
if not cmd:
cmd = Rscript_cmd()
return os.path.dirname(cmd) | [
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223,705 | bcbio/bcbio-nextgen | bcbio/utils.py | clear_java_home | def clear_java_home():
"""Clear JAVA_HOME environment or reset to BCBIO_JAVA_HOME.
Avoids accidental java injection but respects custom BCBIO_JAVA_HOME
command.
"""
if os.environ.get("BCBIO_JAVA_HOME"):
test_cmd = os.path.join(os.environ["BCBIO_JAVA_HOME"], "bin", "java")
if os.path.exists(test_cmd):
return "export JAVA_HOME=%s" % os.environ["BCBIO_JAVA_HOME"]
return "unset JAVA_HOME" | python | def clear_java_home():
"""Clear JAVA_HOME environment or reset to BCBIO_JAVA_HOME.
Avoids accidental java injection but respects custom BCBIO_JAVA_HOME
command.
"""
if os.environ.get("BCBIO_JAVA_HOME"):
test_cmd = os.path.join(os.environ["BCBIO_JAVA_HOME"], "bin", "java")
if os.path.exists(test_cmd):
return "export JAVA_HOME=%s" % os.environ["BCBIO_JAVA_HOME"]
return "unset JAVA_HOME" | [
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223,706 | bcbio/bcbio-nextgen | bcbio/utils.py | perl_cmd | def perl_cmd():
"""Retrieve path to locally installed conda Perl or first in PATH.
"""
perl = which(os.path.join(get_bcbio_bin(), "perl"))
if perl:
return perl
else:
return which("perl") | python | def perl_cmd():
"""Retrieve path to locally installed conda Perl or first in PATH.
"""
perl = which(os.path.join(get_bcbio_bin(), "perl"))
if perl:
return perl
else:
return which("perl") | [
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223,707 | bcbio/bcbio-nextgen | bcbio/utils.py | get_perl_exports | def get_perl_exports(tmpdir=None):
"""Environmental exports to use conda installed perl.
"""
perl_path = os.path.dirname(perl_cmd())
out = "unset PERL5LIB && export PATH=%s:\"$PATH\"" % (perl_path)
if tmpdir:
out += " && export TMPDIR=%s" % (tmpdir)
return out | python | def get_perl_exports(tmpdir=None):
"""Environmental exports to use conda installed perl.
"""
perl_path = os.path.dirname(perl_cmd())
out = "unset PERL5LIB && export PATH=%s:\"$PATH\"" % (perl_path)
if tmpdir:
out += " && export TMPDIR=%s" % (tmpdir)
return out | [
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223,708 | bcbio/bcbio-nextgen | bcbio/utils.py | get_all_conda_bins | def get_all_conda_bins():
"""Retrieve all possible conda bin directories, including environments.
"""
bcbio_bin = get_bcbio_bin()
conda_dir = os.path.dirname(bcbio_bin)
if os.path.join("anaconda", "envs") in conda_dir:
conda_dir = os.path.join(conda_dir[:conda_dir.rfind(os.path.join("anaconda", "envs"))], "anaconda")
return [bcbio_bin] + list(glob.glob(os.path.join(conda_dir, "envs", "*", "bin"))) | python | def get_all_conda_bins():
"""Retrieve all possible conda bin directories, including environments.
"""
bcbio_bin = get_bcbio_bin()
conda_dir = os.path.dirname(bcbio_bin)
if os.path.join("anaconda", "envs") in conda_dir:
conda_dir = os.path.join(conda_dir[:conda_dir.rfind(os.path.join("anaconda", "envs"))], "anaconda")
return [bcbio_bin] + list(glob.glob(os.path.join(conda_dir, "envs", "*", "bin"))) | [
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223,709 | bcbio/bcbio-nextgen | bcbio/utils.py | get_program_python | def get_program_python(cmd):
"""Get the full path to a python version linked to the command.
Allows finding python based programs in python 2 versus python 3
environments.
"""
full_cmd = os.path.realpath(which(cmd))
cmd_python = os.path.join(os.path.dirname(full_cmd), "python")
env_python = None
if "envs" in cmd_python:
parts = cmd_python.split(os.sep)
env_python = os.path.join(os.sep.join(parts[:parts.index("envs") + 2]), "bin", "python")
if os.path.exists(cmd_python):
return cmd_python
elif env_python and os.path.exists(env_python):
return env_python
else:
return os.path.realpath(sys.executable) | python | def get_program_python(cmd):
"""Get the full path to a python version linked to the command.
Allows finding python based programs in python 2 versus python 3
environments.
"""
full_cmd = os.path.realpath(which(cmd))
cmd_python = os.path.join(os.path.dirname(full_cmd), "python")
env_python = None
if "envs" in cmd_python:
parts = cmd_python.split(os.sep)
env_python = os.path.join(os.sep.join(parts[:parts.index("envs") + 2]), "bin", "python")
if os.path.exists(cmd_python):
return cmd_python
elif env_python and os.path.exists(env_python):
return env_python
else:
return os.path.realpath(sys.executable) | [
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223,710 | bcbio/bcbio-nextgen | bcbio/utils.py | local_path_export | def local_path_export(at_start=True, env_cmd=None):
"""Retrieve paths to local install, also including environment paths if env_cmd included.
"""
paths = [get_bcbio_bin()]
if env_cmd:
env_path = os.path.dirname(get_program_python(env_cmd))
if env_path not in paths:
paths.insert(0, env_path)
if at_start:
return "export PATH=%s:\"$PATH\" && " % (":".join(paths))
else:
return "export PATH=\"$PATH\":%s && " % (":".join(paths)) | python | def local_path_export(at_start=True, env_cmd=None):
"""Retrieve paths to local install, also including environment paths if env_cmd included.
"""
paths = [get_bcbio_bin()]
if env_cmd:
env_path = os.path.dirname(get_program_python(env_cmd))
if env_path not in paths:
paths.insert(0, env_path)
if at_start:
return "export PATH=%s:\"$PATH\" && " % (":".join(paths))
else:
return "export PATH=\"$PATH\":%s && " % (":".join(paths)) | [
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223,711 | bcbio/bcbio-nextgen | bcbio/utils.py | rbind | def rbind(dfs):
"""
acts like rbind for pandas dataframes
"""
if len(dfs) == 1:
return dfs[0]
df = dfs[0]
for d in dfs[1:]:
df = df.append(d)
return df | python | def rbind(dfs):
"""
acts like rbind for pandas dataframes
"""
if len(dfs) == 1:
return dfs[0]
df = dfs[0]
for d in dfs[1:]:
df = df.append(d)
return df | [
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223,712 | bcbio/bcbio-nextgen | bcbio/utils.py | sort_filenames | def sort_filenames(filenames):
"""
sort a list of files by filename only, ignoring the directory names
"""
basenames = [os.path.basename(x) for x in filenames]
indexes = [i[0] for i in sorted(enumerate(basenames), key=lambda x:x[1])]
return [filenames[x] for x in indexes] | python | def sort_filenames(filenames):
"""
sort a list of files by filename only, ignoring the directory names
"""
basenames = [os.path.basename(x) for x in filenames]
indexes = [i[0] for i in sorted(enumerate(basenames), key=lambda x:x[1])]
return [filenames[x] for x in indexes] | [
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223,713 | bcbio/bcbio-nextgen | bcbio/utils.py | walk_json | def walk_json(d, func):
""" Walk over a parsed JSON nested structure `d`, apply `func` to each leaf element and replace it with result
"""
if isinstance(d, Mapping):
return OrderedDict((k, walk_json(v, func)) for k, v in d.items())
elif isinstance(d, list):
return [walk_json(v, func) for v in d]
else:
return func(d) | python | def walk_json(d, func):
""" Walk over a parsed JSON nested structure `d`, apply `func` to each leaf element and replace it with result
"""
if isinstance(d, Mapping):
return OrderedDict((k, walk_json(v, func)) for k, v in d.items())
elif isinstance(d, list):
return [walk_json(v, func) for v in d]
else:
return func(d) | [
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223,714 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _link_bam_file | def _link_bam_file(in_file, new_dir, data):
"""Provide symlinks of BAM file and existing indexes if needed.
"""
new_dir = utils.safe_makedir(new_dir)
out_file = os.path.join(new_dir, os.path.basename(in_file))
if not utils.file_exists(out_file):
out_file = os.path.join(new_dir, "%s-prealign.bam" % dd.get_sample_name(data))
if data.get("cwl_keys"):
# Has indexes, we're okay to go with the original file
if utils.file_exists(in_file + ".bai"):
out_file = in_file
else:
utils.copy_plus(in_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return out_file | python | def _link_bam_file(in_file, new_dir, data):
"""Provide symlinks of BAM file and existing indexes if needed.
"""
new_dir = utils.safe_makedir(new_dir)
out_file = os.path.join(new_dir, os.path.basename(in_file))
if not utils.file_exists(out_file):
out_file = os.path.join(new_dir, "%s-prealign.bam" % dd.get_sample_name(data))
if data.get("cwl_keys"):
# Has indexes, we're okay to go with the original file
if utils.file_exists(in_file + ".bai"):
out_file = in_file
else:
utils.copy_plus(in_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return out_file | [
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223,715 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _add_supplemental_bams | def _add_supplemental_bams(data):
"""Add supplemental files produced by alignment, useful for structural
variant calling.
"""
file_key = "work_bam"
if data.get(file_key):
for supext in ["disc", "sr"]:
base, ext = os.path.splitext(data[file_key])
test_file = "%s-%s%s" % (base, supext, ext)
if os.path.exists(test_file):
sup_key = file_key + "_plus"
if sup_key not in data:
data[sup_key] = {}
data[sup_key][supext] = test_file
return data | python | def _add_supplemental_bams(data):
"""Add supplemental files produced by alignment, useful for structural
variant calling.
"""
file_key = "work_bam"
if data.get(file_key):
for supext in ["disc", "sr"]:
base, ext = os.path.splitext(data[file_key])
test_file = "%s-%s%s" % (base, supext, ext)
if os.path.exists(test_file):
sup_key = file_key + "_plus"
if sup_key not in data:
data[sup_key] = {}
data[sup_key][supext] = test_file
return data | [
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223,716 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _add_hla_files | def _add_hla_files(data):
"""Add extracted fastq files of HLA alleles for typing.
"""
if "hla" not in data:
data["hla"] = {}
align_file = dd.get_align_bam(data)
hla_dir = os.path.join(os.path.dirname(align_file), "hla")
if not os.path.exists(hla_dir):
hla_files = None
else:
hla_files = sorted(list(glob.glob(os.path.join(hla_dir, "%s.*.fq" % os.path.basename(align_file)))))
data["hla"]["fastq"] = hla_files
return data | python | def _add_hla_files(data):
"""Add extracted fastq files of HLA alleles for typing.
"""
if "hla" not in data:
data["hla"] = {}
align_file = dd.get_align_bam(data)
hla_dir = os.path.join(os.path.dirname(align_file), "hla")
if not os.path.exists(hla_dir):
hla_files = None
else:
hla_files = sorted(list(glob.glob(os.path.join(hla_dir, "%s.*.fq" % os.path.basename(align_file)))))
data["hla"]["fastq"] = hla_files
return data | [
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223,717 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | prep_samples | def prep_samples(*items):
"""Handle any global preparatory steps for samples with potentially shared data.
Avoids race conditions in postprocess alignment when performing prep tasks
on shared files between multiple similar samples.
Cleans input BED files to avoid issues with overlapping input segments.
"""
out = []
for data in (utils.to_single_data(x) for x in items):
data = cwlutils.normalize_missing(data)
data = cwlutils.unpack_tarballs(data, data)
data = clean_inputs(data)
out.append([data])
return out | python | def prep_samples(*items):
"""Handle any global preparatory steps for samples with potentially shared data.
Avoids race conditions in postprocess alignment when performing prep tasks
on shared files between multiple similar samples.
Cleans input BED files to avoid issues with overlapping input segments.
"""
out = []
for data in (utils.to_single_data(x) for x in items):
data = cwlutils.normalize_missing(data)
data = cwlutils.unpack_tarballs(data, data)
data = clean_inputs(data)
out.append([data])
return out | [
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223,718 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | clean_inputs | def clean_inputs(data):
"""Clean BED input files to avoid overlapping segments that cause downstream issues.
Per-merges inputs to avoid needing to call multiple times during later parallel steps.
"""
if not utils.get_in(data, ("config", "algorithm", "variant_regions_orig")):
data["config"]["algorithm"]["variant_regions_orig"] = dd.get_variant_regions(data)
clean_vr = clean_file(dd.get_variant_regions(data), data, prefix="cleaned-")
merged_vr = merge_overlaps(clean_vr, data)
data["config"]["algorithm"]["variant_regions"] = clean_vr
data["config"]["algorithm"]["variant_regions_merged"] = merged_vr
if dd.get_coverage(data):
if not utils.get_in(data, ("config", "algorithm", "coverage_orig")):
data["config"]["algorithm"]["coverage_orig"] = dd.get_coverage(data)
clean_cov_bed = clean_file(dd.get_coverage(data), data, prefix="cov-", simple=True)
merged_cov_bed = merge_overlaps(clean_cov_bed, data)
data["config"]["algorithm"]["coverage"] = clean_cov_bed
data["config"]["algorithm"]["coverage_merged"] = merged_cov_bed
if 'seq2c' in get_svcallers(data):
seq2c_ready_bed = prep_seq2c_bed(data)
if not seq2c_ready_bed:
logger.warning("Can't run Seq2C without a svregions or variant_regions BED file")
else:
data["config"]["algorithm"]["seq2c_bed_ready"] = seq2c_ready_bed
elif regions.get_sv_bed(data):
dd.set_sv_regions(data, clean_file(regions.get_sv_bed(data), data, prefix="svregions-"))
return data | python | def clean_inputs(data):
"""Clean BED input files to avoid overlapping segments that cause downstream issues.
Per-merges inputs to avoid needing to call multiple times during later parallel steps.
"""
if not utils.get_in(data, ("config", "algorithm", "variant_regions_orig")):
data["config"]["algorithm"]["variant_regions_orig"] = dd.get_variant_regions(data)
clean_vr = clean_file(dd.get_variant_regions(data), data, prefix="cleaned-")
merged_vr = merge_overlaps(clean_vr, data)
data["config"]["algorithm"]["variant_regions"] = clean_vr
data["config"]["algorithm"]["variant_regions_merged"] = merged_vr
if dd.get_coverage(data):
if not utils.get_in(data, ("config", "algorithm", "coverage_orig")):
data["config"]["algorithm"]["coverage_orig"] = dd.get_coverage(data)
clean_cov_bed = clean_file(dd.get_coverage(data), data, prefix="cov-", simple=True)
merged_cov_bed = merge_overlaps(clean_cov_bed, data)
data["config"]["algorithm"]["coverage"] = clean_cov_bed
data["config"]["algorithm"]["coverage_merged"] = merged_cov_bed
if 'seq2c' in get_svcallers(data):
seq2c_ready_bed = prep_seq2c_bed(data)
if not seq2c_ready_bed:
logger.warning("Can't run Seq2C without a svregions or variant_regions BED file")
else:
data["config"]["algorithm"]["seq2c_bed_ready"] = seq2c_ready_bed
elif regions.get_sv_bed(data):
dd.set_sv_regions(data, clean_file(regions.get_sv_bed(data), data, prefix="svregions-"))
return data | [
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223,719 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | postprocess_alignment | def postprocess_alignment(data):
"""Perform post-processing steps required on full BAM files.
Prepares list of callable genome regions allowing subsequent parallelization.
"""
data = cwlutils.normalize_missing(utils.to_single_data(data))
data = cwlutils.unpack_tarballs(data, data)
bam_file = data.get("align_bam") or data.get("work_bam")
ref_file = dd.get_ref_file(data)
if vmulti.bam_needs_processing(data) and bam_file and bam_file.endswith(".bam"):
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data)))
bam_file_ready = os.path.join(out_dir, os.path.basename(bam_file))
if not utils.file_exists(bam_file_ready):
utils.symlink_plus(bam_file, bam_file_ready)
bam.index(bam_file_ready, data["config"])
covinfo = callable.sample_callable_bed(bam_file_ready, ref_file, data)
callable_region_bed, nblock_bed = \
callable.block_regions(covinfo.raw_callable, bam_file_ready, ref_file, data)
data["regions"] = {"nblock": nblock_bed,
"callable": covinfo.raw_callable,
"sample_callable": covinfo.callable,
"mapped_stats": readstats.get_cache_file(data)}
data["depth"] = covinfo.depth_files
data = coverage.assign_interval(data)
data = samtools.run_and_save(data)
data = recalibrate.prep_recal(data)
data = recalibrate.apply_recal(data)
elif dd.get_variant_regions(data):
callable_region_bed, nblock_bed = \
callable.block_regions(dd.get_variant_regions(data), bam_file, ref_file, data)
data["regions"] = {"nblock": nblock_bed, "callable": dd.get_variant_regions(data),
"sample_callable": dd.get_variant_regions(data)}
return [[data]] | python | def postprocess_alignment(data):
"""Perform post-processing steps required on full BAM files.
