Mercurial > repos > mheinzl > variant_analyzer2
view read2mut.py @ 89:1a5974404d4f draft
planemo upload for repository https://github.com/Single-Molecule-Genetics/VariantAnalyzerGalaxy/tree/master/tools/variant_analyzer commit ee4a8e6cf290e6c8a4d55f9cd2839d60ab3b11c8-dirty
| author | mheinzl |
|---|---|
| date | Tue, 25 Apr 2023 17:06:38 +0000 |
| parents | 63e4e5d9a98f |
| children | 24f166c1dba7 |
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#!/usr/bin/env python """read2mut.py Author -- Gundula Povysil Contact -- povysil@bioinf.jku.at Looks for reads with mutation at known positions and calculates frequencies and stats. ======= ========== ================= ================================ Version Date Author Description 0.2.2 2019-10-27 Gundula Povysil - ======= ========== ================= ================================ USAGE: python read2mut.py --mutFile DCS_Mutations.tabular --bamFile Interesting_Reads.trim.bam --inputJson tag_count_dict.json --sscsJson SSCS_counts.json --outputFile mutant_reads_summary_short_trim.xlsx --thresh 10 --phred 20 --trim 10 --chimera_correction """ from __future__ import division import argparse import csv import itertools import json import operator import os import re import sys import numpy as np import pysam import xlsxwriter from cyvcf2 import VCF def make_argparser(): parser = argparse.ArgumentParser(description='Takes a VCF file with mutations, a BAM file and JSON files as input and prints stats about variants to a user specified output file.') parser.add_argument('--mutFile', help='VCF file with DCS mutations.') parser.add_argument('--bamFile', help='BAM file with aligned raw reads of selected tags (FASTQ created by mut2read.py - trimming with Trimmomatic - alignment with bwa).') parser.add_argument('--inputJson', help='JSON file with data collected by mut2read.py.') parser.add_argument('--sscsJson', help='JSON file with SSCS counts collected by mut2sscs.py.') parser.add_argument('--outputFile', help='Output xlsx file with summary of mutations.') parser.add_argument('--outputFile_csv', help='Output csv file with summary of mutations.') parser.add_argument('--outputFile2', help='Output xlsx file with allele frequencies of mutations.') parser.add_argument('--outputFile3', help='Output xlsx file with examples of the tier classification.') parser.add_argument('--thresh', type=int, default=0, help='Integer threshold for displaying mutations. Only mutations occuring less than thresh times are displayed. Default of 0 displays all.') parser.add_argument('--phred', type=int, default=20, help='Integer threshold for Phred score. Only reads higher than this threshold are considered. Default 20.') parser.add_argument('--trim', type=int, default=10, help='Integer threshold for assigning mutations at start and end of reads to lower tier. Default 10.') parser.add_argument('--chimera_correction', action="store_true", help='Count chimeric variants and correct the variant frequencies') parser.add_argument('--softclipping_dist', type=int, default=15, help='Count mutation as an artifact if mutation lies within this parameter away from the softclipping part of the read.') parser.add_argument('--reads_threshold', type=float, default=1.0, help='Float number which specifies the minimum percentage of softclipped reads in a family to be considered in the softclipping tiers. Default: 1.0, means all reads of a family have to be softclipped.') parser.add_argument('--refalttiers', action="store_true", help='Store also information about the reference allele.') return parser def safe_div(x, y): if y == 0: return None return x / y def read2mut(argv): parser = make_argparser() args = parser.parse_args(argv[1:]) file1 = args.mutFile file2 = args.bamFile json_file = args.inputJson sscs_json = args.sscsJson outfile = args.outputFile outfile2 = args.outputFile2 outfile3 = args.outputFile3 outputFile_csv = args.outputFile_csv refalttiers = args.refalttiers thresh = args.thresh phred_score = args.phred trim = args.trim chimera_correction = args.chimera_correction thr = args.softclipping_dist threshold_reads = args.reads_threshold if os.path.isfile(file1) is False: sys.exit("Error: Could not find '{}'".format(file1)) if os.path.isfile(file2) is False: sys.exit("Error: Could not find '{}'".format(file2)) if os.path.isfile(json_file) is False: sys.exit("Error: Could not find '{}'".format(json_file)) if thresh < 0: sys.exit("Error: thresh is '{}', but only non-negative integers allowed".format(thresh)) if phred_score < 0: sys.exit("Error: phred is '{}', but only non-negative integers allowed".format(phred_score)) if trim < 0: sys.exit("Error: trim is '{}', but only non-negative integers allowed".format(thresh)) if thr <= 0: sys.exit("Error: trim is '{}', but only non-negative integers allowed".format(thr)) # load dicts with open(json_file, "r") as f: (tag_dict, cvrg_dict, tag_dict_ref) = json.load(f) with open(sscs_json, "r") as f: (mut_pos_dict, ref_pos_dict) = json.load(f) # read bam file # pysam.index(file2) bam = pysam.AlignmentFile(file2, "rb") # create mut_dict mut_dict = {} mut_read_pos_dict = {} mut_read_dict = {} reads_dict = {} mut_read_cigar_dict = {} real_start_end = {} i = 0 mut_array = [] for count, variant in enumerate(VCF(file1)): chrom = variant.CHROM stop_pos = variant.start ref = variant.REF if len(variant.ALT) == 0: continue else: alt = variant.ALT[0] alt = alt.upper() ref = ref.upper() if "N" in alt: # skip indels with N in alt allele --> it is not an indel but just a mismatch at the position where the N is (checked this in IGV) continue chrom_stop_pos = str(chrom) + "#" + str(stop_pos) + "#" + ref + "#" + alt mut_array.append([chrom, stop_pos, ref, alt]) i += 1 mut_dict[chrom_stop_pos] = {} mut_read_pos_dict[chrom_stop_pos] = {} reads_dict[chrom_stop_pos] = {} mut_read_cigar_dict[chrom_stop_pos] = {} real_start_end[chrom_stop_pos] = {} for pileupcolumn in bam.pileup(chrom, stop_pos - 1, stop_pos + 1, max_depth=1000000000): if pileupcolumn.reference_pos == stop_pos: count_alt = 0 count_ref = 0 count_indel = 0 count_n = 0 count_other = 0 count_lowq = 0 n = 0 for pileupread in pileupcolumn.pileups: n += 1 if not pileupread.is_refskip: if pileupread.is_del: p = pileupread.query_position_or_next e = p + len(alt) - 1 else: p = pileupread.query_position e = p + len(alt) s = p tag = pileupread.alignment.query_name split_cigar = re.split('(\d+)', pileupread.alignment.cigarstring) if len(ref) < len(alt): if "I" in split_cigar: all_insertions = [inser_i for inser_i, ins in enumerate(split_cigar) if ins == "I"] for ai in all_insertions: # if multiple insertions in DCS ins_index = [int(ci) for ci in split_cigar[:ai - 1] if ci.isdigit()] ins_count = split_cigar[ai - 1] # nr of insertions should match with alt allele if "I" in split_cigar and sum(ins_index) == p + 1 and int(ins_count) >= len(alt) - 1: # if pe read matches exatcly to insertion nuc = pileupread.alignment.query_sequence[s:e] phred = ord(pileupread.alignment.qual[s]) - 33 break elif "I" in split_cigar and sum(ins_index) == p + 1 and int(ins_count) < len(alt) - 1: # if pe read has shorter insertion -- not alt nuc = pileupread.alignment.query_sequence[s:s+int(ins_count)+1] phred = ord(pileupread.alignment.qual[s]) - 33 break else: # insertion in pe reads but not at the desired position nuc = pileupread.alignment.query_sequence[s] phred = ord(pileupread.alignment.qual[s]) - 33 elif "D" in split_cigar: # if deletion in pe read, don't count all_deletions = [del_i for del_i, dele in enumerate(split_cigar) if dele == "D"] for di, ai in enumerate(all_deletions): # if multiple insertions in DCS if di > 0: # more than 1 deletion, don't count previous deletion to position all_deletions_mod = split_cigar[:ai - 1] prev_del_idx = [all_deletions_mod.index("D") - 1, all_deletions_mod.index("D")] split_cigar_no_prev = [ad for i, ad in enumerate(all_deletions_mod) if i not in prev_del_idx] del_index = [int(ci) for ci in split_cigar_no_prev[:ai - 1] if ci.isdigit()] else: # first deletion in read, sum all previous (mis)matches and insertions to position del_index = [int(ci) for ci in split_cigar[:ai - 1] if ci.isdigit()] if "D" in split_cigar and sum(del_index) == p + 1: # if deletion at that position nuc = "D" phred = ord(pileupread.alignment.qual[s]) - 33 break else: nuc = pileupread.alignment.query_sequence[s] phred = ord(pileupread.alignment.qual[s]) - 33 else: # insertion in pe reads but not at the desired position nuc = pileupread.alignment.query_sequence[s] phred = ord(pileupread.alignment.qual[s]) - 33 elif len(ref) > len(alt): ref_positions = pileupread.alignment.get_reference_positions(full_length=True)[s:p + len(ref)] if "D" in split_cigar: all_deletions = [del_i for del_i, dele in enumerate(split_cigar) if dele == "D"] for di, ai in enumerate(all_deletions): # if multiple insertions in DCS if di > 0: # more than 1 deletion, don't count previous deletion to position all_deletions_mod = split_cigar[:ai - 1] prev_del_idx = [all_deletions_mod.index("D") - 1, all_deletions_mod.index("D")] split_cigar_no_prev = [ad for i, ad in enumerate(all_deletions_mod) if i not in prev_del_idx] del_index = [int(ci) for ci in split_cigar_no_prev[:ai - 1] if ci.isdigit()] else: # first deletion in read, sum all previous (mis)matches and insertions to position del_index = [int(ci) for ci in split_cigar[:ai - 1] if ci.isdigit()] del_count = split_cigar[ai - 1] # deletion on that position but does not necesserily match