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
def _get_output_cwl_keys(fnargs):
"""Retrieve output_cwl_keys from potentially nested input arguments.
"""
for d in utils.flatten(fnargs):
if isinstance(d, dict) and d.get("output_cwl_keys"):
return d["output_cwl_keys"]
raise ValueError("Did not find output_cwl_keys in %s" % (pprint.pformat(fnargs)))
def _get_vcf_samples(calls, items):
have_full_file = False
all_samples = set([])
sample_matches = False
for f in utils.flatten(calls):
if have_full_file:
cur = set(vcfutils.get_samples(f))
if cur:
if not all_samples:
all_samples = cur
else:
all_samples &= set(cur)
else:
for data in items:
for i, test_name in enumerate([dd.get_sample_name(data)] +
dd.get_batches(data)):
# For tumor/normal batches, want to attach germline VCFs to normals
# Standard somatics go to tumors
if dd.get_phenotype(data) == "normal":
test_name += "-germline"
if os.path.basename(f).startswith(
("%s-" % test_name, "%s." % test_name)):
# Prefer matches to single samples (gVCF) over joint batches
if i == 0:
sample_matches = True
if sample_matches and i > 0:
continue
else:
all_samples.add(dd.get_sample_name(data))
return list(all_samples)
def summarize_sv(items):
"""CWL target: summarize structural variants for multiple samples.
XXX Need to support non-VCF output as tabix indexed output
"""
items = [utils.to_single_data(x) for x in utils.flatten(items)]
out = {"sv": {"calls": []}}
added = set([])
for data in items:
if data.get("sv"):
names = dd.get_batches(data)
if not names:
names = [dd.get_sample_name(data)]
batch_name = names[0]
cur_name = "%s-%s" % (batch_name, data["sv"]["variantcaller"])
if data["sv"].get("vrn_file"):
ext = utils.splitext_plus(data["sv"]["vrn_file"])[-1]
if cur_name not in added and ext.startswith(".vcf"):
added.add(cur_name)
out_file = os.path.join(
utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "sv",
"calls")), "%s%s" % (cur_name, ext))
utils.copy_plus(data["sv"]["vrn_file"], out_file)
out_file = vcfutils.bgzip_and_index(
out_file, data["config"])
out["sv"]["calls"].append(out_file)
return [out]
def _write_wdl_outputs(argfile, out_keys):
"""Write variables as WDL compatible output files.
Writes individual files prefixed with 'wdl.output' that can be read
by WDL standard library functions:
https://github.com/broadinstitute/wdl/blob/develop/SPEC.md#outputs
"""
out_basename = "wdl.output.%s.txt"
with open(argfile) as in_handle:
outputs = json.load(in_handle)
record_name, record_attrs = _get_record_attrs(out_keys)
if record_name:
recs = outputs[record_name]
with open(out_basename % record_name, "w") as out_handle:
writer = csv.writer(out_handle)
if not isinstance(recs, (list, tuple)):
recs = [recs]
recs = list(utils.flatten(recs))
keys = sorted(list(set(reduce(operator.add, [r.keys() for r in recs]))))
writer.writerow(keys)
for rec in recs:
writer.writerow([_cwlvar_to_wdl(rec.get(k)) for k in keys])
else:
for key in out_keys:
with open(out_basename % key, "w") as out_handle:
vals = _cwlvar_to_wdl(outputs.get(key))
if not isinstance(vals, (list, tuple)):
vals = [vals]
for val in vals:
if isinstance(val, (list, tuple)):
val = "\t".join([str(x) for x in val])
out_handle.write(str(val) + "\n")
def _get_output_cwl_keys(fnargs):
"""Retrieve output_cwl_keys from potentially nested input arguments.
"""
for d in utils.flatten(fnargs):
if isinstance(d, dict) and d.get("output_cwl_keys"):
return d["output_cwl_keys"]
raise ValueError("Did not find output_cwl_keys in %s" % (pprint.pformat(fnargs)))
def _get_vcf_samples(calls, items):
have_full_file = False
all_samples = set([])
sample_matches = False
for f in utils.flatten(calls):
if have_full_file:
cur = set(vcfutils.get_samples(f))
if cur:
if not all_samples:
all_samples = cur
else:
all_samples &= set(cur)
else:
for data in items:
for i, test_name in enumerate([dd.get_sample_name(data)] +
dd.get_batches(data)):
if os.path.basename(f).startswith(
("%s-" % test_name, "%s." % test_name)):
# Prefer matches to single samples (gVCF) over joint batches
if i == 0:
sample_matches = True
if sample_matches and i > 0:
continue
else:
all_samples.add(dd.get_sample_name(data))
return list(all_samples)
def _get_vcf_samples(calls, items):
have_full_file = False
all_samples = set([])
sample_matches = False
for f in utils.flatten(calls):
if have_full_file:
cur = set(vcfutils.get_samples(f))
if cur:
if not all_samples:
all_samples = cur
else:
all_samples &= set(cur)
else:
for data in items:
for i, test_name in enumerate([dd.get_sample_name(data)] + dd.get_batches(data)):
# For tumor/normal batches, want to attach germline VCFs to normals
# Standard somatics go to tumors
if dd.get_phenotype(data) == "normal":
test_name += "-germline"
if os.path.basename(f).startswith(("%s-" % test_name,
"%s." % test_name)):
# Prefer matches to single samples (gVCF) over joint batches
if i == 0:
sample_matches = True
if sample_matches and i > 0:
continue
else:
all_samples.add(dd.get_sample_name(data))
return list(all_samples)
def _write_wdl_outputs(argfile, out_keys):
"""Write variables as WDL compatible output files.