Prepares list of callable genome regions allowing subsequent parallelization.
"""
data = cwlutils.normalize_missing(utils.to_single_data(data))
data = cwlutils.unpack_tarballs(data, data)
bam_file = data.get("align_bam") or data.get("work_bam")
ref_file = dd.get_ref_file(data)
if vmulti.bam_needs_processing(data) and bam_file and bam_file.endswith(".bam"):
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data)))
bam_file_ready = os.path.join(out_dir, os.path.basename(bam_file))
if not utils.file_exists(bam_file_ready):
utils.symlink_plus(bam_file, bam_file_ready)
bam.index(bam_file_ready, data["config"])
covinfo = callable.sample_callable_bed(bam_file_ready, ref_file, data)
callable_region_bed, nblock_bed = \
callable.block_regions(covinfo.raw_callable, bam_file_ready, ref_file, data)
data["regions"] = {"nblock": nblock_bed,
"callable": covinfo.raw_callable,
"sample_callable": covinfo.callable,
"mapped_stats": readstats.get_cache_file(data)}
data["depth"] = covinfo.depth_files
data = coverage.assign_interval(data)
data = samtools.run_and_save(data)
data = recalibrate.prep_recal(data)
data = recalibrate.apply_recal(data)
elif dd.get_variant_regions(data):
callable_region_bed, nblock_bed = \
callable.block_regions(dd.get_variant_regions(data), bam_file, ref_file, data)
data["regions"] = {"nblock": nblock_bed, "callable": dd.get_variant_regions(data),
"sample_callable": dd.get_variant_regions(data)}
return [[data]] | [
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223,720 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _merge_out_from_infiles | def _merge_out_from_infiles(in_files):
"""Generate output merged file name from set of input files.
Handles non-shared filesystems where we don't know output path when setting
up split parts.
"""
fname = os.path.commonprefix([os.path.basename(f) for f in in_files])
while fname.endswith(("-", "_", ".")):
fname = fname[:-1]
ext = os.path.splitext(in_files[0])[-1]
dirname = os.path.dirname(in_files[0])
while dirname.endswith(("split", "merge")):
dirname = os.path.dirname(dirname)
return os.path.join(dirname, "%s%s" % (fname, ext)) | python | def _merge_out_from_infiles(in_files):
"""Generate output merged file name from set of input files.
Handles non-shared filesystems where we don't know output path when setting
up split parts.
"""
fname = os.path.commonprefix([os.path.basename(f) for f in in_files])
while fname.endswith(("-", "_", ".")):
fname = fname[:-1]
ext = os.path.splitext(in_files[0])[-1]
dirname = os.path.dirname(in_files[0])
while dirname.endswith(("split", "merge")):
dirname = os.path.dirname(dirname)
return os.path.join(dirname, "%s%s" % (fname, ext)) | [
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223,721 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | delayed_bam_merge | def delayed_bam_merge(data):
"""Perform a merge on previously prepped files, delayed in processing.
Handles merging of associated split read and discordant files if present.
"""
if data.get("combine"):
assert len(data["combine"].keys()) == 1
file_key = list(data["combine"].keys())[0]
extras = []
for x in data["combine"][file_key].get("extras", []):
if isinstance(x, (list, tuple)):
extras.extend(x)
else:
extras.append(x)
if file_key in data:
extras.append(data[file_key])
in_files = sorted(list(set(extras)))
out_file = tz.get_in(["combine", file_key, "out"], data, _merge_out_from_infiles(in_files))
sup_exts = data.get(file_key + "_plus", {}).keys()
for ext in list(sup_exts) + [""]:
merged_file = None
if os.path.exists(utils.append_stem(out_file, "-" + ext)):
cur_out_file, cur_in_files = out_file, []
if ext:
cur_in_files = list(filter(os.path.exists, (utils.append_stem(f, "-" + ext) for f in in_files)))
cur_out_file = utils.append_stem(out_file, "-" + ext) if len(cur_in_files) > 0 else None
else:
cur_in_files, cur_out_file = in_files, out_file
if cur_out_file:
config = copy.deepcopy(data["config"])
if len(cur_in_files) > 0:
merged_file = merge_bam_files(cur_in_files, os.path.dirname(cur_out_file), data,
out_file=cur_out_file)
else:
assert os.path.exists(cur_out_file)
merged_file = cur_out_file
if merged_file:
if ext:
data[file_key + "_plus"][ext] = merged_file
else:
data[file_key] = merged_file
data.pop("region", None)
data.pop("combine", None)
return [[data]] | python | def delayed_bam_merge(data):
"""Perform a merge on previously prepped files, delayed in processing.
Handles merging of associated split read and discordant files if present.
"""
if data.get("combine"):
assert len(data["combine"].keys()) == 1
file_key = list(data["combine"].keys())[0]
extras = []
for x in data["combine"][file_key].get("extras", []):
if isinstance(x, (list, tuple)):
extras.extend(x)
else:
extras.append(x)
if file_key in data:
extras.append(data[file_key])
in_files = sorted(list(set(extras)))
out_file = tz.get_in(["combine", file_key, "out"], data, _merge_out_from_infiles(in_files))
sup_exts = data.get(file_key + "_plus", {}).keys()
for ext in list(sup_exts) + [""]:
merged_file = None
if os.path.exists(utils.append_stem(out_file, "-" + ext)):
cur_out_file, cur_in_files = out_file, []
if ext:
cur_in_files = list(filter(os.path.exists, (utils.append_stem(f, "-" + ext) for f in in_files)))
cur_out_file = utils.append_stem(out_file, "-" + ext) if len(cur_in_files) > 0 else None
else:
cur_in_files, cur_out_file = in_files, out_file
if cur_out_file:
config = copy.deepcopy(data["config"])
if len(cur_in_files) > 0:
merged_file = merge_bam_files(cur_in_files, os.path.dirname(cur_out_file), data,
out_file=cur_out_file)
else:
assert os.path.exists(cur_out_file)
merged_file = cur_out_file
if merged_file:
if ext:
data[file_key + "_plus"][ext] = merged_file
else:
data[file_key] = merged_file
data.pop("region", None)
data.pop("combine", None)
return [[data]] | [
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223,722 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | merge_split_alignments | def merge_split_alignments(data):
"""Merge split BAM inputs generated by common workflow language runs.
"""
data = utils.to_single_data(data)
data = _merge_align_bams(data)
data = _merge_hla_fastq_inputs(data)
return [[data]] | python | def merge_split_alignments(data):
"""Merge split BAM inputs generated by common workflow language runs.
"""
data = utils.to_single_data(data)
data = _merge_align_bams(data)
data = _merge_hla_fastq_inputs(data)
return [[data]] | [
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223,723 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _merge_align_bams | def _merge_align_bams(data):
"""Merge multiple alignment BAMs, including split and discordant reads.
"""
for key in (["work_bam"], ["work_bam_plus", "disc"], ["work_bam_plus", "sr"], ["umi_bam"]):
in_files = tz.get_in(key, data, [])
if not isinstance(in_files, (list, tuple)):
in_files = [in_files]
in_files = [x for x in in_files if x and x != "None"]
if in_files:
ext = "-%s" % key[-1] if len(key) > 1 else ""
out_file = os.path.join(dd.get_work_dir(data), "align", dd.get_sample_name(data),
"%s-sort%s.bam" % (dd.get_sample_name(data), ext))
merged_file = merge_bam_files(in_files, utils.safe_makedir(os.path.dirname(out_file)),
data, out_file=out_file)
data = tz.update_in(data, key, lambda x: merged_file)
else:
data = tz.update_in(data, key, lambda x: None)
if "align_bam" in data and "work_bam" in data:
data["align_bam"] = data["work_bam"]
return data | python | def _merge_align_bams(data):
"""Merge multiple alignment BAMs, including split and discordant reads.
"""
for key in (["work_bam"], ["work_bam_plus", "disc"], ["work_bam_plus", "sr"], ["umi_bam"]):
in_files = tz.get_in(key, data, [])
if not isinstance(in_files, (list, tuple)):
in_files = [in_files]
in_files = [x for x in in_files if x and x != "None"]
if in_files:
ext = "-%s" % key[-1] if len(key) > 1 else ""
out_file = os.path.join(dd.get_work_dir(data), "align", dd.get_sample_name(data),
"%s-sort%s.bam" % (dd.get_sample_name(data), ext))
merged_file = merge_bam_files(in_files, utils.safe_makedir(os.path.dirname(out_file)),
data, out_file=out_file)
data = tz.update_in(data, key, lambda x: merged_file)
else:
data = tz.update_in(data, key, lambda x: None)
if "align_bam" in data and "work_bam" in data:
data["align_bam"] = data["work_bam"]
return data | [
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223,724 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | _merge_hla_fastq_inputs | def _merge_hla_fastq_inputs(data):
"""Merge HLA inputs from a split initial alignment.
"""
hla_key = ["hla", "fastq"]
hla_sample_files = [x for x in (tz.get_in(hla_key, data) or []) if x and x != "None"]
merged_hlas = None
if hla_sample_files:
out_files = collections.defaultdict(list)
for hla_file in utils.flatten(hla_sample_files):
rehla = re.search(r".hla.(?P<hlatype>[\w-]+).fq", hla_file)
if rehla:
hlatype = rehla.group("hlatype")
out_files[hlatype].append(hla_file)
if len(out_files) > 0:
hla_outdir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data), "hla"))
merged_hlas = []
for hlatype, files in out_files.items():
out_file = os.path.join(hla_outdir, "%s-%s.fq" % (dd.get_sample_name(data), hlatype))
optitype.combine_hla_fqs([(hlatype, f) for f in files], out_file, data)
merged_hlas.append(out_file)
data = tz.update_in(data, hla_key, lambda x: merged_hlas)
return data | python | def _merge_hla_fastq_inputs(data):
"""Merge HLA inputs from a split initial alignment.
"""
hla_key = ["hla", "fastq"]
hla_sample_files = [x for x in (tz.get_in(hla_key, data) or []) if x and x != "None"]
merged_hlas = None
if hla_sample_files:
out_files = collections.defaultdict(list)
for hla_file in utils.flatten(hla_sample_files):
rehla = re.search(r".hla.(?P<hlatype>[\w-]+).fq", hla_file)
if rehla:
hlatype = rehla.group("hlatype")
out_files[hlatype].append(hla_file)
if len(out_files) > 0:
hla_outdir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data), "hla"))
merged_hlas = []
for hlatype, files in out_files.items():
out_file = os.path.join(hla_outdir, "%s-%s.fq" % (dd.get_sample_name(data), hlatype))
optitype.combine_hla_fqs([(hlatype, f) for f in files], out_file, data)
merged_hlas.append(out_file)
data = tz.update_in(data, hla_key, lambda x: merged_hlas)
return data | [
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223,725 | bcbio/bcbio-nextgen | bcbio/pipeline/sample.py | prepare_bcbio_samples | def prepare_bcbio_samples(sample):
"""
Function that will use specific function to merge input files
"""
logger.info("Preparing %s files %s to merge into %s." % (sample['name'], sample['files'], sample['out_file']))
if sample['fn'] == "fq_merge":
out_file = fq_merge(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "bam_merge":
out_file = bam_merge(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "query_gsm":
out_file = query_gsm(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "query_srr":
out_file = query_srr(sample['files'], sample['out_file'], sample['config'])
sample['out_file'] = out_file
return [sample] | python | def prepare_bcbio_samples(sample):
"""
Function that will use specific function to merge input files
"""
logger.info("Preparing %s files %s to merge into %s." % (sample['name'], sample['files'], sample['out_file']))
if sample['fn'] == "fq_merge":
out_file = fq_merge(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "bam_merge":
out_file = bam_merge(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "query_gsm":
out_file = query_gsm(sample['files'], sample['out_file'], sample['config'])
elif sample['fn'] == "query_srr":
out_file = query_srr(sample['files'], sample['out_file'], sample['config'])
sample['out_file'] = out_file
return [sample] | [
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223,726 | bcbio/bcbio-nextgen | bcbio/heterogeneity/__init__.py | _get_calls | def _get_calls(data, cnv_only=False):
"""Retrieve calls, organized by name, to use for heterogeneity analysis.
"""
cnvs_supported = set(["cnvkit", "battenberg"])
out = {}
for sv in data.get("sv", []):
if not cnv_only or sv["variantcaller"] in cnvs_supported:
out[sv["variantcaller"]] = sv
return out | python | def _get_calls(data, cnv_only=False):
"""Retrieve calls, organized by name, to use for heterogeneity analysis.
"""
cnvs_supported = set(["cnvkit", "battenberg"])
out = {}
for sv in data.get("sv", []):
if not cnv_only or sv["variantcaller"] in cnvs_supported:
out[sv["variantcaller"]] = sv
return out | [
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223,727 | bcbio/bcbio-nextgen | bcbio/heterogeneity/__init__.py | get_variants | def get_variants(data, include_germline=False):
"""Retrieve set of variant calls to use for heterogeneity analysis.
"""
data = utils.deepish_copy(data)
supported = ["precalled", "vardict", "vardict-java", "vardict-perl",
"freebayes", "octopus", "strelka2"]
# Right now mutect2 and mutect do not provide heterozygous germline calls
# to be useful https://github.com/bcbio/bcbio-nextgen/issues/2464
# supported += ["mutect2", "mutect"]
if include_germline:
supported.insert(1, "gatk-haplotype")
out = []
# CWL based input
if isinstance(data.get("variants"), dict) and "samples" in data["variants"]:
cur_vs = []
# Unpack single sample list of files
if (isinstance(data["variants"]["samples"], (list, tuple)) and
len(data["variants"]["samples"]) == 1 and isinstance(data["variants"]["samples"][0], (list, tuple))):
data["variants"]["samples"] = data["variants"]["samples"][0]
for fname in data["variants"]["samples"]:
variantcaller = utils.splitext_plus(os.path.basename(fname))[0]
variantcaller = variantcaller.replace(dd.get_sample_name(data) + "-", "")
for batch in dd.get_batches(data):
variantcaller = variantcaller.replace(batch + "-", "")
cur_vs.append({"vrn_file": fname, "variantcaller": variantcaller})
data["variants"] = cur_vs
for v in data.get("variants", []):
if v["variantcaller"] in supported and v.get("vrn_file"):
out.append((supported.index(v["variantcaller"]), v))
out.sort()
return [xs[1] for xs in out] | python | def get_variants(data, include_germline=False):
"""Retrieve set of variant calls to use for heterogeneity analysis.
"""
data = utils.deepish_copy(data)
supported = ["precalled", "vardict", "vardict-java", "vardict-perl",
"freebayes", "octopus", "strelka2"]
# Right now mutect2 and mutect do not provide heterozygous germline calls
# to be useful https://github.com/bcbio/bcbio-nextgen/issues/2464
# supported += ["mutect2", "mutect"]
if include_germline:
supported.insert(1, "gatk-haplotype")
out = []
# CWL based input
if isinstance(data.get("variants"), dict) and "samples" in data["variants"]:
cur_vs = []
# Unpack single sample list of files
if (isinstance(data["variants"]["samples"], (list, tuple)) and
len(data["variants"]["samples"]) == 1 and isinstance(data["variants"]["samples"][0], (list, tuple))):
data["variants"]["samples"] = data["variants"]["samples"][0]
for fname in data["variants"]["samples"]:
variantcaller = utils.splitext_plus(os.path.basename(fname))[0]
variantcaller = variantcaller.replace(dd.get_sample_name(data) + "-", "")
for batch in dd.get_batches(data):
variantcaller = variantcaller.replace(batch + "-", "")
cur_vs.append({"vrn_file": fname, "variantcaller": variantcaller})
data["variants"] = cur_vs
for v in data.get("variants", []):
if v["variantcaller"] in supported and v.get("vrn_file"):
out.append((supported.index(v["variantcaller"]), v))
out.sort()
return [xs[1] for xs in out] | [
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223,728 | bcbio/bcbio-nextgen | bcbio/heterogeneity/__init__.py | _ready_for_het_analysis | def _ready_for_het_analysis(items):
"""Check if a sample has input information for heterogeneity analysis.
