Mercurial > repos > cstrittmatter > test_eurl_vtec_wgs_pt
annotate scripts/ReMatCh/modules/rematch_module.py @ 0:965517909457 draft
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author | cstrittmatter |
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date | Wed, 22 Jan 2020 08:41:44 -0500 |
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children | 0cbed1c0a762 |
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1 import os.path |
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2 import multiprocessing |
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3 import utils |
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4 import functools |
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5 import sys |
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6 import pickle |
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7 |
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8 |
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9 def index_fasta_samtools(fasta, region_None, region_outfile_none, print_comand_True): |
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10 command = ['samtools', 'faidx', fasta, '', '', ''] |
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11 shell_true = False |
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12 if region_None is not None: |
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13 command[3] = region_None |
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14 if region_outfile_none is not None: |
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15 command[4] = '>' |
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16 command[5] = region_outfile_none |
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17 shell_true = True |
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18 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, shell_true, None, print_comand_True) |
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19 return run_successfully, stdout |
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20 |
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21 |
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22 # Indexing reference file using Bowtie2 |
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23 def indexSequenceBowtie2(referenceFile, threads): |
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24 if os.path.isfile(str(referenceFile + '.1.bt2')): |
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25 run_successfully = True |
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26 else: |
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27 command = ['bowtie2-build', '--threads', str(threads), referenceFile, referenceFile] |
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28 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, True) |
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29 return run_successfully |
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30 |
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31 |
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32 # Mapping with Bowtie2 |
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33 def mappingBowtie2(fastq_files, referenceFile, threads, outdir, conserved_True, numMapLoc, bowtieOPT): |
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34 sam_file = os.path.join(outdir, str('alignment.sam')) |
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35 |
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36 # Index reference file |
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37 run_successfully = indexSequenceBowtie2(referenceFile, threads) |
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38 |
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39 if run_successfully: |
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40 command = ['bowtie2', '-k', str(numMapLoc), '-q', '', '--threads', str(threads), '-x', referenceFile, '', '--no-unal', '', '-S', sam_file] |
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41 |
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42 if len(fastq_files) == 1: |
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43 command[9] = '-U ' + fastq_files[0] |
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44 elif len(fastq_files) == 2: |
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45 command[9] = '-1 ' + fastq_files[0] + ' -2 ' + fastq_files[1] |
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46 else: |
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47 return False, None |
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48 |
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49 if conserved_True: |
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50 command[4] = '--sensitive' |
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51 else: |
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52 command[4] = '--very-sensitive-local' |
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53 |
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54 if bowtieOPT is not None: |
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55 command[11] = bowtieOPT |
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56 |
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57 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, True) |
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58 |
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59 if not run_successfully: |
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60 sam_file = None |
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61 |
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62 return run_successfully, sam_file |
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63 |
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64 |
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65 def split_cigar(cigar): |
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66 cigars = ['M', 'I', 'D', 'N', 'S', 'H', 'P', '=', 'X'] |
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67 |
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68 splited_cigars = [] |
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69 numbers = '' |
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70 for char in cigar: |
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71 if char not in cigars: |
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72 numbers += char |
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73 else: |
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74 splited_cigars.append([int(numbers), char]) |
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75 numbers = '' |
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76 |
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77 return splited_cigars |
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78 |
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79 |
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80 def recode_cigar_based_on_base_quality(cigar, bases_quality, softClip_baseQuality, mapping_position, direct_strand_true, softClip_cigarFlagRecode): |
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81 cigar = split_cigar(cigar) |
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82 soft_left = [] |
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83 soft_right = [] |
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84 cigar_flags_for_reads_length = ('M', 'I', 'S', '=', 'X') |
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85 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) |
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86 for x, base in enumerate(bases_quality): |
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87 if ord(base) - 33 >= softClip_baseQuality: |
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88 if x <= cigar[0][0] - 1: |
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89 if cigar[0][1] == 'S': |
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90 soft_left.append(x) |
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91 elif x > read_length_without_right_s - 1: |
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92 if cigar[len(cigar) - 1][1] == 'S': |
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93 soft_right.append(x) |
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94 |
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95 left_changed = (False, 0) |
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96 if len(soft_left) > 0: |
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97 soft_left = min(soft_left) + 1 |
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98 if soft_left == 1: |
