--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/convert_VCF_info_fields.py	Wed Oct 12 07:43:59 2022 +0000
@@ -0,0 +1,164 @@
+#!/usr/bin/env python3
+
+# Takes in VCF file annotated with medaka tools annotate and converts
+#
+# Usage statement:
+# python convert_VCF_info_fields.py in_vcf.vcf out_vcf.vcf
+
+# 10/21/2020 - Nathan P. Roach, natproach@gmail.com
+
+import re
+import sys
+from collections import OrderedDict
+from math import log10
+
+import scipy.stats
+
+
+def pval_to_phredqual(pval):
+    try:
+        ret = round(-10 * log10(pval))
+    except ValueError:
+        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):
+    """Postprocess output of medaka tools annotate.
+
+    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.
+                    # We don't need the incomplete line.
+                    contig_ids_simple.add(match_id)
+                    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])