Writes individual files prefixed with 'wdl.output' that can be read
by WDL standard library functions:
https://github.com/broadinstitute/wdl/blob/develop/SPEC.md#outputs
"""
out_basename = "wdl.output.%s.txt"
with open(argfile) as in_handle:
outputs = json.load(in_handle)
record_name, record_attrs = _get_record_attrs(out_keys)
if record_name:
recs = outputs[record_name]
with open(out_basename % record_name, "w") as out_handle:
writer = csv.writer(out_handle)
if not isinstance(recs, (list, tuple)):
recs = [recs]
recs = list(utils.flatten(recs))
keys = sorted(list(set(reduce(operator.add, [r.keys() for r in recs]))))
writer.writerow(keys)
for rec in recs:
writer.writerow([_cwlvar_to_wdl(rec.get(k)) for k in keys])
else:
for key in out_keys:
with open(out_basename % key, "w") as out_handle:
vals = _cwlvar_to_wdl(outputs.get(key))
if not isinstance(vals, (list, tuple)):
vals = [vals]
for val in vals:
if isinstance(val, (list, tuple)):
val = "\t".join([str(x) for x in val])
out_handle.write(str(val) + "\n")
def summarize_grading(samples, vkey="validate"):
"""Provide summaries of grading results across all samples.
Handles both traditional pipelines (validation part of variants) and CWL
pipelines (validation at top level)
"""
samples = list(utils.flatten(samples))
if not _has_grading_info(samples, vkey):
return [[d] for d in samples]
validate_dir = utils.safe_makedir(
os.path.join(samples[0]["dirs"]["work"], vkey))
header = ["sample", "caller", "variant.type", "category", "value"]
validated, out = _group_validate_samples(samples, vkey)
for vname, vitems in validated.items():
out_csv = os.path.join(validate_dir, "grading-summary-%s.csv" % vname)
with open(out_csv, "w") as out_handle:
writer = csv.writer(out_handle)
writer.writerow(header)
plot_data = []
plot_files = []
for data in sorted(
vitems,
key=lambda x: x.get("lane", dd.get_sample_name(x))):
validations = [
variant.get(vkey) for variant in data.get("variants", [])
]
validations = [v for v in validations if v]
if len(validations) == 0 and vkey in data:
validations = [data.get(vkey)]
for validate in validations:
if validate:
validate["grading_summary"] = out_csv
if validate.get("grading"):
for row in _get_validate_plotdata_yaml(
validate["grading"], data):
writer.writerow(row)
plot_data.append(row)
elif validate.get("summary") and not validate.get(
"summary") == "None":
if isinstance(validate["summary"], (list, tuple)):
plot_files.extend(
list(set(validate["summary"])))
else:
plot_files.append(validate["summary"])
if plot_files:
plots = validateplot.classifyplot_from_plotfiles(
plot_files, out_csv)
elif plot_data:
plots = validateplot.create(plot_data, header, 0, data["config"],
os.path.splitext(out_csv)[0])
else:
plots = []
for data in vitems:
if data.get(vkey):
data[vkey]["grading_plots"] = plots
for variant in data.get("variants", []):
if variant.get(vkey):
variant[vkey]["grading_plots"] = plots
out.append([data])
return out
def test_disambiguate(self):
in_files = self.config["input_bamdiff"]
disambiguate = sam.Disambiguate(self.config)
output = list(flatten(disambiguate(in_files)))
out_md5 = map(self._get_md5, output)
correct_files = self._correct_files(output)
correct_md5 = map(self._get_md5, correct_files)
self.assertTrue(out_md5 == correct_md5)
def _get_vcf_samples(calls):
all_samples = set([])
for f in utils.flatten(calls):
cur = set(vcfutils.get_samples(f))
if cur:
if not all_samples:
all_samples = cur
else:
all_samples &= set(cur)
return list(all_samples)
def summary(*samples):
"""Summarize all quality metrics together"""
samples = list(utils.flatten(samples))
work_dir = dd.get_work_dir(samples[0])
multiqc = config_utils.get_program("multiqc", samples[0]["config"])
if not multiqc:
logger.debug("multiqc not found. Update bcbio_nextgen.py tools to fix this issue.")
