Mercurial > repos > iss > eurl_vtec_wgs_pt
diff scripts/ReMatCh/modules/rematch_module.py @ 0:c6bab5103a14 draft
"planemo upload commit 6abf3e299d82d07e6c3cf8642bdea80e96df64c3-dirty"
author | iss |
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date | Mon, 21 Mar 2022 15:23:09 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/ReMatCh/modules/rematch_module.py Mon Mar 21 15:23:09 2022 +0000 @@ -0,0 +1,1255 @@ +import os.path +import multiprocessing +import functools +import sys +import pickle + +# https://chrisyeh96.github.io/2017/08/08/definitive-guide-python-imports.html#case-2-syspath-could-change +sys.path.insert(0, os.path.dirname(os.path.realpath(__file__))) +import utils + + +def index_fasta_samtools(fasta, region_none, region_outfile_none, print_comand_true): + command = ['samtools', 'faidx', fasta, '', '', ''] + shell_true = False + if region_none is not None: + command[3] = region_none + if region_outfile_none is not None: + command[4] = '>' + command[5] = region_outfile_none + shell_true = True + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, shell_true, None, print_comand_true) + return run_successfully, stdout + + +# Indexing reference file using Bowtie2 +def index_sequence_bowtie2(reference_file, threads): + if os.path.isfile(str(reference_file + '.1.bt2')): + run_successfully = True + else: + command = ['bowtie2-build', '--threads', str(threads), reference_file, reference_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, True) + return run_successfully + + +# Mapping with Bowtie2 +def mapping_bowtie2(fastq_files, reference_file, threads, outdir, num_map_loc, + bowtie_algorithm='--very-sensitive-local', bowtie_opt=None): + sam_file = os.path.join(outdir, str('alignment.sam')) + + # Index reference file + run_successfully = index_sequence_bowtie2(reference_file, threads) + + if run_successfully: + command = ['bowtie2', '', '', '-q', bowtie_algorithm, '--threads', str(threads), '-x', + reference_file, '', '--no-unal', '', '-S', sam_file] + + if num_map_loc is not None and num_map_loc > 1: + command[1] = '-k' + command[2] = str(num_map_loc) + + if len(fastq_files) == 1: + command[9] = '-U ' + fastq_files[0] + elif len(fastq_files) == 2: + command[9] = '-1 ' + fastq_files[0] + ' -2 ' + fastq_files[1] + else: + return False, None + + if bowtie_opt is not None: + command[11] = bowtie_opt + + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, True) + + if not run_successfully: + sam_file = None + + return run_successfully, sam_file + + +def split_cigar(cigar): + cigars = ['M', 'I', 'D', 'N', 'S', 'H', 'P', '=', 'X'] + + splited_cigars = [] + numbers = '' + for char in cigar: + if char not in cigars: + numbers += char + else: + splited_cigars.append([int(numbers), char]) + numbers = '' + + return splited_cigars + + +def recode_cigar_based_on_base_quality(cigar, bases_quality, soft_clip_base_quality, mapping_position, + direct_strand_true, soft_clip_cigar_flag_recode): + cigar = split_cigar(cigar) + soft_left = [] + soft_right = [] + cigar_flags_for_reads_length = ('M', 'I', 'S', '=', 'X') + read_length_without_right_s = sum([cigar_part[0] for cigar_part in cigar if + cigar_part[1] in cigar_flags_for_reads_length]) - \ + (cigar[len(cigar) - 1][0] if cigar[len(cigar) - 1][1] == 'S' else 0) + for x, base in enumerate(bases_quality): + if ord(base) - 33 >= soft_clip_base_quality: + if x <= cigar[0][0] - 1: + if cigar[0][1] == 'S': + soft_left.append(x) + elif x > read_length_without_right_s - 1: + if cigar[len(cigar) - 1][1] == 'S': + soft_right.append(x) + + left_changed = (False, 0) + if len(soft_left) > 0: + soft_left = min(soft_left) + 1 + if soft_left == 1: + cigar = [[cigar[0][0], soft_clip_cigar_flag_recode]] + cigar[1:] + left_changed = (True, cigar[0][0]) + elif cigar[0][0] - soft_left > 0: + cigar = [[soft_left, 'S']] + [[cigar[0][0] - soft_left, soft_clip_cigar_flag_recode]] + cigar[1:] + left_changed = (True, cigar[0][0] - soft_left) + + right_changed = (False, 0) + if len(soft_right) > 0: + soft_right = max(soft_right) + 1 + cigar = cigar[:-1] + if soft_right - read_length_without_right_s > 0: + cigar.append([soft_right - read_length_without_right_s, soft_clip_cigar_flag_recode]) + right_changed = (True, soft_right - read_length_without_right_s) + if len(bases_quality) - soft_right > 0: + cigar.append([len(bases_quality) - soft_right, 'S']) + + if left_changed[0]: + # if direct_strand_true: + mapping_position = mapping_position - left_changed[1] + # if right_changed[0]: + # if not direct_strand_true: + # mapping_position = mapping_position + right_changed[1] + + return ''.join([str(cigar_part[0]) + cigar_part[1] for cigar_part in cigar]), str(mapping_position) + + +def verify_is_forward_read(sam_flag_bit): + # 64 = 1000000 + forward_read = False + bit = format(sam_flag_bit, 'b').zfill(7) + if bit[-7] == '1': + forward_read = True + return forward_read + + +def verify_mapped_direct_strand(sam_flag_bit): + # 16 = 10000 -> mapped in reverse strand + direct_strand = False + bit = format(sam_flag_bit, 'b').zfill(5) + if bit[-5] == '0': + direct_strand = True + return direct_strand + + +def verify_mapped_tip(reference_length, mapping_position, cigar): + tip = False + if 'S' in cigar: + cigar = split_cigar(cigar) + if cigar[0][1] == 'S': + if mapping_position - cigar[0][0] < 0: + tip = True + if cigar[len(cigar) - 1][1] == 'S': + if mapping_position + cigar[len(cigar) - 1][0] > reference_length: + tip = True + return tip + + +def change_sam_flag_bit_mapped_reverse_strand_2_direct_strand(sam_flag_bit): + bit = list(format(sam_flag_bit, 'b').zfill(5)) + bit[-5] = '0' + return int(''.join(bit), 2) + + +def change_sam_flag_bit_mate_reverse_strand_2_direct_strand(sam_flag_bit): + bit = list(format(sam_flag_bit, 'b').zfill(6)) + bit[-6] = '0' + return int(''.join(bit), 2) + + +def move_read_mapped_reverse_strand_2_direct_strand(seq, bases_quality, sam_flag_bit, cigar): + seq = utils.reverse_complement(seq) + bases_quality = ''.join(reversed(list(bases_quality))) + sam_flag_bit = change_sam_flag_bit_mapped_reverse_strand_2_direct_strand(sam_flag_bit) + cigar = ''.join([str(cigar_part[0]) + cigar_part[1] for cigar_part in reversed(split_cigar(cigar))]) + return seq, bases_quality, str(sam_flag_bit), cigar + + +@utils.trace_unhandled_exceptions +def parallelized_recode_soft_clipping(line_collection, pickle_file, soft_clip_base_quality, sequences_length, + soft_clip_cigar_flag_recode): + lines_sam = [] + for line in line_collection: + line = line.rstrip('\r\n') + if len(line) > 0: + if line.startswith('@'): + lines_sam.append(line) + else: + line = line.split('\t') + if not verify_mapped_tip(sequences_length[line[2]], int(line[3]), line[5]): + line[5], line[3] = recode_cigar_based_on_base_quality(line[5], line[10], soft_clip_base_quality, + int(line[3]), + verify_mapped_direct_strand(int(line[1])), + soft_clip_cigar_flag_recode) + lines_sam.append('\t'.join(line)) + with open(pickle_file, 'wb') as writer: + pickle.dump(lines_sam, writer) + + +def recode_soft_clipping_from_sam(sam_file, outdir, threads, soft_clip_base_quality, reference_dict, + soft_clip_cigar_flag_recode): + pickle_files = [] + sequences_length = {} + for x, seq_info in list(reference_dict.items()): + sequences_length[seq_info['header']] = seq_info['length'] + + with open(sam_file, 'rtU') as reader: + pool = multiprocessing.Pool(processes=threads) + line_collection = [] + x = 0 + for x, line in enumerate(reader): + line_collection.append(line) + if x % 10000 == 0: + pickle_file = os.path.join(outdir, 'remove_soft_clipping.' + str(x) + '.pkl') + pickle_files.append(pickle_file) + pool.apply_async(parallelized_recode_soft_clipping, args=(line_collection, pickle_file, + soft_clip_base_quality, sequences_length, + soft_clip_cigar_flag_recode,)) + line_collection = [] + if len(line_collection) > 0: + pickle_file = os.path.join(outdir, 'remove_soft_clipping.' + str(x) + '.pkl') + pickle_files.append(pickle_file) + pool.apply_async(parallelized_recode_soft_clipping, args=(line_collection, pickle_file, + soft_clip_base_quality, sequences_length, + soft_clip_cigar_flag_recode,)) + pool.close() + pool.join() + + os.remove(sam_file) + + new_sam_file = os.path.join(outdir, 'alignment_with_soft_clipping_recoded.sam') + with open(new_sam_file, 'wt') as writer: + for pickle_file in pickle_files: + if os.path.isfile(pickle_file): + lines_sam = None + with open(pickle_file, 'rb') as reader: + lines_sam = pickle.load(reader) + if lines_sam is not None: + for line in lines_sam: + writer.write(line + '\n') + os.remove(pickle_file) + + return new_sam_file + + +# Sort alignment file +def sort_alignment(alignment_file, output_file, sort_by_name_true, threads): + out_format_string = os.path.splitext(output_file)[1][1:].lower() + command = ['samtools', 'sort', '-o', output_file, '-O', out_format_string, '', '-@', str(threads), alignment_file] + if sort_by_name_true: + command[6] = '-n' + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, True) + if not run_successfully: + output_file = None + return run_successfully, output_file + + +# Index alignment file +def index_alignment(alignment_file): + command = ['samtools', 'index', alignment_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, True) + return run_successfully + + +def mapping_reads(fastq_files, reference_file, threads, outdir, num_map_loc, rematch_run, + soft_clip_base_quality, soft_clip_recode_run, reference_dict, soft_clip_cigar_flag_recode, + bowtie_algorithm, bowtie_opt, clean_run=True): + # Create a symbolic link to the reference_file + if clean_run: + reference_link = os.path.join(outdir, os.path.basename(reference_file)) + if os.path.islink(reference_link): + os.unlink(reference_link) + os.symlink(reference_file, reference_link) + reference_file = reference_link + + bam_file = None + # Mapping reads using Bowtie2 + run_successfully, sam_file = mapping_bowtie2(fastq_files=fastq_files, reference_file=reference_file, + threads=threads, outdir=outdir, num_map_loc=num_map_loc, + bowtie_algorithm=bowtie_algorithm, bowtie_opt=bowtie_opt) + + if run_successfully: + # Remove soft clipping + if rematch_run == soft_clip_recode_run or soft_clip_recode_run == 'both': + print('Recoding soft clipped regions') + sam_file = recode_soft_clipping_from_sam(sam_file, outdir, threads, soft_clip_base_quality, reference_dict, + soft_clip_cigar_flag_recode) + + # Convert sam to bam and sort bam + run_successfully, bam_file = sort_alignment(sam_file, str(os.path.splitext(sam_file)[0] + '.bam'), False, + threads) + + if run_successfully: + os.remove(sam_file) + # Index bam + run_successfully = index_alignment(bam_file) + + return run_successfully, bam_file, reference_file + + +def create_vcf(bam_file, sequence_to_analyse, outdir, counter, reference_file): + gene_vcf = os.path.join(outdir, 'samtools_mpileup.sequence_' + str(counter) + '.vcf') + + command = ['samtools', 'mpileup', '--count-orphans', '--no-BAQ', '--min-BQ', '0', '--min-MQ', str(7), '--fasta-ref', + reference_file, '--region', sequence_to_analyse, '--output', gene_vcf, '--VCF', '--uncompressed', + '--output-tags', 'INFO/AD,AD,DP', bam_file] + + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, False) + if not run_successfully: + gene_vcf = None + return run_successfully, gene_vcf + + +# Read vcf file +class Vcf: + def __init__(self, vcf_file, encoding=None, newline=None): + try: + self.vcf = open(vcf_file, 'rt', encoding=encoding, newline=newline) + except TypeError: + self.vcf = open(vcf_file, 'rt') + self.line_read = self.vcf.readline() + self.contigs_info_dict = {} + while self.line_read.startswith('#'): + if self.line_read.startswith('##contig=<ID='): + seq = self.line_read.split('=')[2].split(',')[0] + seq_len = self.line_read.split('=')[3].split('>')[0] + self.contigs_info_dict[seq] = int(seq_len) + self.line_read = self.vcf.readline() + self.line = self.line_read + + def readline(self): + line_stored = self.line + self.line = self.vcf.readline() + return line_stored + + def close(self): + self.vcf.close() + + def get_contig_legth(self, contig): + return self.contigs_info_dict[contig] + + +def get_variants(gene_vcf, seq_name, encoding=None, newline=None): + variants = {} + + vfc_file = Vcf(vcf_file=gene_vcf, encoding=encoding, newline=newline) + line = vfc_file.readline() + counter = 1 + while len(line) > 0: + fields = line.rstrip('\r\n').split('\t') + if len(fields) > 0: + fields[1] = int(fields[1]) + + info_field = {} + try: + for i in fields[7].split(';'): + i = i.split('=') + if len(i) > 1: + info_field[i[0]] = i[1] + else: + info_field[i[0]] = None + except IndexError: + if counter > vfc_file.get_contig_legth(contig=seq_name): + break + else: + raise IndexError + + format_field = {} + format_field_name = fields[8].split(':') + format_data = fields[9].split(':') + + for i in range(0, len(format_data)): + format_field[format_field_name[i]] = format_data[i].split(',') + + fields_to_store = {'REF': fields[3], 'ALT': fields[4].split(','), 'info': info_field, + 'format': format_field} + if fields[1] in variants: + variants[fields[1]][len(variants[fields[1]])] = fields_to_store + else: + variants[fields[1]] = {0: fields_to_store} + + try: + line = vfc_file.readline() + except UnicodeDecodeError: + if counter + 1 > vfc_file.get_contig_legth(contig=seq_name): + break + else: + raise UnicodeDecodeError + + counter += 1 + vfc_file.close() + + return variants + + +def indel_entry(variant_position): + entry_with_indel = [] + entry_with_snp = None + for i in variant_position: + keys = list(variant_position[i]['info'].keys()) + if 'INDEL' in keys: + entry_with_indel.append(i) + else: + entry_with_snp = i + + return entry_with_indel, entry_with_snp + + +def get_alt_no_matter(variant_position, indel_true): + dp = sum(map(int, variant_position['format']['AD'])) + index_alleles_sorted_position = sorted(zip(list(map(int, variant_position['format']['AD'])), + list(range(0, len(variant_position['format']['AD'])))), + reverse=True) + index_dominant_allele = None + if not indel_true: + ad_idv = index_alleles_sorted_position[0][0] + + if len([x for x in index_alleles_sorted_position if x[0] == ad_idv]) > 1: + index_alleles_sorted_position = sorted([x for x in index_alleles_sorted_position if x[0] == ad_idv]) + + index_dominant_allele = index_alleles_sorted_position[0][1] + if index_dominant_allele == 0: + alt = '.' + else: + alt = variant_position['ALT'][index_dominant_allele - 1] + + else: + ad_idv = int(variant_position['info']['IDV']) + + if float(ad_idv) / float(dp) >= 0.5: + if len([x for x in index_alleles_sorted_position if x[0] == index_alleles_sorted_position[0][0]]) > 1: + index_alleles_sorted_position = sorted([x for x in index_alleles_sorted_position if + x[0] == index_alleles_sorted_position[0][0]]) + + index_dominant_allele = index_alleles_sorted_position[0][1] + if index_dominant_allele == 0: + alt = '.' + else: + alt = variant_position['ALT'][index_dominant_allele - 1] + else: + ad_idv = int(variant_position['format']['AD'][0]) + alt = '.' + + return alt, dp, ad_idv, index_dominant_allele + + +def count_number_diferences(ref, alt): + number_diferences = 0 + + if len(ref) != len(alt): + number_diferences += 1 + + for i in range(0, min(len(ref), len(alt))): + if alt[i] != 'N' and ref[i] != alt[i]: + number_diferences += 1 + + return number_diferences + + +def get_alt_correct(variant_position, alt_no_matter, dp, ad_idv, index_dominant_allele, minimum_depth_presence, + minimum_depth_call, minimum_depth_frequency_dominant_allele): + alt = None + low_coverage = False + multiple_alleles = False + + if dp >= minimum_depth_presence: + if dp < minimum_depth_call: + alt = 'N' * len(variant_position['REF']) + low_coverage = True + else: + if ad_idv < minimum_depth_call: + alt = 'N' * len(variant_position['REF']) + low_coverage = True + if float(ad_idv) / float(dp) < minimum_depth_frequency_dominant_allele: + multiple_alleles = True + else: + if float(ad_idv) / float(dp) < minimum_depth_frequency_dominant_allele: + alt = 'N' * len(variant_position['REF']) + if index_dominant_allele is not None: + variants_coverage = [int(variant_position['format']['AD'][i]) for i in + range(0, len(variant_position['ALT']) + 1) if i != index_dominant_allele] + if sum(variants_coverage) > 0: + if float(max(variants_coverage)) / float(sum(variants_coverage)) > 0.5: + multiple_alleles = True + elif float(max(variants_coverage)) / float(sum(variants_coverage)) == 0.5 and \ + len(variants_coverage) > 2: + multiple_alleles = True + else: + multiple_alleles = True + else: + alt = alt_no_matter + else: + low_coverage = True + + return alt, low_coverage, multiple_alleles + + +def get_alt_alignment(ref, alt): + if alt is None: + alt = 'N' * len(ref) + else: + if len(ref) != len(alt): + if len(alt) < len(ref): + if alt == '.': + alt = ref + alt += 'N' * (len(ref) - len(alt)) + else: + if alt[:len(ref)] == ref: + alt = '.' + else: + alt = alt[:len(ref)] + + return alt + + +def get_indel_more_likely(variant_position, indels_entry): + indel_coverage = {} + for i in indels_entry: + indel_coverage[i] = int(variant_position['info']['IDV']) + return indel_coverage.index(str(max(indel_coverage.values()))) + + +def determine_variant(variant_position, minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, indel_true): + alt_no_matter, dp, ad_idv, index_dominant_allele = get_alt_no_matter(variant_position, indel_true) + + alt_correct, low_coverage, multiple_alleles = get_alt_correct(variant_position, alt_no_matter, dp, ad_idv, + index_dominant_allele, minimum_depth_presence, + minimum_depth_call, + minimum_depth_frequency_dominant_allele) + + alt_alignment = get_alt_alignment(variant_position['REF'], alt_correct) + + return variant_position['REF'], alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment + + +def confirm_nucleotides_indel(ref, alt, variants, position_start_indel, minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, alignment_true): + alt = list(alt) + + for i in range(0, len(alt) - 1): + if len(alt) < len(ref): + new_position = position_start_indel + len(alt) - i - 1 + alt_position = len(alt) - i - 1 + else: + if i + 1 > len(ref): + break + new_position = position_start_indel + 1 + i + alt_position = 1 + i + + if alt[alt_position] != 'N': + if new_position not in variants: + if alignment_true: + alt[alt_position] = 'N' + else: + alt = alt[: alt_position] + break + + entry_with_indel, entry_with_snp = indel_entry(variants[new_position]) + new_ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment = \ + determine_variant(variants[new_position][entry_with_snp], minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, False) + if alt_no_matter != '.' and alt[alt_position] != alt_no_matter: + alt[alt_position] = alt_no_matter + + return ''.join(alt) + + +def snp_indel(variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele): + entry_with_indel, entry_with_snp = indel_entry(variants[position]) + + if len(entry_with_indel) == 0: + ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment = \ + determine_variant(variants[position][entry_with_snp], minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, False) + else: + ref_snp, alt_correct_snp, low_coverage_snp, multiple_alleles_snp, alt_no_matter_snp, alt_alignment_snp = \ + determine_variant(variants[position][entry_with_snp], minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, False) + + indel_more_likely = entry_with_indel[0] + if len(entry_with_indel) > 1: + indel_more_likely = get_indel_more_likely(variants[position], entry_with_indel) + + ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment = \ + determine_variant(variants[position][indel_more_likely], minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, True) + + if alt_no_matter == '.': + ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment = \ + ref_snp, alt_correct_snp, low_coverage_snp, multiple_alleles_snp, alt_no_matter_snp, alt_alignment_snp + else: + if alt_correct is None and alt_correct_snp is not None: + alt_correct = alt_correct_snp + elif alt_correct is not None and alt_correct_snp is not None: + if alt_correct_snp != '.' and alt_correct[0] != alt_correct_snp: + alt_correct = alt_correct_snp + alt_correct[1:] if len(alt_correct) > 1 else alt_correct_snp + if alt_no_matter_snp != '.' and alt_no_matter[0] != alt_no_matter_snp: + alt_no_matter = alt_no_matter_snp + alt_no_matter[1:] if len(alt_no_matter) > 1 else alt_no_matter_snp + if alt_alignment_snp != '.' and alt_alignment[0] != alt_alignment_snp: + alt_alignment = alt_alignment_snp + alt_alignment[1:] if len(alt_alignment) > 1 else alt_alignment_snp + + # if alt_no_matter != '.': + # alt_no_matter = confirm_nucleotides_indel(ref, alt_no_matter, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) + # if alt_correct is not None and alt_correct != '.': + # alt_correct = confirm_nucleotides_indel(ref, alt_correct, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) + # if alt_alignment != '.': + # alt_alignment = confirm_nucleotides_indel(ref, alt_alignment, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, True) + + return ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment + + +def get_true_variants(variants, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, + sequence): + variants_correct = {} + variants_no_matter = {} + variants_alignment = {} + + correct_absent_positions = {} + correct_last_absent_position = '' + + no_matter_absent_positions = {} + no_matter_last_absent_position = '' + + multiple_alleles_found = [] + + counter = 1 + while counter <= len(sequence): + if counter in variants: + no_matter_last_absent_position = '' + + ref, alt_correct, low_coverage, multiple_alleles, alt_no_matter, alt_alignment = \ + snp_indel(variants, counter, minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele) + + if alt_alignment != '.': + variants_alignment[counter] = {'REF': ref, 'ALT': alt_alignment} + + if alt_no_matter != '.': + variants_no_matter[counter] = {'REF': ref, 'ALT': alt_no_matter} + + if alt_correct is None: + if counter - len(correct_last_absent_position) in correct_absent_positions: + correct_absent_positions[counter - len(correct_last_absent_position)]['REF'] += ref + else: + correct_absent_positions[counter] = {'REF': ref, 'ALT': ''} + correct_last_absent_position += ref + else: + if alt_correct != '.': + if len(alt_correct) < len(ref): + if len(alt_correct) == 1: + correct_absent_positions[counter + 1] = {'REF': ref[1:], 'ALT': ''} + else: + correct_absent_positions[counter + 1] = {'REF': ref[1:], 'ALT': alt_correct[1:]} + + correct_last_absent_position = ref[1:] + else: + variants_correct[counter] = {'REF': ref, 'ALT': alt_correct} + correct_last_absent_position = '' + else: + correct_last_absent_position = '' + + if multiple_alleles: + multiple_alleles_found.append(counter) + + counter += len(ref) + else: + variants_alignment[counter] = {'REF': sequence[counter - 1], 'ALT': 'N'} + + if counter - len(correct_last_absent_position) in correct_absent_positions: + correct_absent_positions[counter - len(correct_last_absent_position)]['REF'] += sequence[counter - 1] + else: + correct_absent_positions[counter] = {'REF': sequence[counter - 1], 'ALT': ''} + correct_last_absent_position += sequence[counter - 1] + + if counter - len(no_matter_last_absent_position) in no_matter_absent_positions: + no_matter_absent_positions[counter - len(no_matter_last_absent_position)]['REF'] += \ + sequence[counter - 1] + else: + no_matter_absent_positions[counter] = {'REF': sequence[counter - 1], 'ALT': ''} + no_matter_last_absent_position += sequence[counter - 1] + + counter += 1 + + for position in correct_absent_positions: + if position == 1: + variants_correct[position] = {'REF': correct_absent_positions[position]['REF'], 'ALT': 'N'} + else: + if position - 1 not in variants_correct: + variants_correct[position - 1] = \ + {'REF': sequence[position - 2] + correct_absent_positions[position]['REF'], + 'ALT': sequence[position - 2] + correct_absent_positions[position]['ALT']} + else: + variants_correct[position - 1] = \ + {'REF': variants_correct[position - 1]['REF'] + + correct_absent_positions[position]['REF'][len(variants_correct[position - 1]['REF']) - 1:], + 'ALT': variants_correct[position - 1]['ALT'] + + correct_absent_positions[position]['ALT'][len(variants_correct[position - 1]['ALT']) - 1 if + len(variants_correct[position - 1]['ALT']) > 0 + else 0:]} + + for position in no_matter_absent_positions: + if position == 1: + variants_no_matter[position] = {'REF': no_matter_absent_positions[position]['REF'], 'ALT': 'N'} + else: + if position - 1 not in variants_no_matter: + variants_no_matter[position - 1] = \ + {'REF': sequence[position - 2] + no_matter_absent_positions[position]['REF'], + 'ALT': sequence[position - 2] + no_matter_absent_positions[position]['ALT']} + else: + variants_no_matter[position - 1] = \ + {'REF': variants_no_matter[position - 1]['REF'] + + no_matter_absent_positions[position]['REF'][len(variants_no_matter[position - 1]['REF']) - + 1:], + 'ALT': variants_no_matter[position - 1]['ALT'] + + no_matter_absent_positions[position]['ALT'][len(variants_no_matter[position - 1]['ALT']) - + 1 if + len(variants_no_matter[position - 1]['ALT']) > 0 + else 0:]} + + return variants_correct, variants_no_matter, variants_alignment, multiple_alleles_found + + +def clean_variant_in_extra_seq_left(variant_dict, position, length_extra_seq, multiple_alleles_found, + number_multi_alleles): + number_diferences = 0 + + if position + len(variant_dict[position]['REF']) - 1 > length_extra_seq: + if multiple_alleles_found is not None and position in multiple_alleles_found: + number_multi_alleles += 1 + + temp_variant = variant_dict[position] + del variant_dict[position] + variant_dict[length_extra_seq] = {} + variant_dict[length_extra_seq]['REF'] = temp_variant['REF'][length_extra_seq - position:] + variant_dict[length_extra_seq]['ALT'] = temp_variant['ALT'][length_extra_seq - position:] if \ + len(temp_variant['ALT']) > length_extra_seq - position else \ + temp_variant['REF'][length_extra_seq - position] + number_diferences = count_number_diferences(variant_dict[length_extra_seq]['REF'], + variant_dict[length_extra_seq]['ALT']) + else: + del variant_dict[position] + + return variant_dict, number_multi_alleles, number_diferences + + +def clean_variant_in_extra_seq_rigth(variant_dict, position, sequence_length, length_extra_seq): + if position + len(variant_dict[position]['REF']) - 1 > sequence_length - length_extra_seq: + variant_dict[position]['REF'] = \ + variant_dict[position]['REF'][: - (position - (sequence_length - length_extra_seq)) + 1] + variant_dict[position]['ALT'] = \ + variant_dict[position]['ALT'][: - (position - (sequence_length - length_extra_seq)) + 1] if \ + len(variant_dict[position]['ALT']) >= - (position - (sequence_length - length_extra_seq)) + 1 else \ + variant_dict[position]['ALT'] + + number_diferences = count_number_diferences(variant_dict[position]['REF'], variant_dict[position]['ALT']) + + return variant_dict, number_diferences + + +def cleanning_variants_extra_seq(variants_correct, variants_no_matter, variants_alignment, multiple_alleles_found, + length_extra_seq, sequence_length): + number_multi_alleles = 0 + number_diferences = 0 + + counter = 1 + while counter <= sequence_length: + if counter <= length_extra_seq: + if counter in variants_correct: + variants_correct, number_multi_alleles, number_diferences = \ + clean_variant_in_extra_seq_left(variants_correct, counter, length_extra_seq, multiple_alleles_found, + number_multi_alleles) + if counter in variants_no_matter: + variants_no_matter, ignore, ignore = \ + clean_variant_in_extra_seq_left(variants_no_matter, counter, length_extra_seq, None, None) + if counter in variants_alignment: + variants_alignment, ignore, ignore = \ + clean_variant_in_extra_seq_left(variants_alignment, counter, length_extra_seq, None, None) + elif sequence_length - length_extra_seq >= counter > length_extra_seq: + if counter in variants_correct: + if counter in multiple_alleles_found: + number_multi_alleles += 1 + variants_correct, number_diferences_found = \ + clean_variant_in_extra_seq_rigth(variants_correct, counter, sequence_length, length_extra_seq) + number_diferences += number_diferences_found + if counter in variants_no_matter: + variants_no_matter, ignore = \ + clean_variant_in_extra_seq_rigth(variants_no_matter, counter, sequence_length, length_extra_seq) + if counter in variants_alignment: + variants_alignment, ignore = \ + clean_variant_in_extra_seq_rigth(variants_alignment, counter, sequence_length, length_extra_seq) + else: + if counter in variants_correct: + del variants_correct[counter] + if counter in variants_no_matter: + del variants_no_matter[counter] + if counter in variants_alignment: + del variants_alignment[counter] + + counter += 1 + + return variants_correct, variants_no_matter, variants_alignment, number_multi_alleles, number_diferences + + +def get_coverage(gene_coverage): + coverage = {} + + with open(gene_coverage, 'rtU') as reader: + for line in reader: + line = line.rstrip('\r\n') + if len(line) > 0: + line = line.split('\t') + coverage[int(line[1])] = int(line[2]) + + return coverage + + +def get_coverage_report(coverage, sequence_length, minimum_depth_presence, minimum_depth_call, length_extra_seq): + if len(coverage) == 0: + return sequence_length - 2 * length_extra_seq, 100.0, 0.0 + + count_absent = 0 + count_low_coverage = 0 + sum_coverage = 0 + + counter = 1 + while counter <= sequence_length: + if sequence_length - length_extra_seq >= counter > length_extra_seq: + if coverage[counter] < minimum_depth_presence: + count_absent += 1 + else: + if coverage[counter] < minimum_depth_call: + count_low_coverage += 1 + sum_coverage += coverage[counter] + counter += 1 + + mean_coverage = 0 + percentage_low_coverage = 0 + if sequence_length - 2 * length_extra_seq - count_absent > 0: + mean_coverage = float(sum_coverage) / float(sequence_length - 2 * length_extra_seq - count_absent) + percentage_low_coverage = \ + float(count_low_coverage) / float(sequence_length - 2 * length_extra_seq - count_absent) * 100 + + return count_absent, percentage_low_coverage, mean_coverage + + +# Get genome coverage data +def compute_genome_coverage_data(alignment_file, sequence_to_analyse, outdir, counter): + genome_coverage_data_file = os.path.join(outdir, 'samtools_depth.sequence_' + str(counter) + '.tab') + command = ['samtools', 'depth', '-a', '-q', '0', '-r', sequence_to_analyse, alignment_file, '>', + genome_coverage_data_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, True, None, False) + return run_successfully, genome_coverage_data_file + + +def write_variants_vcf(variants, outdir, sequence_to_analyse, sufix): + vcf_file = os.path.join(outdir, str(sequence_to_analyse + '.' + sufix + '.vcf')) + with open(vcf_file, 'wt') as writer: + writer.write('##fileformat=VCFv4.2' + '\n') + writer.write('#' + '\t'.join(['SEQUENCE', 'POSITION', 'ID_unused', 'REFERENCE_sequence', 'ALTERNATIVE_sequence', + 'QUALITY_unused', 'FILTER_unused', 'INFO_unused', 'FORMAT_unused']) + '\n') + for i in sorted(variants.keys()): + writer.write('\t'.join([sequence_to_analyse, str(i), '.', variants[i]['REF'], variants[i]['ALT'], '.', '.', + '.', '.']) + '\n') + + compressed_vcf_file = vcf_file + '.gz' + command = ['bcftools', 'convert', '-o', compressed_vcf_file, '-O', 'z', vcf_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, False) + if run_successfully: + command = ['bcftools', 'index', compressed_vcf_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, False) + + if not run_successfully: + compressed_vcf_file = None + + return run_successfully, compressed_vcf_file + + +def parse_fasta_in_memory(fasta_memory): + fasta_memory = fasta_memory.splitlines() + sequence_dict = {} + for line in fasta_memory: + if len(line) > 0: + if line.startswith('>'): + sequence_dict = {'header': line[1:], 'sequence': ''} + else: + sequence_dict['sequence'] += line + + return sequence_dict + + +def compute_consensus_sequence(reference_file, sequence_to_analyse, compressed_vcf_file, outdir): + sequence_dict = None + + gene_fasta = os.path.join(outdir, str(sequence_to_analyse + '.fasta')) + + run_successfully, stdout = index_fasta_samtools(reference_file, sequence_to_analyse, gene_fasta, False) + if run_successfully: + command = ['bcftools', 'norm', '-c', 's', '-f', gene_fasta, '-Ov', compressed_vcf_file] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, False) + if run_successfully: + command = ['bcftools', 'consensus', '-f', gene_fasta, compressed_vcf_file, '-H', '1'] + run_successfully, stdout, stderr = utils.run_command_popen_communicate(command, False, None, False) + if run_successfully: + sequence_dict = parse_fasta_in_memory(stdout) + + return run_successfully, sequence_dict + + +def create_sample_consensus_sequence(outdir, sequence_to_analyse, reference_file, variants, minimum_depth_presence, + minimum_depth_call, minimum_depth_frequency_dominant_allele, sequence, + length_extra_seq): + variants_correct, variants_noMatter, variants_alignment, multiple_alleles_found = \ + get_true_variants(variants, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, + sequence) + + variants_correct, variants_noMatter, variants_alignment, number_multi_alleles, number_diferences = \ + cleanning_variants_extra_seq(variants_correct, variants_noMatter, variants_alignment, multiple_alleles_found, + length_extra_seq, len(sequence)) + + run_successfully = False + consensus = {'correct': {}, 'noMatter': {}, 'alignment': {}} + for variant_type in ['variants_correct', 'variants_noMatter', 'variants_alignment']: + run_successfully, compressed_vcf_file = \ + write_variants_vcf(eval(variant_type), outdir, sequence_to_analyse, variant_type.split('_', 1)[1]) + if run_successfully: + run_successfully, sequence_dict = \ + compute_consensus_sequence(reference_file, sequence_to_analyse, compressed_vcf_file, outdir) + if run_successfully: + consensus[variant_type.split('_', 1)[1]] = \ + {'header': sequence_dict['header'], + 'sequence': sequence_dict['sequence'][length_extra_seq:len(sequence_dict['sequence']) - + length_extra_seq]} + + return run_successfully, number_multi_alleles, consensus, number_diferences + + +@utils.trace_unhandled_exceptions +def analyse_sequence_data(bam_file, sequence_information, outdir, counter, reference_file, length_extra_seq, + minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele): + count_absent = None + percentage_low_coverage = None + mean_coverage = None + number_diferences = 0 + number_multi_alleles = 0 + consensus_sequence = {'correct': {}, 'noMatter': {}, 'alignment': {}} + + # Create vcf file (for multiple alleles check) + run_successfully, gene_vcf = create_vcf(bam_file, sequence_information['header'], outdir, counter, reference_file) + if run_successfully: + # Create coverage tab file + run_successfully, gene_coverage = \ + compute_genome_coverage_data(bam_file, sequence_information['header'], outdir, counter) + + if run_successfully: + try: + variants = get_variants(gene_vcf=gene_vcf, seq_name=sequence_information['header'], + encoding=sys.getdefaultencoding()) + except UnicodeDecodeError: + try: + print('It was found an enconding error while parsing the following VCF, but lets try forcing it to' + ' "utf_8" encoding: {}'.format(gene_vcf)) + variants = get_variants(gene_vcf=gene_vcf, seq_name=sequence_information['header'], + encoding='utf_8') + except UnicodeDecodeError: + print('It was found an enconding error while parsing the following VCF, but lets try forcing it to' + ' "latin_1" encoding: {}'.format(gene_vcf)) + variants = get_variants(gene_vcf=gene_vcf, seq_name=sequence_information['header'], + encoding='latin_1') + + coverage = get_coverage(gene_coverage) + + run_successfully, number_multi_alleles, consensus_sequence, number_diferences = \ + create_sample_consensus_sequence(outdir, sequence_information['header'], reference_file, variants, + minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele, + sequence_information['sequence'], length_extra_seq) + + try: + count_absent, percentage_low_coverage, mean_coverage = \ + get_coverage_report(coverage, sequence_information['length'], minimum_depth_presence, + minimum_depth_call, length_extra_seq) + except KeyError: + print('ERROR: KeyError') + print(sequence_information) + raise KeyError + + utils.save_variable_to_pickle([run_successfully, counter, number_multi_alleles, count_absent, + percentage_low_coverage, mean_coverage, consensus_sequence, number_diferences], + outdir, str('coverage_info.' + str(counter))) + + +def clean_header(header): + problematic_characters = ["|", " ", ",", ".", "(", ")", "'", "/", ":"] + new_header = str(header) + if any(x in header for x in problematic_characters): + for x in problematic_characters: + new_header = new_header.replace(x, '_') + return header, new_header + + +def get_sequence_information(fasta_file, length_extra_seq): + sequence_dict = {} + headers = {} + headers_changed = False + + with open(fasta_file, 'rtU') as reader: + blank_line_found = False + sequence_counter = 0 + temp_sequence_dict = {} + for line in reader: + line = line.rstrip('\r\n') + if len(line) > 0: + if not blank_line_found: + if line.startswith('>'): + if len(temp_sequence_dict) > 0: + if list(temp_sequence_dict.values())[0]['length'] - 2 * length_extra_seq > 0: + sequence_dict[list(temp_sequence_dict.keys())[0]] = list(temp_sequence_dict.values())[0] + else: + print('{header} sequence ignored due to' + ' length = 0'.format(header=list(temp_sequence_dict.values())[0]['header'])) + del headers[list(temp_sequence_dict.values())[0]['header']] + temp_sequence_dict = {} + + original_header, new_header = clean_header(line[1:]) + if new_header in headers: + sys.exit('Found duplicated sequence' + ' headers: {original_header}'.format(original_header=original_header)) + + sequence_counter += 1 + temp_sequence_dict[sequence_counter] = {'header': new_header, 'sequence': '', 'length': 0} + headers[new_header] = str(original_header) + if new_header != original_header: + headers_changed = True + else: + temp_sequence_dict[sequence_counter]['sequence'] += line.replace(' ', '') + temp_sequence_dict[sequence_counter]['length'] += len(line.replace(' ', '')) + else: + sys.exit('It was found a blank line between the fasta file above line ' + line) + else: + blank_line_found = True + + if len(temp_sequence_dict) > 0: + if list(temp_sequence_dict.values())[0]['length'] - 2 * length_extra_seq > 0: + sequence_dict[list(temp_sequence_dict.keys())[0]] = list(temp_sequence_dict.values())[0] + else: + print('{header} sequence ignored due to' + ' length <= 0'.format(header=list(temp_sequence_dict.values())[0]['header'])) + del headers[list(temp_sequence_dict.values())[0]['header']] + + return sequence_dict, headers, headers_changed + + +def sequence_data(sample, reference_file, bam_file, outdir, threads, length_extra_seq, minimum_depth_presence, + minimum_depth_call, minimum_depth_frequency_dominant_allele, debug_mode_true, not_write_consensus): + sequence_data_outdir = os.path.join(outdir, 'sequence_data', '') + utils.remove_directory(sequence_data_outdir) + os.mkdir(sequence_data_outdir) + + sequences, headers, headers_changed = get_sequence_information(reference_file, length_extra_seq) + + pool = multiprocessing.Pool(processes=threads) + for sequence_counter in sequences: + sequence_dir = os.path.join(sequence_data_outdir, str(sequence_counter), '') + utils.remove_directory(sequence_dir) + os.makedirs(sequence_dir) + pool.apply_async(analyse_sequence_data, args=(bam_file, sequences[sequence_counter], sequence_dir, + sequence_counter, reference_file, length_extra_seq, + minimum_depth_presence, minimum_depth_call, + minimum_depth_frequency_dominant_allele,)) + pool.close() + pool.join() + + run_successfully, sample_data, consensus_files, consensus_sequences = \ + gather_data_together(sample, sequence_data_outdir, sequences, outdir.rsplit('/', 2)[0], debug_mode_true, + length_extra_seq, not_write_consensus) + + return run_successfully, sample_data, consensus_files, consensus_sequences + + +def chunkstring(string, length): + return (string[0 + i:length + i] for i in range(0, len(string), length)) + + +def write_consensus(outdir, sample, consensus_sequence): + consensus_files = {} + for consensus_type in ['correct', 'noMatter', 'alignment']: + consensus_files[consensus_type] = os.path.join(outdir, str(sample + '.' + consensus_type + '.fasta')) + with open(consensus_files[consensus_type], 'at') as writer: + writer.write('>' + consensus_sequence[consensus_type]['header'] + '\n') + fasta_sequence_lines = chunkstring(consensus_sequence[consensus_type]['sequence'], 80) + for line in fasta_sequence_lines: + writer.write(line + '\n') + return consensus_files + + +def gather_data_together(sample, data_directory, sequences_information, outdir, debug_mode_true, length_extra_seq, + not_write_consensus): + run_successfully = True + counter = 0 + sample_data = {} + + consensus_files = None + consensus_sequences_together = {'correct': {}, 'noMatter': {}, 'alignment': {}} + + write_consensus_first_time = True + + genes_directories = [d for d in os.listdir(data_directory) if + not d.startswith('.') and + os.path.isdir(os.path.join(data_directory, d, ''))] + for gene_dir in genes_directories: + gene_dir_path = os.path.join(data_directory, gene_dir, '') + + files = [f for f in os.listdir(gene_dir_path) if + not f.startswith('.') and + os.path.isfile(os.path.join(gene_dir_path, f))] + for file_found in files: + if file_found.startswith('coverage_info.') and file_found.endswith('.pkl'): + file_path = os.path.join(gene_dir_path, file_found) + + if