def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" %
(data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError(
"Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"],
data["sam_ref"], data["dirs"], data)
elif sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(
data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method,
out_file)
else:
out_bam = link_bam_file(
fastq1,
os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], data["sam_ref"],
data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" %
dd.get_sample_name(data))
else:
raise ValueError(
"Could not process input file from sample configuration. \n" +
fastq1 + "\nIs the path to the file correct?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
return [[data]]
def find_annotations(data, retriever=None):
"""Find annotation configuration files for vcfanno, using pre-installed inputs.
Creates absolute paths for user specified inputs and finds locally
installed defaults.
Default annotations:
- gemini for variant pipelines
- somatic for variant tumor pipelines
- rnaedit for RNA-seq variant calling
"""
conf_files = dd.get_vcfanno(data)
if not isinstance(conf_files, (list, tuple)):
conf_files = [conf_files]
for c in _default_conf_files(data, retriever):
if c not in conf_files:
conf_files.append(c)
conf_checkers = {"gemini": annotate_gemini, "somatic": _annotate_somatic}
out = []
annodir = os.path.normpath(
os.path.join(os.path.dirname(dd.get_ref_file(data)), os.pardir,
"config", "vcfanno"))
if not retriever:
annodir = os.path.abspath(annodir)
for conf_file in conf_files:
if objectstore.is_remote(conf_file) or (os.path.exists(conf_file)
and os.path.isfile(conf_file)):
conffn = conf_file
elif not retriever:
conffn = os.path.join(annodir, conf_file + ".conf")
else:
conffn = conf_file + ".conf"
luafn = "%s.lua" % utils.splitext_plus(conffn)[0]
if retriever:
conffn, luafn = [
(x if objectstore.is_remote(x) else None)
for x in retriever.add_remotes([conffn, luafn], data["config"])
]
if not conffn:
pass
elif conf_file in conf_checkers and not conf_checkers[conf_file](
data, retriever):
logger.warn(
"Skipping vcfanno configuration: %s. Not all input files found."
% conf_file)
elif not objectstore.file_exists_or_remote(conffn):
build = dd.get_genome_build(data)
CONF_NOT_FOUND = (
"The vcfanno configuration {conffn} was not found for {build}, skipping."
)
logger.warn(CONF_NOT_FOUND.format(**locals()))
else:
out.append(conffn)
if luafn and objectstore.file_exists_or_remote(luafn):
out.append(luafn)
return out
def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
data = utils.to_single_data(data)
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and objectstore.file_exists_or_remote(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError("Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], dd.get_ref_file(data), data["dirs"],
data)
elif bamclean == "fixrg":
out_bam = cleanbam.fixrg(fastq1, data["rgnames"], dd.get_ref_file(data), data["dirs"], data)
elif sort_method:
runner = broad.runner_from_path("picard", config)
out_file = os.path.join(data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
else:
out_bam = link_bam_file(fastq1, os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], dd.get_ref_file(data), data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" % dd.get_sample_name(data))
else:
raise ValueError("Could not process input file from sample configuration. \n" +
fastq1 +
"\nIs the path to the file correct or is empty?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
if data.get("work_bam"):
# Add stable 'align_bam' target to use for retrieving raw alignment
data["align_bam"] = data["work_bam"]
data = _add_hla_files(data)
return [[data]]
def find_annotations(data, retriever=None):
"""Find annotation configuration files for vcfanno, using pre-installed inputs.
Creates absolute paths for user specified inputs and finds locally
installed defaults.
Default annotations:
- gemini for variant pipelines
- somatic for variant tumor pipelines
- rnaedit for RNA-seq variant calling
"""
conf_files = dd.get_vcfanno(data)
if not isinstance(conf_files, (list, tuple)):
conf_files = [conf_files]
for c in _default_conf_files(data, retriever):
if c not in conf_files:
conf_files.append(c)
conf_checkers = {"gemini": annotate_gemini, "somatic": _annotate_somatic}
out = []
annodir = os.path.normpath(os.path.join(os.path.dirname(dd.get_ref_file(data)), os.pardir, "config", "vcfanno"))
if not retriever:
annodir = os.path.abspath(annodir)
for conf_file in conf_files:
if objectstore.is_remote(conf_file) or (os.path.exists(conf_file) and os.path.isfile(conf_file)):
conffn = conf_file
elif not retriever:
conffn = os.path.join(annodir, conf_file + ".conf")
else:
conffn = conf_file + ".conf"
luafn = "%s.lua" % utils.splitext_plus(conffn)[0]
if retriever:
conffn, luafn = [(x if objectstore.is_remote(x) else None)
for x in retriever.add_remotes([conffn, luafn], data["config"])]
if not conffn:
pass
elif conf_file in conf_checkers and not conf_checkers[conf_file](data, retriever):
logger.warn("Skipping vcfanno configuration: %s. Not all input files found." % conf_file)
elif not objectstore.file_exists_or_remote(conffn):
build = dd.get_genome_build(data)
CONF_NOT_FOUND = (
"The vcfanno configuration {conffn} was not found for {build}, skipping.")