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99 cigar = [[cigar[0][0], softClip_cigarFlagRecode]] + cigar[1:] |
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100 left_changed = (True, cigar[0][0]) |
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101 elif cigar[0][0] - soft_left > 0: |
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102 cigar = [[soft_left, 'S']] + [[cigar[0][0] - soft_left, softClip_cigarFlagRecode]] + cigar[1:] |
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103 left_changed = (True, cigar[0][0] - soft_left) |
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104 |
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105 right_changed = (False, 0) |
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106 if len(soft_right) > 0: |
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107 soft_right = max(soft_right) + 1 |
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108 cigar = cigar[:-1] |
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109 if soft_right - read_length_without_right_s > 0: |
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110 cigar.append([soft_right - read_length_without_right_s, softClip_cigarFlagRecode]) |
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111 right_changed = (True, soft_right - read_length_without_right_s) |
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112 if len(bases_quality) - soft_right > 0: |
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113 cigar.append([len(bases_quality) - soft_right, 'S']) |
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114 |
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115 if left_changed[0]: |
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116 # if direct_strand_true: |
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117 mapping_position = mapping_position - left_changed[1] |
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118 # if right_changed[0]: |
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119 # if not direct_strand_true: |
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120 # mapping_position = mapping_position + right_changed[1] |
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121 |
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122 return ''.join([str(cigar_part[0]) + cigar_part[1] for cigar_part in cigar]), str(mapping_position) |
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123 |
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124 |
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125 def verify_is_forward_read(sam_flag_bit): |
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126 # 64 = 1000000 |
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127 forward_read = False |
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128 bit = format(sam_flag_bit, 'b').zfill(7) |
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129 if bit[-7] == '1': |
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130 forward_read = True |
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131 return forward_read |
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132 |
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133 |
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134 def verify_mapped_direct_strand(sam_flag_bit): |
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135 # 16 = 10000 -> mapped in reverse strand |
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136 direct_strand = False |
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137 bit = format(sam_flag_bit, 'b').zfill(5) |
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138 if bit[-5] == '0': |
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139 direct_strand = True |
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140 return direct_strand |
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141 |
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142 |
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143 def verify_mapped_tip(reference_length, mapping_position, read_length, cigar): |
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144 tip = False |
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145 if 'S' in cigar: |
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146 cigar = split_cigar(cigar) |
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147 if cigar[0][1] == 'S': |
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148 if mapping_position - cigar[0][0] < 0: |
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149 tip = True |
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150 if cigar[len(cigar) - 1][1] == 'S': |
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151 if mapping_position + cigar[len(cigar) - 1][0] > reference_length: |
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152 tip = True |
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153 return tip |
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154 |
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155 |
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156 def change_sam_flag_bit_mapped_reverse_strand_2_direct_strand(sam_flag_bit): |
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157 bit = list(format(sam_flag_bit, 'b').zfill(5)) |
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158 bit[-5] = '0' |
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159 return int(''.join(bit), 2) |
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160 |
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161 |
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162 def change_sam_flag_bit_mate_reverse_strand_2_direct_strand(sam_flag_bit): |
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163 bit = list(format(sam_flag_bit, 'b').zfill(6)) |
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164 bit[-6] = '0' |
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165 return int(''.join(bit), 2) |
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166 |
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167 |
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168 def move_read_mapped_reverse_strand_2_direct_strand(seq, bases_quality, sam_flag_bit, cigar): |
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169 seq = utils.reverse_complement(seq) |
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170 bases_quality = ''.join(reversed(list(bases_quality))) |
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171 sam_flag_bit = change_sam_flag_bit_mapped_reverse_strand_2_direct_strand(sam_flag_bit) |
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172 cigar = ''.join([str(cigar_part[0]) + cigar_part[1] for cigar_part in reversed(split_cigar(cigar))]) |
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173 return seq, bases_quality, str(sam_flag_bit), cigar |
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174 |
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175 |
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176 @utils.trace_unhandled_exceptions |
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177 def parallelized_recode_soft_clipping(line_collection, pickleFile, softClip_baseQuality, sequences_length, softClip_cigarFlagRecode): |
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178 lines_sam = [] |
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179 for line in line_collection: |
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180 line = line.splitlines()[0] |
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181 if len(line) > 0: |
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182 if line.startswith('@'): |
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183 lines_sam.append(line) |
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184 else: |
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185 line = line.split('\t') |
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186 if not verify_mapped_tip(sequences_length[line[2]], int(line[3]), len(line[9]), line[5]): |
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187 line[5], line[3] = recode_cigar_based_on_base_quality(line[5], line[10], softClip_baseQuality, int(line[3]), verify_mapped_direct_strand(int(line[1])), softClip_cigarFlagRecode) |
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188 lines_sam.append('\t'.join(line)) |
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189 with open(pickleFile, 'wb') as writer: |
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190 pickle.dump(lines_sam, writer) |
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191 |
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192 |