in the length if "D" in split_cigar and sum(del_index) == p + 1 and int(del_count) >= len(ref) - 1: # if pe read matches exatcly to deletion nuc = pileupread.alignment.query_sequence[s:e] phred = ord(pileupread.alignment.qual[s]) - 33 if nuc == "": nuc = str(alt) break elif "D" in split_cigar and sum(del_index) == p + 1 and int(del_count) < len(ref) - 1: # if pe read has shorter deletion --> not alt nuc = str(ref)[:int(del_count)+1] phred = ord(pileupread.alignment.qual[s]) - 33 break else: # deletion in pe reads but not at the desired position nuc = pileupread.alignment.query_sequence[s:s + len(ref)] phred = ord(pileupread.alignment.qual[s]) - 33 elif "I" in split_cigar: # if pe read has insertion --> not count all_insertions = [inser_i for inser_i, ins in enumerate(split_cigar) if ins == "I"] for ai in all_insertions: # if multiple insertions in DCS ins_index = [int(ci) for ci in split_cigar[:ai - 1] if ci.isdigit()] ins_count = split_cigar[ai - 1] # nr of insertions should match with alt allele if "I" in split_cigar and sum(ins_index) == p + 1: nuc = "I" phred = ord(pileupread.alignment.qual[s]) - 33 break else: nuc = pileupread.alignment.query_sequence[s] phred = ord(pileupread.alignment.qual[s]) - 33 elif len(ref_positions) < len(ref): # DCS has reference but the position is at the very end of the DCS and therefore not the full reference positions are there nuc = pileupread.alignment.get_reference_sequence()[s:s + len(ref)] phred = ord(pileupread.alignment.qual[s]) - 33 if nuc.upper() == ref[:len(nuc)]: nuc = str(ref) else: # deletion in pe reads but not at the desired position nuc = pileupread.alignment.query_sequence[s:s + len(ref)] phred = ord(pileupread.alignment.qual[s]) - 33 else: # SNV: query position is None if is_del or is_refskip is set. nuc = pileupread.alignment.query_sequence[s] phred = ord(pileupread.alignment.qual[s]) - 33 nuc = nuc.upper() # if read is softclipped, store real position in reference if "S" in pileupread.alignment.cigarstring: # spftclipped at start if re.search(r"^[0-9]+S", pileupread.alignment.cigarstring): start = pileupread.alignment.reference_start - int(pileupread.alignment.cigarstring.split("S")[0]) end = pileupread.alignment.reference_end # softclipped at end elif re.search(r"S$", pileupread.alignment.cigarstring): end = pileupread.alignment.reference_end + int(re.split("[A-Z]", str(pileupread.alignment.cigarstring))[-2]) start = pileupread.alignment.reference_start else: end = pileupread.alignment.reference_end start = pileupread.alignment.reference_start if phred < phred_score: nuc = "lowQ" if tag not in mut_dict[chrom_stop_pos]: mut_dict[chrom_stop_pos][tag] = {} if nuc in mut_dict[chrom_stop_pos][tag]: mut_dict[chrom_stop_pos][tag][nuc] += 1 else: mut_dict[chrom_stop_pos][tag][nuc] = 1 if tag not in mut_read_pos_dict[chrom_stop_pos]: mut_read_pos_dict[chrom_stop_pos][tag] = [s + 1] reads_dict[chrom_stop_pos][tag] = [len(pileupread.alignment.query_sequence)] mut_read_cigar_dict[chrom_stop_pos][tag] = [pileupread.alignment.cigarstring] real_start_end[chrom_stop_pos][tag] = [(start, end)] else: mut_read_pos_dict[chrom_stop_pos][tag].append(s + 1) reads_dict[chrom_stop_pos][tag].append(len(pileupread.alignment.query_sequence)) mut_read_cigar_dict[chrom_stop_pos][tag].append(pileupread.alignment.cigarstring) real_start_end[chrom_stop_pos][tag].append((start, end)) if nuc == alt: count_alt += 1 if tag not in mut_read_dict: mut_read_dict[tag] = {} mut_read_dict[tag][chrom_stop_pos] = (alt, ref) else: mut_read_dict[tag][chrom_stop_pos] = (alt, ref) elif nuc == ref: count_ref += 1 elif nuc == "N": count_n += 1 elif nuc == "lowQ": count_lowq += 1 else: count_other += 1 else: count_indel += 1 print("coverage at pos %s = %s, ref = %s, alt = %s, other bases = %s, N = %s, low quality = %s\n" % (pileupcolumn.pos, count_ref + count_alt, count_ref, count_alt, count_other, count_n, count_lowq)) mut_array = np.array(mut_array) for read in bam.fetch(until_eof=True): if read.is_unmapped: pure_tag = read.query_name[:-5] nuc = "na" if pure_tag in tag_dict.keys(): # stored all ref and alt reads --> get only alt reads for key in tag_dict[pure_tag].keys(): if key not in mut_dict: mut_dict[key] = {} if read.query_name not in mut_dict[key]: mut_dict[key][read.query_name] = {} if nuc in mut_dict[key][read.query_name]: mut_dict[key][read.query_name][nuc] += 1 else: mut_dict[key][read.query_name][nuc] = 1 bam.close() # create pure_tags_dict pure_tags_dict = {} pure_tags_dict_ref = {} for key1, value1 in sorted(mut_dict.items()): i = np.where(np.array(['#'.join(str(i) for i in z) for z in zip(mut_array[:, 0], mut_array[:, 1], mut_array[:, 2], mut_array[:, 3])]) == key1)[0][0] ref = mut_array[i, 2] alt = mut_array[i, 3] pure_tags_dict[key1] = {} pure_tags_dict_ref[key1] = {} for key2, value2 in sorted(value1.items()): for key3, value3 in value2.items(): pure_tag = key2[:-5] if key3 == alt: if pure_tag in pure_tags_dict[key1]: pure_tags_dict[key1][pure_tag] += 1 else: pure_tags_dict[key1][pure_tag] = 1 if key3 == ref: if pure_tag in pure_tags_dict_ref[key1]: pure_tags_dict_ref[key1][pure_tag] += 1 else: pure_tags_dict_ref[key1][pure_tag] = 1 # create pure_tags_dict_short with thresh if thresh > 0: pure_tags_dict_short = {} for key, value in sorted(pure_tags_dict.items()): if len(value) < thresh: pure_tags_dict_short[key] = value else: pure_tags_dict_short = pure_tags_dict csv_data = open(outputFile_csv, "w") csv_writer = csv.writer(csv_data, delimiter=",") # output summary with threshold workbook = xlsxwriter.Workbook(outfile) workbook2 = xlsxwriter.Workbook(outfile2) workbook3 = xlsxwriter.Workbook(outfile3) count_sheet = 1 ws1 = workbook.add_worksheet("Results" + str(count_sheet)) ws2 = workbook2.add_worksheet("Allele frequencies") ws3 = workbook3.add_worksheet("Tiers") current_result_sheet = ws1 format1 = workbook.add_format({'bg_color': '#BCF5A9'}) # green format2 = workbook.add_format({'bg_color': '#FFC7CE'}) # red format3 = workbook.add_format({'bg_color': '#FACC2E'}) # yellow format12 = workbook2.add_format({'bg_color': '#BCF5A9'}) # green format22 = workbook2.add_format({'bg_color': '#FFC7CE'}) # red format32 = workbook2.add_format({'bg_color': '#FACC2E'}) # yellow format13 = workbook3.add_format({'bg_color': '#BCF5A9'}) # green format23 = workbook3.add_format({'bg_color': '#FFC7CE'}) # red format33 = workbook3.add_format({'bg_color': '#FACC2E'}) # yellow header_line = ('variant ID', 'tier', 'allele', 'tag', 'mate', 'read pos.ab', 'read pos.ba', 'read median length.ab', 'read median length.ba', 'DCS median length', 'FS.ab', 'FS.ba', 'FSqc.ab', 'FSqc.ba', 'ref.ab', 'ref.ba', 'alt.ab', 'alt.ba', 'rel. ref.ab', 'rel. ref.ba', 'rel. alt.ab', 'rel. alt.ba', 'na.ab', 'na.ba', 'lowq.ab', 'lowq.ba', 'trim.ab', 'trim.ba', 'SSCS alt.ab', 'SSCS alt.ba', 'SSCS ref.ab', 'SSCS ref.ba', 'in phase', 'chimeric tag') current_result_sheet.write_row(0, 0, header_line) csv_writer.writerow(header_line) counter_tier11 = 0 counter_tier12 = 0 counter_tier21 = 0 counter_tier22 = 0 counter_tier23 = 0 counter_tier24 = 0 counter_tier31 = 0 counter_tier32 = 0 counter_tier25 = 0 counter_tier4 = 0 counter_tier51 = 0 counter_tier52 = 0 counter_tier53 = 0 counter_tier54 = 0 counter_tier55 = 0 counter_tier6 = 0 counter_tier7 = 0 row = 1 tier_dict = {} tier_dict_ref = {} chimera_dict = {} for key1, value1 in sorted(mut_dict.items()): counts_mut = 0 chimeric_tag_list = [] chimeric_tag = {} if (key1 in pure_tags_dict_short.keys()) or (key1 in pure_tags_dict_ref.keys()): # ref or alt # if key1 not in np.array(['#'.join(str(i) for i in z) # for z in zip(mut_array[:, 0], mut_array[:, 1], mut_array[:, 2], mut_array[:, 3])]): # continue change_tier_after_print = [] i = np.where(np.array(['#'.join(str(i) for i in z) for z in zip(mut_array[:, 0], mut_array[:, 1], mut_array[:, 2], mut_array[:, 3])]) == key1)[0][0] ref = mut_array[i, 2] alt = mut_array[i, 3] dcs_median = cvrg_dict[key1][2] whole_array = list(pure_tags_dict_short[key1].keys()) tier_dict[key1] = {} tier_dict_ref[key1] = {} values_tier_dict = [("tier 1.1", 0), ("tier 1.2", 0), ("tier 2.1", 0), ("tier 2.2", 0), ("tier 2.3", 0), ("tier 2.4", 0), ("tier 2.5", 0), ("tier 3.1", 0), ("tier 3.2", 0), ("tier 4", 0), ("tier 5.1", 0), ("tier 5.2", 0), ("tier 5.3", 0), ("tier 5.4", 0), ("tier 5.5", 0), ("tier 6", 0), ("tier 7", 0)] for k, v in values_tier_dict: tier_dict[key1][k] = v tier_dict_ref[key1][k] = v used_keys = [] # used_keys_ref = [] if 'ab' in mut_pos_dict[key1].keys(): sscs_mut_ab = mut_pos_dict[key1]['ab'] else: sscs_mut_ab = 0 if 'ba' in mut_pos_dict[key1].keys(): sscs_mut_ba = mut_pos_dict[key1]['ba'] else: sscs_mut_ba = 0 if 'ab' in ref_pos_dict[key1].keys(): sscs_ref_ab = ref_pos_dict[key1]['ab'] else: sscs_ref_ab = 0 if 'ba' in ref_pos_dict[key1].keys(): sscs_ref_ba = ref_pos_dict[key1]['ba'] else: sscs_ref_ba = 0 for key2, value2 in sorted(value1.items()): add_mut14 = "" add_mut23 = "" if key2[:-5] not in tag_dict.keys() and key2[:-5] not in tag_dict_ref.keys(): # skip reads that have not alt or ref continue if (((key2[:-5] in tag_dict.keys()) and (key2[:-5] in pure_tags_dict_short[key1].keys()) and (key1 in tag_dict[key2[:-5]].keys()) and (key2[:-5] not in used_keys)) or ((key2[:-5] in tag_dict_ref.keys()) and (key2[:-5] in pure_tags_dict_ref[key1].keys()) and (key1 in tag_dict_ref[key2[:-5]].keys()) and (key2[:-5] not in used_keys))): if key2[:-5] in tag_dict.keys() and key1 in tag_dict[key2[:-5]].keys(): variant_type = "alt" elif key2[:-5] in tag_dict_ref.keys() and key1 in tag_dict_ref[key2[:-5]].keys(): variant_type = "ref" if refalttiers is False and variant_type == "ref": # if we only want information about alt tiers, skip all refs continue if key2[:-5] + '.ab.1' in mut_dict[key1].keys(): total1 = sum(mut_dict[key1][key2[:-5] + '.ab.1'].values()) if 'na' in mut_dict[key1][key2[:-5] + '.ab.1'].keys(): na1 = mut_dict[key1][key2[:-5] + '.ab.1']['na'] # na1f = na1/total1 else: # na1 = na1f = 0 na1 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ab.1'].keys(): lowq1 = mut_dict[key1][key2[:-5] + '.ab.1']['lowQ'] # lowq1f = lowq1 / total1 else: # lowq1 = lowq1f = 0 lowq1 = 0 if ref in mut_dict[key1][key2[:-5] + '.ab.1'].keys(): ref1 = mut_dict[key1][key2[:-5] + '.ab.1'][ref] ref1f = ref1 / (total1 - na1 - lowq1) else: ref1 = ref1f = 0 if alt in mut_dict[key1][key2[:-5] + '.ab.1'].keys(): alt1 = mut_dict[key1][key2[:-5] + '.ab.1'][alt] alt1f = alt1 / (total1 - na1 - lowq1) else: alt1 = alt1f = 0 total1new = total1 - na1 - lowq1 if (key2[:-5] + '.ab.1') in mut_read_dict.keys(): k1 = mut_read_dict[(key2[:-5] + '.ab.1')].keys() add_mut1 = len(k1) if add_mut1 > 1: for k, v in mut_read_dict[(key2[:-5] + '.ab.1')].items(): if k != key1: new_mut = str(k).split("#")[0] + "-" + str(int(str(k).split("#")[1]) + 1) + "-" + v[1] + "-" + v[0] if len(add_mut14) == 0: add_mut14 = new_mut else: add_mut14 = add_mut14 + ", " + new_mut else: k1 = [] else: total1 = total1new = na1 = lowq1 = 0 ref1 = alt1 = ref1f = alt1f = 0 k1 = [] if key2[:-5] + '.ab.2' in mut_dict[key1].keys(): total2 = sum(mut_dict[key1][key2[:-5] + '.ab.2'].values()) if 'na' in mut_dict[key1][key2[:-5] + '.ab.2'].keys(): na2 = mut_dict[key1][key2[:-5] + '.ab.2']['na'] # na2f = na2 / total2 else: # na2 = na2f = 0 na2 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ab.2'].keys(): lowq2 = mut_dict[key1][key2[:-5] + '.ab.2']['lowQ'] # lowq2f = lowq2 / total2 else: # lowq2 = lowq2f = 0 lowq2 = 0 if ref in mut_dict[key1][key2[:-5] + '.ab.2'].keys(): ref2 = mut_dict[key1][key2[:-5] + '.ab.2'][ref] ref2f = ref2 / (total2 - na2 - lowq2) else: ref2 = ref2f = 0 if alt in mut_dict[key1][key2[:-5] + '.ab.2'].keys(): alt2 = mut_dict[key1][key2[:-5] + '.ab.2'][alt] alt2f = alt2 / (total2 - na2 - lowq2) else: alt2 = alt2f = 0 total2new = total2 - na2 - lowq2 if (key2[:-5] + '.ab.2') in mut_read_dict.keys(): k2 = mut_read_dict[(key2[:-5] + '.ab.2')].keys() add_mut2 = len(k2) if add_mut2 > 1: for k, v in mut_read_dict[(key2[:-5] + '.ab.2')].items(): if k != key1: new_mut = str(k).split("#")[0] + "-" + str(int(str(k).split("#")[1]) + 1) + "-" + v[1] + "-" + v[0] if len(add_mut23) == 0: add_mut23 = new_mut else: add_mut23 = add_mut23 + ", " + new_mut else: k2 = [] else: total2 = total2new = na2 = lowq2 = 0 ref2 = alt2 = ref2f = alt2f = 0 k2 = [] if key2[:-5] + '.ba.1' in mut_dict[key1].keys(): total3 = sum(mut_dict[key1][key2[:-5] + '.ba.1'].values()) if 'na' in mut_dict[key1][key2[:-5] + '.ba.1'].keys(): na3 = mut_dict[key1][key2[:-5] + '.ba.1']['na'] # na3f = na3 / total3 else: # na3 = na3f = 0 na3 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ba.1'].keys(): lowq3 = mut_dict[key1][key2[:-5] + '.ba.1']['lowQ'] # lowq3f = lowq3 / total3 else: # lowq3 = lowq3f = 0 lowq3 = 0 if ref in mut_dict[key1][key2[:-5] + '.ba.1'].keys(): ref3 = mut_dict[key1][key2[:-5] + '.ba.1'][ref] ref3f = ref3 / (total3 - na3 - lowq3) else: ref3 = ref3f = 0 if alt in mut_dict[key1][key2[:-5] + '.ba.1'].keys(): alt3 = mut_dict[key1][key2[:-5] + '.ba.1'][alt] alt3f = alt3 / (total3 - na3 - lowq3) else: alt3 = alt3f = 0 total3new = total3 - na3 - lowq3 if (key2[:-5] + '.ba.1') in mut_read_dict.keys(): add_mut3 = len(mut_read_dict[(key2[:-5] + '.ba.1')].keys()) if add_mut3 > 1: for k, v in mut_read_dict[(key2[:-5] + '.ba.1')].items(): if k != key1 and k not in k2: new_mut = str(k).split("#")[0] + "-" + str(int(str(k).split("#")[1]) + 1) + "-" + v[1] + "-" + v[0] if len(add_mut23) == 0: add_mut23 = new_mut else: add_mut23 = add_mut23 + ", " + new_mut else: total3 = total3new = na3 = lowq3 = 0 ref3 = alt3 = ref3f = alt3f = 0 if key2[:-5] + '.ba.2' in mut_dict[key1].keys(): total4 = sum(mut_dict[key1][key2[:-5] + '.ba.2'].values()) if 'na' in mut_dict[key1][key2[:-5] + '.ba.2'].keys(): na4 = mut_dict[key1][key2[:-5] + '.ba.2']['na'] # na4f = na4 / total4 else: # na4 = na4f = 0 na4 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ba.2'].keys(): lowq4 = mut_dict[key1][key2[:-5] + '.ba.2']['lowQ'] # lowq4f = lowq4 / total4 else: # lowq4 = lowq4f = 0 lowq4 = 0 if ref in mut_dict[key1][key2[:-5] + '.ba.2'].keys(): ref4 = mut_dict[key1][key2[:-5] + '.ba.2'][ref] ref4f = ref4 / (total4 - na4 - lowq4) else: ref4 = ref4f = 0 if alt in mut_dict[key1][key2[:-5] + '.ba.2'].keys(): alt4 = mut_dict[key1][key2[:-5] + '.ba.2'][alt] alt4f = alt4 / (total4 - na4 - lowq4) else: alt4 = alt4f = 0 total4new = total4 - na4 - lowq4 if (key2[:-5] + '.ba.2') in mut_read_dict.keys(): add_mut4 = len(mut_read_dict[(key2[:-5] + '.ba.2')].keys()) if add_mut4 > 1: for k, v in mut_read_dict[(key2[:-5] + '.ba.2')].items(): if k != key1 and k not in k1: new_mut = str(k).split("#")[0] + "-" + str(int(str(k).split("#")[1]) + 1) + "-" + v[1] + "-" + v[0] if len(add_mut14) == 0: add_mut14 = new_mut else: add_mut14 = add_mut14 + ", " + new_mut else: total4 = total4new = na4 = lowq4 = 0 ref4 = alt4 = ref4f = alt4f = 0 read_pos1 = read_pos2 = read_pos3 = read_pos4 = -1 read_len_median1 = read_len_median2 = read_len_median3 = read_len_median4 = 0 cigars_dcs1 = cigars_dcs2 = cigars_dcs3 = cigars_dcs4 = [] pos_read1 = pos_read2 = pos_read3 = pos_read4 = [] end_read1 = end_read2 = end_read3 = end_read4 = [] if key2[:-5] + '.ab.1' in mut_read_pos_dict[key1].keys(): read_pos1 = np.median(np.array(mut_read_pos_dict[key1][key2[:-5] + '.ab.1'])) read_len_median1 = np.median(np.array(reads_dict[key1][key2[:-5] + '.ab.1'])) cigars_dcs1 = mut_read_cigar_dict[key1][key2[:-5] + '.ab.1'] pos_read1 = mut_read_pos_dict[key1][key2[:-5] + '.ab.1'] end_read1 = reads_dict[key1][key2[:-5] + '.ab.1'] ref_positions1 = real_start_end[key1][key2[:-5] + '.ab.1'] if key2[:-5] + '.ab.2' in mut_read_pos_dict[key1].keys(): read_pos2 = np.median(np.array(mut_read_pos_dict[key1][key2[:-5] + '.ab.2'])) read_len_median2 = np.median(np.array(reads_dict[key1][key2[:-5] + '.ab.2'])) cigars_dcs2 = mut_read_cigar_dict[key1][key2[:-5] + '.ab.2'] pos_read2 = mut_read_pos_dict[key1][key2[:-5] + '.ab.2'] end_read2 = reads_dict[key1][key2[:-5] + '.ab.2'] ref_positions2 = real_start_end[key1][key2[:-5] + '.ab.2'] if key2[:-5] + '.ba.1' in mut_read_pos_dict[key1].keys(): read_pos3 = np.median(np.array(mut_read_pos_dict[key1][key2[:-5] + '.ba.1'])) read_len_median3 = np.median(np.array(reads_dict[key1][key2[:-5] + '.ba.1'])) cigars_dcs3 = mut_read_cigar_dict[key1][key2[:-5] + '.ba.1'] pos_read3 = mut_read_pos_dict[key1][key2[:-5] + '.ba.1'] end_read3 = reads_dict[key1][key2[:-5] + '.ba.1'] ref_positions3 = real_start_end[key1][key2[:-5] + '.ba.1'] if key2[:-5] + '.ba.2' in mut_read_pos_dict[key1].keys(): read_pos4 = np.median(np.array(mut_read_pos_dict[key1][key2[:-5] + '.ba.2'])) read_len_median4 = np.median(np.array(reads_dict[key1][key2[:-5] + '.ba.2'])) cigars_dcs4 = mut_read_cigar_dict[key1][key2[:-5] + '.ba.2'] pos_read4 = mut_read_pos_dict[key1][key2[:-5] + '.ba.2'] end_read4 = reads_dict[key1][key2[:-5] + '.ba.2'] ref_positions4 = real_start_end[key1][key2[:-5] + '.ba.2'] # if variant_type == "alt": used_keys.append(key2[:-5]) # elif variant_type == "ref": # used_keys_ref.append(key2[:-5]) counts_mut += 1 if (variant_type == "alt" and ((alt1f + alt2f + alt3f + alt4f) > 0.5)) or (variant_type == "ref" and ((ref1f + ref2f + ref3f + ref4f) > 0.5)): if row > 1000000: count_sheet += 1 ws_new = workbook.add_worksheet("Results" + str(count_sheet)) current_result_sheet = ws_new current_result_sheet.write_row(0, 0, header_line) row = 1 if variant_type == "alt": tier1ff, tier2ff, tier3ff, tier4ff = alt1f, alt2f, alt3f, alt4f tier1ff_trim, tier2ff_trim, tier3ff_trim, tier4ff_trim = alt1f, alt2f, alt3f, alt4f elif variant_type == "ref": tier1ff, tier2ff, tier3ff, tier4ff = ref1f, ref2f, ref3f, ref4f tier1ff_trim, tier2ff_trim, tier3ff_trim, tier4ff_trim = ref1f, ref2f, ref3f, ref4f total1new_trim, total2new_trim, total3new_trim, total4new_trim = total1new, total2new, total3new, total4new if total1new == 0: ref1f = alt1f = None alt1ff = -1 alt1ff_trim = -1 tier1ff = -1 tier1ff_trim = -1 else: alt1ff = alt1f alt1ff_trim = alt1f tier1ff = tier1ff tier1ff_trim = tier1ff_trim if total2new == 0: ref2f = alt2f = None alt2ff = -1 alt2ff_trim = -1 tier2ff = -1 tier2ff_trim = -1 else: alt2ff = alt2f alt2ff_trim = alt2f tier2ff = tier2ff tier2ff_trim = tier2ff_trim if total3new == 0: ref3f = alt3f = None alt3ff = -1 alt3ff_trim = -1 tier3ff = -1 tier3ff_trim = -1 else: alt3ff = alt3f alt3ff_trim = alt3f tier3ff = tier3ff tier3ff_trim = tier3ff_trim if total4new == 0: ref4f = alt4f = None alt4ff = -1 alt4ff_trim = -1 tier4ff = -1 tier4ff_trim = -1 else: alt4ff = alt4f alt4ff_trim = alt4f tier4ff = tier4ff tier4ff_trim = tier4ff_trim beg1 = beg4 = beg2 = beg3 = 0 details1 = (total1, total4, total1new, total4new, ref1, ref4, alt1, alt4, ref1f, ref4f, alt1f, alt4f, na1, na4, lowq1, lowq4, beg1, beg4) details2 = (total2, total3, total2new, total3new, ref2, ref3, alt2, alt3, ref2f, ref3f, alt2f, alt3f, na2, na3, lowq2, lowq3, beg2, beg3) trimmed = False contradictory = False