Beispiel #1
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
Beispiel #2
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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
Beispiel #3
0
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
Beispiel #4
0
def run(bam_file, data, out_dir):
    """Run coverage QC analysis
    """
    out = dict()

    if dd.get_coverage(data) and dd.get_coverage(data) not in ["None"]:
        merged_bed_file = dd.get_coverage_merged(data)
        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(data, bam_file, merged_bed_file, target_name)
    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)

    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 = sambamba.number_of_mapped_reads(data, bam_file, keep_dups=False)
    out['Mapped_unique_reads'] = mapped_unique

    if merged_bed_file:
        ontarget = sambamba.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 = sambamba.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

    out_files = cov.coverage_region_detailed_stats(data, out_dir,
                                                   extra_cutoffs=set([max(1, int(avg_depth * 0.8))]))
    for ext in ["coverage.bed", "summary.bed"]:
        out_files += [x for x in glob.glob(os.path.join(out_dir, "*%s" % ext)) if os.path.isfile(x)]
    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