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193 def recode_soft_clipping_from_sam(sam_file, outdir, threads, softClip_baseQuality, reference_dict, softClip_cigarFlagRecode): |
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194 pickle_files = [] |
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195 sequences_length = {} |
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196 for x, seq_info in reference_dict.items(): |
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197 sequences_length[seq_info['header']] = seq_info['length'] |
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198 |
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199 with open(sam_file, 'rtU') as reader: |
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200 pool = multiprocessing.Pool(processes=threads) |
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201 line_collection = [] |
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202 x = 0 |
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203 for x, line in enumerate(reader): |
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204 line_collection.append(line) |
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205 if x % 10000 == 0: |
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206 pickleFile = os.path.join(outdir, 'remove_soft_clipping.' + str(x) + '.pkl') |
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207 pickle_files.append(pickleFile) |
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208 pool.apply_async(parallelized_recode_soft_clipping, args=(line_collection, pickleFile, softClip_baseQuality, sequences_length, softClip_cigarFlagRecode,)) |
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209 line_collection = [] |
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210 if len(line_collection) > 0: |
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211 pickleFile = os.path.join(outdir, 'remove_soft_clipping.' + str(x) + '.pkl') |
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212 pickle_files.append(pickleFile) |
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213 pool.apply_async(parallelized_recode_soft_clipping, args=(line_collection, pickleFile, softClip_baseQuality, sequences_length, softClip_cigarFlagRecode,)) |
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214 line_collection = [] |
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215 pool.close() |
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216 pool.join() |
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217 |
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218 os.remove(sam_file) |
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219 |
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220 new_sam_file = os.path.join(outdir, 'alignment_with_soft_clipping_recoded.sam') |
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221 with open(new_sam_file, 'wt') as writer: |
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222 for pickleFile in pickle_files: |
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223 if os.path.isfile(pickleFile): |
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224 lines_sam = None |
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225 with open(pickleFile, 'rb') as reader: |
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226 lines_sam = pickle.load(reader) |
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227 if lines_sam is not None: |
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228 for line in lines_sam: |
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229 writer.write(line + '\n') |
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230 os.remove(pickleFile) |
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231 |
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232 return new_sam_file |
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233 |
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234 |
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235 # Sort alignment file |
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236 def sortAlignment(alignment_file, output_file, sortByName_True, threads): |
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237 outFormat_string = os.path.splitext(output_file)[1][1:].lower() |
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238 command = ['samtools', 'sort', '-o', output_file, '-O', outFormat_string, '', '-@', str(threads), alignment_file] |
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239 if sortByName_True: |
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240 command[6] = '-n' |
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241 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, True) |
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242 if not run_successfully: |
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243 output_file = None |
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244 return run_successfully, output_file |
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245 |
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246 |
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247 # Index alignment file |
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248 def indexAlignment(alignment_file): |
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249 command = ['samtools', 'index', alignment_file] |
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250 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, True) |
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251 return run_successfully |
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252 |
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253 |
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254 def mapping_reads(fastq_files, reference_file, threads, outdir, conserved_True, numMapLoc, rematch_run, softClip_baseQuality, softClip_recodeRun, reference_dict, softClip_cigarFlagRecode, bowtieOPT): |
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255 # Create a symbolic link to the reference_file |
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256 reference_link = os.path.join(outdir, os.path.basename(reference_file)) |
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257 os.symlink(reference_file, reference_link) |
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258 |
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259 bam_file = None |
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260 # Mapping reads using Bowtie2 |
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261 run_successfully, sam_file = mappingBowtie2(fastq_files, reference_link, threads, outdir, conserved_True, numMapLoc, bowtieOPT) |
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262 |
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263 if run_successfully: |
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264 # Remove soft clipping |
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265 if rematch_run == softClip_recodeRun or softClip_recodeRun == 'both': |
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266 print 'Recoding soft clipped regions' |
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267 sam_file = recode_soft_clipping_from_sam(sam_file, outdir, threads, softClip_baseQuality, reference_dict, softClip_cigarFlagRecode) |
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268 |
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269 # Convert sam to bam and sort bam |
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270 run_successfully, bam_file = sortAlignment(sam_file, str(os.path.splitext(sam_file)[0] + '.bam'), False, threads) |
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271 |
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272 if run_successfully: |
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273 os.remove(sam_file) |
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274 # Index bam |
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275 run_successfully = indexAlignment(bam_file) |
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276 |
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277 return run_successfully, bam_file, reference_link |
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278 |
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279 |
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280 def create_vcf(bam_file, sequence_to_analyse, outdir, counter, reference_file): |
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281 gene_vcf = os.path.join(outdir, 'samtools_mpileup.sequence_' + str(counter) + '.vcf') |
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282 |
