Mercurial > repos > mheinzl > variant_analyzer2
view read2mut.py @ 18:d910b6dfd826 draft
planemo upload for repository https://github.com/Single-Molecule-Genetics/VariantAnalyzerGalaxy/tree/master/tools/variant_analyzer commit ee4a8e6cf290e6c8a4d55f9cd2839d60ab3b11c8
| author | mheinzl |
|---|---|
| date | Mon, 22 Feb 2021 14:42:31 +0000 |
| parents | f06067bedfc5 |
| children | 858ca8b7ad43 |
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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 2.0.0 2020-10-30 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 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') 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 thresh = args.thresh phred_score = args.phred trim = args.trim chimera_correction = args.chimera_correction 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)) # load dicts with open(json_file, "r") as f: (tag_dict, cvrg_dict) = json.load(f) with open(sscs_json, "r") as f: (mut_pos_dict, ref_pos_dict) = json.load(f) # read bam file bam = pysam.AlignmentFile(file2, "rb") # create mut_dict mut_dict = {} mut_read_pos_dict = {} mut_read_dict = {} reads_dict = {} i = 0 mut_array = [] for variant in VCF(file1): chrom = variant.CHROM stop_pos = variant.start #chrom_stop_pos = str(chrom) + "#" + str(stop_pos) ref = variant.REF alt = variant.ALT[0] chrom_stop_pos = str(chrom) + "#" + str(stop_pos) + "#" + ref + "#" + alt if len(ref) == len(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] = {} for pileupcolumn in bam.pileup(chrom, stop_pos - 1, stop_pos + 1, max_depth=100000000): 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 print("unfiltered reads=", pileupcolumn.n, "filtered reads=", len(pileupcolumn.pileups), "difference= ", len(pileupcolumn.pileups) - pileupcolumn.n) for pileupread in pileupcolumn.pileups: n += 1 if not pileupread.is_del and not pileupread.is_refskip: tag = pileupread.alignment.query_name nuc = pileupread.alignment.query_sequence[pileupread.query_position] phred = ord(pileupread.alignment.qual[pileupread.query_position]) - 33 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] = np.array(pileupread.query_position) + 1 reads_dict[chrom_stop_pos][tag] = len(pileupread.alignment.query_sequence) else: mut_read_pos_dict[chrom_stop_pos][tag] = np.append( mut_read_pos_dict[chrom_stop_pos][tag], pileupread.query_position + 1) reads_dict[chrom_stop_pos][tag] = np.append( reads_dict[chrom_stop_pos][tag], len(pileupread.alignment.query_sequence)) 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, indel = %s, low quality = %s\n" % (pileupcolumn.pos, count_ref + count_alt, count_ref, count_alt, count_other, count_n, count_indel, count_lowq)) else: print("indels are currently not evaluated") 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" 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 = {} 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] = {} 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 # 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) ws1 = workbook.add_worksheet("Results") ws2 = workbook2.add_worksheet("Allele frequencies") ws3 = workbook3.add_worksheet("Tiers") 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', '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') ws1.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_tier41 = 0 counter_tier42 = 0 counter_tier5 = 0 row = 1 tier_dict = {} chimera_dict = {} for key1, value1 in sorted(mut_dict.items()): counts_mut = 0 chimeric_tag = {} 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] dcs_median = cvrg_dict[key1][2] whole_array = list(pure_tags_dict_short[key1].keys()) tier_dict[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 3.1", 0), ("tier 3.2", 0), ("tier 4.1", 0), ("tier 4.2", 0), ("tier 5", 0)] for k, v in values_tier_dict: tier_dict[key1][k] = v used_keys = [] 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] in pure_tags_dict_short[key1].keys()) and (key2[:-5] not in used_keys) and (key1 in tag_dict[key2[:-5]].keys()): 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'] else: na1 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ab.1'].keys(): lowq1 = mut_dict[key1][key2[:-5] + '.ab.1']['lowQ'] else: 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'] else: na2 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ab.2'].keys(): lowq2 = mut_dict[key1][key2[:-5] + '.ab.2']['lowQ'] else: 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'] else: na3 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ba.1'].keys(): lowq3 = mut_dict[key1][key2[:-5] + '.ba.1']['lowQ'] else: 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'] else: na4 = 0 if 'lowQ' in mut_dict[key1][key2[:-5] + '.ba.2'].keys(): lowq4 = mut_dict[key1][key2[:-5] + '.ba.2']['lowQ'] else: 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 if key2[:-5] + '.ab.1' in mut_read_pos_dict[key1].keys(): read_pos1 = np.median(mut_read_pos_dict[key1][key2[:-5] + '.ab.1']) read_len_median1 = np.median(reads_dict[key1][key2[:-5] + '.ab.1']) if key2[:-5] + '.ab.2' in mut_read_pos_dict[key1].keys(): read_pos2 = np.median(mut_read_pos_dict[key1][key2[:-5] + '.ab.2']) read_len_median2 = np.median(reads_dict[key1][key2[:-5] + '.ab.2']) if key2[:-5] + '.ba.1' in mut_read_pos_dict[key1].keys(): read_pos3 = np.median(mut_read_pos_dict[key1][key2[:-5] + '.ba.1']) read_len_median3 = np.median(reads_dict[key1][key2[:-5] + '.ba.1']) if key2[:-5] + '.ba.2' in mut_read_pos_dict[key1].keys(): read_pos4 = np.median(mut_read_pos_dict[key1][key2[:-5] + '.ba.2']) read_len_median4 = np.median(reads_dict[key1][key2[:-5] + '.ba.2']) used_keys.append(key2[:-5]) counts_mut += 1 if (alt1f + alt2f + alt3f + alt4f) > 0.5: if total1new == 0: ref1f = alt1f = None alt1ff = -1 else: alt1ff = alt1f if total2new == 0: ref2f = alt2f = None alt2ff = -1 else: alt2ff = alt2f if total3new == 0: ref3f = alt3f = None alt3ff = -1 else: alt3ff = alt3f if total4new == 0: ref4f = alt4f = None alt4ff = -1 else: alt4ff = alt4f 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 if ((all(float(ij) >= 0.5 for ij in [alt1ff, alt4ff]) & all(float(ij) == 0. for ij in [alt2ff, alt3ff])) | (all(float(ij) >= 0.5 for ij in [alt2ff, alt3ff]) & all(float(ij) == 0. for ij in [alt1ff, alt4ff]))): alt1ff = 0 alt4ff = 0 alt2ff = 0 alt3ff = 0 trimmed = False contradictory = True else: if ((read_pos1 >= 0) and ((read_pos1 <= trim) | (abs(read_len_median1 - read_pos1) <= trim))): beg1 = total1new total1new = 0 alt1ff = 0 alt1f = 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 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 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 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 [alt1ff, alt4ff])) | (all(int(ij) >= 3 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [alt2ff, alt3ff]))): tier = "1.1" counter_tier11 += 1 tier_dict[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 [alt1ff, alt2ff, alt3ff, alt4ff])): tier = "1.2" counter_tier12 += 1 tier_dict[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 [alt1ff, alt4ff])) | (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 [alt2ff, alt3ff]))): tier = "2.1" counter_tier21 += 1 tier_dict[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 [alt1ff, alt2ff, alt3ff, alt4ff])): tier = "2.2" counter_tier22 += 1 tier_dict[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 [alt1ff, alt4ff]) & any(float(ij) >= 0.75 for ij in [alt2ff, alt3ff])) | (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 [alt2ff, alt3ff]) & any(float(ij) >= 0.75 for ij in [alt1ff, alt4ff]))): tier = "2.3" counter_tier23 += 1 tier_dict[key1]["tier 2.3"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & all(float(ij) >= 0.75 for ij in [alt1ff, alt4ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & all(float(ij) >= 0.75 for ij in [alt2ff, alt3ff]))): tier = "2.4" counter_tier24 += 1 tier_dict[key1]["tier 2.4"] += 1 elif ((len(pure_tags_dict_short[key1]) > 1) & (all(float(ij) >= 0.5 for ij in [alt1ff, alt4ff]) | all(float(ij) >= 0.5 for ij in [alt2ff, alt3ff]))): tier = "3.1" counter_tier31 += 1 tier_dict[key1]["tier 3.1"] += 1 elif ((all(int(ij) >= 1 for ij in [total1new, total4new]) & all(float(ij) >= 0.5 for ij in [alt1ff, alt4ff])) | (all(int(ij) >= 1 for ij in [total2new, total3new]) & all(float(ij) >= 0.5 for ij in [alt2ff, alt3ff]))): tier = "3.2" counter_tier32 += 1 tier_dict[key1]["tier 3.2"] += 1 elif (trimmed): tier = "4.1" counter_tier41 += 1 tier_dict[key1]["tier 4.1"] += 1 elif (contradictory): tier = "4.2" counter_tier42 += 1 tier_dict[key1]["tier 4.2"] += 1 else: tier = "5" counter_tier5 += 1 tier_dict[key1]["tier 5"] += 1 chrom, pos, ref_a, alt_a = re.split(r'\#', key1) var_id = '-'.join([chrom, str(int(pos) + 1), ref, 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(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(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 = "" 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, 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) ws1.write_row(row, 0, line) csv_writer.writerow(line) line = ("", "", 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) ws1.write_row(row + 1, 0, line) csv_writer.writerow(line) ws1.conditional_format('L{}:M{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="1.1", $B${}="1.2")'.format(row + 1, row + 1), 'format': format1, 'multi_range': 'L{}:M{} T{}:U{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1)}) ws1.conditional_format('L{}:M{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=OR($B${}="2.1", $B${}="2.2", $B${}="2.3", $B${}="2.4")'.format(row + 1, row + 1, row + 1, row + 1), 'format': format3, 'multi_range': 'L{}:M{} T{}:U{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1)}) ws1.conditional_format('L{}:M{}'.format(row + 1, row + 2), {'type': 'formula', 'criteria': '=$B${}>="3"'.format(row + 1), 'format': format2, 'multi_range': 'L{}:M{} T{}:U{} B{}'.format(row + 1, row + 2, row + 1, row + 2, row + 1)}) row += 3 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) # sheet 2 if chimera_correction: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'chimeras in AC alt (all tiers)', 'chimera-corrected cvrg', 'chimera-corrected AF (all tiers)', 'cvrg (tiers 1.1-2.4)', 'AC alt (tiers 1.1-2.4)', 'AF (tiers 1.1-2.4)', 'chimeras in AC alt (tiers 1.1-2.4)', 'chimera-corrected cvrg (tiers 1.1-2.4)', 'chimera-corrected AF (tiers 1.1-2.4)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1', 'tier 1.2', 'tier 2.1', 'tier 2.2', 'tier 2.3', 'tier 2.4', 'tier 3.1', 'tier 3.2', 'tier 4.1', 'tier 4.2', 'tier 5', 'AF 1.1-1.2', 'AF 1.1-2.1', 'AF 1.1-2.2', 'AF 1.1-2.3', 'AF 1.1-2.4', 'AF 1.1-3.1', 'AF 1.1-3.2', 'AF 1.1-4.1', 'AF 1.1-4.2', 'AF 1.1-5') else: header_line2 = ('variant ID', 'cvrg', 'AC alt (all tiers)', 'AF (all tiers)', 'cvrg (tiers 1.1-2.4)', 'AC alt (tiers 1.1-2.4)', 'AF (tiers 1.1-2.4)', 'AC alt (orginal DCS)', 'AF (original DCS)', 'tier 1.1', 'tier 1.2', 'tier 2.1', 'tier 2.2', 'tier 2.3', 'tier 2.4', 'tier 3.1', 'tier 3.2', 'tier 4.1', 'tier 4.2', 'tier 5', 'AF 1.1-1.2', 'AF 1.1-2.1', 'AF 1.1-2.2', 'AF 1.1-2.3', 'AF 1.1-2.4', 'AF 1.1-3.1', 'AF 1.1-3.2', 'AF 1.1-4.1', 'AF 1.1-4.2', 'AF 1.1-5') 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 var_id = '-'.join([chrom, str(int(pos) + 1), ref, alt]) lst = [var_id, cvrg] used_tiers = [] 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) lst.extend([sum(used_tiers), safe_div(sum(used_tiers), cvrg)]) if chimera_correction: chimeras_all = chimera_dict[key1][0] new_alt = sum(used_tiers) - chimeras_all fraction_chimeras = safe_div(chimeras_all, float(sum(used_tiers))) if fraction_chimeras is None: fraction_chimeras = 0. new_cvrg = cvrg * (1. - fraction_chimeras) lst.extend([chimeras_all, new_cvrg, safe_div(new_alt, new_cvrg)]) lst.extend([(cvrg - sum(used_tiers[-5:])), sum(used_tiers[0:6]), safe_div(sum(used_tiers[0:6]), (cvrg - sum(used_tiers[-5:])))]) if chimera_correction: chimeras_all = chimera_dict[key1][1] new_alt = sum(used_tiers[0:6]) - chimeras_all fraction_chimeras = safe_div(chimeras_all, float(sum(used_tiers[0:6]))) if fraction_chimeras is None: fraction_chimeras = 0. new_cvrg = (cvrg - sum(used_tiers[-5:])) * (1. - fraction_chimeras) lst.extend([chimeras_all, new_cvrg, safe_div(new_alt, new_cvrg)]) lst.extend([alt_count, safe_div(alt_count, cvrg)]) lst.extend(used_tiers) lst.extend(cum_af) lst = tuple(lst) ws2.write_row(row + 1, 0, lst) if chimera_correction: ws2.conditional_format('P{}:Q{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$P$1="tier 1.1"', 'format': format1, 'multi_range': 'P{}:Q{} P1:Q1'.format(row + 2, row + 2)}) ws2.conditional_format('R{}:U{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$R$1="tier 2.1"', 'format': format3, 'multi_range': 'R{}:U{} R1:U1'.format(row + 2, row + 2)}) ws2.conditional_format('V{}:Z{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$V$1="tier 3.1"', 'format': format2, 'multi_range': 'V{}:Z{} V1:Z1'.format(row + 2, row + 2)}) else: ws2.conditional_format('J{}:K{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$J$1="tier 1.1"', 'format': format1, 'multi_range': 'J{}:K{} J1:K1'.format(row + 2, row + 2)}) ws2.conditional_format('L{}:O{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$L$1="tier 2.1"', 'format': format3, 'multi_range': 'L{}:O{} L1:O1'.format(row + 2, row + 2)}) ws2.conditional_format('P{}:T{}'.format(row + 2, row + 2), {'type': 'formula', 'criteria': '=$P$1="tier 3.1"', 'format': format2, 'multi_range': 'P{}:T{} P1:T1'.format(row + 2, row + 2)}) row += 1 # sheet 3 sheet3 = [("tier 1.1", counter_tier11), ("tier 1.2", counter_tier12), ("tier 2.1", counter_tier21), ("tier 2.2", counter_tier22), ("tier 2.3", counter_tier23), ("tier 2.4", counter_tier24), ("tier 3.1", counter_tier31), ("tier 3.2", counter_tier32), ("tier 4.1", counter_tier41), ("tier 4.2", counter_tier42), ("tier 5", counter_tier5)] header = ("tier", "count") ws3.write_row(0, 0, header) for i in range(len(sheet3)): ws3.write_row(i + 1, 0, sheet3[i]) ws3.conditional_format('A{}:B{}'.format(i + 2, i + 2), {'type': 'formula', 'criteria': '=OR($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")'.format(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 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 4.1", "variants at the start or end of the reads"), ("Tier 4.2", "mates with contradictory information"), ("Tier 5", "remaining variants")] examples_tiers = [[("Chr5:5-20000-11068-C-G", "1.1", "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", "", "")], [("Chr5:5-20000-11068-C-G", "1.1", "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", "", "")], [("Chr5:5-20000-10776-G-T", "1.2", "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", "", "")], [("Chr5:5-20000-11068-C-G", "2.1", "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", "", "")], [("Chr5:5-20000-11068-C-G", "2.2", "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", "", "")], [("Chr5:5-20000-11068-C-G", "2.3", "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", "", "")], [("Chr5:5-20000-11068-C-G", "2.4", "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", "", "")], [("Chr5:5-20000-10776-G-T", "3.1", "ATGCCTACCTCATTTGTCGT", "ab1.ba2", "46", "15", "287", "288", "290", "3", "3", "3", "2", "3", "1", "0", "1", "1", "0.5", "0", 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None, None, None, None, "0", "0", "0", "0", "0", "0", "1", "1", "5348", "5350", "", "")]] start_row = 15 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('L{}:M{}'.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': format1, 'multi_range': 'L{}:M{} T{}:U{} 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)}) ws3.conditional_format('L{}:M{}'.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")'.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), 'format': format3, 'multi_range': 'L{}:M{} T{}:U{} 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)}) ws3.conditional_format('L{}:M{}'.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': format2, 'multi_range': 'L{}:M{} T{}:U{} 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)}) row += 3 workbook.close() workbook2.close() workbook3.close() csv_data.close() if __name__ == '__main__': sys.exit(read2mut(sys.argv))