def _merge_target_information(samples, metrics_dir):
out_file = os.path.abspath(os.path.join(metrics_dir, "target_info.yaml"))
if utils.file_exists(out_file):
return samples
genomes = set(dd.get_genome_build(data) for data in samples)
coverage_beds = set(dd.get_coverage(data) for data in samples)
original_variant_regions = set(dd.get_variant_regions_orig(data) for data in samples)
data = samples[0]
info = {}
# Reporting in MultiQC only if the genome is the same across all samples
if len(genomes) == 1:
info["genome_info"] = {
"name": dd.get_genome_build(data),
"size": sum([c.size for c in ref.file_contigs(dd.get_ref_file(data), data["config"])]),
}
# Reporting in MultiQC only if the target is the same across all samples
vcr_orig = None
if len(original_variant_regions) == 1 and list(original_variant_regions)[0] is not None:
vcr_orig = list(original_variant_regions)[0]
vcr_clean = bedutils.clean_file(vcr_orig, data)
info["variants_regions_info"] = {
"bed": vcr_orig,
"size": sum(len(x) for x in pybedtools.BedTool(dd.get_variant_regions_merged(data))),
"regions": pybedtools.BedTool(vcr_clean).count(),
}
gene_num = annotate.count_genes(vcr_clean, data)
if gene_num is not None:
info["variants_regions_info"]["genes"] = gene_num
else:
info["variants_regions_info"] = {
"bed": "callable regions",
}
# Reporting in MultiQC only if the target is the same across samples
if len(coverage_beds) == 1:
cov_bed = list(coverage_beds)[0]
if cov_bed not in [None, "None"]:
if vcr_orig and vcr_orig == cov_bed:
info["coverage_bed_info"] = info["variants_regions_info"]
else:
clean_bed = bedutils.clean_file(cov_bed, data, prefix="cov-", simple=True)
info["coverage_bed_info"] = {
"bed": cov_bed,
"size": pybedtools.BedTool(cov_bed).total_coverage(),
"regions": pybedtools.BedTool(clean_bed).count(),
}
gene_num = annotate.count_genes(clean_bed, data)
if gene_num is not None:
info["coverage_bed_info"]["genes"] = gene_num
else:
info["coverage_bed_info"] = info["variants_regions_info"]
coverage_intervals = set(data["config"]["algorithm"]["coverage_interval"] for data in samples)
if len(coverage_intervals) == 1:
info["coverage_interval"] = list(coverage_intervals)[0]
if info:
with open(out_file, "w") as out_handle:
yaml.safe_dump(info, out_handle)
return samples
def _merge_target_information(samples, metrics_dir):
out_file = os.path.abspath(os.path.join(metrics_dir, "target_info.yaml"))
if utils.file_exists(out_file):
return samples
genomes = set(dd.get_genome_build(data) for data in samples)
coverage_beds = set(dd.get_coverage(data) for data in samples)
original_variant_regions = set(
dd.get_variant_regions_orig(data) for data in samples)
data = samples[0]
info = {}
# Reporting in MultiQC only if the genome is the same across all samples
if len(genomes) == 1:
info["genome_info"] = {
"name":
dd.get_genome_build(data),
"size":
sum([
c.size for c in ref.file_contigs(dd.get_ref_file(data),
data["config"])
]),
}
# Reporting in MultiQC only if the target is the same across all samples
vcr_orig = None
if len(original_variant_regions) == 1 and list(
original_variant_regions)[0] is not None:
vcr_orig = list(original_variant_regions)[0]
vcr_clean = bedutils.clean_file(vcr_orig, data)
info["variants_regions_info"] = {
"bed":
vcr_orig,
"size":
sum(
len(x) for x in pybedtools.BedTool(
dd.get_variant_regions_merged(data))),
"regions":
pybedtools.BedTool(vcr_clean).count(),
}
gene_num = annotate.count_genes(vcr_clean, data)
if gene_num is not None:
info["variants_regions_info"]["genes"] = gene_num
else:
info["variants_regions_info"] = {
"bed": "callable regions",
}
# Reporting in MultiQC only if the target is the same across samples
if len(coverage_beds) == 1:
cov_bed = list(coverage_beds)[0]
if cov_bed not in [None, "None"]:
if vcr_orig and vcr_orig == cov_bed:
info["coverage_bed_info"] = info["variants_regions_info"]
else:
clean_bed = bedutils.clean_file(cov_bed,
data,
prefix="cov-",
simple=True)
info["coverage_bed_info"] = {
"bed": cov_bed,
"size": pybedtools.BedTool(cov_bed).total_coverage(),
"regions": pybedtools.BedTool(clean_bed).count(),
}
gene_num = annotate.count_genes(clean_bed, data)
if gene_num is not None:
info["coverage_bed_info"]["genes"] = gene_num
else:
info["coverage_bed_info"] = info["variants_regions_info"]
coverage_intervals = set(data["config"]["algorithm"]["coverage_interval"]
for data in samples)
if len(coverage_intervals) == 1:
info["coverage_interval"] = list(coverage_intervals)[0]
if info:
with open(out_file, "w") as out_handle:
yaml.safe_dump(info, out_handle)
return samples
def _merge_target_information(samples):
out_file = os.path.join("metrics", "target_info.yaml")
if utils.file_exists(out_file):
return samples
genomes = set(dd.get_genome_build(data) for data in samples)
coverage_beds = set(dd.get_coverage(data) for data in samples)
variant_regions = set(dd.get_variant_regions(data) for data in samples)
data = samples[0]
info = {}
# Reporting in MultiQC only if the genome is the sample across samples
if len(genomes) == 1:
info["genome_info"] = {
"name":
dd.get_genome_build(data),
"size":
sum([
c.size for c in ref.file_contigs(dd.get_ref_file(data),
data["config"])
]),
}
# Reporting in MultiQC only if the target is the sample across samples
vcr = None
if len(variant_regions) == 1:
vcr = dd.get_variant_regions_orig(data)
vcr_merged = dd.get_variant_regions_merged(data)
vcr_ann = annotate.add_genes(vcr, data)
info["variants_regions_info"] = {
"bed":
variant_regions,
"size":
sum(len(x) for x in pybedtools.BedTool(vcr_merged)),
"regions":
pybedtools.BedTool(vcr).count(),
"genes":
len(
list(
set(r.name for r in pybedtools.BedTool(vcr_ann)
if r.name and r.name != "."))),
}
elif len(variant_regions) == 0:
info["variants_regions_info"] = {"bed": None}
# Reporting in MultiQC only if the target is the sample across samples
if len(coverage_beds) == 1:
bed = dd.get_coverage(data)
if vcr and vcr == bed:
info["coverage_bed_info"] = info["variants_regions_info"]
elif bed:
ann_bed = annotate.add_genes(bed, data)
info["coverage_bed_info"] = {
"bed":
bed,
"size":
pybedtools.BedTool(bed).total_coverage(),
"regions":
pybedtools.BedTool(bed).count(),
"genes":
len(
list(
set(r.name for r in pybedtools.BedTool(ann_bed)
if r.name and r.name != "."))),
}
if info:
with open(out_file, "w") as out_handle:
yaml.safe_dump(info, out_handle)
return samples