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283 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] |
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284 |
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285 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, False) |
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286 if not run_successfully: |
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287 gene_vcf = None |
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288 return run_successfully, gene_vcf |
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289 |
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290 |
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291 # Read vcf file |
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292 class Vcf(): |
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293 def __init__(self, vcfFile): |
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294 self.vcf = open(vcfFile, 'rtU') |
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295 self.line_read = self.vcf.readline() |
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296 while self.line_read.startswith('#'): |
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297 self.line_read = self.vcf.readline() |
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298 self.line = self.line_read |
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299 |
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300 def readline(self): |
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301 self.line_stored = self.line |
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302 self.line = self.vcf.readline() |
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303 return self.line_stored |
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304 |
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305 def close(self): |
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306 self.vcf.close() |
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307 |
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308 |
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309 def get_variants(gene_vcf): |
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310 variants = {} |
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311 |
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312 vfc_file = Vcf(gene_vcf) |
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313 line = vfc_file.readline() |
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314 while len(line) > 0: |
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315 fields = line.splitlines()[0].split('\t') |
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316 if len(fields) > 0: |
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317 fields[1] = int(fields[1]) |
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318 |
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319 info_field = {} |
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320 for i in fields[7].split(';'): |
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321 i = i.split('=') |
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322 if len(i) > 1: |
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323 info_field[i[0]] = i[1] |
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324 else: |
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325 info_field[i[0]] = None |
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326 |
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327 format_field = {} |
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328 format_field_name = fields[8].split(':') |
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329 format_data = fields[9].split(':') |
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330 |
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331 for i in range(0, len(format_data)): |
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332 format_field[format_field_name[i]] = format_data[i].split(',') |
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333 |
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334 fields_to_store = {'REF': fields[3], 'ALT': fields[4].split(','), 'info': info_field, 'format': format_field} |
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335 if fields[1] in variants: |
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336 variants[fields[1]][len(variants[fields[1]])] = fields_to_store |
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337 else: |
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338 variants[fields[1]] = {0: fields_to_store} |
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339 |
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340 line = vfc_file.readline() |
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341 vfc_file.close() |
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342 |
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343 return variants |
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344 |
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345 |
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346 def indel_entry(variant_position): |
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347 entry_with_indel = [] |
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348 entry_with_snp = None |
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349 for i in variant_position: |
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350 keys = variant_position[i]['info'].keys() |
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351 if 'INDEL' in keys: |
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352 entry_with_indel.append(i) |
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353 else: |
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354 entry_with_snp = i |
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355 |
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356 return entry_with_indel, entry_with_snp |
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357 |
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358 |
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359 def get_alt_noMatter(variant_position, indel_true): |
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360 dp = sum(map(int, variant_position['format']['AD'])) |
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361 index_alleles_sorted_position = sorted(zip(map(int, variant_position['format']['AD']), range(0, len(variant_position['format']['AD']))), reverse=True) |
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362 index_dominant_allele = None |
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363 if not indel_true: |
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364 ad_idv = index_alleles_sorted_position[0][0] |
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365 |
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366 if len([x for x in index_alleles_sorted_position if x[0] == ad_idv]) > 1: |
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367 index_alleles_sorted_position = sorted([x for x in index_alleles_sorted_position if x[0] == ad_idv]) |
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368 |
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369 index_dominant_allele = index_alleles_sorted_position[0][1] |
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370 if index_dominant_allele == 0: |
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371 alt = '.' |
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372 else: |
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373 alt = variant_position['ALT'][index_dominant_allele - 1] |
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374 |
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375 else: |
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376 ad_idv = int(variant_position['info']['IDV']) |
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377 |
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378 if float(ad_idv) / float(dp) >= 0.5: |
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379 if len([x for x in index_alleles_sorted_position if x[0] == index_alleles_sorted_position[0][0]]) > 1: |
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380 index_alleles_sorted_position = sorted([x for x in index_alleles_sorted_position if x[0] == index_alleles_sorted_position[0][0]]) |
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381 |
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382 index_dominant_allele = index_alleles_sorted_position[0][1] |
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383 if index_dominant_allele == 0: |