We currently require a tumor/normal sample containing both CNV and variant calls.
"""
paired = vcfutils.get_paired_bams([dd.get_align_bam(d) for d in items], items)
has_het = any(dd.get_hetcaller(d) for d in items)
if has_het and paired:
return get_variants(paired.tumor_data) and _get_calls(paired.tumor_data, cnv_only=True) | python | def _ready_for_het_analysis(items):
"""Check if a sample has input information for heterogeneity analysis.
We currently require a tumor/normal sample containing both CNV and variant calls.
"""
paired = vcfutils.get_paired_bams([dd.get_align_bam(d) for d in items], items)
has_het = any(dd.get_hetcaller(d) for d in items)
if has_het and paired:
return get_variants(paired.tumor_data) and _get_calls(paired.tumor_data, cnv_only=True) | [
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223,729 | bcbio/bcbio-nextgen | bcbio/heterogeneity/__init__.py | run | def run(items, run_parallel):
"""Top level entry point for calculating heterogeneity, handles organization and job distribution.
"""
to_process = []
extras = []
for batch, cur_items in _group_by_batches(items).items():
if _ready_for_het_analysis(cur_items):
to_process.append((batch, cur_items))
else:
for data in cur_items:
extras.append([data])
processed = run_parallel("heterogeneity_estimate", ([xs, b, xs[0]["config"]] for b, xs in to_process))
return _group_by_sample_and_batch(extras + processed) | python | def run(items, run_parallel):
"""Top level entry point for calculating heterogeneity, handles organization and job distribution.
"""
to_process = []
extras = []
for batch, cur_items in _group_by_batches(items).items():
if _ready_for_het_analysis(cur_items):
to_process.append((batch, cur_items))
else:
for data in cur_items:
extras.append([data])
processed = run_parallel("heterogeneity_estimate", ([xs, b, xs[0]["config"]] for b, xs in to_process))
return _group_by_sample_and_batch(extras + processed) | [
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] | 6a9348c0054ccd5baffd22f1bb7d0422f6978b20 | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/heterogeneity/__init__.py#L122-L134 |
223,730 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | create_inputs | def create_inputs(data):
"""Index input reads and prepare groups of reads to process concurrently.
Allows parallelization of alignment beyond processors available on a single
machine. Prepares a bgzip and grabix indexed file for retrieving sections
of files.
"""
from bcbio.pipeline import sample
data = cwlutils.normalize_missing(data)
aligner = tz.get_in(("config", "algorithm", "aligner"), data)
# CRAM files must be converted to bgzipped fastq, unless not aligning.
# Also need to prep and download remote files.
if not ("files" in data and data["files"] and aligner and (_is_cram_input(data["files"]) or
objectstore.is_remote(data["files"][0]))):
# skip indexing on samples without input files or not doing alignment
if ("files" not in data or not data["files"] or data["files"][0] is None or not aligner):
return [[data]]
data["files_orig"] = data["files"]
data["files"] = prep_fastq_inputs(data["files"], data)
# preparation converts illumina into sanger format
data["config"]["algorithm"]["quality_format"] = "standard"
# Handle any necessary trimming
data = utils.to_single_data(sample.trim_sample(data)[0])
_prep_grabix_indexes(data["files"], data)
data = _set_align_split_size(data)
out = []
if tz.get_in(["config", "algorithm", "align_split_size"], data):
splits = _find_read_splits(data["files"][0], int(data["config"]["algorithm"]["align_split_size"]))
for split in splits:
cur_data = copy.deepcopy(data)
cur_data["align_split"] = split
out.append([cur_data])
else:
out.append([data])
if "output_cwl_keys" in data:
out = cwlutils.samples_to_records([utils.to_single_data(x) for x in out],
["files", "align_split", "config__algorithm__quality_format"])
return out | python | def create_inputs(data):
"""Index input reads and prepare groups of reads to process concurrently.
Allows parallelization of alignment beyond processors available on a single
machine. Prepares a bgzip and grabix indexed file for retrieving sections
of files.
"""
from bcbio.pipeline import sample
data = cwlutils.normalize_missing(data)
aligner = tz.get_in(("config", "algorithm", "aligner"), data)
# CRAM files must be converted to bgzipped fastq, unless not aligning.
# Also need to prep and download remote files.
if not ("files" in data and data["files"] and aligner and (_is_cram_input(data["files"]) or
objectstore.is_remote(data["files"][0]))):
# skip indexing on samples without input files or not doing alignment
if ("files" not in data or not data["files"] or data["files"][0] is None or not aligner):
return [[data]]
data["files_orig"] = data["files"]
data["files"] = prep_fastq_inputs(data["files"], data)
# preparation converts illumina into sanger format
data["config"]["algorithm"]["quality_format"] = "standard"
# Handle any necessary trimming
data = utils.to_single_data(sample.trim_sample(data)[0])
_prep_grabix_indexes(data["files"], data)
data = _set_align_split_size(data)
out = []
if tz.get_in(["config", "algorithm", "align_split_size"], data):
splits = _find_read_splits(data["files"][0], int(data["config"]["algorithm"]["align_split_size"]))
for split in splits:
cur_data = copy.deepcopy(data)
cur_data["align_split"] = split
out.append([cur_data])
else:
out.append([data])
if "output_cwl_keys" in data:
out = cwlutils.samples_to_records([utils.to_single_data(x) for x in out],
["files", "align_split", "config__algorithm__quality_format"])
return out | [
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Allows parallelization of alignment beyond processors available on a single
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223,731 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _set_align_split_size | def _set_align_split_size(data):
"""Set useful align_split_size, generating an estimate if it doesn't exist.
We try to split on larger inputs and avoid too many pieces, aiming for size
chunks of 5Gb or at most 50 maximum splits.
The size estimate used in calculations is 20 million reads for ~5Gb.
For UMI calculations we skip splitting since we're going to align and
re-align after consensus.
For CWL runs, we pick larger split sizes to avoid overhead of staging each chunk.
"""
if cwlutils.is_cwl_run(data):
target_size = 20 # Gb
target_size_reads = 80 # million reads
else:
target_size = 5 # Gb
target_size_reads = 20 # million reads
max_splits = 100 # Avoid too many pieces, causing merge memory problems
val = dd.get_align_split_size(data)
umi_consensus = dd.get_umi_consensus(data)
if val is None:
if not umi_consensus:
total_size = 0 # Gb
# Use original files if we might have reduced the size of our prepped files
input_files = data.get("files_orig", []) if dd.get_save_diskspace(data) else data.get("files", [])
for fname in input_files:
if os.path.exists(fname):
total_size += os.path.getsize(fname) / (1024.0 * 1024.0 * 1024.0)
# Only set if we have files and are bigger than the target size
if total_size > target_size:
data["config"]["algorithm"]["align_split_size"] = \
int(1e6 * _pick_align_split_size(total_size, target_size,
target_size_reads, max_splits))
elif val:
assert not umi_consensus, "Cannot set align_split_size to %s with UMI conensus specified" % val
return data | python | def _set_align_split_size(data):
"""Set useful align_split_size, generating an estimate if it doesn't exist.
We try to split on larger inputs and avoid too many pieces, aiming for size
chunks of 5Gb or at most 50 maximum splits.
The size estimate used in calculations is 20 million reads for ~5Gb.
For UMI calculations we skip splitting since we're going to align and
re-align after consensus.
For CWL runs, we pick larger split sizes to avoid overhead of staging each chunk.
"""
if cwlutils.is_cwl_run(data):
target_size = 20 # Gb
target_size_reads = 80 # million reads
else:
target_size = 5 # Gb
target_size_reads = 20 # million reads
max_splits = 100 # Avoid too many pieces, causing merge memory problems
val = dd.get_align_split_size(data)
umi_consensus = dd.get_umi_consensus(data)
if val is None:
if not umi_consensus:
total_size = 0 # Gb
# Use original files if we might have reduced the size of our prepped files
input_files = data.get("files_orig", []) if dd.get_save_diskspace(data) else data.get("files", [])
for fname in input_files:
if os.path.exists(fname):
total_size += os.path.getsize(fname) / (1024.0 * 1024.0 * 1024.0)
# Only set if we have files and are bigger than the target size
if total_size > target_size:
data["config"]["algorithm"]["align_split_size"] = \
int(1e6 * _pick_align_split_size(total_size, target_size,
target_size_reads, max_splits))
elif val:
assert not umi_consensus, "Cannot set align_split_size to %s with UMI conensus specified" % val
return data | [
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The size estimate used in calculations is 20 million reads for ~5Gb.
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223,732 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _pick_align_split_size | def _pick_align_split_size(total_size, target_size, target_size_reads, max_splits):
"""Do the work of picking an alignment split size for the given criteria.
"""
# Too many pieces, increase our target size to get max_splits pieces
if total_size // target_size > max_splits:
piece_size = total_size // max_splits
return int(piece_size * target_size_reads / target_size)
else:
return int(target_size_reads) | python | def _pick_align_split_size(total_size, target_size, target_size_reads, max_splits):
"""Do the work of picking an alignment split size for the given criteria.
"""
# Too many pieces, increase our target size to get max_splits pieces
if total_size // target_size > max_splits:
piece_size = total_size // max_splits
return int(piece_size * target_size_reads / target_size)
else:
return int(target_size_reads) | [
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223,733 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | split_namedpipe_cls | def split_namedpipe_cls(pair1_file, pair2_file, data):
"""Create a commandline suitable for use as a named pipe with reads in a given region.
"""
if "align_split" in data:
start, end = [int(x) for x in data["align_split"].split("-")]
else:
start, end = None, None
if pair1_file.endswith(".sdf"):
assert not pair2_file, pair2_file
return rtg.to_fastq_apipe_cl(pair1_file, start, end)
else:
out = []
for in_file in pair1_file, pair2_file:
if in_file:
assert _get_grabix_index(in_file), "Need grabix index for %s" % in_file
out.append("<(grabix grab {in_file} {start} {end})".format(**locals()))
else:
out.append(None)
return out | python | def split_namedpipe_cls(pair1_file, pair2_file, data):
"""Create a commandline suitable for use as a named pipe with reads in a given region.
"""
if "align_split" in data:
start, end = [int(x) for x in data["align_split"].split("-")]
else:
start, end = None, None
if pair1_file.endswith(".sdf"):
assert not pair2_file, pair2_file
return rtg.to_fastq_apipe_cl(pair1_file, start, end)
else:
out = []
for in_file in pair1_file, pair2_file:
if in_file:
assert _get_grabix_index(in_file), "Need grabix index for %s" % in_file
out.append("<(grabix grab {in_file} {start} {end})".format(**locals()))
else:
out.append(None)
return out | [
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223,734 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _seqtk_fastq_prep_cl | def _seqtk_fastq_prep_cl(data, in_file=None, read_num=0):
"""Provide a commandline for prep of fastq inputs with seqtk.
Handles fast conversion of fastq quality scores and trimming.
"""
needs_convert = dd.get_quality_format(data).lower() == "illumina"
trim_ends = dd.get_trim_ends(data)
seqtk = config_utils.get_program("seqtk", data["config"])
if in_file:
in_file = objectstore.cl_input(in_file)
else:
in_file = "/dev/stdin"
cmd = ""
if needs_convert:
cmd += "{seqtk} seq -Q64 -V {in_file}".format(**locals())
if trim_ends:
left_trim, right_trim = trim_ends[0:2] if data.get("read_num", read_num) == 0 else trim_ends[2:4]
if left_trim or right_trim:
trim_infile = "/dev/stdin" if needs_convert else in_file
pipe = " | " if needs_convert else ""
cmd += "{pipe}{seqtk} trimfq -b {left_trim} -e {right_trim} {trim_infile}".format(**locals())
return cmd | python | def _seqtk_fastq_prep_cl(data, in_file=None, read_num=0):
"""Provide a commandline for prep of fastq inputs with seqtk.
Handles fast conversion of fastq quality scores and trimming.
"""
needs_convert = dd.get_quality_format(data).lower() == "illumina"
trim_ends = dd.get_trim_ends(data)
seqtk = config_utils.get_program("seqtk", data["config"])
if in_file:
in_file = objectstore.cl_input(in_file)
else:
in_file = "/dev/stdin"
cmd = ""
if needs_convert:
cmd += "{seqtk} seq -Q64 -V {in_file}".format(**locals())
if trim_ends:
left_trim, right_trim = trim_ends[0:2] if data.get("read_num", read_num) == 0 else trim_ends[2:4]
if left_trim or right_trim:
trim_infile = "/dev/stdin" if needs_convert else in_file
pipe = " | " if needs_convert else ""
cmd += "{pipe}{seqtk} trimfq -b {left_trim} -e {right_trim} {trim_infile}".format(**locals())
return cmd | [
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223,735 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | fastq_convert_pipe_cl | def fastq_convert_pipe_cl(in_file, data):
"""Create an anonymous pipe converting Illumina 1.3-1.7 to Sanger.
Uses seqtk: https://github.com/lh3/seqt
"""
cmd = _seqtk_fastq_prep_cl(data, in_file)
if not cmd:
cat_cmd = "zcat" if in_file.endswith(".gz") else "cat"
cmd = cat_cmd + " " + in_file
return "<(%s)" % cmd | python | def fastq_convert_pipe_cl(in_file, data):
"""Create an anonymous pipe converting Illumina 1.3-1.7 to Sanger.
Uses seqtk: https://github.com/lh3/seqt
"""
cmd = _seqtk_fastq_prep_cl(data, in_file)
if not cmd:
cat_cmd = "zcat" if in_file.endswith(".gz") else "cat"
cmd = cat_cmd + " " + in_file
return "<(%s)" % cmd | [
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223,736 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | parallel_multiplier | def parallel_multiplier(items):
"""Determine if we will be parallelizing items during processing.
"""
multiplier = 1
for data in (x[0] for x in items):
if (tz.get_in(["config", "algorithm", "align_split_size"], data) is not False and
tz.get_in(["algorithm", "align_split_size"], data) is not False):
multiplier += 50
return multiplier | python | def parallel_multiplier(items):
"""Determine if we will be parallelizing items during processing.
"""
multiplier = 1
for data in (x[0] for x in items):
if (tz.get_in(["config", "algorithm", "align_split_size"], data) is not False and
tz.get_in(["algorithm", "align_split_size"], data) is not False):
multiplier += 50
return multiplier | [
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223,737 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | setup_combine | def setup_combine(final_file, data):
"""Setup the data and outputs to allow merging data back together.
"""
if "align_split" not in data:
return final_file, data
align_dir = os.path.dirname(final_file)
base, ext = os.path.splitext(os.path.basename(final_file))
start, end = [int(x) for x in data["align_split"].split("-")]
out_file = os.path.join(utils.safe_makedir(os.path.join(align_dir, "split")),
"%s-%s_%s%s" % (base, start, end, ext))
data["combine"] = {"work_bam": {"out": final_file, "extras": []}}
return out_file, data | python | def setup_combine(final_file, data):
"""Setup the data and outputs to allow merging data back together.
"""
if "align_split" not in data:
return final_file, data
align_dir = os.path.dirname(final_file)
base, ext = os.path.splitext(os.path.basename(final_file))
start, end = [int(x) for x in data["align_split"].split("-")]
out_file = os.path.join(utils.safe_makedir(os.path.join(align_dir, "split")),
"%s-%s_%s%s" % (base, start, end, ext))
data["combine"] = {"work_bam": {"out": final_file, "extras": []}}
return out_file, data | [
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223,738 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | merge_split_alignments | def merge_split_alignments(samples, run_parallel):
"""Manage merging split alignments back into a final working BAM file.
Perform de-duplication on the final merged file.