out_dir = utils.safe_makedir(os.path.join(work_dir, "qc", "multiqc"))
out_data = os.path.join(out_dir, "multiqc_data")
out_file = os.path.join(out_dir, "multiqc_report.html")
file_list = os.path.join(out_dir, "list_files.txt")
work_samples = [cwlutils.unpack_tarballs(utils.deepish_copy(x), x) for x in samples]
work_samples = _report_summary(work_samples, os.path.join(out_dir, "report"))
if not utils.file_exists(out_file):
with tx_tmpdir(samples[0], work_dir) as tx_out:
in_files = _get_input_files(work_samples, out_dir, tx_out)
in_files += _merge_metrics(work_samples, out_dir)
if _one_exists(in_files):
with utils.chdir(out_dir):
_create_config_file(out_dir, work_samples)
input_list_file = _create_list_file(in_files, file_list)
if dd.get_tmp_dir(samples[0]):
export_tmp = "export TMPDIR=%s &&" % dd.get_tmp_dir(samples[0])
else:
export_tmp = ""
path_export = utils.local_path_export()
other_opts = config_utils.get_resources("multiqc", samples[0]["config"]).get("options", [])
other_opts = " ".join([str(x) for x in other_opts])
cmd = "{path_export}{export_tmp} {multiqc} -f -l {input_list_file} {other_opts} -o {tx_out}"
do.run(cmd.format(**locals()), "Run multiqc")
if utils.file_exists(os.path.join(tx_out, "multiqc_report.html")):
shutil.move(os.path.join(tx_out, "multiqc_report.html"), out_file)
shutil.move(os.path.join(tx_out, "multiqc_data"), out_data)
samples = _group_by_sample_and_batch(samples)
if utils.file_exists(out_file) and samples:
data_files = set()
for i, data in enumerate(samples):
data_files.add(os.path.join(out_dir, "report", "metrics", dd.get_sample_name(data) + "_bcbio.txt"))
data_files.add(os.path.join(out_dir, "report", "metrics", "target_info.yaml"))
data_files.add(os.path.join(out_dir, "multiqc_config.yaml"))
data_files = [f for f in data_files if f and utils.file_exists(f)]
if "summary" not in samples[0]:
samples[0]["summary"] = {}
samples[0]["summary"]["multiqc"] = {"base": out_file, "secondary": data_files}
data_json = os.path.join(out_dir, "multiqc_data", "multiqc_data.json")
data_json_final = _save_uploaded_data_json(samples, data_json, os.path.join(out_dir, "multiqc_data"))
if data_json_final:
samples[0]["summary"]["multiqc"]["secondary"].append(data_json_final)
file_list_final = _save_uploaded_file_list(samples, file_list, out_dir)
if file_list_final:
samples[0]["summary"]["multiqc"]["secondary"].append(file_list_final)
return [[data] for data in samples]
def summarize_grading(samples, vkey="validate"):
"""Provide summaries of grading results across all samples.
Handles both traditional pipelines (validation part of variants) and CWL
pipelines (validation at top level)
"""
samples = list(utils.flatten(samples))
if not _has_grading_info(samples, vkey):
return [[d] for d in samples]
validate_dir = utils.safe_makedir(os.path.join(samples[0]["dirs"]["work"], vkey))
header = ["sample", "caller", "variant.type", "category", "value"]
_summarize_combined(samples, vkey)
validated, out = _group_validate_samples(samples, vkey,
(["metadata", "validate_batch"], ["metadata", "batch"], ["description"]))
for vname, vitems in validated.items():
out_csv = os.path.join(validate_dir, "grading-summary-%s.csv" % vname)
with open(out_csv, "w") as out_handle:
writer = csv.writer(out_handle)
writer.writerow(header)
plot_data = []
plot_files = []
for data in sorted(vitems, key=lambda x: x.get("lane", dd.get_sample_name(x)) or ""):
validations = [variant.get(vkey) for variant in data.get("variants", [])
if isinstance(variant, dict)]
validations = [v for v in validations if v]
if len(validations) == 0 and vkey in data:
validations = [data.get(vkey)]
for validate in validations:
if validate:
validate["grading_summary"] = out_csv
if validate.get("grading"):
for row in _get_validate_plotdata_yaml(validate["grading"], data):
writer.writerow(row)
plot_data.append(row)
elif validate.get("summary") and not validate.get("summary") == "None":
if isinstance(validate["summary"], (list, tuple)):
plot_files.extend(list(set(validate["summary"])))
else:
plot_files.append(validate["summary"])
if plot_files:
plots = validateplot.classifyplot_from_plotfiles(plot_files, out_csv)
elif plot_data:
plots = validateplot.create(plot_data, header, 0, data["config"],
os.path.splitext(out_csv)[0])
else:
plots = []
for data in vitems:
if data.get(vkey):
data[vkey]["grading_plots"] = plots
for variant in data.get("variants", []):
if isinstance(variant, dict) and variant.get(vkey):
variant[vkey]["grading_plots"] = plots
out.append([data])
return out
def summarize_vc(items):
"""CWL target: summarize variant calls and validation for multiple samples.