logger.warn(CONF_NOT_FOUND.format(**locals()))
else:
out.append(conffn)
if luafn and objectstore.file_exists_or_remote(luafn):
out.append(luafn)
return out
def annotate_gemini(data, retriever=None):
"""Annotate with population calls if have data installed.
"""
r = dd.get_variation_resources(data)
return all([
r.get(k) and objectstore.file_exists_or_remote(r[k])
for k in ["exac", "gnomad_exome"]
])
def _annotate_somatic(data, retriever=None):
"""Annotate somatic calls if we have cosmic data installed.
"""
if is_human(data):
paired = vcfutils.get_paired([data])
if paired:
r = dd.get_variation_resources(data)
if r.get("cosmic") and objectstore.file_exists_or_remote(r["cosmic"]):
return True
return False
def _annotate_somatic(data, retriever=None):
"""Annotate somatic calls if we have cosmic data installed.
"""
if is_human(data):
paired = vcfutils.get_paired([data])
if paired:
r = dd.get_variation_resources(data)
if r.get("cosmic") and objectstore.file_exists_or_remote(r["cosmic"]):
return True
return False
def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
data = cwlutils.normalize_missing(utils.to_single_data(data))
data = cwlutils.unpack_tarballs(data, data)
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" %
(data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
if dd.get_correct_umis(data):
data["work_bam"] = postalign.correct_umis(data)
if dd.get_umi_consensus(data):
data["umi_bam"] = dd.get_work_bam(data)
if fastq2:
f1, f2, avg_cov = postalign.umi_consensus(data)
data["config"]["algorithm"]["rawumi_avg_cov"] = avg_cov
del data["config"]["algorithm"]["umi_type"]
data["config"]["algorithm"]["mark_duplicates"] = False
data = align_to_sort_bam(f1, f2, aligner, data)
else:
raise ValueError(
"Single fastq input for UMI processing; fgbio needs paired reads: %s"
% dd.get_sample_name(data))
data = _add_supplemental_bams(data)
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError(
"Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
ref_file = dd.get_ref_file(data)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], ref_file,
data["dirs"], data)
elif bamclean == "fixrg":
out_bam = cleanbam.fixrg(fastq1, data["rgnames"],
dd.get_ref_file(data), data["dirs"], data)
elif bamclean == "remove_extracontigs":
out_bam = cleanbam.remove_extracontigs(fastq1, data)
elif sort_method:
runner = broad.runner_from_path("picard", config)
out_file = os.path.join(
data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
if not utils.file_exists(out_file):
work_dir = utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "bamclean",
dd.get_sample_name(data)))
out_file = os.path.join(
work_dir, "{}-sort.bam".format(dd.get_sample_name(data)))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method,
out_file)
else:
out_bam = _link_bam_file(
fastq1,
os.path.join(dd.get_work_dir(data), "prealign",
dd.get_sample_name(data)), data)
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], dd.get_ref_file(data),
data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and not dd.get_aligner(data):
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" %
dd.get_sample_name(data))
elif "kraken" in config["algorithm"]: # kraken doesn's need bam
pass
else:
raise ValueError(
"Could not process input file from sample configuration. \n" +
fastq1 + "\nIs the path to the file correct or is empty?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
if data.get("work_bam"):
# Add stable 'align_bam' target to use for retrieving raw alignment
data["align_bam"] = data["work_bam"]
data = _add_hla_files(data)
return [[data]]
def annotate_gemini(data, retriever=None):
"""Annotate with population calls if have data installed.
"""
r = dd.get_variation_resources(data)
return all([r.get(k) and objectstore.file_exists_or_remote(r[k]) for k in ["exac", "gnomad_exome"]])