"""
ready = []
file_key = "work_bam"
to_merge = collections.defaultdict(list)
for data in (xs[0] for xs in samples):
if data.get("combine"):
out_key = tz.get_in(["combine", file_key, "out"], data)
if not out_key:
out_key = data["rgnames"]["lane"]
to_merge[out_key].append(data)
else:
ready.append([data])
ready_merge = []
hla_merges = []
for mgroup in to_merge.values():
cur_data = mgroup[0]
del cur_data["align_split"]
for x in mgroup[1:]:
cur_data["combine"][file_key]["extras"].append(x[file_key])
ready_merge.append([cur_data])
cur_hla = None
for d in mgroup:
hla_files = tz.get_in(["hla", "fastq"], d)
if hla_files:
if not cur_hla:
cur_hla = {"rgnames": {"sample": dd.get_sample_name(cur_data)},
"config": cur_data["config"], "dirs": cur_data["dirs"],
"hla": {"fastq": []}}
cur_hla["hla"]["fastq"].append(hla_files)
if cur_hla:
hla_merges.append([cur_hla])
if not tz.get_in(["config", "algorithm", "kraken"], data):
# kraken requires fasta filenames from data['files'] as input.
# We don't want to remove those files if kraken qc is required.
_save_fastq_space(samples)
merged = run_parallel("delayed_bam_merge", ready_merge)
hla_merge_raw = run_parallel("merge_split_alignments", hla_merges)
hla_merges = {}
for hla_merge in [x[0] for x in hla_merge_raw]:
hla_merges[dd.get_sample_name(hla_merge)] = tz.get_in(["hla", "fastq"], hla_merge)
# Add stable 'align_bam' target to use for retrieving raw alignment
out = []
for data in [x[0] for x in merged + ready]:
if data.get("work_bam"):
data["align_bam"] = data["work_bam"]
if dd.get_sample_name(data) in hla_merges:
data["hla"]["fastq"] = hla_merges[dd.get_sample_name(data)]
else:
hla_files = glob.glob(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data), "hla", "*.fq"))
if hla_files:
data["hla"]["fastq"] = hla_files
out.append([data])
return out | python | def merge_split_alignments(samples, run_parallel):
"""Manage merging split alignments back into a final working BAM file.
Perform de-duplication on the final merged file.
"""
ready = []
file_key = "work_bam"
to_merge = collections.defaultdict(list)
for data in (xs[0] for xs in samples):
if data.get("combine"):
out_key = tz.get_in(["combine", file_key, "out"], data)
if not out_key:
out_key = data["rgnames"]["lane"]
to_merge[out_key].append(data)
else:
ready.append([data])
ready_merge = []
hla_merges = []
for mgroup in to_merge.values():
cur_data = mgroup[0]
del cur_data["align_split"]
for x in mgroup[1:]:
cur_data["combine"][file_key]["extras"].append(x[file_key])
ready_merge.append([cur_data])
cur_hla = None
for d in mgroup:
hla_files = tz.get_in(["hla", "fastq"], d)
if hla_files:
if not cur_hla:
cur_hla = {"rgnames": {"sample": dd.get_sample_name(cur_data)},
"config": cur_data["config"], "dirs": cur_data["dirs"],
"hla": {"fastq": []}}
cur_hla["hla"]["fastq"].append(hla_files)
if cur_hla:
hla_merges.append([cur_hla])
if not tz.get_in(["config", "algorithm", "kraken"], data):
# kraken requires fasta filenames from data['files'] as input.
# We don't want to remove those files if kraken qc is required.
_save_fastq_space(samples)
merged = run_parallel("delayed_bam_merge", ready_merge)
hla_merge_raw = run_parallel("merge_split_alignments", hla_merges)
hla_merges = {}
for hla_merge in [x[0] for x in hla_merge_raw]:
hla_merges[dd.get_sample_name(hla_merge)] = tz.get_in(["hla", "fastq"], hla_merge)
# Add stable 'align_bam' target to use for retrieving raw alignment
out = []
for data in [x[0] for x in merged + ready]:
if data.get("work_bam"):
data["align_bam"] = data["work_bam"]
if dd.get_sample_name(data) in hla_merges:
data["hla"]["fastq"] = hla_merges[dd.get_sample_name(data)]
else:
hla_files = glob.glob(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data), "hla", "*.fq"))
if hla_files:
data["hla"]["fastq"] = hla_files
out.append([data])
return out | [
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223,739 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _save_fastq_space | def _save_fastq_space(items):
"""Potentially save fastq space prior to merging, since alignments done.
"""
to_cleanup = {}
for data in (utils.to_single_data(x) for x in items):
for fname in data.get("files", []):
if os.path.realpath(fname).startswith(dd.get_work_dir(data)):
to_cleanup[fname] = data["config"]
for fname, config in to_cleanup.items():
utils.save_diskspace(fname, "Cleanup prep files after alignment finished", config) | python | def _save_fastq_space(items):
"""Potentially save fastq space prior to merging, since alignments done.
"""
to_cleanup = {}
for data in (utils.to_single_data(x) for x in items):
for fname in data.get("files", []):
if os.path.realpath(fname).startswith(dd.get_work_dir(data)):
to_cleanup[fname] = data["config"]
for fname, config in to_cleanup.items():
utils.save_diskspace(fname, "Cleanup prep files after alignment finished", config) | [
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223,740 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | total_reads_from_grabix | def total_reads_from_grabix(in_file):
"""Retrieve total reads in a fastq file from grabix index.
"""
gbi_file = _get_grabix_index(in_file)
if gbi_file:
with open(gbi_file) as in_handle:
next(in_handle) # throw away
num_lines = int(next(in_handle).strip())
assert num_lines % 4 == 0, "Expected lines to be multiple of 4"
return num_lines // 4
else:
return 0 | python | def total_reads_from_grabix(in_file):
"""Retrieve total reads in a fastq file from grabix index.
"""
gbi_file = _get_grabix_index(in_file)
if gbi_file:
with open(gbi_file) as in_handle:
next(in_handle) # throw away
num_lines = int(next(in_handle).strip())
assert num_lines % 4 == 0, "Expected lines to be multiple of 4"
return num_lines // 4
else:
return 0 | [
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223,741 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _find_read_splits | def _find_read_splits(in_file, split_size):
"""Determine sections of fastq files to process in splits.
Assumes a 4 line order to input files (name, read, name, quality).
grabix is 1-based inclusive, so return coordinates in that format.
"""
num_lines = total_reads_from_grabix(in_file) * 4
assert num_lines and num_lines > 0, "Did not find grabix index reads: %s %s" % (in_file, num_lines)
split_lines = split_size * 4
chunks = []
last = 1
for chunki in range(num_lines // split_lines + min(1, num_lines % split_lines)):
new = last + split_lines - 1
chunks.append((last, min(new, num_lines)))
last = new + 1
return ["%s-%s" % (s, e) for s, e in chunks] | python | def _find_read_splits(in_file, split_size):
"""Determine sections of fastq files to process in splits.
Assumes a 4 line order to input files (name, read, name, quality).
grabix is 1-based inclusive, so return coordinates in that format.
"""
num_lines = total_reads_from_grabix(in_file) * 4
assert num_lines and num_lines > 0, "Did not find grabix index reads: %s %s" % (in_file, num_lines)
split_lines = split_size * 4
chunks = []
last = 1
for chunki in range(num_lines // split_lines + min(1, num_lines % split_lines)):
new = last + split_lines - 1
chunks.append((last, min(new, num_lines)))
last = new + 1
return ["%s-%s" % (s, e) for s, e in chunks] | [
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223,742 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _ready_gzip_fastq | def _ready_gzip_fastq(in_files, data, require_bgzip=False):
"""Check if we have gzipped fastq and don't need format conversion or splitting.
Avoid forcing bgzip if we don't need indexed files.
"""
all_gzipped = all([not x or x.endswith(".gz") for x in in_files])
if require_bgzip and all_gzipped:
all_gzipped = all([not x or not _check_gzipped_input(x, data)[0] for x in in_files])
needs_convert = dd.get_quality_format(data).lower() == "illumina"
needs_trim = dd.get_trim_ends(data)
do_splitting = dd.get_align_split_size(data) is not False
return (all_gzipped and not needs_convert and not do_splitting and
not objectstore.is_remote(in_files[0]) and not needs_trim and not get_downsample_params(data)) | python | def _ready_gzip_fastq(in_files, data, require_bgzip=False):
"""Check if we have gzipped fastq and don't need format conversion or splitting.
Avoid forcing bgzip if we don't need indexed files.
"""
all_gzipped = all([not x or x.endswith(".gz") for x in in_files])
if require_bgzip and all_gzipped:
all_gzipped = all([not x or not _check_gzipped_input(x, data)[0] for x in in_files])
needs_convert = dd.get_quality_format(data).lower() == "illumina"
needs_trim = dd.get_trim_ends(data)
do_splitting = dd.get_align_split_size(data) is not False
return (all_gzipped and not needs_convert and not do_splitting and
not objectstore.is_remote(in_files[0]) and not needs_trim and not get_downsample_params(data)) | [
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223,743 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | prep_fastq_inputs | def prep_fastq_inputs(in_files, data):
"""Prepare bgzipped fastq inputs
"""
if len(in_files) == 1 and _is_bam_input(in_files):
out = _bgzip_from_bam(in_files[0], data["dirs"], data)
elif len(in_files) == 1 and _is_cram_input(in_files):
out = _bgzip_from_cram(in_files[0], data["dirs"], data)
elif len(in_files) in [1, 2] and _ready_gzip_fastq(in_files, data):
out = _symlink_in_files(in_files, data)
else:
if len(in_files) > 2:
fpairs = fastq.combine_pairs(in_files)
pair_types = set([len(xs) for xs in fpairs])
assert len(pair_types) == 1
fpairs.sort(key=lambda x: os.path.basename(x[0]))
organized = [[xs[0] for xs in fpairs]]
if len(fpairs[0]) > 1:
organized.append([xs[1] for xs in fpairs])
in_files = organized
parallel = {"type": "local", "num_jobs": len(in_files),
"cores_per_job": max(1, data["config"]["algorithm"]["num_cores"] // len(in_files))}
inputs = [{"in_file": x, "read_num": i, "dirs": data["dirs"], "config": data["config"],
"is_cwl": "cwl_keys" in data,
"rgnames": data["rgnames"]}
for i, x in enumerate(in_files) if x]
out = run_multicore(_bgzip_from_fastq_parallel, [[d] for d in inputs], data["config"], parallel)
return out | python | def prep_fastq_inputs(in_files, data):
"""Prepare bgzipped fastq inputs
"""
if len(in_files) == 1 and _is_bam_input(in_files):
out = _bgzip_from_bam(in_files[0], data["dirs"], data)
elif len(in_files) == 1 and _is_cram_input(in_files):
out = _bgzip_from_cram(in_files[0], data["dirs"], data)
elif len(in_files) in [1, 2] and _ready_gzip_fastq(in_files, data):
out = _symlink_in_files(in_files, data)
else:
if len(in_files) > 2:
fpairs = fastq.combine_pairs(in_files)
pair_types = set([len(xs) for xs in fpairs])
assert len(pair_types) == 1
fpairs.sort(key=lambda x: os.path.basename(x[0]))
organized = [[xs[0] for xs in fpairs]]
if len(fpairs[0]) > 1:
organized.append([xs[1] for xs in fpairs])
in_files = organized
parallel = {"type": "local", "num_jobs": len(in_files),
"cores_per_job": max(1, data["config"]["algorithm"]["num_cores"] // len(in_files))}
inputs = [{"in_file": x, "read_num": i, "dirs": data["dirs"], "config": data["config"],
"is_cwl": "cwl_keys" in data,
"rgnames": data["rgnames"]}
for i, x in enumerate(in_files) if x]
out = run_multicore(_bgzip_from_fastq_parallel, [[d] for d in inputs], data["config"], parallel)
return out | [
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223,744 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _symlink_or_copy_grabix | def _symlink_or_copy_grabix(in_file, out_file, data):
"""We cannot symlink in CWL, but may be able to use inputs or copy
"""
if cwlutils.is_cwl_run(data):
# Has grabix indexes, we're okay to go
if utils.file_exists(in_file + ".gbi"):
out_file = in_file
else:
utils.copy_plus(in_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return out_file | python | def _symlink_or_copy_grabix(in_file, out_file, data):
"""We cannot symlink in CWL, but may be able to use inputs or copy
"""
if cwlutils.is_cwl_run(data):
# Has grabix indexes, we're okay to go
if utils.file_exists(in_file + ".gbi"):
out_file = in_file
else:
utils.copy_plus(in_file, out_file)
else:
utils.symlink_plus(in_file, out_file)
return out_file | [
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223,745 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _prep_grabix_indexes | def _prep_grabix_indexes(in_files, data):
"""Parallel preparation of grabix indexes for files.
"""
# if we have gzipped but not bgzipped, add a fake index for CWL support
# Also skips bgzip indexing if we don't need alignment splitting
if _ready_gzip_fastq(in_files, data) and (not _ready_gzip_fastq(in_files, data, require_bgzip=True) or
dd.get_align_split_size(data) is False):
for in_file in in_files:
if not utils.file_exists(in_file + ".gbi"):
with file_transaction(data, in_file + ".gbi") as tx_gbi_file:
with open(tx_gbi_file, "w") as out_handle:
out_handle.write("Not grabix indexed; index added for compatibility.\n")
else:
items = [[{"bgzip_file": x, "config": copy.deepcopy(data["config"])}] for x in in_files if x]
run_multicore(_grabix_index, items, data["config"])
return data | python | def _prep_grabix_indexes(in_files, data):
"""Parallel preparation of grabix indexes for files.
"""
# if we have gzipped but not bgzipped, add a fake index for CWL support
# Also skips bgzip indexing if we don't need alignment splitting
if _ready_gzip_fastq(in_files, data) and (not _ready_gzip_fastq(in_files, data, require_bgzip=True) or
dd.get_align_split_size(data) is False):
for in_file in in_files:
if not utils.file_exists(in_file + ".gbi"):
with file_transaction(data, in_file + ".gbi") as tx_gbi_file:
with open(tx_gbi_file, "w") as out_handle:
out_handle.write("Not grabix indexed; index added for compatibility.\n")
else:
items = [[{"bgzip_file": x, "config": copy.deepcopy(data["config"])}] for x in in_files if x]
run_multicore(_grabix_index, items, data["config"])
return data | [
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223,746 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _bgzip_from_cram | def _bgzip_from_cram(cram_file, dirs, data):
"""Create bgzipped fastq files from an input CRAM file in regions of interest.
Returns a list with a single file, for single end CRAM files, or two
files for paired end input.
"""
import pybedtools
region_file = (tz.get_in(["config", "algorithm", "variant_regions"], data)
if tz.get_in(["config", "algorithm", "coverage_interval"], data)
in ["regional", "exome", "amplicon"]
else None)
if region_file:
regions = ["%s:%s-%s" % tuple(r[:3]) for r in pybedtools.BedTool(region_file)]
else:
regions = [None]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep"))
out_s, out_p1, out_p2 = [os.path.join(work_dir, "%s-%s.fq.gz" %
(utils.splitext_plus(os.path.basename(cram_file))[0], fext))
for fext in ["s1", "p1", "p2"]]
if (not utils.file_exists(out_s) and
(not utils.file_exists(out_p1) or not utils.file_exists(out_p2))):
cram.index(cram_file, data["config"])
fastqs, part_dir = _cram_to_fastq_regions(regions, cram_file, dirs, data)
if len(fastqs[0]) == 1:
with file_transaction(data, out_s) as tx_out_file:
_merge_and_bgzip([xs[0] for xs in fastqs], tx_out_file, out_s)
else:
for i, out_file in enumerate([out_p1, out_p2]):
if not utils.file_exists(out_file):
ext = "/%s" % (i + 1)
with file_transaction(data, out_file) as tx_out_file:
_merge_and_bgzip([xs[i] for xs in fastqs], tx_out_file, out_file, ext)
shutil.rmtree(part_dir)
if utils.file_exists(out_p1):
return [out_p1, out_p2]
else:
assert utils.file_exists(out_s)
return [out_s] | python | def _bgzip_from_cram(cram_file, dirs, data):
"""Create bgzipped fastq files from an input CRAM file in regions of interest.
Returns a list with a single file, for single end CRAM files, or two
files for paired end input.