softclipped_mutation_allMates = False softclipped_mutation_oneOfTwoMates = False softclipped_mutation_oneOfTwoSSCS = False softclipped_mutation_oneOfTwoSSCS_diffMates = False softclipped_mutation_oneMate = False softclipped_mutation_oneMateOneSSCS = False trimmed_actual_high_tier = False dist_start_read1 = dist_start_read2 = dist_start_read3 = dist_start_read4 = [] dist_end_read1 = dist_end_read2 = dist_end_read3 = dist_end_read4 = [] ratio_dist_start1 = ratio_dist_start2 = ratio_dist_start3 = ratio_dist_start4 = False ratio_dist_end1 = ratio_dist_end2 = ratio_dist_end3 = ratio_dist_end4 = False # mate 1 - SSCS ab softclipped_idx1 = [True if re.search(r"^[0-9]+S", string) or re.search(r"S$", string) else False for string in cigars_dcs1] safe_div_result = safe_div(sum(softclipped_idx1), float(len(softclipped_idx1))) if (safe_div_result is None): ratio1 = False else: ratio1 = safe_div_result >= threshold_reads if any(ij is True for ij in softclipped_idx1): softclipped_both_ends_idx1 = [True if (re.search(r"^[0-9]+S", string) and re.search(r"S$", string)) else False for string in cigars_dcs1] softclipped_start1 = [int(string.split("S")[0]) if re.search(r"^[0-9]+S", string) else -1 for string in cigars_dcs1] softclipped_end1 = [int(re.split("[A-Z]", str(string))[-2]) if re.search(r"S$", string) else -1 for string in cigars_dcs1] dist_start_read1 = [(pos - soft) if soft != -1 else thr + 1000 for soft, pos in zip(softclipped_start1, pos_read1)] dist_end_read1 = [(length_read - pos - soft) if soft != -1 else thr + 1000 for soft, pos, length_read in zip(softclipped_end1, pos_read1, end_read1)] # if read at both ends softclipped --> select end with smallest distance between mut position and softclipping if any(ij is True for ij in softclipped_both_ends_idx1): for nr, indx in enumerate(softclipped_both_ends_idx1): if indx: if dist_start_read1[nr] <= dist_end_read1[nr]: dist_end_read1[nr] = thr + 1000 # use dist of start and set start to very large number else: dist_start_read1[nr] = thr + 1000 # use dist of end and set start to very large number ratio_dist_start1 = safe_div(sum([True if x <= thr else False for x in dist_start_read1]), float(sum(softclipped_idx1))) >= threshold_reads ratio_dist_end1 = safe_div(sum([True if x <= thr else False for x in dist_end_read1]), float(sum(softclipped_idx1))) >= threshold_reads # mate 1 - SSCS ba softclipped_idx4 = [True if re.search(r"^[0-9]+S", string) or re.search(r"S$", string) else False for string in cigars_dcs4] safe_div_result = safe_div(sum(softclipped_idx4), float(len(softclipped_idx4))) if (safe_div_result is None): ratio4 = False else: ratio4 = safe_div_result >= threshold_reads if any(ij is True for ij in softclipped_idx4): softclipped_both_ends_idx4 = [True if (re.search(r"^[0-9]+S", string) and re.search(r"S$", string)) else False for string in cigars_dcs4] softclipped_start4 = [int(string.split("S")[0]) if re.search(r"^[0-9]+S", string) else -1 for string in cigars_dcs4] softclipped_end4 = [int(re.split("[A-Z]", str(string))[-2]) if re.search(r"S$", string) else -1 for string in cigars_dcs4] dist_start_read4 = [(pos - soft) if soft != -1 else thr + 1000 for soft, pos in zip(softclipped_start4, pos_read4)] dist_end_read4 = [(length_read - pos - soft) if soft != -1 else thr + 1000 for soft, pos, length_read in zip(softclipped_end4, pos_read4, end_read4)] # if read at both ends softclipped --> select end with smallest distance between mut position and softclipping if any(ij is True for ij in softclipped_both_ends_idx4): for nr, indx in enumerate(softclipped_both_ends_idx4): if indx: if dist_start_read4[nr] <= dist_end_read4[nr]: dist_end_read4[nr] = thr + 1000 # use dist of start and set start to very large number else: dist_start_read4[nr] = thr + 1000 # use dist of end and set start to very large number ratio_dist_start4 = safe_div(sum([True if x <= thr else False for x in dist_start_read4]), float(sum(softclipped_idx4))) >= threshold_reads ratio_dist_end4 = safe_div(sum([True if x <= thr else False for x in dist_end_read4]), float(sum(softclipped_idx4))) >= threshold_reads # mate 2 - SSCS ab softclipped_idx2 = [True if re.search(r"^[0-9]+S", string) or re.search(r"S$", string) else False for string in cigars_dcs2] safe_div_result = safe_div(sum(softclipped_idx2), float(len(softclipped_idx2))) if (safe_div_result is None): ratio2 = False else: ratio2 = safe_div_result >= threshold_reads if any(ij is True for ij in softclipped_idx2): softclipped_both_ends_idx2 = [True if (re.search(r"^[0-9]+S", string) and re.search(r"S$", string)) else False for string in cigars_dcs2] softclipped_start2 = [int(string.split("S")[0]) if re.search(r"^[0-9]+S", string) else -1 for string in cigars_dcs2] softclipped_end2 = [int(re.split("[A-Z]", str(string))[-2]) if re.search(r"S$", string) else -1 for string in cigars_dcs2] dist_start_read2 = [(pos - soft) if soft != -1 else thr + 1000 for soft, pos in zip(softclipped_start2, pos_read2)] dist_end_read2 = [(length_read - pos - soft) if soft != -1 else thr + 1000 for soft, pos, length_read in zip(softclipped_end2, pos_read2, end_read2)] # if read at both ends softclipped --> select end with smallest distance between mut position and softclipping if any(ij is True for ij in softclipped_both_ends_idx2): for nr, indx in enumerate(softclipped_both_ends_idx2): if indx: if dist_start_read2[nr] <= dist_end_read2[nr]: dist_end_read2[nr] = thr + 1000 # use dist of start and set start to very large number else: dist_start_read2[nr] = thr + 1000 # use dist of end and set start to very large number ratio_dist_start2 = safe_div(sum([True if x <= thr else False for x in dist_start_read2]), float(sum(softclipped_idx2))) >= threshold_reads ratio_dist_end2 = safe_div(sum([True if x <= thr else False for x in dist_end_read2]), float(sum(softclipped_idx2))) >= threshold_reads # mate 2 - SSCS ba softclipped_idx3 = [True if re.search(r"^[0-9]+S", string) or re.search(r"S$", string) else False for string in cigars_dcs3] safe_div_result = safe_div(sum(softclipped_idx3), float(len(softclipped_idx3))) if (safe_div_result is None): ratio3 = False else: ratio3 = safe_div_result >= threshold_reads if any(ij is True for ij in softclipped_idx3): softclipped_both_ends_idx3 = [True if (re.search(r"^[0-9]+S", string) and re.search(r"S$", string)) else False for string in cigars_dcs3] softclipped_start3 = [int(string.split("S")[0]) if re.search(r"^[0-9]+S", string) else -1 for string in cigars_dcs3] softclipped_end3 = [int(re.split("[A-Z]", str(string))[-2]) if re.search(r"S$", string) else -1 for string in cigars_dcs3] dist_start_read3 = [(pos - soft) if soft != -1 else thr + 1000 for soft, pos in zip(softclipped_start3, pos_read3)] dist_end_read3 = [(length_read - pos - soft) if soft != -1 else thr + 1000 for soft, pos, length_read in zip(softclipped_end3, pos_read3, end_read3)] # if read at both ends softclipped --> select end with smallest distance between mut position and softclipping if any(ij is True for ij in softclipped_both_ends_idx3): for nr, indx in enumerate(softclipped_both_ends_idx3): if indx: if dist_start_read3[nr] <= dist_end_read3[nr]: dist_end_read3[nr] = thr + 1000 # use dist of start and set start to a larger number than thresh else: dist_start_read3[nr] = thr + 1000 # use dist of end and set start to very large number ratio_dist_start3 = safe_div(sum([True if x <= thr else False for x in dist_start_read3]), float(sum(softclipped_idx3))) >= threshold_reads ratio_dist_end3 = safe_div(sum([True if x <= thr else False for x in dist_end_read3]), float(sum(softclipped_idx3))) >= threshold_reads if ((all(float(ij) >= 0.5 for ij in [tier1ff, tier4ff]) & # contradictory variant all(float(ij) == 0. for ij in [tier2ff, tier3ff])) | (all(float(ij) >= 0.5 for ij in [tier2ff, tier3ff]) & all(float(ij) == 0. for ij in [tier1ff, tier4ff]))): tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = True # softclipping tiers # information of both mates available --> all reads for both mates and SSCS are softclipped elif (ratio1 & ratio4 & ratio2 & ratio3 & (ratio_dist_start1 | ratio_dist_end1) & (ratio_dist_start4 | ratio_dist_end4) & (ratio_dist_start2 | ratio_dist_end2) & (ratio_dist_start3 | ratio_dist_end3) & all(float(ij) > 0. for ij in [tier1ff, tier2ff, tier3ff, tier4ff])): # all mates available # if distance between softclipping and mutation is at start or end of the read smaller than threshold softclipped_mutation_allMates = True softclipped_mutation_oneOfTwoMates = False softclipped_mutation_oneOfTwoSSCS = False softclipped_mutation_oneOfTwoSSCS_diffMates = False softclipped_mutation_oneMate = False softclipped_mutation_oneMateOneSSCS = False tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = False # information of both mates available --> only one mate softclipped elif (((ratio1 & ratio4 & (ratio_dist_start1 | ratio_dist_end1) & (ratio_dist_start4 | ratio_dist_end4)) | (ratio2 & ratio3 & (ratio_dist_start2 | ratio_dist_end2) & (ratio_dist_start3 | ratio_dist_end3))) & all(float(ij) > 0. for ij in [tier1ff, tier2ff, tier3ff, tier4ff])): # all mates available # if distance between softclipping and mutation is at start or end of the read smaller than threshold min_start1 = min(min([ij[0] for ij in ref_positions1]), min([ij[0] for ij in ref_positions4])) # red min_start2 = min(min([ij[0] for ij in ref_positions2]), min([ij[0] for ij in ref_positions3])) # blue max_end1 = max(max([ij[1] for ij in