def _detect_duplicates(bam_file, out_dir, data):
"""
count duplicate percentage
"""
out_file = os.path.join(out_dir, "dup_metrics.txt")
if not utils.file_exists(out_file):
dup_align_bam = postalign.dedup_bam(bam_file, data)
logger.info("Detecting duplicates in %s." % dup_align_bam)
dup_count = readstats.number_of_mapped_reads(data,
dup_align_bam,
keep_dups=False)
tot_count = readstats.number_of_mapped_reads(data,
dup_align_bam,
keep_dups=True)
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
out_handle.write("%s\n%s\n" % (dup_count, tot_count))
with open(out_file) as in_handle:
dupes = float(next(in_handle).strip())
total = float(next(in_handle).strip())
if total == 0:
rate = "NA"
else:
rate = dupes / total
return {"Duplication Rate of Mapped": rate}
def _count_offtarget(data, bam_file, bed_file, target_name):
mapped_unique = readstats.number_of_mapped_reads(data, bam_file, keep_dups=False)
ontarget = readstats.number_of_mapped_reads(
data, bam_file, keep_dups=False, bed_file=bed_file, target_name=target_name)
if mapped_unique:
return float(mapped_unique - ontarget) / mapped_unique
else:
return 0.0
def _count_offtarget(data, bam_file, bed_file, target_name):
mapped_unique = readstats.number_of_mapped_reads(data, bam_file, keep_dups=False)
ontarget = readstats.number_of_mapped_reads(
data, bam_file, keep_dups=False, bed_file=bed_file, target_name=target_name)
if mapped_unique:
return float(mapped_unique - ontarget) / mapped_unique
else:
return 0.0
def _prep_real_counts(bam_file, data, samtools_stats):
out = {}
if dd.get_coverage(data) and dd.get_coverage(data) not in ["None"]:
bed = dd.get_coverage_merged(data)
target_name = "coverage"
elif dd.get_coverage_interval(data) != "genome":
bed = dd.get_variant_regions_merged(data) or dd.get_sample_callable(
data)
target_name = "variant_regions"
else:
bed = None
target_name = "genome"
dedupped = utils.get_in(data, ("config", "algorithm", "mark_duplicates"),
True)
if bed:
out["Preseq_genome_size"] = pybedtools.BedTool(bed).total_coverage()
out["Preseq_read_count"] = readstats.number_of_mapped_reads(
data,
bam_file,
keep_dups=True,
bed_file=bed,
target_name=target_name)
ontrg_unique_depth = cov.get_average_coverage(target_name, bed, data,
bam_file)
if dedupped:
out["Preseq_unique_count"] = readstats.number_of_mapped_reads(
data,
bam_file,
keep_dups=False,
bed_file=bed,
target_name=target_name)
# Counting average on-target alignment length, based on the equation:
# avg depth ~~ num (unique) on-target alignments * avg on-target aln length / target size
total_alignments = out.get(
"Preseq_unique_count") or out["Preseq_read_count"]
out["Preseq_read_length"] = ontrg_unique_depth * out[
"Preseq_genome_size"] // total_alignments
else: # WGS
out["Preseq_genome_size"] = sum([
c.size
for c in ref.file_contigs(dd.get_ref_file(data), data["config"])
])
out["Preseq_read_count"] = int(samtools_stats["Total_reads"])
out["Preseq_read_length"] = int(samtools_stats["Average_read_length"])
if dedupped:
out["Preseq_unique_count"] = out["Preseq_read_count"] - int(
samtools_stats["Duplicates"])
return out
def _reads_in_peaks(bam_file, peaks_file, sample):
"""Calculate number of reads in peaks"""
if not peaks_file:
return {}
rip = number_of_mapped_reads(sample, bam_file, bed_file=peaks_file)
return {"metrics": {"RiP": rip}}
def _detect_duplicates(bam_file, out_dir, data):
"""
count duplicate percentage
"""
out_file = os.path.join(out_dir, "dup_metrics.txt")
if not utils.file_exists(out_file):
dup_align_bam = postalign.dedup_bam(bam_file, data)
logger.info("Detecting duplicates in %s." % dup_align_bam)
dup_count = readstats.number_of_mapped_reads(data, dup_align_bam, keep_dups=False)
tot_count = readstats.number_of_mapped_reads(data, dup_align_bam, keep_dups=True)
with file_transaction(data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
out_handle.write("%s\n%s\n" % (dup_count, tot_count))
with open(out_file) as in_handle:
dupes = float(next(in_handle).strip())
total = float(next(in_handle).strip())
if total == 0:
rate = "NA"
else:
rate = dupes / total
return {"Duplication Rate of Mapped": rate}
def _prep_real_counts(bam_file, data, samtools_stats):
out = {}
if dd.get_coverage(data) and dd.get_coverage(data) not in ["None"]:
bed = dd.get_coverage_merged(data)
target_name = "coverage"