"""
import pybedtools
region_file = (tz.get_in(["config", "algorithm", "variant_regions"], data)
if tz.get_in(["config", "algorithm", "coverage_interval"], data)
in ["regional", "exome", "amplicon"]
else None)
if region_file:
regions = ["%s:%s-%s" % tuple(r[:3]) for r in pybedtools.BedTool(region_file)]
else:
regions = [None]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep"))
out_s, out_p1, out_p2 = [os.path.join(work_dir, "%s-%s.fq.gz" %
(utils.splitext_plus(os.path.basename(cram_file))[0], fext))
for fext in ["s1", "p1", "p2"]]
if (not utils.file_exists(out_s) and
(not utils.file_exists(out_p1) or not utils.file_exists(out_p2))):
cram.index(cram_file, data["config"])
fastqs, part_dir = _cram_to_fastq_regions(regions, cram_file, dirs, data)
if len(fastqs[0]) == 1:
with file_transaction(data, out_s) as tx_out_file:
_merge_and_bgzip([xs[0] for xs in fastqs], tx_out_file, out_s)
else:
for i, out_file in enumerate([out_p1, out_p2]):
if not utils.file_exists(out_file):
ext = "/%s" % (i + 1)
with file_transaction(data, out_file) as tx_out_file:
_merge_and_bgzip([xs[i] for xs in fastqs], tx_out_file, out_file, ext)
shutil.rmtree(part_dir)
if utils.file_exists(out_p1):
return [out_p1, out_p2]
else:
assert utils.file_exists(out_s)
return [out_s] | [
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223,747 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _bgzip_from_cram_sambamba | def _bgzip_from_cram_sambamba(cram_file, dirs, data):
"""Use sambamba to extract from CRAM via regions.
"""
raise NotImplementedError("sambamba doesn't yet support retrieval from CRAM by BED file")
region_file = (tz.get_in(["config", "algorithm", "variant_regions"], data)
if tz.get_in(["config", "algorithm", "coverage_interval"], data) in ["regional", "exome"]
else None)
base_name = utils.splitext_plus(os.path.basename(cram_file))[0]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep",
"%s-parts" % base_name))
f1, f2, o1, o2, si = [os.path.join(work_dir, "%s.fq" % x) for x in ["match1", "match2", "unmatch1", "unmatch2",
"single"]]
ref_file = dd.get_ref_file(data)
region = "-L %s" % region_file if region_file else ""
cmd = ("sambamba view -f bam -l 0 -C {cram_file} -T {ref_file} {region} | "
"bamtofastq F={f1} F2={f2} S={si} O={o1} O2={o2}")
do.run(cmd.format(**locals()), "Convert CRAM to fastq in regions") | python | def _bgzip_from_cram_sambamba(cram_file, dirs, data):
"""Use sambamba to extract from CRAM via regions.
"""
raise NotImplementedError("sambamba doesn't yet support retrieval from CRAM by BED file")
region_file = (tz.get_in(["config", "algorithm", "variant_regions"], data)
if tz.get_in(["config", "algorithm", "coverage_interval"], data) in ["regional", "exome"]
else None)
base_name = utils.splitext_plus(os.path.basename(cram_file))[0]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep",
"%s-parts" % base_name))
f1, f2, o1, o2, si = [os.path.join(work_dir, "%s.fq" % x) for x in ["match1", "match2", "unmatch1", "unmatch2",
"single"]]
ref_file = dd.get_ref_file(data)
region = "-L %s" % region_file if region_file else ""
cmd = ("sambamba view -f bam -l 0 -C {cram_file} -T {ref_file} {region} | "
"bamtofastq F={f1} F2={f2} S={si} O={o1} O2={o2}")
do.run(cmd.format(**locals()), "Convert CRAM to fastq in regions") | [
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223,748 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _cram_to_fastq_regions | def _cram_to_fastq_regions(regions, cram_file, dirs, data):
"""Convert CRAM files to fastq, potentially within sub regions.
Returns multiple fastq files that can be merged back together.
"""
base_name = utils.splitext_plus(os.path.basename(cram_file))[0]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep",
"%s-parts" % base_name))
fnames = run_multicore(_cram_to_fastq_region,
[(cram_file, work_dir, base_name, region, data) for region in regions],
data["config"])
# check if we have paired or single end data
if any(not _is_gzip_empty(p1) for p1, p2, s in fnames):
out = [[p1, p2] for p1, p2, s in fnames]
else:
out = [[s] for p1, p2, s in fnames]
return out, work_dir | python | def _cram_to_fastq_regions(regions, cram_file, dirs, data):
"""Convert CRAM files to fastq, potentially within sub regions.
Returns multiple fastq files that can be merged back together.
"""
base_name = utils.splitext_plus(os.path.basename(cram_file))[0]
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep",
"%s-parts" % base_name))
fnames = run_multicore(_cram_to_fastq_region,
[(cram_file, work_dir, base_name, region, data) for region in regions],
data["config"])
# check if we have paired or single end data
if any(not _is_gzip_empty(p1) for p1, p2, s in fnames):
out = [[p1, p2] for p1, p2, s in fnames]
else:
out = [[s] for p1, p2, s in fnames]
return out, work_dir | [
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223,749 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _cram_to_fastq_region | def _cram_to_fastq_region(cram_file, work_dir, base_name, region, data):
"""Convert CRAM to fastq in a specified region.
"""
ref_file = tz.get_in(["reference", "fasta", "base"], data)
resources = config_utils.get_resources("bamtofastq", data["config"])
cores = tz.get_in(["config", "algorithm", "num_cores"], data, 1)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G")) * cores
rext = "-%s" % region.replace(":", "_").replace("-", "_") if region else "full"
out_s, out_p1, out_p2, out_o1, out_o2 = [os.path.join(work_dir, "%s%s-%s.fq.gz" %
(base_name, rext, fext))
for fext in ["s1", "p1", "p2", "o1", "o2"]]
if not utils.file_exists(out_p1):
with file_transaction(data, out_s, out_p1, out_p2, out_o1, out_o2) as \
(tx_out_s, tx_out_p1, tx_out_p2, tx_out_o1, tx_out_o2):
cram_file = objectstore.cl_input(cram_file)
sortprefix = "%s-sort" % utils.splitext_plus(tx_out_s)[0]
cmd = ("bamtofastq filename={cram_file} inputformat=cram T={sortprefix} "
"gz=1 collate=1 colsbs={max_mem} exclude=SECONDARY,SUPPLEMENTARY "
"F={tx_out_p1} F2={tx_out_p2} S={tx_out_s} O={tx_out_o1} O2={tx_out_o2} "
"reference={ref_file}")
if region:
cmd += " ranges='{region}'"
do.run(cmd.format(**locals()), "CRAM to fastq %s" % region if region else "")
return [[out_p1, out_p2, out_s]] | python | def _cram_to_fastq_region(cram_file, work_dir, base_name, region, data):
"""Convert CRAM to fastq in a specified region.
"""
ref_file = tz.get_in(["reference", "fasta", "base"], data)
resources = config_utils.get_resources("bamtofastq", data["config"])
cores = tz.get_in(["config", "algorithm", "num_cores"], data, 1)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G")) * cores
rext = "-%s" % region.replace(":", "_").replace("-", "_") if region else "full"
out_s, out_p1, out_p2, out_o1, out_o2 = [os.path.join(work_dir, "%s%s-%s.fq.gz" %
(base_name, rext, fext))
for fext in ["s1", "p1", "p2", "o1", "o2"]]
if not utils.file_exists(out_p1):
with file_transaction(data, out_s, out_p1, out_p2, out_o1, out_o2) as \
(tx_out_s, tx_out_p1, tx_out_p2, tx_out_o1, tx_out_o2):
cram_file = objectstore.cl_input(cram_file)
sortprefix = "%s-sort" % utils.splitext_plus(tx_out_s)[0]
cmd = ("bamtofastq filename={cram_file} inputformat=cram T={sortprefix} "
"gz=1 collate=1 colsbs={max_mem} exclude=SECONDARY,SUPPLEMENTARY "
"F={tx_out_p1} F2={tx_out_p2} S={tx_out_s} O={tx_out_o1} O2={tx_out_o2} "
"reference={ref_file}")
if region:
cmd += " ranges='{region}'"
do.run(cmd.format(**locals()), "CRAM to fastq %s" % region if region else "")
return [[out_p1, out_p2, out_s]] | [
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223,750 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _bgzip_from_bam | def _bgzip_from_bam(bam_file, dirs, data, is_retry=False, output_infix=''):
"""Create bgzipped fastq files from an input BAM file.
"""
# tools
config = data["config"]
bamtofastq = config_utils.get_program("bamtofastq", config)
resources = config_utils.get_resources("bamtofastq", config)
cores = config["algorithm"].get("num_cores", 1)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G")) * cores
bgzip = tools.get_bgzip_cmd(config, is_retry)
# files
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep"))
out_file_1 = os.path.join(work_dir, "%s%s-1.fq.gz" % (os.path.splitext(os.path.basename(bam_file))[0], output_infix))
out_file_2 = out_file_1.replace("-1.fq.gz", "-2.fq.gz")
needs_retry = False
if is_retry or not utils.file_exists(out_file_1):
if not bam.is_paired(bam_file):
out_file_2 = None
with file_transaction(config, out_file_1) as tx_out_file:
for f in [tx_out_file, out_file_1, out_file_2]:
if f and os.path.exists(f):
os.remove(f)
fq1_bgzip_cmd = "%s -c /dev/stdin > %s" % (bgzip, tx_out_file)
prep_cmd = _seqtk_fastq_prep_cl(data, read_num=0)
if prep_cmd:
fq1_bgzip_cmd = prep_cmd + " | " + fq1_bgzip_cmd
sortprefix = "%s-sort" % os.path.splitext(tx_out_file)[0]
if bam.is_paired(bam_file):
prep_cmd = _seqtk_fastq_prep_cl(data, read_num=1)
fq2_bgzip_cmd = "%s -c /dev/stdin > %s" % (bgzip, out_file_2)
if prep_cmd:
fq2_bgzip_cmd = prep_cmd + " | " + fq2_bgzip_cmd
out_str = ("F=>({fq1_bgzip_cmd}) F2=>({fq2_bgzip_cmd}) S=/dev/null O=/dev/null "
"O2=/dev/null collate=1 colsbs={max_mem}")
else:
out_str = "S=>({fq1_bgzip_cmd})"
bam_file = objectstore.cl_input(bam_file)
extra_opts = " ".join([str(x) for x in resources.get("options", [])])
cmd = "{bamtofastq} filename={bam_file} T={sortprefix} {extra_opts} " + out_str
try:
do.run(cmd.format(**locals()), "BAM to bgzipped fastq",
checks=[do.file_reasonable_size(tx_out_file, bam_file)],
log_error=False)
except subprocess.CalledProcessError as msg:
if not is_retry and "deflate failed" in str(msg):
logger.info("bamtofastq deflate IO failure preparing %s. Retrying with single core."
% (bam_file))
needs_retry = True
else:
logger.exception()
raise
if needs_retry:
return _bgzip_from_bam(bam_file, dirs, data, is_retry=True)
else:
return [x for x in [out_file_1, out_file_2] if x is not None and utils.file_exists(x)] | python | def _bgzip_from_bam(bam_file, dirs, data, is_retry=False, output_infix=''):
"""Create bgzipped fastq files from an input BAM file.
"""
# tools
config = data["config"]
bamtofastq = config_utils.get_program("bamtofastq", config)
resources = config_utils.get_resources("bamtofastq", config)
cores = config["algorithm"].get("num_cores", 1)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G")) * cores
bgzip = tools.get_bgzip_cmd(config, is_retry)
# files
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "align_prep"))
out_file_1 = os.path.join(work_dir, "%s%s-1.fq.gz" % (os.path.splitext(os.path.basename(bam_file))[0], output_infix))
out_file_2 = out_file_1.replace("-1.fq.gz", "-2.fq.gz")
needs_retry = False
if is_retry or not utils.file_exists(out_file_1):
if not bam.is_paired(bam_file):
out_file_2 = None
with file_transaction(config, out_file_1) as tx_out_file:
for f in [tx_out_file, out_file_1, out_file_2]:
if f and os.path.exists(f):
os.remove(f)
fq1_bgzip_cmd = "%s -c /dev/stdin > %s" % (bgzip, tx_out_file)
prep_cmd = _seqtk_fastq_prep_cl(data, read_num=0)
if prep_cmd:
fq1_bgzip_cmd = prep_cmd + " | " + fq1_bgzip_cmd
sortprefix = "%s-sort" % os.path.splitext(tx_out_file)[0]
if bam.is_paired(bam_file):
prep_cmd = _seqtk_fastq_prep_cl(data, read_num=1)
fq2_bgzip_cmd = "%s -c /dev/stdin > %s" % (bgzip, out_file_2)
if prep_cmd:
fq2_bgzip_cmd = prep_cmd + " | " + fq2_bgzip_cmd
out_str = ("F=>({fq1_bgzip_cmd}) F2=>({fq2_bgzip_cmd}) S=/dev/null O=/dev/null "
"O2=/dev/null collate=1 colsbs={max_mem}")
else:
out_str = "S=>({fq1_bgzip_cmd})"
bam_file = objectstore.cl_input(bam_file)
extra_opts = " ".join([str(x) for x in resources.get("options", [])])
cmd = "{bamtofastq} filename={bam_file} T={sortprefix} {extra_opts} " + out_str
try:
do.run(cmd.format(**locals()), "BAM to bgzipped fastq",
checks=[do.file_reasonable_size(tx_out_file, bam_file)],
log_error=False)
except subprocess.CalledProcessError as msg:
if not is_retry and "deflate failed" in str(msg):
logger.info("bamtofastq deflate IO failure preparing %s. Retrying with single core."
% (bam_file))
needs_retry = True
else:
logger.exception()
raise
if needs_retry:
return _bgzip_from_bam(bam_file, dirs, data, is_retry=True)
else:
return [x for x in [out_file_1, out_file_2] if x is not None and utils.file_exists(x)] | [
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223,751 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _grabix_index | def _grabix_index(data):
"""Create grabix index of bgzip input file.
grabix does not allow specification of output file, so symlink the original
file into a transactional directory.
"""
in_file = data["bgzip_file"]
config = data["config"]
grabix = config_utils.get_program("grabix", config)
gbi_file = _get_grabix_index(in_file)
# We always build grabix input so we can use it for counting reads and doing downsampling
if not gbi_file or _is_partial_index(gbi_file):
if gbi_file:
utils.remove_safe(gbi_file)
else:
gbi_file = in_file + ".gbi"
with file_transaction(data, gbi_file) as tx_gbi_file:
tx_in_file = os.path.splitext(tx_gbi_file)[0]
utils.symlink_plus(in_file, tx_in_file)
do.run([grabix, "index", tx_in_file], "Index input with grabix: %s" % os.path.basename(in_file))
assert utils.file_exists(gbi_file)
return [gbi_file] | python | def _grabix_index(data):
"""Create grabix index of bgzip input file.
grabix does not allow specification of output file, so symlink the original
file into a transactional directory.
"""
in_file = data["bgzip_file"]
config = data["config"]
grabix = config_utils.get_program("grabix", config)
gbi_file = _get_grabix_index(in_file)
# We always build grabix input so we can use it for counting reads and doing downsampling
if not gbi_file or _is_partial_index(gbi_file):
if gbi_file:
utils.remove_safe(gbi_file)
else:
gbi_file = in_file + ".gbi"
with file_transaction(data, gbi_file) as tx_gbi_file:
tx_in_file = os.path.splitext(tx_gbi_file)[0]
utils.symlink_plus(in_file, tx_in_file)
do.run([grabix, "index", tx_in_file], "Index input with grabix: %s" % os.path.basename(in_file))
assert utils.file_exists(gbi_file)
return [gbi_file] | [
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223,752 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _is_partial_index | def _is_partial_index(gbi_file):
"""Check for truncated output since grabix doesn't write to a transactional directory.
"""
with open(gbi_file) as in_handle:
for i, _ in enumerate(in_handle):
if i > 2:
return False
return True | python | def _is_partial_index(gbi_file):
"""Check for truncated output since grabix doesn't write to a transactional directory.
"""
with open(gbi_file) as in_handle:
for i, _ in enumerate(in_handle):
if i > 2:
return False
return True | [
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223,753 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _bgzip_file | def _bgzip_file(finput, config, work_dir, needs_bgzip, needs_gunzip, needs_convert, data):
"""Handle bgzip of input file, potentially gunzipping an existing file.
Handles cases where finput might be multiple files and need to be concatenated.