ref_positions1]), max([ij[1] for ij in ref_positions4])) # red max_end2 = max(max([ij[1] for ij in ref_positions2]), max([ij[1] for ij in ref_positions3])) # blue if (min_start1 > min_start2) or (max_end1 > max_end2): # if mate1 is red and mate2 is blue softclipped_mutation_oneOfTwoMates = False # blue mate at beginning softclipped if min_start1 > min_start2: n_spacer_barcode = min_start1 - min_start2 read_pos2 = read_pos2 - n_spacer_barcode read_pos3 = read_pos3 - n_spacer_barcode read_len_median2 = read_len_median2 - n_spacer_barcode read_len_median3 = read_len_median3 - n_spacer_barcode # red mate at end softclipped if max_end1 > max_end2: n_spacer_barcode = max_end1 - max_end2 read_len_median1 = read_len_median1 - n_spacer_barcode read_len_median4 = read_len_median4 - n_spacer_barcode elif (min_start1 < min_start2) or (max_end1 < max_end2): # if mate1 is blue and mate2 is red softclipped_mutation_oneOfTwoMates = False if min_start1 < min_start2: n_spacer_barcode = min_start2 - min_start1 read_pos1 = read_pos1 - n_spacer_barcode read_pos4 = read_pos4 - n_spacer_barcode read_len_median1 = read_len_median1 - n_spacer_barcode read_len_median4 = read_len_median4 - n_spacer_barcode if max_end1 < max_end2: # if mate1 ends after mate 2 starts n_spacer_barcode = max_end2 - max_end1 read_len_median2 = read_len_median2 - n_spacer_barcode read_len_median3 = read_len_median3 - n_spacer_barcode else: softclipped_mutation_oneOfTwoMates = True tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = False softclipped_mutation_allMates = False softclipped_mutation_oneOfTwoSSCS = False softclipped_mutation_oneMate = False softclipped_mutation_oneMateOneSSCS = False if softclipped_mutation_oneOfTwoMates is False: # check trimming tier if ((read_pos1 >= 0) and ((read_pos1 <= trim) | (abs(read_len_median1 - read_pos1) <= trim))): beg1 = total1new total1new = 0 alt1ff = 0 alt1f = 0 tier1ff = 0 trimmed = True if ((read_pos4 >= 0) and ((read_pos4 <= trim) | (abs(read_len_median4 - read_pos4) <= trim))): beg4 = total4new total4new = 0 alt4ff = 0 alt4f = 0 tier4ff = 0 trimmed = True if ((read_pos2 >= 0) and ((read_pos2 <= trim) | (abs(read_len_median2 - read_pos2) <= trim))): beg2 = total2new total2new = 0 alt2ff = 0 alt2f = 0 tier2ff = 0 trimmed = True if ((read_pos3 >= 0) and ((read_pos3 <= trim) | (abs(read_len_median3 - read_pos3) <= trim))): beg3 = total3new total3new = 0 alt3ff = 0 alt3f = 0 tier3ff = 0 trimmed = True details1 = (total1, total4, total1new, total4new, ref1, ref4, alt1, alt4, ref1f, ref4f, alt1f, alt4f, na1, na4, lowq1, lowq4, beg1, beg4) details2 = (total2, total3, total2new, total3new, ref2, ref3, alt2, alt3, ref2f, ref3f, alt2f, alt3f, na2, na3, lowq2, lowq3, beg2, beg3) # information of both mates available --> only one mate softclipped elif (((ratio1 & (ratio_dist_start1 | ratio_dist_end1)) | (ratio4 & (ratio_dist_start4 | ratio_dist_end4))) & ((ratio2 & (ratio_dist_start2 | ratio_dist_end2)) | (ratio3 & (ratio_dist_start3 | ratio_dist_end3))) & all(float(ij) > 0. for ij in [tier1ff, tier2ff, tier3ff, tier4ff])): # all mates available # if distance between softclipping and mutation is at start or end of the read smaller than threshold softclipped_mutation_allMates = False softclipped_mutation_oneOfTwoMates = False softclipped_mutation_oneOfTwoSSCS = True softclipped_mutation_oneOfTwoSSCS_diffMates = False softclipped_mutation_oneMate = False softclipped_mutation_oneMateOneSSCS = False tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = False # information of one mate available --> all reads of one mate are softclipped elif ((ratio1 & ratio4 & (ratio_dist_start1 | ratio_dist_end1) & (ratio_dist_start4 | ratio_dist_end4) & all(float(ij) < 0. for ij in [tier2ff, tier3ff]) & all(float(ij) > 0. for ij in [tier1ff, tier4ff])) | (ratio2 & ratio3 & (ratio_dist_start2 | ratio_dist_end2) & (ratio_dist_start3 | ratio_dist_end3) & all(float(ij) < 0. for ij in [tier1ff, tier4ff]) & all(float(ij) > 0. for ij in [tier2ff, tier3ff]))): # all mates available # if distance between softclipping and mutation is at start or end of the read smaller than threshold softclipped_mutation_allMates = False softclipped_mutation_oneOfTwoMates = False softclipped_mutation_oneOfTwoSSCS = False softclipped_mutation_oneOfTwoSSCS_diffMates = False softclipped_mutation_oneMate = True softclipped_mutation_oneMateOneSSCS = False tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = False # information of one mate available --> only one SSCS is softclipped elif ((((ratio1 & (ratio_dist_start1 | ratio_dist_end1)) | (ratio4 & (ratio_dist_start4 | ratio_dist_end4))) & (all(float(ij) < 0. for ij in [tier2ff, tier3ff]) & all(float(ij) > 0. for ij in [tier1ff, tier4ff]))) | (((ratio2 & (ratio_dist_start2 | ratio_dist_end2)) | (ratio3 & (ratio_dist_start3 | ratio_dist_end3))) & (all(float(ij) < 0. for ij in [tier1ff, tier4ff]) & all(float(ij) < 0. for ij in [tier2ff, tier3ff])))): # all mates available # if distance between softclipping and mutation is at start or end of the read smaller than threshold softclipped_mutation_allMates = False softclipped_mutation_oneOfTwoMates = False softclipped_mutation_oneOfTwoSSCS = False softclipped_mutation_oneOfTwoSSCS_diffMates = False softclipped_mutation_oneMate = False softclipped_mutation_oneMateOneSSCS = True tier1ff = 0 tier4ff = 0 tier2ff = 0 tier3ff = 0 trimmed = False contradictory = False else: if ((read_pos1 >= 0) and ((read_pos1 <= trim) | (abs(read_len_median1 - read_pos1) <= trim))): beg1 = total1new total1new = 0 alt1ff = 0 alt1f = 0 tier1ff = 0 trimmed = True if ((read_pos4 >= 0) and ((read_pos4 <= trim) | (abs(read_len_median4 - read_pos4) <= trim))): beg4 = total4new total4new = 0 alt4ff = 0 alt4f = 0 tier4ff = 0 trimmed = True if ((read_pos2 >= 0) and ((read_pos2 <= trim) | (abs(read_len_median2 - read_pos2) <= trim))): beg2 = total2new total2new = 0 alt2ff = 0 alt2f = 0 tier2ff = 0 trimmed = True if ((read_pos3 >= 0) and ((read_pos3 <= trim) | (abs(read_len_median3 - read_pos3) <= trim))): beg3 = total3new total3new = 0 alt3ff = 0 alt3f = 0 tier3ff = 0 trimmed = True details1 = (total1, total4, total1new, total4new, ref1, ref4, alt1, alt4, ref1f, ref4f, alt1f, alt4f, na1, na4, lowq1, lowq4, beg1, beg4) details2 = (total2, total3, total2new, total3new, ref2, ref3, alt2, alt3, ref2f, ref3f, alt2f, alt3f, na2, na3, lowq2, lowq3, beg2, beg3) # assign tiers if ((all(int(ij) >= 3 for ij in [total1new, total4new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier4ff])) | (all(int(ij) >= 3 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [tier2ff, tier3ff]))): tier = "1.1" counter_tier11 += 1 if variant_type == "alt": tier_dict[key1]["tier 1.1"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 1.1"] += 1 elif (all(int(ij) >= 1 for ij in [total1new, total2new, total3new, total4new]) & any(int(ij) >= 3 for ij in [total1new, total4new]) & any(int(ij) >= 3 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier2ff, tier3ff, tier4ff])): tier = "1.2" counter_tier12 += 1 if variant_type == "alt": tier_dict[key1]["tier 1.2"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 1.2"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & any(int(ij) >= 3 for ij in [total1new, total4new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier4ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & any(int(ij) >= 3 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [tier2ff, tier3ff]))): tier = "2.1" counter_tier21 += 1 if variant_type == "alt": tier_dict[key1]["tier 2.1"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 2.1"] += 1 elif (all(int(ij) >= 1 for ij in [total1new, total2new, total3new, total4new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier2ff, tier3ff, tier4ff])): tier = "2.2" counter_tier22 += 1 if variant_type == "alt": tier_dict[key1]["tier 2.2"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 2.2"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & any(int(ij) >= 3 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier4ff]) & any(float(ij) >= 0.75 for ij in [tier2ff, tier3ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & any(int(ij) >= 3 for ij in [total1new, total4new]) & all(float(ij) >= 0.75 for ij in [tier2ff, tier3ff]) & any(float(ij) >= 0.75 for ij in [tier1ff, tier4ff]))): tier = "2.3" counter_tier23 += 1 if variant_type == "alt": tier_dict[key1]["tier 2.3"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 2.3"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & all(float(ij) >= 0.75 for ij in [tier1ff, tier4ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [tier2ff, tier3ff]))): tier = "2.4" counter_tier24 += 1 if variant_type == "alt": tier_dict[key1]["tier 2.4"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 2.4"] += 1 elif ((len(pure_tags_dict_short[key1]) > 1) & (all(float(ij) >= 0.5 for ij in [tier1ff, tier4ff]) | all(float(ij) >= 0.5 for ij in [tier2ff, tier3ff]))): tier = "3.1" counter_tier31 += 1 if variant_type == "alt": tier_dict[key1]["tier 3.1"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 3.1"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & all(float(ij) >= 0.5 for ij in [tier1ff, tier4ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & all(float(ij) >= 0.5 for ij in [tier2ff, tier3ff]))): tier = "3.2" counter_tier32 += 1 if variant_type == "alt": tier_dict[key1]["tier 3.2"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 3.2"] += 1 elif (trimmed): tier = "4" counter_tier4 += 1 if variant_type == "alt": tier_dict[key1]["tier 4"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 4"] += 1 # assign tiers if ((all(int(ij) >= 3 for ij in [total1new_trim, total4new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier4ff_trim])) | (all(int(ij) >= 3 for ij in [total2new_trim, total3new_trim]) & all(float(ij) >= 0.75 for ij in [tier2ff_trim, tier3ff_trim]))): trimmed_actual_high_tier = True elif (all(int(ij) >= 1 for ij in [total1new_trim, total2new_trim, total3new_trim, total4new_trim]) & any(int(ij) >= 3 for ij in [total1new_trim, total4new_trim]) & any(int(ij) >= 3 for ij in [total2new_trim, total3new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier2ff_trim, tier3ff_trim, tier4ff_trim])): trimmed_actual_high_tier = True elif ((all(int(ij) >= 1 for ij in [total1new_trim, total4new_trim]) & any(int(ij) >= 3 for ij in [total1new_trim, total4new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier4ff_trim])) | (all(int(ij) >= 1 for ij in [total2new_trim, total3new_trim]) & any(int(ij) >= 3 for ij in [total2new_trim, total3new_trim]) & all(float(ij) >= 0.75 for ij in [tier2ff_trim, tier3ff_trim]))): trimmed_actual_high_tier = True elif (all(int(ij) >= 1 for ij in [total1new_trim, total2new_trim, total3new_trim, total4new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier2ff_trim, tier3ff_trim, tier4ff_trim])): trimmed_actual_high_tier = True elif ((all(int(ij) >= 1 for ij in [total1new_trim, total4new_trim]) & any(int(ij) >= 3 for ij in [total2new_trim, total3new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier4ff_trim]) & any(float(ij) >= 0.75 for ij in [tier2ff_trim, tier3ff_trim])) | (all(int(ij) >= 1 for ij in [total2new_trim, total3new_trim]) & any(int(ij) >= 3 for ij in [total1new_trim, total4new_trim]) & all(float(ij) >= 0.75 for ij in [tier2ff_trim, tier3ff_trim]) & any(float(ij) >= 0.75 for ij in [tier1ff_trim, tier4ff_trim]))): trimmed_actual_high_tier = True elif ((all(int(ij) >= 1 for ij in [total1new_trim, total4new_trim]) & all(float(ij) >= 0.75 for ij in [tier1ff_trim, tier4ff_trim])) | (all(int(ij) >= 1 for ij in [total2new_trim, total3new_trim]) & all(float(ij) >= 0.75 for ij in [tier2ff_trim, tier3ff_trim]))): trimmed_actual_high_tier = True else: trimmed_actual_high_tier = False elif softclipped_mutation_allMates: tier = "5.1" counter_tier51 += 1 if variant_type == "alt": tier_dict[key1]["tier 5.1"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 5.1"] += 1 elif softclipped_mutation_oneOfTwoMates: tier = "5.2" counter_tier52 += 1 if variant_type == "alt": tier_dict[key1]["tier 5.2"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 5.2"] += 1 elif softclipped_mutation_oneOfTwoSSCS: tier = "5.3" counter_tier53 += 1 if variant_type == "alt": tier_dict[key1]["tier 5.3"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 5.3"] += 1 elif softclipped_mutation_oneMate: tier = "5.4" counter_tier54 += 1 if variant_type == "alt": tier_dict[key1]["tier 5.4"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 5.4"] += 1 elif softclipped_mutation_oneMateOneSSCS: tier = "5.5" counter_tier55 += 1 if variant_type == "alt": tier_dict[key1]["tier 5.5"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 5.5"] += 1 elif (contradictory): tier = "6" counter_tier6 += 1 if variant_type == "alt": tier_dict[key1]["tier 6"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 6"] += 1 else: tier = "7" counter_tier7 += 1 if variant_type == "alt": tier_dict[key1]["tier 7"] += 1 elif variant_type == "ref": tier_dict_ref[key1]["tier 7"] += 1 chrom, pos, ref_a, alt_a = re.split(r'\#', key1) var_id = '-'.join([chrom, str(int(pos)+1), ref, alt]) if variant_type == "alt": sample_tag = key2[:-5] array2 = np.unique(whole_array) # remove duplicate sequences to decrease running time # exclude identical tag from array2, to prevent comparison to itself same_tag = np.where(array2 == sample_tag) index_array2 = np.arange(0, len(array2), 1) index_withoutSame = np.delete(index_array2, same_tag) # delete identical tag from the data array2 = array2[index_withoutSame] if len(array2) != 0: # only perform chimera analysis if there is more than 1 variant array1_half = sample_tag[0:int(len(sample_tag) / 2)] # mate1 part1 array1_half2 = sample_tag[int(len(sample_tag) / 2):int(len(sample_tag))] # mate1 part 2 array2_half = np.array([ii[0:int(len(ii) / 2)] for ii in array2]) # mate2 part1 array2_half2 = np.array([ii[int(len(ii) / 2):int(len(ii))] for ii in array2]) # mate2 part2 min_tags_list_zeros = [] chimera_tags = [] for mate_b in [False, True]: i = 0 # counter, only used to see how many HDs of tags were already calculated if mate_b is False: # HD calculation for all a's half1_mate1 = array1_half half2_mate1 = array1_half2 half1_mate2 = array2_half half2_mate2 = array2_half2 elif mate_b is True: # HD calculation for all b's half1_mate1 = array1_half2 half2_mate1 = array1_half half1_mate2 = array2_half2 half2_mate2 = array2_half # calculate HD of "a" in the tag to all "a's" or "b" in the tag to all "b's" dist = np.array([sum(itertools.map(operator.ne, half1_mate1, c)) for c in half1_mate2]) min_index = np.where(dist == dist.min()) # get index of min HD # get all "b's" of the tag or all "a's" of the tag with minimum HD min_tag_half2 = half2_mate2[min_index] min_tag_array2 = array2[min_index] # get whole tag with min HD min_value = dist.min() # calculate HD of "b" to all "b's" or "a" to all "a's" dist_second_half = np.array([sum(itertools.map(operator.ne, half2_mate1, e)) for e in min_tag_half2]) dist2 = dist_second_half.max() max_index = np.where(dist_second_half == dist_second_half.max())[0] # get index of max HD max_tag = min_tag_array2[max_index] # tags which have identical parts: if min_value == 0 or dist2 == 0: min_tags_list_zeros.append(tag) chimera_tags.append(max_tag) i += 1 chimera_tags = [x for x in chimera_tags if x != []] chimera_tags_new = [] for i in chimera_tags: if len(i) > 1: for t in i: chimera_tags_new.append(t) else: chimera_tags_new.extend(i) chimera = ", ".join(chimera_tags_new) else: chimera_tags_new = [] chimera = "" else: chimera_tags_new = [] chimera = "" if len(chimera_tags_new) > 0: chimera_tags_new.append(sample_tag) key_chimera = ",".join(sorted(chimera_tags_new)) if key_chimera in chimeric_tag.keys(): chimeric_tag[key_chimera].append(float(tier)) else: chimeric_tag[key_chimera] = [float(tier)] if (read_pos1 == -1): read_pos1 = read_len_median1 = None if (read_pos4 == -1): read_pos4 = read_len_median4 = None if (read_pos2 == -1): read_pos2 = read_len_median2 = None if (read_pos3 == -1): read_pos3 = read_len_median3 = None line = (var_id, tier, variant_type, key2[:-5], 'ab1.ba2', read_pos1, read_pos4, read_len_median1, read_len_median4, dcs_median) + details1 + (sscs_mut_ab, sscs_mut_ba, sscs_ref_ab, sscs_ref_ba, add_mut14, chimera) line2 = ("", "", "", key2[:-5], 'ab2.ba1', read_pos2, read_pos3, read_len_median2, read_len_median3, dcs_median) + details2 + (sscs_mut_ab, sscs_mut_ba, sscs_ref_ab, sscs_ref_ba, add_mut23, chimera) if tier != "4": current_result_sheet.write_row(row, 0, line) csv_writer.writerow(line) current_result_sheet.write_row(row + 1, 0, line2) csv_writer.writerow(line2) if variant_type == "alt": current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(row + 1, row + 1), 'format': format1, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="2.1", $B${}="2.2", $B${}="2.3", $B${}="2.4", $B${}="2.5")'.format(row + 1, row + 1, row + 1, row + 1, row + 1), 'format': format3, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(row + 1), 'format': format2, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) elif variant_type == "ref": current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(row + 1, row + 1), 'format': format1, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="2.1", $B${}="2.2", $B${}="2.3", $B${}="2.4", $B${}="2.5")'.format(row + 1, row + 1, row + 1, row + 1, row + 1), 'format': format3, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(row + 1), 'format': format2, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) row += 3 else: change_tier_after_print.append((line, line2, trimmed_actual_high_tier)) if chimera_correction: chimeric_dcs_high_tiers = 0 chimeric_dcs = 0 for keys_chimera in chimeric_tag.keys(): tiers = chimeric_tag[keys_chimera] chimeric_dcs += len(tiers) - 1 high_tiers = sum(1 for t in tiers if t < 3.) if high_tiers == len(tiers): chimeric_dcs_high_tiers += high_tiers - 1 else: chimeric_dcs_high_tiers += high_tiers chimera_dict[key1] = (chimeric_dcs, chimeric_dcs_high_tiers) # write to file # move tier 4 counts to tier 2.5 if there other mutations with tier <= 2.4 sum_highTiers = sum([tier_dict[key1][ij] for ij in list(sorted(tier_dict[key1].keys()))[:6]]) sum_highTiers_ref = sum([tier_dict_ref[key1][ij] for ij in list(sorted(tier_dict_ref[key1].keys()))[:6]]) correct_tier = False correct_tier_ref = False if tier_dict[key1]["tier 4"] > 0 and sum_highTiers > 0: # tier_dict[key1]["tier 2.5"] = tier_dict[key1]["tier 4"] # tier_dict[key1]["tier 4"] = 0 correct_tier = True elif tier_dict_ref[key1]["tier 4"] > 0 and sum_highTiers_ref > 0: # tier_dict_ref[key1]["tier 2.5"] = tier_dict_ref[key1]["tier 4"] # tier_dict_ref[key1]["tier 4"] = 0 correct_tier_ref = True # print(key1, "change tiers from tier 4 to tier 2.5 for {} DCS ...".format(len(change_tier_after_print))) if len(change_tier_after_print) > 0: for sample in change_tier_after_print: # row_number = sample[0] line1 = sample[0] line2 = sample[1] actual_high_tier = sample[2] current_tier = list(line1)[1] if line1[2] == "alt" and correct_tier and (current_tier == "4") and actual_high_tier: line1 = list(line1) line1[1] = "2.5" line1 = tuple(line1) counter_tier25 += 1 counter_tier4 -= 1 tier_dict[key1]["tier 2.5"] += 1 tier_dict[key1]["tier 4"] -= 1 elif line1[2] == "ref" and correct_tier_ref and (current_tier == "4") and actual_high_tier: line1 = list(line1) line1[1] = "2.5" line1 = tuple(line1) counter_tier25 += 1 counter_tier4 -= 1 tier_dict_ref[key1]["tier 2.5"] += 1 tier_dict_ref[key1]["tier 4"] -= 1 current_result_sheet.write_row(row, 0, line1) csv_writer.writerow(line1) current_result_sheet.write_row(row + 1, 0, line2) csv_writer.writerow(line2) if line1[2] == "alt": current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(row + 1, row + 1), 'format': format1, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="2.1", $B${}="2.2", $B${}="2.3", $B${}="2.4", $B${}="2.5")'.format(row + 1, row + 1, row + 1, row + 1, row + 1), 'format': format3, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(row + 1), 'format': format2, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) elif line1[2] == "ref": current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(row + 1, row + 1), 'format': format1, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="2.1", $B${}="2.2", $B${}="2.3", $B${}="2.4", $B${}="2.5")'.format(row + 1, row + 1, row + 1, row + 1, row + 1), 'format': format3, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) current_result_sheet.conditional_format('M{}:N{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(row + 1), 'format': format2, 'multi_range': 'M{}:N{} S{}:T{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1, row + 2)}) row += 3 # sheet 2 if refalttiers: if chimera_correction: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'cvrg (tiers 1.1-2.5)', 'AC ref (tiers 1.1-2.5)', 'AC alt (tiers 1.1-2.5)', 'AF (tiers 1.1-2.5)', 'chimera-corrected cvrg (tiers 1.1-2.5)', 'chimeras in AC alt (tiers 1.1-2.5)', 'chimera-corrected AF (tiers 1.1-2.5)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1 (alt)', 'tier 1.2 (alt)', 'tier 2.1 (alt)', 'tier 2.2 (alt)', 'tier 2.3 (alt)', 'tier 2.4 (alt)', 'tier 2.5 (alt)', 'tier 3.1 (alt)', 'tier 3.2 (alt)', 'tier 4 (alt)', 'tier 5.1 (alt)', 'tier 5.2 (alt)', 'tier 5.3 (alt)', 'tier 5.4 (alt)', 'tier 5.5 (alt)', 'tier 6 (alt)', 'tier 7 (alt)', 'tier 1.1 (ref)', 'tier 1.2 (ref)', 'tier 2.1 (ref)', 'tier 2.2 (ref)', 'tier 2.3 (ref)', 'tier 2.4 (ref)', 'tier 2.5 (ref)', 'tier 3.1 (ref)', 'tier 3.2 (ref)', 'tier 4 (ref)', 'tier 5.1 (ref)', 'tier 5.2 (ref)', 'tier 5.3 (ref)', 'tier 5.4 (ref)', 'tier 5.5 (ref)', 'tier 6 (ref)', 'tier 7 (ref)' ) else: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'cvrg (tiers 1.1-2.5)', 'AC ref (tiers 1.1-2.5)', 'AC alt (tiers 1.1-2.5)', 'AF (tiers 1.1-2.5)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1 (alt)', 'tier 1.2 (alt)', 'tier 2.1 (alt)', 'tier 2.2 (alt)', 'tier 2.3 (alt)', 'tier 2.4 (alt)', 'tier 2.5 (alt)', 'tier 3.1 (alt)', 'tier 3.2 (alt)', 'tier 4 (alt)', 'tier 5.1 (alt)', 'tier 5.2 (alt)', 'tier 5.3 (alt)', 'tier 5.4 (alt)', 'tier 5.5 (alt)', 'tier 6 (alt)', 'tier 7 (alt)', 'tier 1.1 (ref)', 'tier 1.2 (ref)', 'tier 2.1 (ref)', 'tier 2.2 (ref)', 'tier 2.3 (ref)', 'tier 2.4 (ref)', 'tier 2.5 (ref)', 'tier 3.1 (ref)', 'tier 3.2 (ref)', 'tier 4 (ref)', 'tier 5.1 (ref)', 'tier 5.2 (ref)', 'tier 5.3 (ref)', 'tier 5.4 (ref)', 'tier 5.5 (ref)', 'tier 6 (ref)', 'tier 7 (ref)' ) else: if chimera_correction: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'cvrg (tiers 1.1-2.5)', 'AC alt (tiers 1.1-2.5)', 'AF (tiers 1.1-2.5)', 'chimera-corrected cvrg (tiers 1.1-2.5)', 'chimeras in AC alt (tiers 1.1-2.5)', 'chimera-corrected AF (tiers 1.1-2.5)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1 (alt)', 'tier 1.2 (alt)', 'tier 2.1 (alt)', 'tier 2.2 (alt)', 'tier 2.3 (alt)', 'tier 2.4 (alt)', 'tier 2.5 (alt)', 'tier 3.1 (alt)', 'tier 3.2 (alt)', 'tier 4 (alt)', 'tier 5.1 (alt)', 'tier 5.2 (alt)', 'tier 5.3 (alt)', 'tier 5.4 (alt)', 'tier 5.5 (alt)', 'tier 6 (alt)', 'tier 7 (alt)' ) else: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'cvrg (tiers 1.1-2.5)', 'AC alt (tiers 1.1-2.5)', 'AF (tiers 1.1-2.5)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1 (alt)', 'tier 1.2 (alt)', 'tier 2.1 (alt)', 'tier 2.2 (alt)', 'tier 2.3 (alt)', 'tier 2.4 (alt)', 'tier 2.5 (alt)', 'tier 3.1 (alt)', 'tier 3.2 (alt)', 'tier 4 (alt)', 'tier 5.1 (alt)', 'tier 5.2 (alt)', 'tier 5.3 (alt)', 'tier 5.4 (alt)', 'tier 5.5 (alt)', 'tier 6 (alt)', 'tier 7 (alt)' ) ws2.write_row(0, 0, header_line2) row = 0 for key1, value1 in sorted(tier_dict.items()): if key1 in pure_tags_dict_short.keys(): i = np.where(np.array(['#'.join(str(i) for i in z) for z in zip(mut_array[:, 0], mut_array[:, 1], mut_array[:, 2], mut_array[:, 3])]) == key1)[0][0] ref = mut_array[i, 2] alt = mut_array[i, 3] chrom, pos, ref_a, alt_a = re.split(r'\#', key1) ref_count = cvrg_dict[key1][0] alt_count = cvrg_dict[key1][1] cvrg = ref_count + alt_count ref_tiers = tier_dict_ref[key1] var_id = '-'.join([chrom, str(int(pos)+1), ref, alt]) lst = [var_id, cvrg] used_tiers = [] used_tiers_ref = [t for k, t in sorted(ref_tiers.items())] cum_af = [] for key2, value2 in sorted(value1.items()): # calculate cummulative AF used_tiers.append(value2) if len(used_tiers) > 1: cum = safe_div(sum(used_tiers), cvrg) cum_af.append(cum) if sum(used_tiers) == 0: # skip mutations that are filtered by the VA in the first place continue lst.extend([sum(used_tiers), safe_div(sum(used_tiers), cvrg)]) if refalttiers: lst.extend([(sum(used_tiers_ref[0:7]) + sum(used_tiers[0:7])), sum(used_tiers_ref[0:7]), sum(used_tiers[0:7]), safe_div(sum(used_tiers[0:7]), (sum(used_tiers_ref[0:7]) + sum(used_tiers[0:7])))]) else: lst.extend([(cvrg - sum(used_tiers[-10:])), sum(used_tiers[0:7]), safe_div(sum(used_tiers[0:7]), (cvrg - sum(used_tiers[-10:])))]) if chimera_correction: chimeras_all = chimera_dict[key1][1] new_alt = sum(used_tiers[0:7]) - chimeras_all fraction_chimeras = safe_div(chimeras_all, float(sum(used_tiers[0:7]))) if fraction_chimeras is None: fraction_chimeras = 0. new_cvrg = (cvrg - sum(used_tiers[-10:])) * (1. - fraction_chimeras) lst.extend([new_cvrg, chimeras_all, safe_div(new_alt, new_cvrg)]) lst.extend([alt_count, safe_div(alt_count, cvrg)]) lst.extend(used_tiers) if refalttiers: lst.extend(used_tiers_ref) # lst.extend(cum_af) lst = tuple(lst) ws2.write_row(row + 1, 0, lst) if refalttiers: if chimera_correction: ws2.conditional_format('N{}:O{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$N$1="tier 1.1 (alt)"', 'format': format12, 'multi_range': 'N{}:O{} N1:O1 AE{}:AF{} AE1:AF1'.format(row + 2, row + 2, row + 2, row + 2)}) ws2.conditional_format('P{}:T{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$P$1="tier 2.1 (alt)"', 'format': format32, 'multi_range': 'P{}:T{} P1:T1 AG{}:AK{} AG1:AK1'.format(row + 2, row + 2, row + 2, row + 2)}) ws2.conditional_format('U{}:AD{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$U$1="tier 3.1 (alt)"', 'format': format22, 'multi_range': 'U{}:AD{} U1:AD1 AL{}:AU{} AL1:AU1'.format(row + 2, row + 2, row + 2, row + 2)}) else: ws2.conditional_format('K{}:L{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$K$1="tier 1.1 (alt)"', 'format': format12, 'multi_range': 'K{}:L{} K1:L1 AB{}:AC{} AB1:AC1'.format(row + 2, row + 2, row + 2, row + 2)}) ws2.conditional_format('M{}:Q{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$M$1="tier 2.1 (alt)"', 'format': format32, 'multi_range': 'M{}:Q{} M1:Q1 AD{}:AH{} AD1:AH1'.format(row + 2, row + 2, row + 2, row + 2)}) ws2.conditional_format('R{}:AA{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$R$1="tier 3.1 (alt)"', 'format': format22, 'multi_range': 'R{}:AA{} R1:AA1 AI{}:AR{} AI1:AR1'.format(row + 2, row + 2, row + 2, row + 2)}) else: if chimera_correction: ws2.conditional_format('M{}:N{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$M$1="tier 1.1 (alt)"', 'format': format12, 'multi_range': 'M{}:N{} M1:N1'.format(row + 2, row + 2)}) ws2.conditional_format('O{}:S{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$O$1="tier 2.1 (alt)"', 'format': format32, 'multi_range': 'O{}:S{} O1:S1'.format(row + 2, row + 2)}) ws2.conditional_format('T{}:AC{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$T$1="tier 3.1 (alt)"', 'format': format22, 'multi_range': 'T{}:AC{} T1:AC1'.format(row + 2, row + 2)}) else: ws2.conditional_format('J{}:K{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$J$1="tier 1.1 (alt)"', 'format': format12, 'multi_range': 'J{}:K{} J1:K1'.format(row + 2, row + 2)}) ws2.conditional_format('L{}:P{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$L$1="tier 2.1 (alt)"', 'format': format32, 'multi_range': 'L{}:P{} L1:P1'.format(row + 2, row + 2)}) ws2.conditional_format('Q{}:Z{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$Q$1="tier 3.1 (alt)"', 'format': format22, 'multi_range': 'Q{}:Z{} Q1:Z1'.format(row + 2, row + 2)}) row += 1 # sheet 3 sheet3 = [("tier 1.1", counter_tier11), ("tier 1.2", counter_tier12), ("tier 2.1", counter_tier21), ("tier 2.2", counter_tier22), ("tier 2.3", counter_tier23), ("tier 2.4", counter_tier24), ("tier 2.5", counter_tier25), ("tier 3.1", counter_tier31), ("tier 3.2", counter_tier32), ("tier 4", counter_tier4), ("tier 5.1", counter_tier51), ("tier 5.2", counter_tier52), ("tier 