"""
items = [utils.to_single_data(x) for x in utils.flatten(items)]
items = [_normalize_vc_input(x) for x in items]
items = validate.summarize_grading(items)
items = [utils.to_single_data(x) for x in items]
out = {
"validate": validate.combine_validations(items),
"variants": {
"calls": [],
"gvcf": [],
"samples": []
}
}
added = set([])
variants_by_sample = collections.defaultdict(list)
sample_order = []
for data in items:
batch_samples = data.get("batch_samples", [dd.get_sample_name(data)])
for s in batch_samples:
if s not in sample_order:
sample_order.append(s)
if data.get("vrn_file"):
# Only get batches if we're actually doing variantcalling in bcbio
# otherwise we'll be using the original files
names = dd.get_batches(data) if dd.get_variantcaller(
data) else None
if not names:
names = [dd.get_sample_name(data)]
batch_name = names[0]
if data.get("vrn_file_joint") is not None:
to_add = [("vrn_file", "gvcf", dd.get_sample_name(data)),
("vrn_file_joint", "calls", batch_name)]
else:
to_add = [("vrn_file", "calls", batch_name)]
for vrn_key, out_key, name in to_add:
cur_name = "%s-%s" % (name, dd.get_variantcaller(data))
out_file = os.path.join(
utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "variants",
out_key)), "%s.vcf.gz" % cur_name)
for s in batch_samples:
variants_by_sample[s].append(out_file)
if cur_name not in added:
added.add(cur_name)
# Ideally could symlink here but doesn't appear to work with
# Docker container runs on Toil where PATHs don't get remapped
utils.copy_plus(os.path.realpath(data[vrn_key]), out_file)
vcfutils.bgzip_and_index(out_file, data["config"])
out["variants"][out_key].append(out_file)
for sample in sample_order:
out["variants"]["samples"].append(variants_by_sample[sample])
return [out]
def summary(*samples):
"""Summarize all quality metrics together"""
samples = list(utils.flatten(samples))
work_dir = dd.get_work_dir(samples[0])
multiqc = config_utils.get_program("multiqc", samples[0]["config"])
if not multiqc:
logger.debug("multiqc not found. Update bcbio_nextgen.py tools to fix this issue.")
out_dir = utils.safe_makedir(os.path.join(work_dir, "qc", "multiqc"))
out_data = os.path.join(out_dir, "multiqc_data")
out_file = os.path.join(out_dir, "multiqc_report.html")
file_list = os.path.join(out_dir, "list_files.txt")
work_samples = [cwlutils.unpack_tarballs(utils.deepish_copy(x), x) for x in samples]
work_samples = _report_summary(work_samples, os.path.join(out_dir, "report"))
if not utils.file_exists(out_file):
with tx_tmpdir(samples[0], work_dir) as tx_out:
in_files = _get_input_files(work_samples, out_dir, tx_out)
in_files += _merge_metrics(work_samples, out_dir)
if _one_exists(in_files):
with utils.chdir(out_dir):
_create_config_file(out_dir, work_samples)
input_list_file = _create_list_file(in_files, file_list)
if dd.get_tmp_dir(samples[0]):
export_tmp = "export TMPDIR=%s &&" % dd.get_tmp_dir(samples[0])
else:
export_tmp = ""
path_export = utils.local_path_export()
other_opts = config_utils.get_resources("multiqc", samples[0]["config"]).get("options", [])
other_opts = " ".join([str(x) for x in other_opts])
cmd = "{path_export}{export_tmp} {multiqc} -f -l {input_list_file} {other_opts} -o {tx_out}"
do.run(cmd.format(**locals()), "Run multiqc")
if utils.file_exists(os.path.join(tx_out, "multiqc_report.html")):
shutil.move(os.path.join(tx_out, "multiqc_report.html"), out_file)
shutil.move(os.path.join(tx_out, "multiqc_data"), out_data)
out = []
for i, data in enumerate(_group_by_samplename(samples)):
if i == 0:
if utils.file_exists(out_file):
data_files = glob.glob(os.path.join(out_dir, "multiqc_data", "*.txt"))
data_files += glob.glob(os.path.join(out_dir, "report", "*", "*.bed"))
data_files += glob.glob(os.path.join(out_dir, "report", "*", "*.txt"))
data_files += glob.glob(os.path.join(out_dir, "report", "*", "*.tsv"))
data_files += glob.glob(os.path.join(out_dir, "report", "*", "*.yaml"))
data_files += glob.glob(os.path.join(out_dir, "report", "*.R*"))
data_files += glob.glob(os.path.join(out_dir, "multiqc_config.yaml"))
data_files.append(file_list)
if "summary" not in data:
data["summary"] = {}