"""
if isinstance(finput, six.string_types):
in_file = finput
else:
assert not needs_convert, "Do not yet handle quality conversion with multiple inputs"
return _bgzip_multiple_files(finput, work_dir, data)
out_file = os.path.join(work_dir, os.path.basename(in_file).replace(".bz2", "") +
(".gz" if not in_file.endswith(".gz") else ""))
if not utils.file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
bgzip = tools.get_bgzip_cmd(config)
is_remote = objectstore.is_remote(in_file)
in_file = objectstore.cl_input(in_file, unpack=needs_gunzip or needs_convert or
needs_bgzip or dd.get_trim_ends(data))
if needs_convert or dd.get_trim_ends(data):
in_file = fastq_convert_pipe_cl(in_file, data)
if needs_gunzip and not (needs_convert or dd.get_trim_ends(data)):
if in_file.endswith(".bz2"):
gunzip_cmd = "bunzip2 -c {in_file} |".format(**locals())
else:
gunzip_cmd = "gunzip -c {in_file} |".format(**locals())
bgzip_in = "/dev/stdin"
else:
gunzip_cmd = ""
bgzip_in = in_file
if needs_bgzip:
do.run("{gunzip_cmd} {bgzip} -c {bgzip_in} > {tx_out_file}".format(**locals()),
"bgzip input file")
elif is_remote:
bgzip = "| bgzip -c" if (needs_convert or dd.get_trim_ends(data)) else ""
do.run("cat {in_file} {bgzip} > {tx_out_file}".format(**locals()), "Get remote input")
else:
raise ValueError("Unexpected inputs: %s %s %s %s" % (in_file, needs_bgzip,
needs_gunzip, needs_convert))
return out_file | python | def _bgzip_file(finput, config, work_dir, needs_bgzip, needs_gunzip, needs_convert, data):
"""Handle bgzip of input file, potentially gunzipping an existing file.
Handles cases where finput might be multiple files and need to be concatenated.
"""
if isinstance(finput, six.string_types):
in_file = finput
else:
assert not needs_convert, "Do not yet handle quality conversion with multiple inputs"
return _bgzip_multiple_files(finput, work_dir, data)
out_file = os.path.join(work_dir, os.path.basename(in_file).replace(".bz2", "") +
(".gz" if not in_file.endswith(".gz") else ""))
if not utils.file_exists(out_file):
with file_transaction(config, out_file) as tx_out_file:
bgzip = tools.get_bgzip_cmd(config)
is_remote = objectstore.is_remote(in_file)
in_file = objectstore.cl_input(in_file, unpack=needs_gunzip or needs_convert or
needs_bgzip or dd.get_trim_ends(data))
if needs_convert or dd.get_trim_ends(data):
in_file = fastq_convert_pipe_cl(in_file, data)
if needs_gunzip and not (needs_convert or dd.get_trim_ends(data)):
if in_file.endswith(".bz2"):
gunzip_cmd = "bunzip2 -c {in_file} |".format(**locals())
else:
gunzip_cmd = "gunzip -c {in_file} |".format(**locals())
bgzip_in = "/dev/stdin"
else:
gunzip_cmd = ""
bgzip_in = in_file
if needs_bgzip:
do.run("{gunzip_cmd} {bgzip} -c {bgzip_in} > {tx_out_file}".format(**locals()),
"bgzip input file")
elif is_remote:
bgzip = "| bgzip -c" if (needs_convert or dd.get_trim_ends(data)) else ""
do.run("cat {in_file} {bgzip} > {tx_out_file}".format(**locals()), "Get remote input")
else:
raise ValueError("Unexpected inputs: %s %s %s %s" % (in_file, needs_bgzip,
needs_gunzip, needs_convert))
return out_file | [
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223,754 | bcbio/bcbio-nextgen | bcbio/ngsalign/alignprep.py | _check_gzipped_input | def _check_gzipped_input(in_file, data):
"""Determine if a gzipped input file is blocked gzip or standard.
"""
grabix = config_utils.get_program("grabix", data["config"])
is_bgzip = subprocess.check_output([grabix, "check", in_file])
if is_bgzip.strip() == "yes":
return False, False
else:
return True, True | python | def _check_gzipped_input(in_file, data):
"""Determine if a gzipped input file is blocked gzip or standard.
"""
grabix = config_utils.get_program("grabix", data["config"])
is_bgzip = subprocess.check_output([grabix, "check", in_file])
if is_bgzip.strip() == "yes":
return False, False
else:
return True, True | [
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223,755 | bcbio/bcbio-nextgen | bcbio/qc/fastqc.py | run | def run(bam_file, data, fastqc_out):
"""Run fastqc, generating report in specified directory and parsing metrics.
Downsamples to 10 million reads to avoid excessive processing times with large
files, unless we're running a Standard/smallRNA-seq/QC pipeline.
Handles fastqc 0.11+, which use a single HTML file and older versions that use
a directory of files + images. The goal is to eventually move to only 0.11+
"""
sentry_file = os.path.join(fastqc_out, "fastqc_report.html")
if not os.path.exists(sentry_file):
work_dir = os.path.dirname(fastqc_out)
utils.safe_makedir(work_dir)
ds_file = (bam.downsample(bam_file, data, 1e7, work_dir=work_dir)
if data.get("analysis", "").lower() not in ["standard", "smallrna-seq"]
else None)
if ds_file is not None:
bam_file = ds_file
frmt = "bam" if bam_file.endswith("bam") else "fastq"
fastqc_name = utils.splitext_plus(os.path.basename(bam_file))[0]
fastqc_clean_name = dd.get_sample_name(data)
num_cores = data["config"]["algorithm"].get("num_cores", 1)
with tx_tmpdir(data, work_dir) as tx_tmp_dir:
with utils.chdir(tx_tmp_dir):
cl = [config_utils.get_program("fastqc", data["config"]),
"-d", tx_tmp_dir,
"-t", str(num_cores), "--extract", "-o", tx_tmp_dir, "-f", frmt, bam_file]
cl = "%s %s %s" % (utils.java_freetype_fix(),
utils.local_path_export(), " ".join([str(x) for x in cl]))
do.run(cl, "FastQC: %s" % dd.get_sample_name(data))
tx_fastqc_out = os.path.join(tx_tmp_dir, "%s_fastqc" % fastqc_name)
tx_combo_file = os.path.join(tx_tmp_dir, "%s_fastqc.html" % fastqc_name)
if not os.path.exists(sentry_file) and os.path.exists(tx_combo_file):
utils.safe_makedir(fastqc_out)
# Use sample name for reports instead of bam file name
with open(os.path.join(tx_fastqc_out, "fastqc_data.txt"), 'r') as fastqc_bam_name, \
open(os.path.join(tx_fastqc_out, "_fastqc_data.txt"), 'w') as fastqc_sample_name:
for line in fastqc_bam_name:
fastqc_sample_name.write(line.replace(os.path.basename(bam_file), fastqc_clean_name))
shutil.move(os.path.join(tx_fastqc_out, "_fastqc_data.txt"), os.path.join(fastqc_out, 'fastqc_data.txt'))
shutil.move(tx_combo_file, sentry_file)
if os.path.exists("%s.zip" % tx_fastqc_out):
shutil.move("%s.zip" % tx_fastqc_out, os.path.join(fastqc_out, "%s.zip" % fastqc_clean_name))
elif not os.path.exists(sentry_file):
raise ValueError("FastQC failed to produce output HTML file: %s" % os.listdir(tx_tmp_dir))
logger.info("Produced HTML report %s" % sentry_file)
parser = FastQCParser(fastqc_out, dd.get_sample_name(data))
stats = parser.get_fastqc_summary()
parser.save_sections_into_file()
return stats | python | def run(bam_file, data, fastqc_out):
"""Run fastqc, generating report in specified directory and parsing metrics.
Downsamples to 10 million reads to avoid excessive processing times with large
files, unless we're running a Standard/smallRNA-seq/QC pipeline.
Handles fastqc 0.11+, which use a single HTML file and older versions that use
a directory of files + images. The goal is to eventually move to only 0.11+
"""
sentry_file = os.path.join(fastqc_out, "fastqc_report.html")
if not os.path.exists(sentry_file):
work_dir = os.path.dirname(fastqc_out)
utils.safe_makedir(work_dir)
ds_file = (bam.downsample(bam_file, data, 1e7, work_dir=work_dir)
if data.get("analysis", "").lower() not in ["standard", "smallrna-seq"]
else None)
if ds_file is not None:
bam_file = ds_file
frmt = "bam" if bam_file.endswith("bam") else "fastq"
fastqc_name = utils.splitext_plus(os.path.basename(bam_file))[0]
fastqc_clean_name = dd.get_sample_name(data)
num_cores = data["config"]["algorithm"].get("num_cores", 1)
with tx_tmpdir(data, work_dir) as tx_tmp_dir:
with utils.chdir(tx_tmp_dir):
cl = [config_utils.get_program("fastqc", data["config"]),
"-d", tx_tmp_dir,
"-t", str(num_cores), "--extract", "-o", tx_tmp_dir, "-f", frmt, bam_file]
cl = "%s %s %s" % (utils.java_freetype_fix(),
utils.local_path_export(), " ".join([str(x) for x in cl]))
do.run(cl, "FastQC: %s" % dd.get_sample_name(data))
tx_fastqc_out = os.path.join(tx_tmp_dir, "%s_fastqc" % fastqc_name)
tx_combo_file = os.path.join(tx_tmp_dir, "%s_fastqc.html" % fastqc_name)
if not os.path.exists(sentry_file) and os.path.exists(tx_combo_file):
utils.safe_makedir(fastqc_out)
# Use sample name for reports instead of bam file name
with open(os.path.join(tx_fastqc_out, "fastqc_data.txt"), 'r') as fastqc_bam_name, \
open(os.path.join(tx_fastqc_out, "_fastqc_data.txt"), 'w') as fastqc_sample_name:
for line in fastqc_bam_name:
fastqc_sample_name.write(line.replace(os.path.basename(bam_file), fastqc_clean_name))
shutil.move(os.path.join(tx_fastqc_out, "_fastqc_data.txt"), os.path.join(fastqc_out, 'fastqc_data.txt'))
shutil.move(tx_combo_file, sentry_file)
if os.path.exists("%s.zip" % tx_fastqc_out):
shutil.move("%s.zip" % tx_fastqc_out, os.path.join(fastqc_out, "%s.zip" % fastqc_clean_name))
elif not os.path.exists(sentry_file):
raise ValueError("FastQC failed to produce output HTML file: %s" % os.listdir(tx_tmp_dir))
logger.info("Produced HTML report %s" % sentry_file)
parser = FastQCParser(fastqc_out, dd.get_sample_name(data))
stats = parser.get_fastqc_summary()
parser.save_sections_into_file()
return stats | [
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Handles fastqc 0.11+, which use a single HTML file and older versions that use
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223,756 | bcbio/bcbio-nextgen | bcbio/qc/fastqc.py | FastQCParser._get_module | def _get_module(self, parser, module):
"""
Get module using fadapa package
"""
dt = []
lines = parser.clean_data(module)
header = lines[0]
for data in lines[1:]:
if data[0].startswith("#"): # some modules have two headers
header = data
continue
if data[0].find("-") > -1: # expand positions 1-3 to 1, 2, 3
f, s = map(int, data[0].split("-"))
for pos in range(f, s):
dt.append([str(pos)] + data[1:])
else:
dt.append(data)
dt = pd.DataFrame(dt)
dt.columns = [h.replace(" ", "_") for h in header]
dt['sample'] = self.sample
return dt | python | def _get_module(self, parser, module):
"""
Get module using fadapa package
"""
dt = []
lines = parser.clean_data(module)
header = lines[0]
for data in lines[1:]:
if data[0].startswith("#"): # some modules have two headers
header = data
continue
if data[0].find("-") > -1: # expand positions 1-3 to 1, 2, 3
f, s = map(int, data[0].split("-"))
for pos in range(f, s):
dt.append([str(pos)] + data[1:])
else:
dt.append(data)
dt = pd.DataFrame(dt)
dt.columns = [h.replace(" ", "_") for h in header]
dt['sample'] = self.sample
return dt | [
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223,757 | bcbio/bcbio-nextgen | bcbio/server/background.py | GenericSubprocess.start | def start(self):
"""Spawn the task.
Throws RuntimeError if the task was already started."""
if not self.pipe is None:
raise RuntimeError("Cannot start task twice")
self.ioloop = tornado.ioloop.IOLoop.instance()
if self.timeout > 0:
self.expiration = self.ioloop.add_timeout( time.time() + self.timeout, self.on_timeout )
self.pipe = subprocess.Popen(**self.args)
self.streams = [ (self.pipe.stdout.fileno(), []),
(self.pipe.stderr.fileno(), []) ]
for fd, d in self.streams:
flags = fcntl.fcntl(fd, fcntl.F_GETFL)| os.O_NDELAY
fcntl.fcntl( fd, fcntl.F_SETFL, flags)
self.ioloop.add_handler( fd,
self.stat,
self.ioloop.READ|self.ioloop.ERROR) | python | def start(self):
"""Spawn the task.
Throws RuntimeError if the task was already started."""
if not self.pipe is None:
raise RuntimeError("Cannot start task twice")
self.ioloop = tornado.ioloop.IOLoop.instance()
if self.timeout > 0:
self.expiration = self.ioloop.add_timeout( time.time() + self.timeout, self.on_timeout )
self.pipe = subprocess.Popen(**self.args)
self.streams = [ (self.pipe.stdout.fileno(), []),
(self.pipe.stderr.fileno(), []) ]
for fd, d in self.streams:
flags = fcntl.fcntl(fd, fcntl.F_GETFL)| os.O_NDELAY
fcntl.fcntl( fd, fcntl.F_SETFL, flags)
self.ioloop.add_handler( fd,
self.stat,
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223,758 | bcbio/bcbio-nextgen | bcbio/workflow/xprize.py | get_fc_date | def get_fc_date(out_config_file):
"""Retrieve flowcell date, reusing older dates if refreshing a present workflow.
"""
if os.path.exists(out_config_file):
with open(out_config_file) as in_handle:
old_config = yaml.safe_load(in_handle)
fc_date = old_config["fc_date"]
else:
fc_date = datetime.datetime.now().strftime("%y%m%d")
return fc_date | python | def get_fc_date(out_config_file):
"""Retrieve flowcell date, reusing older dates if refreshing a present workflow.
"""
if os.path.exists(out_config_file):
with open(out_config_file) as in_handle:
old_config = yaml.safe_load(in_handle)
fc_date = old_config["fc_date"]
else:
fc_date = datetime.datetime.now().strftime("%y%m%d")
return fc_date | [
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223,759 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | draw_quality_plot | def draw_quality_plot(db_file, plot_file, position_select, title):
"""Draw a plot of remapped qualities using ggplot2.
Remapping information is pulled from the sqlite3 database using sqldf
according to the position select attribute, which is a selection phrase like
'> 50' or '=28'.
plyr is used to summarize data by the original and remapped score for all
selected positions.
ggplot2 plots a heatmap of remapped counts at each (original, remap)
coordinate, with a x=y line added for reference.
"""
robjects.r.assign('db.file', db_file)
robjects.r.assign('plot.file', plot_file)
robjects.r.assign('position.select', position_select)
robjects.r.assign('title', title)
robjects.r('''
library(sqldf)
library(plyr)
library(ggplot2)
sql <- paste("select * from data WHERE position", position.select, sep=" ")
exp.data <- sqldf(sql, dbname=db.file)
remap.data <- ddply(exp.data, c("orig", "remap"), transform, count=sum(count))
p <- ggplot(remap.data, aes(orig, remap)) +
geom_tile(aes(fill = count)) +
scale_fill_gradient(low = "white", high = "steelblue", trans="log") +
opts(panel.background = theme_rect(fill = "white"),
title=title) +
geom_abline(intercept=0, slope=1)
ggsave(plot.file, p, width=6, height=6)
''') | python | def draw_quality_plot(db_file, plot_file, position_select, title):
"""Draw a plot of remapped qualities using ggplot2.
Remapping information is pulled from the sqlite3 database using sqldf
according to the position select attribute, which is a selection phrase like
'> 50' or '=28'.
plyr is used to summarize data by the original and remapped score for all
selected positions.
ggplot2 plots a heatmap of remapped counts at each (original, remap)
coordinate, with a x=y line added for reference.