elif dd.get_coverage_interval(data) != "genome":
bed = dd.get_variant_regions_merged(data)
target_name = "variant_regions"
else:
bed = None
target_name = "genome"
dedupped = utils.get_in(data, ("config", "algorithm", "mark_duplicates"), True)
if bed:
out["Preseq_genome_size"] = pybedtools.BedTool(bed).total_coverage()
out["Preseq_read_count"] = readstats.number_of_mapped_reads(
data, bam_file, keep_dups=True, bed_file=bed, target_name=target_name)
ontrg_unique_depth = cov.get_average_coverage(target_name, bed, data, bam_file)
if dedupped:
out["Preseq_unique_count"] = readstats.number_of_mapped_reads(
data, bam_file, keep_dups=False, bed_file=bed, target_name=target_name)
# Counting average on-target alignment length, based on the equation:
# avg depth ~~ num (unique) on-target alignments * avg on-target aln length / target size
total_alignments = out.get("Preseq_unique_count") or out["Preseq_read_count"]
out["Preseq_read_length"] = ontrg_unique_depth * out["Preseq_genome_size"] // total_alignments
else: # WGS
out["Preseq_genome_size"] = sum([c.size for c in ref.file_contigs(dd.get_ref_file(data), data["config"])])
out["Preseq_read_count"] = int(samtools_stats["Total_reads"])
out["Preseq_read_length"] = int(samtools_stats["Average_read_length"])
if dedupped:
out["Preseq_unique_count"] = out["Preseq_read_count"] - int(samtools_stats["Duplicates"])
return out
def run(bam_file, data, out_dir):
"""Run coverage QC analysis
"""
out = dict()
out_dir = utils.safe_makedir(out_dir)
if dd.get_coverage(data) and dd.get_coverage(data) not in ["None"]:
merged_bed_file = bedutils.clean_file(dd.get_coverage_merged(data),
data,
prefix="cov-",
simple=True)
target_name = "coverage"
elif dd.get_coverage_interval(data) != "genome":
merged_bed_file = dd.get_variant_regions_merged(data)
target_name = "variant_regions"
else:
merged_bed_file = None
target_name = "genome"
avg_depth = cov.get_average_coverage(target_name, merged_bed_file, data)
if target_name == "coverage":
out_files = cov.coverage_region_detailed_stats(target_name,
merged_bed_file, data,
out_dir)
else:
out_files = []
out['Avg_coverage'] = avg_depth
samtools_stats_dir = os.path.join(out_dir, os.path.pardir, 'samtools')
from bcbio.qc import samtools
samtools_stats = samtools.run(bam_file, data,
samtools_stats_dir)["metrics"]
out["Total_reads"] = total_reads = int(samtools_stats["Total_reads"])
out["Mapped_reads"] = mapped = int(samtools_stats["Mapped_reads"])
out["Mapped_paired_reads"] = int(samtools_stats["Mapped_paired_reads"])
out['Duplicates'] = dups = int(samtools_stats["Duplicates"])
if total_reads:
out["Mapped_reads_pct"] = 100.0 * mapped / total_reads
if mapped:
out['Duplicates_pct'] = 100.0 * dups / mapped
if dd.get_coverage_interval(data) == "genome":
mapped_unique = mapped - dups
else:
mapped_unique = readstats.number_of_mapped_reads(data,
bam_file,
keep_dups=False)
out['Mapped_unique_reads'] = mapped_unique
if merged_bed_file:
ontarget = readstats.number_of_mapped_reads(data,
bam_file,
keep_dups=False,
bed_file=merged_bed_file,
target_name=target_name)
out["Ontarget_unique_reads"] = ontarget
if mapped_unique:
out["Ontarget_pct"] = 100.0 * ontarget / mapped_unique
out['Offtarget_pct'] = 100.0 * (mapped_unique -
ontarget) / mapped_unique
if dd.get_coverage_interval(data) != "genome":
# Skip padded calculation for WGS even if the "coverage" file is specified
# the padded statistic makes only sense for exomes and panels
padded_bed_file = bedutils.get_padded_bed_file(
out_dir, merged_bed_file, 200, data)
ontarget_padded = readstats.number_of_mapped_reads(
data,
bam_file,
keep_dups=False,
bed_file=padded_bed_file,
target_name=target_name + "_padded")
out["Ontarget_padded_pct"] = 100.0 * ontarget_padded / mapped_unique
if total_reads:
out['Usable_pct'] = 100.0 * ontarget / total_reads
indexcov_files = _goleft_indexcov(bam_file, data, out_dir)
out_files += [x for x in indexcov_files if x and utils.file_exists(x)]
out = {"metrics": out}
if len(out_files) > 0:
out["base"] = out_files[0]
out["secondary"] = out_files[1:]
return out
def _reads_in_peaks(bam_file, peaks_file, sample):
"""Calculate number of reads in peaks"""
if not peaks_file:
return {}
rip = number_of_mapped_reads(sample, bam_file, bed_file = peaks_file)
return {"metrics": {"RiP": rip}}