data["summary"]["multiqc"] = {"base": out_file, "secondary": data_files}
file_list_final = _save_uploaded_file_list(samples, file_list, out_dir)
if file_list_final:
data["summary"]["multiqc"]["secondary"].append(file_list_final)
out.append([data])
return out
def stringtie_merge(*samples):
to_merge = filter_missing(flatten([dd.get_assembled_gtf(data) for data in
dd.sample_data_iterator(samples)]))
data = samples[0][0]
ref_file = dd.get_sam_ref(data)
gtf_file = dd.get_gtf_file(data)
num_cores = dd.get_num_cores(data)
merged_gtf = stringtie.merge(to_merge, ref_file, gtf_file, num_cores, data)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_merged_gtf(data, merged_gtf)
updated_samples.append([data])
return updated_samples
def stringtie_merge(*samples):
to_merge = filter_missing(flatten([dd.get_assembled_gtf(data) for data in
dd.sample_data_iterator(samples)]))
data = samples[0][0]
ref_file = dd.get_sam_ref(data)
gtf_file = dd.get_gtf_file(data)
num_cores = dd.get_num_cores(data)
merged_gtf = stringtie.merge(to_merge, ref_file, gtf_file, num_cores, data)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_merged_gtf(data, merged_gtf)
updated_samples.append([data])
return updated_samples
def _get_vcf_samples(calls, data):
have_full_file = False
all_samples = set([])
for f in utils.flatten(calls):
if have_full_file:
cur = set(vcfutils.get_samples(f))
if cur:
if not all_samples:
all_samples = cur
else:
all_samples &= set(cur)
else:
for test_name in [dd.get_sample_name(data)] + dd.get_batches(data):
if os.path.basename(f).startswith("%s-" % test_name):
all_samples.add(dd.get_sample_name(data))
return list(all_samples)
def cufflinks_merge(*samples):
to_merge = filter_missing(flatten([dd.get_assembled_gtf(data) for data in
dd.sample_data_iterator(samples)]))
data = samples[0][0]
bam_file = dd.get_work_bam(data)
ref_file = dd.get_sam_ref(data)
gtf_file = dd.get_gtf_file(data)
out_dir = os.path.join(dd.get_work_dir(data), "assembly")
num_cores = dd.get_num_cores(data)
merged_gtf = cufflinks.merge(to_merge, ref_file, gtf_file, num_cores,
samples[0][0])
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_merged_gtf(data, merged_gtf)
updated_samples.append([data])
return updated_samples
def summarize_vc(items):
"""CWL target: summarize variant calls and validation for multiple samples.
"""
items = [utils.to_single_data(x) for x in utils.flatten(items)]
items = [_normalize_vc_input(x) for x in items]
items = validate.summarize_grading(items)
items = [utils.to_single_data(x) for x in items]
out = {"validate": validate.combine_validations(items),
"variants": {"calls": [], "gvcf": [], "samples": []}}
added = set([])
variants_by_sample = collections.defaultdict(list)
sample_order = []
for data in items:
batch_samples = data.get("batch_samples", [dd.get_sample_name(data)])
for s in batch_samples:
if s not in sample_order:
sample_order.append(s)
if data.get("vrn_file"):
# Only get batches if we're actually doing variantcalling in bcbio
# otherwise we'll be using the original files
names = dd.get_batches(data) if dd.get_variantcaller(data) else None
if not names:
names = [dd.get_sample_name(data)]
batch_name = names[0]
if data.get("vrn_file_joint") is not None:
to_add = [("vrn_file", "gvcf", dd.get_sample_name(data)),
("vrn_file_joint", "calls", batch_name)]
else:
to_add = [("vrn_file", "calls", batch_name)]
for vrn_key, out_key, name in to_add:
cur_name = "%s-%s" % (name, dd.get_variantcaller(data))
out_file = os.path.join(utils.safe_makedir(os.path.join(dd.get_work_dir(data),
"variants", out_key)),
"%s.vcf.gz" % cur_name)
for s in batch_samples:
variants_by_sample[s].append(out_file)
if cur_name not in added:
added.add(cur_name)
# Ideally could symlink here but doesn't appear to work with
# Docker container runs on Toil where PATHs don't get remapped
utils.copy_plus(os.path.realpath(data[vrn_key]), out_file)
vcfutils.bgzip_and_index(out_file, data["config"])
out["variants"][out_key].append(out_file)
for sample in sample_order:
out["variants"]["samples"].append(variants_by_sample[sample])
return [out]
def _merge_metadata(samples):
"""Merge all metadata into CSV file"""
samples = list(utils.flatten(samples))
