comparison convert_VCF_info_fields.py @ 14:b2aae698b9d3 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/medaka commit 52289bc7b99bfa8a3bda46cb35cea98399419dab"

13:fa11aa8103b2

        ret = 2147483647  # transform pval of 0.0 to max signed 32 bit int
    return ret


def parseInfoField(info):
    info_fields = info.split(';')
    info_dict = OrderedDict()
    for info_field in info_fields:
        code, val = info_field.split('=')
        info_dict[code] = val
    return info_dict


def annotateVCF(in_vcf_filepath, out_vcf_filepath):

    Splits multiallelic sites into separate records.
    Replaces medaka INFO fields that might represent information of the ref
    and multiple alternate alleles with simple ref, alt allele counterparts.
    """

    in_vcf = open(in_vcf_filepath, 'r')
    # medaka INFO fields that do not make sense after splitting of
    # multi-allelic records
    # DP will be overwritten with the value of DPSP because medaka tools
    # annotate currently only calculates the latter correctly
    # (https://github.com/nanoporetech/medaka/issues/192).
    # DPS, which is as unreliable as DP, gets skipped and the code
    # calculates the spanning reads equivalent DPSPS instead.
    to_skip = {'SC', 'SR', 'AR', 'DP', 'DPSP', 'DPS'}
    struct_meta_pat = re.compile('##(.+)=<ID=([^,]+)(,.+)?>')
    header_lines = []
    contig_ids = set()
    contig_ids_simple = set()
    # parse the metadata lines of the input VCF and drop:
    # - duplicate lines
    # - INFO lines declaring keys we are not going to write
    # - redundant contig information
    while True:
        line = in_vcf.readline()
        if line[:2] != '##':
            assert line.startswith('#CHROM')
            break
        if line in header_lines:
            # the annotate tool may generate lines already written by
            # medaka variant again (example: medaka version line)
            continue
        match = struct_meta_pat.match(line)
        if match:
            match_type, match_id, match_misc = match.groups()
            if match_type == 'INFO':
                if match_id == 'DPSP':
                    line = line.replace('DPSP', 'DP')
                elif match_id in to_skip:
                    continue
            elif match_type == 'contig':
                contig_ids.add(match_id)
                if not match_misc:
                    # the annotate tools writes its own contig info,
                    # which is redundant with contig info generated by
                    # medaka variant, but lacks a length value.

                    continue
        header_lines.append(line)
    # Lets check the above assumption about each ID-only contig line
    # having a more complete counterpart.
    assert not (contig_ids_simple - contig_ids)
    header_lines.insert(1, '##convert_VCF_info_fields=0.2\n')
    header_lines += [
        '##INFO=<ID=DPSPS,Number=2,Type=Integer,Description="Depth of spanning reads by strand">\n',
        '##INFO=<ID=AF,Number=1,Type=Float,Description="Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=FAF,Number=1,Type=Float,Description="Forward Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=RAF,Number=1,Type=Float,Description="Reverse Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=SB,Number=1,Type=Integer,Description="Phred-scaled strand bias of spanning reads at this position">\n',
        '##INFO=<ID=DP4,Number=4,Type=Integer,Description="Counts for ref-forward bases, ref-reverse, alt-forward and alt-reverse bases in spanning reads">\n',
        '##INFO=<ID=AS,Number=4,Type=Integer,Description="Total alignment score to ref and alt allele of spanning reads by strand (ref fwd, ref rev, alt fwd, alt rev) aligned with parasail match 5, mismatch -4, open 5, extend 3">\n',
        line
    ]

