comparison vsnp_add_zero_coverage.py @ 4:4535ad8b74f3 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/vsnp commit c38fd63f7980c70390d104a73ba4c72b266444c3

3:ae7b1b97a2a0

def get_zero_df(reference):
    # Create a zero coverage dictionary.
    zero_dict = {}
    for record in SeqIO.parse(reference, "fasta"):
        chrom = record.id
        total_len = len(record.seq)
        for pos in list(range(1, total_len + 1)):
            zero_dict["%s-%s" % (str(chrom), str(pos))] = 0
    # Convert it to a data frame with depth_x
    # and depth_y columns - index is NaN.
    zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"])
    return zero_df


def output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf):
    column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"]
    vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#')

    else:
        shutil.move(vcf_file, output_vcf)
    return good_snp_count


def output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics):
    bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage]
    vcf_metrics = [base_file_name, str(good_snp_count), "", ""]
    metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"]
    with open(output_metrics, "w") as fh:
        fh.write("# %s\n" % "\t".join(metrics_columns))
        fh.write("%s\n" % "\t".join(bam_metrics))
        fh.write("%s\n" % "\t".join(vcf_metrics))


def output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics):
    base_file_name = get_sample_name(vcf_file)
    good_snp_count = output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf)
    output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics)


def get_coverage_and_snp_count(bam_file, vcf_file, reference, output_metrics, output_vcf):
    coverage_df = get_coverage_df(bam_file)
    zero_df = get_zero_df(reference)
    coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer')
    # depth_x "0" column no longer needed.
    coverage_df = coverage_df.drop(columns=['depth_x'])
    coverage_df = coverage_df.rename(columns={'depth_y': 'depth'})
    # Covert the NaN to 0 coverage and get some metrics.
    coverage_df = coverage_df.fillna(0)
    coverage_df['depth'] = coverage_df['depth'].apply(int)
    total_length = len(coverage_df)
    average_coverage = coverage_df['depth'].mean()
    zero_df = coverage_df[coverage_df['depth'] == 0]
    total_zero_coverage = len(zero_df)
    total_coverage = total_length - total_zero_coverage
    genome_coverage = "{:.2%}".format(total_coverage / total_length)
    # Output a zero-coverage vcf fil and the metrics file.
    output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument('--bam_input', action='store', dest='bam_input', help='bam input file')
    parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file')
    parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file')
    parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset')
    parser.add_argument('--vcf_input', action='store', dest='vcf_input', help='vcf input file')

    args = parser.parse_args()

    get_coverage_and_snp_count(args.bam_input, args.vcf_input, args.reference, args.output_metrics, args.output_vcf)

4:4535ad8b74f3

def get_zero_df(reference):
    # Create a zero coverage dictionary.
    zero_dict = {}
    reference_length = 0
    for record in SeqIO.parse(reference, "fasta"):
        chrom = record.id
        total_len = len(record.seq)
        reference_length = reference_length + len(record.seq)
        for pos in list(range(1, total_len + 1)):
            zero_dict["%s-%s" % (str(chrom), str(pos))] = 0
    # Convert it to a data frame with depth_x
    # and depth_y columns - index is NaN.
    zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"])
    return zero_df, reference_length


def output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf):
    column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"]
    vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#')

    else:
        shutil.move(vcf_file, output_vcf)
    return good_snp_count


def output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, dbkey, reference,
                        reference_length, total_zero_coverage, percent_ref_with_zero_coverage, output_metrics):
    columns = ["BAM File", "Reference", "reference Length", "Genome with Coverage", "Average Depth",
               "No Coverage Bases", "Percent Ref with Zero Coverage", "Quality SNPs"]
    values = [base_file_name, dbkey, str(reference_length), genome_coverage, average_coverage,
              str(total_zero_coverage), percent_ref_with_zero_coverage, str(good_snp_count)]
    with open(output_metrics, "w") as fh:
        fh.write("# %s\n" % "\t".join(columns))
        fh.write("%s\n" % "\t".join(values))


def output_files(vcf_file, total_zero_coverage, percent_ref_with_zero_coverage, zero_df, output_vcf,
                 average_coverage, genome_coverage, output_metrics, reference, reference_length, dbkey):
    base_file_name = get_sample_name(vcf_file)
    good_snp_count = output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf)
    output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, dbkey, reference,
                        reference_length, total_zero_coverage, percent_ref_with_zero_coverage, output_metrics)


def get_coverage_and_snp_count(bam_file, vcf_file, dbkey, reference, output_metrics, output_vcf):
    coverage_df = get_coverage_df(bam_file)
    zero_df, reference_length = get_zero_df(reference)
    coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer')
    # depth_x "0" column no longer needed.
    coverage_df = coverage_df.drop(columns=['depth_x'])
    coverage_df = coverage_df.rename(columns={'depth_y': 'depth'})
    # Covert the NaN to 0 coverage and get some metrics.
    coverage_df = coverage_df.fillna(0)
    coverage_df['depth'] = coverage_df['depth'].apply(int)
    total_length = len(coverage_df)
    average_coverage = "{:.2f}".format(coverage_df['depth'].mean())
    zero_df = coverage_df[coverage_df['depth'] == 0]
    total_zero_coverage = len(zero_df)
    percent_ref_with_zero_coverage = "{:.6%}".format(total_zero_coverage / reference_length * 100)
    total_coverage = total_length - total_zero_coverage
    genome_coverage = "{:.2%}".format(total_coverage / total_length)
    # Output a zero-coverage vcf fil and the metrics file.
    output_files(vcf_file, total_zero_coverage, percent_ref_with_zero_coverage, zero_df, output_vcf,
                 average_coverage, genome_coverage, output_metrics, reference, reference_length, dbkey)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument('--bam_input', action='store', dest='bam_input', help='bam input file')
    parser.add_argument('--dbkey', action='store', dest='dbkey', help='bam input dbkey')
    parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file')
    parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file')
    parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset')
    parser.add_argument('--vcf_input', action='store', dest='vcf_input', help='vcf input file')

    args = parser.parse_args()

    get_coverage_and_snp_count(args.bam_input, args.vcf_input, args.dbkey, args.reference, args.output_metrics, args.output_vcf)