out_dir = dd.get_work_dir(samples[0])
logger.info("summarize metadata")
out_file = os.path.join(out_dir, "metadata.csv")
sample_metrics = collections.defaultdict(dict)
for s in samples:
m = tz.get_in(['metadata'], s)
if isinstance(m, six.string_types):
m = json.loads(m)
if m:
for me in list(m.keys()):
if isinstance(m[me], list) or isinstance(m[me], dict) or isinstance(m[me], tuple):
m.pop(me, None)
sample_metrics[dd.get_sample_name(s)].update(m)
pd.DataFrame(sample_metrics).transpose().to_csv(out_file)
return out_file
def _merge_metadata(samples):
"""Merge all metadata into CSV file"""
samples = list(utils.flatten(samples))
out_dir = dd.get_work_dir(samples[0])
logger.info("summarize metadata")
out_file = os.path.join(out_dir, "metadata.csv")
sample_metrics = collections.defaultdict(dict)
for s in samples:
m = tz.get_in(['metadata'], s)
if isinstance(m, six.string_types):
m = json.loads(m)
if m:
for me in list(m.keys()):
if isinstance(m[me], list) or isinstance(m[me], dict) or isinstance(m[me], tuple):
m.pop(me, None)
sample_metrics[dd.get_sample_name(s)].update(m)
pd.DataFrame(sample_metrics).transpose().to_csv(out_file)
return out_file
def _get_coverage_per_region(name):
"""
Parse coverage file if it exists to get average value.
"""
fns = tz.get_in(["summary", "qc", "coverage"], name, {})
if fns:
fns = utils.flatten(fns.values())
fn = [fn for fn in fns if fn.find("coverage_fixed.bed") > -1]
if fn:
fn = fn[0]
if utils.file_exists(fn):
logger.debug("Reading meanCoverage for: %s" % fn)
try:
dt = pd.read_csv(fn, sep="\t", index_col=False)
if "meanCoverage" in dt:
if len(dt["meanCoverage"]) > 0:
return "%.3f" % (sum(map(float, dt['meanCoverage'])) / len(dt['meanCoverage']))
except TypeError:
logger.debug("%s has no lines in coverage.bed" % name)
return "NA"
def _get_coverage_per_region(name):
"""
Parse coverage file if it exists to get average value.
"""
fns = tz.get_in(["summary", "qc", "coverage"], name, {})
if fns:
fns = utils.flatten(fns.values())
fn = [fn for fn in fns if fn.find("coverage_fixed.bed") > -1]
if fn:
fn = fn[0]
if utils.file_exists(fn):
logger.debug("Reading meanCoverage for: %s" % fn)
try:
dt = pd.read_csv(fn, sep="\t", index_col=False)
if "meanCoverage" in dt:
if len(dt["meanCoverage"]) > 0:
return "%.3f" % (
sum(map(float, dt['meanCoverage'])) /
len(dt['meanCoverage']))
except TypeError:
logger.debug("%s has no lines in coverage.bed" % name)
return "NA"
def main(config_file, fastq_dir):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
barcode_info = config["barcodes"]
print "Processing %s." % (fastq_dir)
in_files = glob.glob(os.path.join(fastq_dir, "*.fastq"))
print "Found %s in %s. " % (in_files, fastq_dir)
print "Combining paired-end files, if found."
pairs = combine_pairs(in_files)
print "Calulcated pairs: %s." % (pairs)
out_files = []
for pair in pairs:
barcode = _determine_barcode_from_filename(pair[0])
print "Detected barcode: %s" % barcode
if barcode not in barcode_info.keys():
print "barcode %s not found in the YAML file, skipping." % (
barcode)
continue
print "Sample ID: %s" % (barcode_info[barcode][0])
type = barcode_info[barcode][1]
print "Sample type: %s" % (barcode_info[barcode][1])
to_trim = config["to_trim"][type]
cutadapt_dir = "cutadapt"
print("Trimming off %s and any bases before it from %s." %
(to_trim[0], pair[0]))
out_dir = os.path.join(cutadapt_dir, os.path.basename(pair[0]))
out_files.append(_trim_from_front(pair[0], to_trim[0]))
if len(pair) > 1:
print("Trimming off %s and any bases before it from %s." %
(to_trim[1], pair[1]))
out_files.append(_trim_from_front(pair[1], to_trim[1]))
out_files = list(flatten(out_files))
out_files = combine_pairs(out_files)
for pair in out_files:
if len(pair) > 1:
filter_reads_by_length(pair[0], pair[1], "fastq-sanger")
else:
filter_single_reads_by_length(pair[0], "fastq-sanger")
def main(config_file, fastq_dir):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
barcode_info = config["barcodes"]
print "Processing %s." % (fastq_dir)
in_files = glob.glob(os.path.join(fastq_dir, "*.fastq"))
print "Found %s in %s. " % (in_files, fastq_dir)
print "Combining paired-end files, if found."