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384 alt = '.' |
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385 else: |
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386 alt = variant_position['ALT'][index_dominant_allele - 1] |
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387 else: |
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388 ad_idv = int(variant_position['format']['AD'][0]) |
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389 alt = '.' |
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390 |
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391 return alt, dp, ad_idv, index_dominant_allele |
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392 |
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393 |
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394 def count_number_diferences(ref, alt): |
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395 number_diferences = 0 |
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396 |
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397 if len(ref) != len(alt): |
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398 number_diferences += 1 |
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399 |
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400 for i in range(0, min(len(ref), len(alt))): |
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401 if alt[i] != 'N' and ref[i] != alt[i]: |
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402 number_diferences += 1 |
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403 |
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404 return number_diferences |
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405 |
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406 |
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407 def get_alt_correct(variant_position, alt_noMatter, dp, ad_idv, index_dominant_allele, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele): |
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408 alt = None |
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409 low_coverage = False |
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410 multiple_alleles = False |
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411 |
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412 if dp >= minimum_depth_presence: |
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413 if dp < minimum_depth_call: |
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414 alt = 'N' * len(variant_position['REF']) |
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415 low_coverage = True |
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416 else: |
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417 if ad_idv < minimum_depth_call: |
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418 alt = 'N' * len(variant_position['REF']) |
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419 low_coverage = True |
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420 if float(ad_idv) / float(dp) < minimum_depth_frequency_dominant_allele: |
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421 multiple_alleles = True |
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422 else: |
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423 if float(ad_idv) / float(dp) < minimum_depth_frequency_dominant_allele: |
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424 alt = 'N' * len(variant_position['REF']) |
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425 if index_dominant_allele is not None: |
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426 variants_coverage = [int(variant_position['format']['AD'][i]) for i in range(0, len(variant_position['ALT']) + 1) if i != index_dominant_allele] |
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427 if sum(variants_coverage) > 0: |
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428 if float(max(variants_coverage)) / float(sum(variants_coverage)) > 0.5: |
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429 multiple_alleles = True |
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430 elif float(max(variants_coverage)) / float(sum(variants_coverage)) == 0.5 and len(variants_coverage) > 2: |
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431 multiple_alleles = True |
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432 else: |
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433 multiple_alleles = True |
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434 else: |
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435 alt = alt_noMatter |
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436 else: |
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437 low_coverage = True |
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438 |
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439 return alt, low_coverage, multiple_alleles |
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440 |
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441 |
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442 def get_alt_alignment(ref, alt): |
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443 if alt is None: |
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444 alt = 'N' * len(ref) |
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445 else: |
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446 if len(ref) != len(alt): |
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447 if len(alt) < len(ref): |
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448 if alt == '.': |
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449 alt = ref |
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450 alt += 'N' * (len(ref) - len(alt)) |
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451 else: |
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452 if alt[:len(ref)] == ref: |
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453 alt = '.' |
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454 else: |
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455 alt = alt[:len(ref)] |
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456 |
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457 return alt |
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458 |
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459 |
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460 def get_indel_more_likely(variant_position, indels_entry): |
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461 indel_coverage = {} |
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462 for i in indels_entry: |
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463 indel_coverage[i] = int(variant_position['info']['IDV']) |
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464 return indel_coverage.index(str(max(indel_coverage.values()))) |
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465 |
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466 |
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467 def determine_variant(variant_position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, indel_true): |
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468 alt_noMatter, dp, ad_idv, index_dominant_allele = get_alt_noMatter(variant_position, indel_true) |
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469 |
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470 alt_correct, low_coverage, multiple_alleles = get_alt_correct(variant_position, alt_noMatter, dp, ad_idv, index_dominant_allele, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele) |
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471 |
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472 alt_alignment = get_alt_alignment(variant_position['REF'], alt_correct) |
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473 |
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474 return variant_position['REF'], alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment |
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475 |
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476 |
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477 def confirm_nucleotides_indel(ref, alt, variants, position_start_indel, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, alignment_true): |
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478 alt = list(alt) |
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479 |
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480 for i in range(0, len(alt) - 1): |
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481 if len(alt) < len(ref): |
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482 new_position = position_start_indel + len(alt) - i - 1 |