run_successfully: + run_successfully, sequence_counter, multiple_alleles_found, count_absent, percentage_low_coverage, \ + mean_coverage, consensus_sequence, \ + number_diferences = utils.extract_variable_from_pickle(file_path) + + if not not_write_consensus: + for consensus_type in consensus_sequence: + consensus_sequences_together[consensus_type][sequence_counter] = \ + {'header': consensus_sequence[consensus_type]['header'], + 'sequence': consensus_sequence[consensus_type]['sequence']} + + if write_consensus_first_time: + for consensus_type in ['correct', 'noMatter', 'alignment']: + file_to_remove = os.path.join(outdir, str(sample + '.' + consensus_type + '.fasta')) + if os.path.isfile(file_to_remove): + os.remove(file_to_remove) + write_consensus_first_time = False + consensus_files = write_consensus(outdir, sample, consensus_sequence) + + gene_identity = 0 + if sequences_information[sequence_counter]['length'] - 2 * length_extra_seq - count_absent > 0: + gene_identity = 100 - \ + (float(number_diferences) / + (sequences_information[sequence_counter]['length'] - 2 * length_extra_seq - + count_absent)) * 100 + + sample_data[sequence_counter] = \ + {'header': sequences_information[sequence_counter]['header'], + 'gene_coverage': 100 - (float(count_absent) / + (sequences_information[sequence_counter]['length'] - 2 * + length_extra_seq)) * 100, + 'gene_low_coverage': percentage_low_coverage, + 'gene_number_positions_multiple_alleles': multiple_alleles_found, + 'gene_mean_read_coverage': mean_coverage, + 'gene_identity': gene_identity} + counter += 1 + + if not debug_mode_true: + utils.remove_directory(gene_dir_path) + + if counter != len(sequences_information): + run_successfully = False + + return run_successfully, sample_data, consensus_files, consensus_sequences_together + + +rematch_timer = functools.partial(utils.timer, name='ReMatCh module') + + +@rematch_timer +def run_rematch_module(sample, fastq_files, reference_file, threads, outdir, length_extra_seq, minimum_depth_presence, + minimum_depth_call, minimum_depth_frequency_dominant_allele, minimum_gene_coverage, + debug_mode_true, num_map_loc, minimum_gene_identity, rematch_run, + soft_clip_base_quality, soft_clip_recode_run, reference_dict, soft_clip_cigar_flag_recode, + bowtie_algorithm, bowtie_opt, gene_list_reference, not_write_consensus, clean_run=True): + rematch_folder = os.path.join(outdir, 'rematch_module', '') + + utils.remove_directory(rematch_folder) + os.mkdir(rematch_folder) + + # Map reads + run_successfully, bam_file, reference_file = mapping_reads(fastq_files=fastq_files, reference_file=reference_file, + threads=threads, outdir=rematch_folder, + num_map_loc=num_map_loc, rematch_run=rematch_run, + soft_clip_base_quality=soft_clip_base_quality, + soft_clip_recode_run=soft_clip_recode_run, + reference_dict=reference_dict, + soft_clip_cigar_flag_recode=soft_clip_cigar_flag_recode, + bowtie_algorithm=bowtie_algorithm, bowtie_opt=bowtie_opt, + clean_run=clean_run) + if run_successfully: + # Index reference file + run_successfully, stdout = index_fasta_samtools(reference_file, None, None, True) + if run_successfully: + print('Analysing alignment data') + run_successfully, sample_data, consensus_files, consensus_sequences = \ + sequence_data(sample, reference_file, bam_file, rematch_folder, threads, length_extra_seq, + minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, + debug_mode_true, not_write_consensus) + + if run_successfully: + print('Writing report file') + number_absent_genes = 0 + number_genes_multiple_alleles = 0 + mean_sample_coverage = 0 + with open(os.path.join(outdir, 'rematchModule_report.txt'), 'wt') as writer: + print('\n'.join(outdir, 'rematchModule_report.txt')) + writer.write('\t'.join(['#gene', 'percentage_gene_coverage', 'gene_mean_read_coverage', + 'percentage_gene_low_coverage', 'number_positions_multiple_alleles', + 'percentage_gene_identity']) + '\n') + for i in range(1, len(sample_data) + 1): + writer.write('\t'.join([gene_list_reference[sample_data[i]['header']], + str(round(sample_data[i]['gene_coverage'], 2)), + str(round(sample_data[i]['gene_mean_read_coverage'], 2)), + str(round(sample_data[i]['gene_low_coverage'], 2)), + str(sample_data[i]['gene_number_positions_multiple_alleles']), + str(round(sample_data[i]['gene_identity'], 2))]) + '\n') + + if sample_data[i]['gene_coverage'] < minimum_gene_coverage or \ + sample_data[i]['gene_identity'] < minimum_gene_identity: + number_absent_genes += 1 + else: + mean_sample_coverage += sample_data[i]['gene_mean_read_coverage'] + if sample_data[i]['gene_number_positions_multiple_alleles'] > 0: + number_genes_multiple_alleles += 1 + + if len(sample_data) - number_absent_genes > 0: + mean_sample_coverage = \ + float(mean_sample_coverage) / float(len(sample_data) - number_absent_genes) + else: + mean_sample_coverage = 0 + + writer.write('\n'.join(['#general', + '>number_absent_genes', str(number_absent_genes), + '>number_genes_multiple_alleles', str(number_genes_multiple_alleles), + '>mean_sample_coverage', str(round(mean_sample_coverage, 2))]) + '\n') + + print('\n'.join([str('number_absent_genes: ' + str(number_absent_genes)), + str('number_genes_multiple_alleles: ' + str(number_genes_multiple_alleles)), + str('mean_sample_coverage: ' + str(round(mean_sample_coverage, 2)))])) + + if not debug_mode_true: + utils.remove_directory(rematch_folder) + + return run_successfully, sample_data if 'sample_data' in locals() else None, \ + {'number_absent_genes': number_absent_genes if 'number_absent_genes' in locals() else None, + 'number_genes_multiple_alleles': number_genes_multiple_alleles if + 'number_genes_multiple_alleles' in locals() else None, + 'mean_sample_coverage': round(mean_sample_coverage, 2) if 'mean_sample_coverage' in locals() else None}, \ + consensus_files if 'consensus_files' in locals() else None,\ + consensus_sequences if 'consensus_sequences' in locals() else None