pairs = combine_pairs(in_files)
print "Calulcated pairs: %s." % (pairs)
out_files = []
for pair in pairs:
barcode = _determine_barcode_from_filename(pair[0])
print "Detected barcode: %s" % barcode
if barcode not in barcode_info.keys():
print "barcode %s not found in the YAML file, skipping." % (barcode)
continue
print "Sample ID: %s" % (barcode_info[barcode][0])
type = barcode_info[barcode][1]
print "Sample type: %s" % (barcode_info[barcode][1])
to_trim = config["to_trim"][type]
cutadapt_dir = "cutadapt"
print ("Trimming off %s and any bases before it from %s."
% (to_trim[0], pair[0]))
out_dir = os.path.join(cutadapt_dir, os.path.basename(pair[0]))
out_files.append(_trim_from_front(pair[0], to_trim[0]))
if len(pair) > 1:
print ("Trimming off %s and any bases before it from %s."
% (to_trim[1], pair[1]))
out_files.append(_trim_from_front(pair[1], to_trim[1]))
out_files = list(flatten(out_files))
out_files = combine_pairs(out_files)
for pair in out_files:
if len(pair) > 1:
filter_reads_by_length(pair[0], pair[1], "fastq-sanger")
else:
filter_single_reads_by_length(pair[0], "fastq-sanger")
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
def update_summary_qc(data, key, base=None, secondary=None):
"""
updates summary_qc, keyed by key. key is generally the program the quality
control metrics came from. if key already exists, the specified
base/secondary files are added as secondary files to the existing
key, removing duplicates.
stick files into summary_qc if you want them propagated forward
and available for multiqc
"""
summary = deepish_copy(get_summary_qc(data, {}))
files = [[base], [secondary],
tz.get_in([key, "base"], summary, []),
tz.get_in([key, "secondary"], summary, [])]
files = list(set([x for x in flatten(files) if x]))
base = tz.first(files)
secondary = list(tz.drop(1, files))
if base and secondary:
summary[key] = {"base": base, "secondary": secondary}
elif base:
summary[key] = {"base": base}
data = set_summary_qc(data, summary)
return data
def summary(*samples):
"""Summarize all quality metrics together"""
samples = list(utils.flatten(samples))
work_dir = dd.get_work_dir(samples[0])
multiqc = config_utils.get_program("multiqc", samples[0]["config"])
if not multiqc:
logger.debug("multiqc not found. Update bcbio_nextgen.py tools to fix this issue.")
out_dir = utils.safe_makedir(os.path.join(work_dir, "qc", "multiqc"))
out_data = os.path.join(out_dir, "multiqc_data")
out_file = os.path.join(out_dir, "multiqc_report.html")
file_list = os.path.join(out_dir, "list_files.txt")
work_samples = cwlutils.unpack_tarballs([utils.deepish_copy(x) for x in samples], samples[0])
work_samples = _summarize_inputs(work_samples, out_dir)
if not utils.file_exists(out_file):
with tx_tmpdir(samples[0], work_dir) as tx_out:
in_files = _get_input_files(work_samples, out_dir, tx_out)
in_files += _merge_metrics(work_samples, out_dir)
if _one_exists(in_files):
with utils.chdir(out_dir):
_create_config_file(out_dir, work_samples)
input_list_file = _create_list_file(in_files, file_list)
if dd.get_tmp_dir(samples[0]):
export_tmp = "export TMPDIR=%s && " % dd.get_tmp_dir(samples[0])
else:
export_tmp = ""
locale_export = utils.locale_export()
path_export = utils.local_path_export()
other_opts = config_utils.get_resources("multiqc", samples[0]["config"]).get("options", [])
other_opts = " ".join([str(x) for x in other_opts])
cmd = ("{path_export}{export_tmp}{locale_export} "
"{multiqc} -f -l {input_list_file} {other_opts} -o {tx_out}")
do.run(cmd.format(**locals()), "Run multiqc")
if utils.file_exists(os.path.join(tx_out, "multiqc_report.html")):
shutil.move(os.path.join(tx_out, "multiqc_report.html"), out_file)
shutil.move(os.path.join(tx_out, "multiqc_data"), out_data)
samples = _group_by_sample_and_batch(samples)
if utils.file_exists(out_file) and samples:
data_files = set()
for i, data in enumerate(samples):
data_files.add(os.path.join(out_dir, "report", "metrics", dd.get_sample_name(data) + "_bcbio.txt"))
data_files.add(os.path.join(out_dir, "report", "metrics", "target_info.yaml"))