    with open(out_vcf_filepath, 'w') as out_vcf:
        out_vcf.writelines(header_lines)
        for line in in_vcf:
            fields = line.split('\t')
            info_dict = parseInfoField(fields[7])
            sr_list = [int(x) for x in info_dict["SR"].split(',')]
            sc_list = [int(x) for x in info_dict["SC"].split(',')]
            if len(sr_list) != len(sc_list):
                print(
                    'WARNING - SR and SC are different lengths, '
                    'skipping variant'
                )
                print(line.strip())  # Print the line for debugging purposes
                continue
            variant_list = fields[4].split(',')
            dpsp = int(info_dict['DPSP'])
            ref_fwd, ref_rev = 0, 1
            dpspf, dpspr = (int(x) for x in info_dict['AR'].split(','))
            for i in range(0, len(sr_list), 2):
                dpspf += sr_list[i]
                dpspr += sr_list[i + 1]
            for j, i in enumerate(range(2, len(sr_list), 2)):
                dp4 = (
                    sr_list[ref_fwd],
                    sr_list[ref_rev],
                    sr_list[i],
                    sr_list[i + 1]
                )
                dp2x2 = [[dp4[0], dp4[1]], [dp4[2], dp4[3]]]
                _, p_val = scipy.stats.fisher_exact(dp2x2)
                sb = pval_to_phredqual(p_val)

                as_ = (
                    sc_list[ref_fwd],
                    sc_list[ref_rev],
                    sc_list[i],
                    sc_list[i + 1]
                )

                info = []
                for code in info_dict:
                    if code in to_skip:
                        continue
                    val = info_dict[code]
                    info.append("%s=%s" % (code, val))

                info.append('DP=%d' % dpsp)
                info.append('DPSPS=%d,%d' % (dpspf, dpspr))

                if dpsp == 0:
                    info.append('AF=NaN')
                else:
                    af = (dp4[2] + dp4[3]) / dpsp
                    info.append('AF=%.6f' % af)
                if dpspf == 0:
                    info.append('FAF=NaN')
                else:
                    faf = dp4[2] / dpspf
                    info.append('FAF=%.6f' % faf)
                if dpspr == 0:
                    info.append('RAF=NaN')
                else:
                    raf = dp4[3] / dpspr
                    info.append('RAF=%.6f' % raf)
                info.append('SB=%d' % sb)
                info.append('DP4=%d,%d,%d,%d' % dp4)
                info.append('AS=%d,%d,%d,%d' % as_)
                new_info = ';'.join(info)
                fields[4] = variant_list[j]
                fields[7] = new_info
                out_vcf.write('\t'.join(fields))
    in_vcf.close()


if __name__ == "__main__":
    annotateVCF(sys.argv[1], sys.argv[2])

14:b2aae698b9d3

        ret = 2147483647  # transform pval of 0.0 to max signed 32 bit int
    return ret


def parseInfoField(info):
    info_fields = info.split(";")
    info_dict = OrderedDict()
    for info_field in info_fields:
        code, val = info_field.split("=")
        info_dict[code] = val
    return info_dict


def annotateVCF(in_vcf_filepath, out_vcf_filepath):

    Splits multiallelic sites into separate records.
    Replaces medaka INFO fields that might represent information of the ref
    and multiple alternate alleles with simple ref, alt allele counterparts.
    """

    in_vcf = open(in_vcf_filepath, "r")
    # medaka INFO fields that do not make sense after splitting of
    # multi-allelic records
    # DP will be overwritten with the value of DPSP because medaka tools
    # annotate currently only calculates the latter correctly
    # (https://github.com/nanoporetech/medaka/issues/192).
    # DPS, which is as unreliable as DP, gets skipped and the code
    # calculates the spanning reads equivalent DPSPS instead.
    to_skip = {"SC", "SR", "AR", "DP", "DPSP", "DPS"}
    struct_meta_pat = re.compile("##(.+)=<ID=([^,]+)(,.+)?>")
    header_lines = []
    contig_ids = set()
    contig_ids_simple = set()
    # parse the metadata lines of the input VCF and drop:
    # - duplicate lines
    # - INFO lines declaring keys we are not going to write
    # - redundant contig information
    while True:
        line = in_vcf.readline()
        if line[:2] != "##":
            assert line.startswith("#CHROM")
            break
        if line in header_lines:
            # the annotate tool may generate lines already written by
            # medaka variant again (example: medaka version line)
            continue
        match = struct_meta_pat.match(line)
        if match:
            match_type, match_id, match_misc = match.groups()
            if match_type == "INFO":
                if match_id == "DPSP":
                    line = line.replace("DPSP", "DP")
                elif match_id in to_skip:
                    continue
            elif match_type == "contig":
                contig_ids.add(match_id)
                if not match_misc:
                    # the annotate tools writes its own contig info,
                    # which is redundant with contig info generated by
                    # medaka variant, but lacks a length value.