5.3", counter_tier53), ("tier 5.4", counter_tier54), ("tier 5.5", counter_tier55), ("tier 6", counter_tier6), ("tier 7", counter_tier7)] header = ("tier", "count") ws3.write_row(0, 0, header) for i in range(len(sheet3)): ws3.write_row(i + 1, 0, sheet3[i]) ws3.conditional_format('A{}:B{}'.format(i + 2, i + 2), {'type': 'formula', 'criteria': '=OR($A${}="tier 1.1", $A${}="tier 1.2")'.format(i + 2, i + 2), 'format': format1}) ws3.conditional_format('A{}:B{}'.format(i + 2, i + 2), {'type': 'formula', 'criteria': '=OR($A${}="tier 2.1", $A${}="tier 2.2", $A${}="tier 2.3", $A${}="tier 2.4", $A${}="tier 2.5")'.format(i + 2, i + 2, i + 2, i + 2, i + 2), 'format': format3}) ws3.conditional_format('A{}:B{}'.format(i + 2, i + 2), {'type': 'formula', 'criteria': '=$A${}>="3"'.format(i + 2), 'format': format2}) description_tiers = [("Tier 1.1", "both ab and ba SSCS present (>75% of the sites with alternative base) and minimal FS>=3 for both SSCS in at least one mate"), ("", ""), ("Tier 1.2", "both ab and ba SSCS present (>75% of the sites with alt. base) and mate pair validation (min. FS=1) and minimal FS>=3 for at least one of the SSCS"), ("Tier 2.1", "both ab and ba SSCS present (>75% of the sites with alt. base) and minimal FS>=3 for at least one of the SSCS in at least one mate"), ("Tier 2.2", "both ab and ba SSCS present (>75% of the sites with alt. base) and mate pair validation (min. FS=1)"), ("Tier 2.3", "both ab and ba SSCS present (>75% of the sites with alt. base) and minimal FS=1 for both SSCS in one mate and minimal FS>=3 for at least one of the SSCS in the other mate"), ("Tier 2.4", "both ab and ba SSCS present (>75% of the sites with alt. base) and minimal FS=1 for both SSCS in at least one mate"), ("Tier 2.5", "variants at the start or end of the read (ignoring variant position tier 1.1-2.4) and recurring mutation on this position in tier 1.1-2.4"), ("Tier 3.1", "both ab and ba SSCS present (>50% of the sites with alt. base) and recurring mutation on this position"), ("Tier 3.2", "both ab and ba SSCS present (>50% of the sites with alt. base) and minimal FS>=1 for both SSCS in at least one mate"), ("Tier 4", "variants at the start or end of the reads"), ("Tier 5.1", "variant is close to softclipping in both mates and SSCS"), ("Tier 5.2", "variant is close to softclipping in one of the mates but both SSCS"), ("Tier 5.3", "variant is close to softclipping in one of the SSCS of both mates"), ("Tier 5.4", "variant is close to softclipping in one mate and both SSCS (no information of second mate)"), ("Tier 5.5", "variant is close to softclipping in one of the SSCS (no information of the second mate)"), ("Tier 6", "mates with contradictory information"), ("Tier 7", "remaining variants")] examples_tiers = [[("chr5-11068-C-G", "1.1", "alt", "AAAAAGATGCCGACTACCTT", "ab1.ba2", "254", "228", "287", "288", "289", "3", "6", "3", "6", "0", "0", "3", "6", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "AAAAAGATGCCGACTACCTT", "ab2.ba1", None, None, None, None, "289", "0", "0", "0", "0", "0", "0", "0", "0", None, None, None, None, "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-11068-C-G", "1.1", "alt", "AAAAATGCGTAGAAATATGC", "ab1.ba2", "254", "228", "287", "288", "289", "33", "43", "33", "43", "0", "0", "33", "43", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "AAAAATGCGTAGAAATATGC", "ab2.ba1", "268", "268", "270", "288", "289", "11", "34", "10", "27", "0", "0", "10", "27", "0", "0", "1", "1", "0", "0", "1", "7", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-10776-G-T", "1.2", "alt", "CTATGACCCGTGAGCCCATG", "ab1.ba2", "132", "132", "287", "288", "290", "4", "1", "4", "1", "0", "0", "4", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "6", "47170", "41149", "", ""), ("", "", "", "CTATGACCCGTGAGCCCATG", "ab2.ba1", "77", "132", "233", "200", "290", "4", "1", "4", "1", "0", "0", "4", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "6", "47170", "41149", "", "")], [("chr5-11068-C-G", "2.1", "alt", "AAAAAAACATCATACACCCA", "ab1.ba2", "246", "244", "287", "288", "289", "2", "8", "2", "8", "0", "0", "2", "8", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "AAAAAAACATCATACACCCA", "ab2.ba1", None, None, None, None, "289", "0", "0", "0", "0", "0", "0", "0", "0", None, None, None, None, "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-11068-C-G", "2.2", "alt", "ATCAGCCATGGCTATTATTG", "ab1.ba2", "72", "72", "217", "288", "289", "1", "1", "1", "1", "0", "0", "1", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "ATCAGCCATGGCTATTATTG", "ab2.ba1", "153", "164", "217", "260", "289", "1", "1", "1", "1", "0", "0", "1", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-11068-C-G", "2.3", "alt", "ATCAATATGGCCTCGCCACG", "ab1.ba2", None, None, None, None, "289", "0", "5", "0", "5", "0", "0", "0", "5", None, None, None, "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "ATCAATATGGCCTCGCCACG", "ab2.ba1", "202", "255", "277", "290", "289", "1", "3", "1", "3", "0", "0", "1", "3", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-11068-C-G", "2.4", "alt", "ATCAGCCATGGCTATTTTTT", "ab1.ba2", "72", "72", "217", "288", "289", "1", "1", "1", "1", "0", "0", "1", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "4081", "4098", "5", "10", "", ""), ("", "", "", "ATCAGCCATGGCTATTTTTT", "ab2.ba1", "153", "164", "217", "260", "289", "1", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "4081", "4098", "5", "10", "", "")], [("chr5-11068-C-G", "2.5", "alt", "ATTGAAAGAATAACCCACAC", "ab1.ba2", "1", "100", "255", "276", "269", "5", "6", "0", "6", "0", "0", "5", "6", "0", "0", "0", "1", "0", "0", "0", "0", "5", "0", "1", "1", "5348", "5350", "", ""), ("", "", "", "AAAAAAAGAATAACCCACAC", "ab2.ba1", None, None, None, None, "269", "0", "0", "0", "0", "0", "0", "0", "0", None, None, None, None, "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", "")], [("chr5-10776-G-T", "3.1", "alt", "ATGCCTACCTCATTTGTCGT", "ab1.ba2", "46", "15", "287", "288", "290", "3", "3", "3", "2", "3", "1", "0", "1", "1", "0.5", "0", "0.5", "0", "0", "0", "1", "0", "0", "3", "3", "47170", "41149", "", ""), ("", "", "", "ATGCCTACCTCATTTGTCGT", "ab2.ba1", None, "274", None, "288", "290", "0", "3", "0", "2", "0", "1", "0", "1", None, "0.5", None, "0.5", "0", "0", "0", "1", "0", "0", "3", "3", "47170", "41149", "", "")], [("chr5-11315-C-T", "3.2", "alt", "ACAACATCACGTATTCAGGT", "ab1.ba2", "197", "197", "240", "255", "271", "2", "3", "2", "3", "0", "1", "2", "2", "0", "0.333333333333333", "1", "0.666666666666667", "0", "0", "0", "0", "0", "0", "1", "1", "6584", "6482", "", ""), ("", "", "", "ACAACATCACGTATTCAGGT", "ab2.ba1", "35", "35", "240", "258", "271", "2", "3", "2", "3", "0", "1", "2", "2", "0", "0.333333333333333", "1", "0.666666666666667", "0", "0", "0", "0", "0", "0", "1", "1", "6584", "6482", "", "")], [("chr5-13983-G-C", "4", "alt", "AAAAAAAGAATAACCCACAC", "ab1.ba2", "1", "100", "255", "276", "269", "5", "6", "0", "6", "0", "0", "5", "6", "0", "0", "0", "1", "0", "0", "0", "0", "5", "0", "1", "1", "5348", "5350", "", ""), ("", "", "", "AAAAAAAGAATAACCCACAC", "ab2.ba1", None, None, None, None, "269", "0", "0", "0", "0", "0", "0", "0", "0", None, None, None, None, "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", "")], [("" * 34), ("" * 34)], [("" * 34), ("" * 34)], [("" * 34), ("" * 34)], [("" * 34), ("" * 34)], [("" * 34), ("" * 34)], [("chr5-13963-T-C", "6", "alt", "TTTTTAAGAATAACCCACAC", "ab1.ba2", "38", "38", "240", "283", "263", "110", "54", "110", "54", "0", "0", "110", "54", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", ""), ("", "", "", "TTTTTAAGAATAACCCACAC", "ab2.ba1", "100", "112", "140", "145", "263", "7", "12", "7", "12", "7", "12", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", "")], [("chr5-13983-G-C", "7", "alt", "ATGTTGTGAATAACCCACAC", "ab1.ba2", None, "186", None, "276", "269", "0", "6", "0", "6", "0", "0", "0", "6", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", ""), ("", "", "", "ATGTTGTGAATAACCCACAC", "ab2.ba1", None, None, None, None, "269", "0", "0", "0", "0", "0", "0", "0", "0", None, None, None, None, "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", "")]] start_row = 20 ws3.write(start_row, 0, "Description of tiers with examples") ws3.write_row(start_row + 1, 0, header_line) row = 0 for i in range(len(description_tiers)): ws3.write_row(start_row + 2 + row + i + 1, 0, description_tiers[i]) ex = examples_tiers[i] for k in range(len(ex)): ws3.write_row(start_row + 2 + row + i + k + 2, 0, ex[k]) ws3.conditional_format('M{}:N{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 2), 'format': format13, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3)}) ws3.conditional_format('M{}:N{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3), {'type': 'formula', 'criteria': '=OR($B${}="2.1",$B${}="2.2", $B${}="2.3", $B${}="2.4", $B${}="2.5")'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 2), 'format': format33, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3)}) ws3.conditional_format('M{}:N{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(start_row + 2 + row + i + k + 2), 'format': format23, 'multi_range': 'M{}:N{} U{}:V{} B{}'.format(start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3, start_row + 2 + row + i + k + 2, start_row + 2 + row + i + k + 3)}) row += 3 workbook.close() workbook2.close() workbook3.close() csv_data.close() if __name__ == '__main__': sys.exit(read2mut(sys.argv))