"""
robjects.r.assign('db.file', db_file)
robjects.r.assign('plot.file', plot_file)
robjects.r.assign('position.select', position_select)
robjects.r.assign('title', title)
robjects.r('''
library(sqldf)
library(plyr)
library(ggplot2)
sql <- paste("select * from data WHERE position", position.select, sep=" ")
exp.data <- sqldf(sql, dbname=db.file)
remap.data <- ddply(exp.data, c("orig", "remap"), transform, count=sum(count))
p <- ggplot(remap.data, aes(orig, remap)) +
geom_tile(aes(fill = count)) +
scale_fill_gradient(low = "white", high = "steelblue", trans="log") +
opts(panel.background = theme_rect(fill = "white"),
title=title) +
geom_abline(intercept=0, slope=1)
ggsave(plot.file, p, width=6, height=6)
''') | [
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plyr is used to summarize data by the original and remapped score for all
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223,760 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | _positions_to_examine | def _positions_to_examine(db_file):
"""Determine how to sub-divide recalibration analysis based on read length.
"""
conn = sqlite3.connect(db_file)
cursor = conn.cursor()
cursor.execute("""SELECT MAX(position) FROM data""")
position = cursor.fetchone()[0]
if position is not None:
position = int(position)
cursor.close()
split_at = 50
if position is None:
return []
elif position < split_at:
return [("<= %s" % position, "lt%s" % position)]
else:
return [("< %s" % split_at, "lt%s" % split_at),
(">= %s" % split_at, "gt%s" % split_at)] | python | def _positions_to_examine(db_file):
"""Determine how to sub-divide recalibration analysis based on read length.
"""
conn = sqlite3.connect(db_file)
cursor = conn.cursor()
cursor.execute("""SELECT MAX(position) FROM data""")
position = cursor.fetchone()[0]
if position is not None:
position = int(position)
cursor.close()
split_at = 50
if position is None:
return []
elif position < split_at:
return [("<= %s" % position, "lt%s" % position)]
else:
return [("< %s" % split_at, "lt%s" % split_at),
(">= %s" % split_at, "gt%s" % split_at)] | [
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223,761 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | _organize_by_position | def _organize_by_position(orig_file, cmp_file, chunk_size):
"""Read two CSV files of qualities, organizing values by position.
"""
with open(orig_file) as in_handle:
reader1 = csv.reader(in_handle)
positions = len(next(reader1)) - 1
for positions in _chunks(range(positions), chunk_size):
with open(orig_file) as orig_handle:
with open(cmp_file) as cmp_handle:
orig_reader = csv.reader(orig_handle)
cmp_reader = csv.reader(cmp_handle)
for item in _counts_at_position(positions,
orig_reader, cmp_reader):
yield item | python | def _organize_by_position(orig_file, cmp_file, chunk_size):
"""Read two CSV files of qualities, organizing values by position.
"""
with open(orig_file) as in_handle:
reader1 = csv.reader(in_handle)
positions = len(next(reader1)) - 1
for positions in _chunks(range(positions), chunk_size):
with open(orig_file) as orig_handle:
with open(cmp_file) as cmp_handle:
orig_reader = csv.reader(orig_handle)
cmp_reader = csv.reader(cmp_handle)
for item in _counts_at_position(positions,
orig_reader, cmp_reader):
yield item | [
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223,762 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | _counts_at_position | def _counts_at_position(positions, orig_reader, cmp_reader):
"""Combine orignal and new qualities at each position, generating counts.
"""
pos_counts = collections.defaultdict(lambda:
collections.defaultdict(lambda:
collections.defaultdict(int)))
for orig_parts in orig_reader:
cmp_parts = next(cmp_reader)
for pos in positions:
try:
pos_counts[pos][int(orig_parts[pos+1])][int(cmp_parts[pos+1])] += 1
except IndexError:
pass
for pos, count_dict in pos_counts.iteritems():
for orig_val, cmp_dict in count_dict.iteritems():
for cmp_val, count in cmp_dict.iteritems():
yield pos+1, orig_val, cmp_val, count | python | def _counts_at_position(positions, orig_reader, cmp_reader):
"""Combine orignal and new qualities at each position, generating counts.
"""
pos_counts = collections.defaultdict(lambda:
collections.defaultdict(lambda:
collections.defaultdict(int)))
for orig_parts in orig_reader:
cmp_parts = next(cmp_reader)
for pos in positions:
try:
pos_counts[pos][int(orig_parts[pos+1])][int(cmp_parts[pos+1])] += 1
except IndexError:
pass
for pos, count_dict in pos_counts.iteritems():
for orig_val, cmp_dict in count_dict.iteritems():
for cmp_val, count in cmp_dict.iteritems():
yield pos+1, orig_val, cmp_val, count | [
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223,763 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | sort_csv | def sort_csv(in_file):
"""Sort a CSV file by read name, allowing direct comparison.
"""
out_file = "%s.sort" % in_file
if not (os.path.exists(out_file) and os.path.getsize(out_file) > 0):
cl = ["sort", "-k", "1,1", in_file]
with open(out_file, "w") as out_handle:
child = subprocess.Popen(cl, stdout=out_handle)
child.wait()
return out_file | python | def sort_csv(in_file):
"""Sort a CSV file by read name, allowing direct comparison.
"""
out_file = "%s.sort" % in_file
if not (os.path.exists(out_file) and os.path.getsize(out_file) > 0):
cl = ["sort", "-k", "1,1", in_file]
with open(out_file, "w") as out_handle:
child = subprocess.Popen(cl, stdout=out_handle)
child.wait()
return out_file | [
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223,764 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | fastq_to_csv | def fastq_to_csv(in_file, fastq_format, work_dir):
"""Convert a fastq file into a CSV of phred quality scores.
"""
out_file = "%s.csv" % (os.path.splitext(os.path.basename(in_file))[0])
out_file = os.path.join(work_dir, out_file)
if not (os.path.exists(out_file) and os.path.getsize(out_file) > 0):
with open(in_file) as in_handle:
with open(out_file, "w") as out_handle:
writer = csv.writer(out_handle)
for rec in SeqIO.parse(in_handle, fastq_format):
writer.writerow([rec.id] + rec.letter_annotations["phred_quality"])
return out_file | python | def fastq_to_csv(in_file, fastq_format, work_dir):
"""Convert a fastq file into a CSV of phred quality scores.
"""
out_file = "%s.csv" % (os.path.splitext(os.path.basename(in_file))[0])
out_file = os.path.join(work_dir, out_file)
if not (os.path.exists(out_file) and os.path.getsize(out_file) > 0):
with open(in_file) as in_handle:
with open(out_file, "w") as out_handle:
writer = csv.writer(out_handle)
for rec in SeqIO.parse(in_handle, fastq_format):
writer.writerow([rec.id] + rec.letter_annotations["phred_quality"])
return out_file | [
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223,765 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | bam_to_fastq | def bam_to_fastq(bam_file, is_paired):
"""Convert a BAM file to fastq files.
"""
out_files, out_handles = _get_fastq_handles(bam_file,
is_paired)
if len(out_handles) > 0:
in_bam = pysam.Samfile(bam_file, mode='rb')
for read in in_bam:
num = 1 if (not read.is_paired or read.is_read1) else 2
# reverse the sequence and quality if mapped to opposite strand
if read.is_reverse:
seq = str(Seq.reverse_complement(Seq.Seq(read.seq)))
qual = "".join(reversed(read.qual))
else:
seq = read.seq
qual = read.qual
out_handles[num].write("@%s\n%s\n+\n%s\n" % (read.qname,
seq, qual))
[h.close() for h in out_handles.values()]
return out_files | python | def bam_to_fastq(bam_file, is_paired):
"""Convert a BAM file to fastq files.
"""
out_files, out_handles = _get_fastq_handles(bam_file,
is_paired)
if len(out_handles) > 0:
in_bam = pysam.Samfile(bam_file, mode='rb')
for read in in_bam:
num = 1 if (not read.is_paired or read.is_read1) else 2
# reverse the sequence and quality if mapped to opposite strand
if read.is_reverse:
seq = str(Seq.reverse_complement(Seq.Seq(read.seq)))
qual = "".join(reversed(read.qual))
else:
seq = read.seq
qual = read.qual
out_handles[num].write("@%s\n%s\n+\n%s\n" % (read.qname,
seq, qual))
[h.close() for h in out_handles.values()]
return out_files | [
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223,766 | bcbio/bcbio-nextgen | scripts/utils/analyze_quality_recal.py | run_latex_report | def run_latex_report(base, report_dir, section_info):
"""Generate a pdf report with plots using latex.
"""
out_name = "%s_recal_plots.tex" % base
out = os.path.join(report_dir, out_name)
with open(out, "w") as out_handle:
out_tmpl = Template(out_template)
out_handle.write(out_tmpl.render(sections=section_info))
start_dir = os.getcwd()
try:
os.chdir(report_dir)
cl = ["pdflatex", out_name]
child = subprocess.Popen(cl)
child.wait()
finally:
os.chdir(start_dir) | python | def run_latex_report(base, report_dir, section_info):
"""Generate a pdf report with plots using latex.
"""
out_name = "%s_recal_plots.tex" % base
out = os.path.join(report_dir, out_name)
with open(out, "w") as out_handle:
out_tmpl = Template(out_template)
out_handle.write(out_tmpl.render(sections=section_info))
start_dir = os.getcwd()
try:
os.chdir(report_dir)
cl = ["pdflatex", out_name]
child = subprocess.Popen(cl)
child.wait()
finally:
os.chdir(start_dir) | [
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223,767 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | bam_needs_processing | def bam_needs_processing(data):
"""Check if a work input needs processing for parallelization.
"""
return ((data.get("work_bam") or data.get("align_bam")) and
(any(tz.get_in(["config", "algorithm", x], data) for x in
["variantcaller", "mark_duplicates", "recalibrate", "realign", "svcaller",
"jointcaller", "variant_regions"])
or any(k in data for k in ["cwl_keys", "output_cwl_keys"]))) | python | def bam_needs_processing(data):
"""Check if a work input needs processing for parallelization.
"""
return ((data.get("work_bam") or data.get("align_bam")) and
(any(tz.get_in(["config", "algorithm", x], data) for x in
["variantcaller", "mark_duplicates", "recalibrate", "realign", "svcaller",
"jointcaller", "variant_regions"])
or any(k in data for k in ["cwl_keys", "output_cwl_keys"]))) | [
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223,768 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | get_batch_for_key | def get_batch_for_key(data):
"""Retrieve batch information useful as a unique key for the sample.
"""
batches = _get_batches(data, require_bam=False)
if len(batches) == 1:
return batches[0]
else:
return tuple(batches) | python | def get_batch_for_key(data):
"""Retrieve batch information useful as a unique key for the sample.
"""
batches = _get_batches(data, require_bam=False)
if len(batches) == 1:
return batches[0]
else:
return tuple(batches) | [
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223,769 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _find_all_groups | def _find_all_groups(items, require_bam=True):
"""Find all groups
"""
all_groups = []
for data in items:
batches = _get_batches(data, require_bam)
all_groups.append(batches)
return all_groups | python | def _find_all_groups(items, require_bam=True):
"""Find all groups
"""
all_groups = []
for data in items:
batches = _get_batches(data, require_bam)
all_groups.append(batches)
return all_groups | [
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223,770 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _get_representative_batch | def _get_representative_batch(merged):
"""Prepare dictionary matching batch items to a representative within a group.
"""
out = {}
for mgroup in merged:
mgroup = sorted(list(mgroup))
for x in mgroup:
out[x] = mgroup[0]
return out | python | def _get_representative_batch(merged):
"""Prepare dictionary matching batch items to a representative within a group.
"""
out = {}
for mgroup in merged:
mgroup = sorted(list(mgroup))
for x in mgroup:
out[x] = mgroup[0]
return out | [
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223,771 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _group_batches_shared | def _group_batches_shared(xs, caller_batch_fn, prep_data_fn):
"""Shared functionality for grouping by batches for variant calling and joint calling.
"""
singles = []
batch_groups = collections.defaultdict(list)
for args in xs:
data = utils.to_single_data(args)
caller, batch = caller_batch_fn(data)
region = _list_to_tuple(data["region"]) if "region" in data else ()
if batch is not None:
batches = batch if isinstance(batch, (list, tuple)) else [batch]
for b in batches:
batch_groups[(b, region, caller)].append(utils.deepish_copy(data))
else:
data = prep_data_fn(data, [data])
singles.append(data)
batches = []
for batch, items in batch_groups.items():
batch_data = utils.deepish_copy(_pick_lead_item(items))
# For nested primary batches, split permanently by batch
if tz.get_in(["metadata", "batch"], batch_data):
batch_name = batch[0]
batch_data["metadata"]["batch"] = batch_name
batch_data = prep_data_fn(batch_data, items)
batch_data["group_orig"] = _collapse_subitems(batch_data, items)
batch_data["group"] = batch
batches.append(batch_data)
return singles + batches | python | def _group_batches_shared(xs, caller_batch_fn, prep_data_fn):
"""Shared functionality for grouping by batches for variant calling and joint calling.
"""
singles = []
batch_groups = collections.defaultdict(list)
for args in xs:
data = utils.to_single_data(args)
caller, batch = caller_batch_fn(data)
region = _list_to_tuple(data["region"]) if "region" in data else ()
if batch is not None:
batches = batch if isinstance(batch, (list, tuple)) else [batch]
for b in batches:
batch_groups[(b, region, caller)].append(utils.deepish_copy(data))
else:
data = prep_data_fn(data, [data])
singles.append(data)
batches = []
for batch, items in batch_groups.items():
batch_data = utils.deepish_copy(_pick_lead_item(items))
# For nested primary batches, split permanently by batch
if tz.get_in(["metadata", "batch"], batch_data):
batch_name = batch[0]
batch_data["metadata"]["batch"] = batch_name
batch_data = prep_data_fn(batch_data, items)
batch_data["group_orig"] = _collapse_subitems(batch_data, items)
batch_data["group"] = batch
batches.append(batch_data)
return singles + batches | [
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223,772 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | group_batches | def group_batches(xs):
"""Group samples into batches for simultaneous variant calling.
Identify all samples to call together: those in the same batch
and variant caller.
Pull together all BAM files from this batch and process together,
Provide details to pull these finalized files back into individual
expected files.
Only batches files if joint calling not specified.
"""
def _caller_batches(data):
caller = tz.get_in(("config", "algorithm", "variantcaller"), data)
jointcaller = tz.get_in(("config", "algorithm", "jointcaller"), data)
batch = tz.get_in(("metadata", "batch"), data) if not jointcaller else None
return caller, batch
def _prep_data(data, items):
data["region_bams"] = [x["region_bams"] for x in items]
return data
return _group_batches_shared(xs, _caller_batches, _prep_data) | python | def group_batches(xs):
"""Group samples into batches for simultaneous variant calling.
Identify all samples to call together: those in the same batch
and variant caller.
Pull together all BAM files from this batch and process together,
Provide details to pull these finalized files back into individual
expected files.
Only batches files if joint calling not specified.
"""
def _caller_batches(data):
caller = tz.get_in(("config", "algorithm", "variantcaller"), data)
jointcaller = tz.get_in(("config", "algorithm", "jointcaller"), data)
batch = tz.get_in(("metadata", "batch"), data) if not jointcaller else None
return caller, batch
def _prep_data(data, items):
data["region_bams"] = [x["region_bams"] for x in items]
return data
return _group_batches_shared(xs, _caller_batches, _prep_data) | [
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223,773 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _collapse_subitems | def _collapse_subitems(base, items):
"""Collapse full data representations relative to a standard base.
"""
out = []
for d in items:
newd = _diff_dict(base, d)
out.append(newd)
return out | python | def _collapse_subitems(base, items):
"""Collapse full data representations relative to a standard base.
"""
out = []
for d in items:
newd = _diff_dict(base, d)
out.append(newd)
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223,774 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _pick_lead_item | def _pick_lead_item(items):
"""Pick single representative sample for batch calling to attach calls to.
For cancer samples, attach to tumor.
"""
if vcfutils.is_paired_analysis([dd.get_align_bam(x) for x in items], items):
for data in items:
if vcfutils.get_paired_phenotype(data) == "tumor":
return data
raise ValueError("Did not find tumor sample in paired tumor/normal calling")
else:
return items[0] | python | def _pick_lead_item(items):
"""Pick single representative sample for batch calling to attach calls to.
For cancer samples, attach to tumor.