                    continue
        header_lines.append(line)
    # Lets check the above assumption about each ID-only contig line
    # having a more complete counterpart.
    assert not (contig_ids_simple - contig_ids)
    header_lines.insert(1, "##convert_VCF_info_fields=0.2\n")
    header_lines += [
        '##INFO=<ID=DPSPS,Number=2,Type=Integer,Description="Depth of spanning reads by strand">\n',
        '##INFO=<ID=AF,Number=1,Type=Float,Description="Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=FAF,Number=1,Type=Float,Description="Forward Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=RAF,Number=1,Type=Float,Description="Reverse Spanning Reads Allele Frequency">\n',
        '##INFO=<ID=SB,Number=1,Type=Integer,Description="Phred-scaled strand bias of spanning reads at this position">\n',
        '##INFO=<ID=DP4,Number=4,Type=Integer,Description="Counts for ref-forward bases, ref-reverse, alt-forward and alt-reverse bases in spanning reads">\n',
        '##INFO=<ID=AS,Number=4,Type=Integer,Description="Total alignment score to ref and alt allele of spanning reads by strand (ref fwd, ref rev, alt fwd, alt rev) aligned with parasail match 5, mismatch -4, open 5, extend 3">\n',
        line,
    ]

    with open(out_vcf_filepath, "w") as out_vcf:
        out_vcf.writelines(header_lines)
        for line in in_vcf:
            fields = line.split("\t")
            info_dict = parseInfoField(fields[7])
            sr_list = [int(x) for x in info_dict["SR"].split(",")]
            sc_list = [int(x) for x in info_dict["SC"].split(",")]
            if len(sr_list) != len(sc_list):
                print("WARNING - SR and SC are different lengths, " "skipping variant")
                print(line.strip())  # Print the line for debugging purposes
                continue
            variant_list = fields[4].split(",")
            dpsp = int(info_dict["DPSP"])
            ref_fwd, ref_rev = 0, 1
            dpspf, dpspr = (int(x) for x in info_dict["AR"].split(","))
            for i in range(0, len(sr_list), 2):
                dpspf += sr_list[i]
                dpspr += sr_list[i + 1]
            for j, i in enumerate(range(2, len(sr_list), 2)):
                dp4 = (sr_list[ref_fwd], sr_list[ref_rev], sr_list[i], sr_list[i + 1])
                dp2x2 = [[dp4[0], dp4[1]], [dp4[2], dp4[3]]]
                _, p_val = scipy.stats.fisher_exact(dp2x2)
                sb = pval_to_phredqual(p_val)

                as_ = (sc_list[ref_fwd], sc_list[ref_rev], sc_list[i], sc_list[i + 1])

                info = []
                for code in info_dict:
                    if code in to_skip:
                        continue
                    val = info_dict[code]
                    info.append("%s=%s" % (code, val))

                info.append("DP=%d" % dpsp)
                info.append("DPSPS=%d,%d" % (dpspf, dpspr))

                if dpsp == 0:
                    info.append("AF=NaN")
                else:
                    af = (dp4[2] + dp4[3]) / dpsp
                    info.append("AF=%.6f" % af)
                if dpspf == 0:
                    info.append("FAF=NaN")
                else:
                    faf = dp4[2] / dpspf
                    info.append("FAF=%.6f" % faf)
                if dpspr == 0:
                    info.append("RAF=NaN")
                else:
                    raf = dp4[3] / dpspr
                    info.append("RAF=%.6f" % raf)
                info.append("SB=%d" % sb)
                info.append("DP4=%d,%d,%d,%d" % dp4)
                info.append("AS=%d,%d,%d,%d" % as_)
                new_info = ";".join(info)
                fields[4] = variant_list[j]
                fields[7] = new_info
                out_vcf.write("\t".join(fields))
    in_vcf.close()


if __name__ == "__main__":
    annotateVCF(sys.argv[1], sys.argv[2])