data_files.add(os.path.join(out_dir, "multiqc_config.yaml"))
[data_files.add(f) for f in glob.glob(os.path.join(out_dir, "multiqc_data", "*"))]
data_files = [f for f in data_files if f and utils.file_exists(f)]
if "summary" not in samples[0]:
samples[0]["summary"] = {}
samples[0]["summary"]["multiqc"] = {"base": out_file, "secondary": data_files}
data_json = os.path.join(out_dir, "multiqc_data", "multiqc_data.json")
data_json_final = _save_uploaded_data_json(samples, data_json, os.path.join(out_dir, "multiqc_data"))
if data_json_final:
samples[0]["summary"]["multiqc"]["secondary"].append(data_json_final)
# Prepare final file list and inputs for downstream usage
file_list_final = _save_uploaded_file_list(samples, file_list, out_dir)
if file_list_final:
samples[0]["summary"]["multiqc"]["secondary"].append(file_list_final)
if any([cwlutils.is_cwl_run(d) for d in samples]):
for indir in ["inputs", "report"]:
tarball = os.path.join(out_dir, "multiqc-%s.tar.gz" % (indir))
if not utils.file_exists(tarball):
with utils.chdir(out_dir):
cmd = ["tar", "-czvpf", tarball, indir]
do.run(cmd, "Compress multiqc inputs: %s" % indir)
samples[0]["summary"]["multiqc"]["secondary"].append(tarball)
if any([cwlutils.is_cwl_run(d) for d in samples]):
samples = _add_versions(samples)
return [[data] for data in samples]
def _get_rv_adapters(data):
builtin = [RV_ADAPTERS[x] for x in dd.get_adapters(data) if x in FW_ADAPTERS]
return flatten(builtin + dd.get_custom_trim(data))
def _get_rv_adapters(data):
builtin = [
RV_ADAPTERS[x] for x in dd.get_adapters(data) if x in FW_ADAPTERS
]
return flatten(builtin + dd.get_custom_trim(data))
def summary(*samples):
"""Summarize all quality metrics together"""
samples = list(utils.flatten(samples))
work_dir = dd.get_work_dir(samples[0])
multiqc = config_utils.get_program("multiqc", samples[0]["config"])
if not multiqc:
logger.debug(
"multiqc not found. Update bcbio_nextgen.py tools to fix this issue."
)
out_dir = utils.safe_makedir(os.path.join(work_dir, "qc", "multiqc"))
out_data = os.path.join(out_dir, "multiqc_data")
out_file = os.path.join(out_dir, "multiqc_report.html")
file_list = os.path.join(out_dir, "list_files.txt")
work_samples = [
cwlutils.unpack_tarballs(utils.deepish_copy(x), x) for x in samples
]
work_samples = _report_summary(work_samples,
os.path.join(out_dir, "report"))
if not utils.file_exists(out_file):
with tx_tmpdir(samples[0], work_dir) as tx_out:
in_files = _get_input_files(work_samples, out_dir, tx_out)
in_files += _merge_metrics(work_samples, out_dir)
if _one_exists(in_files):
with utils.chdir(out_dir):
_create_config_file(out_dir, work_samples)
input_list_file = _create_list_file(in_files, file_list)
if dd.get_tmp_dir(samples[0]):
export_tmp = "export TMPDIR=%s &&" % dd.get_tmp_dir(
samples[0])
else:
export_tmp = ""
path_export = utils.local_path_export()
cmd = "{path_export}{export_tmp} {multiqc} -f -l {input_list_file} -o {tx_out}"
do.run(cmd.format(**locals()), "Run multiqc")
if utils.file_exists(
os.path.join(tx_out, "multiqc_report.html")):
shutil.move(
os.path.join(tx_out, "multiqc_report.html"),
out_file)
shutil.move(os.path.join(tx_out, "multiqc_data"),
out_data)
out = []
for i, data in enumerate(_group_by_samplename(samples)):
if i == 0:
if utils.file_exists(out_file):
data_files = glob.glob(
os.path.join(out_dir, "multiqc_data", "*.txt"))
data_files += glob.glob(
os.path.join(out_dir, "report", "*", "*.bed"))
data_files += glob.glob(
os.path.join(out_dir, "report", "*", "*.txt"))
data_files += glob.glob(
os.path.join(out_dir, "report", "*", "*.tsv"))
data_files += glob.glob(
os.path.join(out_dir, "report", "*", "*.yaml"))
data_files += glob.glob(os.path.join(out_dir, "report",
"*.R*"))
data_files.append(file_list)
if "summary" not in data:
data["summary"] = {}
data["summary"]["multiqc"] = {
"base": out_file,
"secondary": data_files
}
file_list_final = _save_uploaded_file_list(
samples, file_list, out_dir)
if file_list_final:
data["summary"]["multiqc"]["secondary"].append(
file_list_final)
out.append([data])
return out