"""
if vcfutils.is_paired_analysis([dd.get_align_bam(x) for x in items], items):
for data in items:
if vcfutils.get_paired_phenotype(data) == "tumor":
return data
raise ValueError("Did not find tumor sample in paired tumor/normal calling")
else:
return items[0] | [
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223,775 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | get_orig_items | def get_orig_items(base):
"""Retrieve original items from a diffed set of nested samples.
"""
assert "group_orig" in base
out = []
for data_diff in base["group_orig"]:
new = utils.deepish_copy(base)
new.pop("group_orig")
out.append(_patch_dict(data_diff, new))
return out | python | def get_orig_items(base):
"""Retrieve original items from a diffed set of nested samples.
"""
assert "group_orig" in base
out = []
for data_diff in base["group_orig"]:
new = utils.deepish_copy(base)
new.pop("group_orig")
out.append(_patch_dict(data_diff, new))
return out | [
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223,776 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | _patch_dict | def _patch_dict(diff, base):
"""Patch a dictionary, substituting in changed items from the nested diff.
"""
for k, v in diff.items():
if isinstance(v, dict):
base[k] = _patch_dict(v, base.get(k, {}))
elif not v:
base.pop(k, None)
else:
base[k] = v
return base | python | def _patch_dict(diff, base):
"""Patch a dictionary, substituting in changed items from the nested diff.
"""
for k, v in diff.items():
if isinstance(v, dict):
base[k] = _patch_dict(v, base.get(k, {}))
elif not v:
base.pop(k, None)
else:
base[k] = v
return base | [
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223,777 | bcbio/bcbio-nextgen | bcbio/variation/multi.py | split_variants_by_sample | def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif (vcfutils.get_paired_phenotype(data)
and "tumor" in [vcfutils.get_paired_phenotype(d) for d in get_orig_items(data)]):
out = []
for i, sub_data in enumerate(get_orig_items(data)):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
cur_batch = tz.get_in(["metadata", "batch"], data)
if cur_batch:
sub_data["metadata"]["batch"] = cur_batch
sub_data["vrn_file"] = data["vrn_file"]
else:
sub_data.pop("vrn_file", None)
out.append([sub_data])
return out
# joint calling or population runs, do not split back up and keep in batches
else:
out = []
for sub_data in get_orig_items(data):
cur_batch = tz.get_in(["metadata", "batch"], data)
if cur_batch:
sub_data["metadata"]["batch"] = cur_batch
sub_data["vrn_file_batch"] = data["vrn_file"]
sub_data["vrn_file"] = data["vrn_file"]
out.append([sub_data])
return out | python | def split_variants_by_sample(data):
"""Split a multi-sample call file into inputs for individual samples.
For tumor/normal paired analyses, do not split the final file and attach
it to the tumor input.
"""
# not split, do nothing
if "group_orig" not in data:
return [[data]]
# cancer tumor/normal
elif (vcfutils.get_paired_phenotype(data)
and "tumor" in [vcfutils.get_paired_phenotype(d) for d in get_orig_items(data)]):
out = []
for i, sub_data in enumerate(get_orig_items(data)):
if vcfutils.get_paired_phenotype(sub_data) == "tumor":
cur_batch = tz.get_in(["metadata", "batch"], data)
if cur_batch:
sub_data["metadata"]["batch"] = cur_batch
sub_data["vrn_file"] = data["vrn_file"]
else:
sub_data.pop("vrn_file", None)
out.append([sub_data])
return out
# joint calling or population runs, do not split back up and keep in batches
else:
out = []
for sub_data in get_orig_items(data):
cur_batch = tz.get_in(["metadata", "batch"], data)
if cur_batch:
sub_data["metadata"]["batch"] = cur_batch
sub_data["vrn_file_batch"] = data["vrn_file"]
sub_data["vrn_file"] = data["vrn_file"]
out.append([sub_data])
return out | [
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223,778 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | run | def run(call_file, ref_file, vrn_files, data):
"""Run filtering on the input call file, handling SNPs and indels separately.
"""
algs = [data["config"]["algorithm"]] * len(data.get("vrn_files", [1]))
if includes_missingalt(data):
logger.info("Removing variants with missing alts from %s." % call_file)
call_file = gatk_remove_missingalt(call_file, data)
if "gatkcnn" in dd.get_tools_on(data):
return _cnn_filter(call_file, vrn_files, data)
elif config_utils.use_vqsr(algs, call_file):
if vcfutils.is_gvcf_file(call_file):
raise ValueError("Cannot force gVCF output with joint calling using tools_on: [gvcf] and use VQSR. "
"Try using cutoff-based soft filtering with tools_off: [vqsr]")
snp_file, indel_file = vcfutils.split_snps_indels(call_file, ref_file, data["config"])
snp_filter_file = _variant_filtration(snp_file, ref_file, vrn_files, data, "SNP",
vfilter.gatk_snp_cutoff)
indel_filter_file = _variant_filtration(indel_file, ref_file, vrn_files, data, "INDEL",
vfilter.gatk_indel_cutoff)
orig_files = [snp_filter_file, indel_filter_file]
out_file = "%scombined.vcf.gz" % os.path.commonprefix(orig_files)
combined_file = vcfutils.combine_variant_files(orig_files, out_file, ref_file, data["config"])
return combined_file
else:
snp_filter = vfilter.gatk_snp_cutoff(call_file, data)
indel_filter = vfilter.gatk_indel_cutoff(snp_filter, data)
return indel_filter | python | def run(call_file, ref_file, vrn_files, data):
"""Run filtering on the input call file, handling SNPs and indels separately.
"""
algs = [data["config"]["algorithm"]] * len(data.get("vrn_files", [1]))
if includes_missingalt(data):
logger.info("Removing variants with missing alts from %s." % call_file)
call_file = gatk_remove_missingalt(call_file, data)
if "gatkcnn" in dd.get_tools_on(data):
return _cnn_filter(call_file, vrn_files, data)
elif config_utils.use_vqsr(algs, call_file):
if vcfutils.is_gvcf_file(call_file):
raise ValueError("Cannot force gVCF output with joint calling using tools_on: [gvcf] and use VQSR. "
"Try using cutoff-based soft filtering with tools_off: [vqsr]")
snp_file, indel_file = vcfutils.split_snps_indels(call_file, ref_file, data["config"])
snp_filter_file = _variant_filtration(snp_file, ref_file, vrn_files, data, "SNP",
vfilter.gatk_snp_cutoff)
indel_filter_file = _variant_filtration(indel_file, ref_file, vrn_files, data, "INDEL",
vfilter.gatk_indel_cutoff)
orig_files = [snp_filter_file, indel_filter_file]
out_file = "%scombined.vcf.gz" % os.path.commonprefix(orig_files)
combined_file = vcfutils.combine_variant_files(orig_files, out_file, ref_file, data["config"])
return combined_file
else:
snp_filter = vfilter.gatk_snp_cutoff(call_file, data)
indel_filter = vfilter.gatk_indel_cutoff(snp_filter, data)
return indel_filter | [
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223,779 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _cnn_filter | def _cnn_filter(in_file, vrn_files, data):
"""Perform CNN filtering on input VCF using pre-trained models.
"""
#tensor_type = "reference" # 1D, reference sequence
tensor_type = "read_tensor" # 2D, reads, flags, mapping quality
score_file = _cnn_score_variants(in_file, tensor_type, data)
return _cnn_tranch_filtering(score_file, vrn_files, tensor_type, data) | python | def _cnn_filter(in_file, vrn_files, data):
"""Perform CNN filtering on input VCF using pre-trained models.
"""
#tensor_type = "reference" # 1D, reference sequence
tensor_type = "read_tensor" # 2D, reads, flags, mapping quality
score_file = _cnn_score_variants(in_file, tensor_type, data)
return _cnn_tranch_filtering(score_file, vrn_files, tensor_type, data) | [
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223,780 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _cnn_tranch_filtering | def _cnn_tranch_filtering(in_file, vrn_files, tensor_type, data):
"""Filter CNN scored VCFs in tranches using standard SNP and Indel truth sets.
"""
out_file = "%s-filter.vcf.gz" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file):
runner = broad.runner_from_config(data["config"])
gatk_type = runner.gatk_type()
assert gatk_type == "gatk4", "CNN filtering requires GATK4"
if "train_hapmap" not in vrn_files:
raise ValueError("CNN filtering requires HapMap training inputs: %s" % vrn_files)
with file_transaction(data, out_file) as tx_out_file:
params = ["-T", "FilterVariantTranches", "--variant", in_file,
"--output", tx_out_file,
"--snp-truth-vcf", vrn_files["train_hapmap"],
"--indel-truth-vcf", vrn_files["train_indels"]]
if tensor_type == "reference":
params += ["--info-key", "CNN_1D", "--tranche", "99"]
else:
assert tensor_type == "read_tensor"
params += ["--info-key", "CNN_2D", "--tranche", "99"]
runner.run_gatk(params)
return vcfutils.bgzip_and_index(out_file, data["config"]) | python | def _cnn_tranch_filtering(in_file, vrn_files, tensor_type, data):
"""Filter CNN scored VCFs in tranches using standard SNP and Indel truth sets.
"""
out_file = "%s-filter.vcf.gz" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file):
runner = broad.runner_from_config(data["config"])
gatk_type = runner.gatk_type()
assert gatk_type == "gatk4", "CNN filtering requires GATK4"
if "train_hapmap" not in vrn_files:
raise ValueError("CNN filtering requires HapMap training inputs: %s" % vrn_files)
with file_transaction(data, out_file) as tx_out_file:
params = ["-T", "FilterVariantTranches", "--variant", in_file,
"--output", tx_out_file,
"--snp-truth-vcf", vrn_files["train_hapmap"],
"--indel-truth-vcf", vrn_files["train_indels"]]
if tensor_type == "reference":
params += ["--info-key", "CNN_1D", "--tranche", "99"]
else:
assert tensor_type == "read_tensor"
params += ["--info-key", "CNN_2D", "--tranche", "99"]
runner.run_gatk(params)
return vcfutils.bgzip_and_index(out_file, data["config"]) | [
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223,781 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _cnn_score_variants | def _cnn_score_variants(in_file, tensor_type, data):
"""Score variants with pre-trained CNN models.
"""
out_file = "%s-cnnscore.vcf.gz" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file):
runner = broad.runner_from_config(data["config"])
gatk_type = runner.gatk_type()
assert gatk_type == "gatk4", "CNN filtering requires GATK4"
with file_transaction(data, out_file) as tx_out_file:
params = ["-T", "CNNScoreVariants", "--variant", in_file, "--reference", dd.get_ref_file(data),
"--output", tx_out_file, "--input", dd.get_align_bam(data)]
params += ["--tensor-type", tensor_type]
runner.run_gatk(params)
return vcfutils.bgzip_and_index(out_file, data["config"]) | python | def _cnn_score_variants(in_file, tensor_type, data):
"""Score variants with pre-trained CNN models.
"""
out_file = "%s-cnnscore.vcf.gz" % utils.splitext_plus(in_file)[0]
if not utils.file_uptodate(out_file, in_file):
runner = broad.runner_from_config(data["config"])
gatk_type = runner.gatk_type()
assert gatk_type == "gatk4", "CNN filtering requires GATK4"
with file_transaction(data, out_file) as tx_out_file:
params = ["-T", "CNNScoreVariants", "--variant", in_file, "--reference", dd.get_ref_file(data),
"--output", tx_out_file, "--input", dd.get_align_bam(data)]
params += ["--tensor-type", tensor_type]
runner.run_gatk(params)
return vcfutils.bgzip_and_index(out_file, data["config"]) | [
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223,782 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _apply_vqsr | def _apply_vqsr(in_file, ref_file, recal_file, tranch_file,
sensitivity_cutoff, filter_type, data):
"""Apply VQSR based on the specified tranche, returning a filtered VCF file.
"""
base, ext = utils.splitext_plus(in_file)
out_file = "{base}-{filter}filter{ext}".format(base=base, ext=ext,
filter=filter_type)
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
broad_runner = broad.runner_from_config(data["config"])
gatk_type = broad_runner.gatk_type()
if gatk_type == "gatk4":
params = ["-T", "ApplyVQSR",
"--variant", in_file,
"--output", tx_out_file,
"--recal-file", recal_file,
"--tranches-file", tranch_file]
else:
params = ["-T", "ApplyRecalibration",
"--input", in_file,
"--out", tx_out_file,
"--recal_file", recal_file,
"--tranches_file", tranch_file]
params += ["-R", ref_file,
"--mode", filter_type]
resources = config_utils.get_resources("gatk_apply_recalibration", data["config"])
opts = resources.get("options", [])
if not opts:
if gatk_type == "gatk4":
opts += ["--truth-sensitivity-filter-level", sensitivity_cutoff]
else:
opts += ["--ts_filter_level", sensitivity_cutoff]
params += opts
broad_runner.run_gatk(params)
return out_file | python | def _apply_vqsr(in_file, ref_file, recal_file, tranch_file,
sensitivity_cutoff, filter_type, data):
"""Apply VQSR based on the specified tranche, returning a filtered VCF file.
"""
base, ext = utils.splitext_plus(in_file)
out_file = "{base}-{filter}filter{ext}".format(base=base, ext=ext,
filter=filter_type)
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
broad_runner = broad.runner_from_config(data["config"])
gatk_type = broad_runner.gatk_type()
if gatk_type == "gatk4":
params = ["-T", "ApplyVQSR",
"--variant", in_file,
"--output", tx_out_file,
"--recal-file", recal_file,
"--tranches-file", tranch_file]
else:
params = ["-T", "ApplyRecalibration",
"--input", in_file,
"--out", tx_out_file,
"--recal_file", recal_file,
"--tranches_file", tranch_file]
params += ["-R", ref_file,
"--mode", filter_type]
resources = config_utils.get_resources("gatk_apply_recalibration", data["config"])
opts = resources.get("options", [])
if not opts:
if gatk_type == "gatk4":
opts += ["--truth-sensitivity-filter-level", sensitivity_cutoff]
else:
opts += ["--ts_filter_level", sensitivity_cutoff]
params += opts
broad_runner.run_gatk(params)
return out_file | [
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223,783 | bcbio/bcbio-nextgen | bcbio/variation/gatkfilter.py | _get_training_data | def _get_training_data(vrn_files):
"""Retrieve training data, returning an empty set of information if not available.
"""
out = {"SNP": [], "INDEL": []}
# SNPs
for name, train_info in [("train_hapmap", "known=false,training=true,truth=true,prior=15.0"),
("train_omni", "known=false,training=true,truth=true,prior=12.0"),
("train_1000g", "known=false,training=true,truth=false,prior=10.0"),
("dbsnp", "known=true,training=false,truth=false,prior=2.0")]:
if name not in vrn_files:
return {}
else:
out["SNP"].append((name.replace("train_", ""), train_info, vrn_files[name]))
# Indels
if "train_indels" in vrn_files:
out["INDEL"].append(("mills", "known=true,training=true,truth=true,prior=12.0",
vrn_files["train_indels"]))
else:
return {}
return out | python | def _get_training_data(vrn_files):
"""Retrieve training data, returning an empty set of information if not available.
"""
out = {"SNP": [], "INDEL": []}
# SNPs
for name, train_info in [("train_hapmap", "known=false,training=true,truth=true,prior=15.0"),
("train_omni", "known=false,training=true,truth=true,prior=12.0"),
("train_1000g", "known=false,training=true,truth=false,prior=10.0"),
("dbsnp", "known=true,training=false,truth=false,prior=2.0")]:
if name not in vrn_files:
return {}
else:
out["SNP"].append((name.replace("train_", ""), train_info, vrn_files[name]))
# Indels
if "train_indels" in vrn_files:
out["INDEL"].append(("mills", "known=true,training=true,truth=true,prior=12.0",
vrn_files["train_indels"]))
else:
return {}
return out | [
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223,784 | 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):
"""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 | [
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223,785 | 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):
"""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 | [
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223,786 | 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):
"""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 | [
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223,787 | 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):
"""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) | [
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223,788 | 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):
"""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) | [
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223,789 | 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):
"""
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"]) | [
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223,790 | 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):
"""
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 | [
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223,791 | 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):
"""
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 | [
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223,792 | 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):
"""
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 | [
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223,793 | 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):
"""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 | [
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223,794 | 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):
"""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) | [
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223,795 | 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):
"""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)) | [
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| strand1 | strand2 | VCF |
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223,796 | 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):
"""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))) | [
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223,797 | 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):
"""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)) | [
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223,798 | 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):
"""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) | [
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223,799 | 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):
"""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 | [
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