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483 alt_position = len(alt) - i - 1 |
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484 else: |
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485 if i + 1 > len(ref): |
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486 break |
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487 new_position = position_start_indel + 1 + i |
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488 alt_position = 1 + i |
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489 |
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490 if alt[alt_position] != 'N': |
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491 if new_position not in variants: |
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492 if alignment_true: |
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493 alt[alt_position] = 'N' |
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494 else: |
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495 alt = alt[: alt_position] |
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496 break |
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497 |
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498 entry_with_indel, entry_with_snp = indel_entry(variants[new_position]) |
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499 new_ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment = determine_variant(variants[new_position][entry_with_snp], minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) |
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500 if alt_noMatter != '.' and alt[alt_position] != alt_noMatter: |
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501 alt[alt_position] = alt_noMatter |
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502 |
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503 return ''.join(alt) |
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504 |
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505 |
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506 def snp_indel(variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele): |
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507 entry_with_indel, entry_with_snp = indel_entry(variants[position]) |
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508 |
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509 if len(entry_with_indel) == 0: |
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510 ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment = determine_variant(variants[position][entry_with_snp], minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) |
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511 else: |
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512 ref_snp, alt_correct_snp, low_coverage_snp, multiple_alleles_snp, alt_noMatter_snp, alt_alignment_snp = determine_variant(variants[position][entry_with_snp], minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) |
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513 |
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514 indel_more_likely = entry_with_indel[0] |
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515 if len(entry_with_indel) > 1: |
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516 indel_more_likely = get_indel_more_likely(variants[position], entry_with_indel) |
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517 |
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518 ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment = determine_variant(variants[position][indel_more_likely], minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, True) |
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519 |
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520 if alt_noMatter == '.': |
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521 ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment = ref_snp, alt_correct_snp, low_coverage_snp, multiple_alleles_snp, alt_noMatter_snp, alt_alignment_snp |
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522 else: |
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523 if alt_correct is None and alt_correct_snp is not None: |
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524 alt_correct = alt_correct_snp |
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525 elif alt_correct is not None and alt_correct_snp is not None: |
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526 if alt_correct_snp != '.' and alt_correct[0] != alt_correct_snp: |
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527 alt_correct = alt_correct_snp + alt_correct[1:] if len(alt_correct) > 1 else alt_correct_snp |
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528 if alt_noMatter_snp != '.' and alt_noMatter[0] != alt_noMatter_snp: |
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529 alt_noMatter = alt_noMatter_snp + alt_noMatter[1:] if len(alt_noMatter) > 1 else alt_noMatter_snp |
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530 if alt_alignment_snp != '.' and alt_alignment[0] != alt_alignment_snp: |
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531 alt_alignment = alt_alignment_snp + alt_alignment[1:] if len(alt_alignment) > 1 else alt_alignment_snp |
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532 |
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533 # if alt_noMatter != '.': |
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534 # alt_noMatter = confirm_nucleotides_indel(ref, alt_noMatter, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) |
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535 # if alt_correct is not None and alt_correct != '.': |
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536 # alt_correct = confirm_nucleotides_indel(ref, alt_correct, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, False) |
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537 # if alt_alignment != '.': |
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538 # alt_alignment = confirm_nucleotides_indel(ref, alt_alignment, variants, position, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, True) |
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539 |
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540 return ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment |
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541 |
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542 |
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543 def get_true_variants(variants, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, sequence): |
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544 variants_correct = {} |
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545 variants_noMatter = {} |
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546 variants_alignment = {} |
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547 |
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548 correct_absent_positions = {} |
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549 correct_last_absent_position = '' |
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550 |
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551 noMatter_absent_positions = {} |
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552 noMatter_last_absent_position = '' |
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553 |
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554 multiple_alleles_found = [] |
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555 |
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556 counter = 1 |
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557 while counter <= len(sequence): |
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558 if counter in variants: |
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559 noMatter_last_absent_position = '' |
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560 |
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561 ref, alt_correct, low_coverage, multiple_alleles, alt_noMatter, alt_alignment = snp_indel(variants, counter, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele) |
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562 |
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563 if alt_alignment != '.': |
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564 variants_alignment[counter] = {'REF': ref, 'ALT': alt_alignment} |
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565 |
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566 if alt_noMatter != '.': |
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567 variants_noMatter[counter] = {'REF': ref, 'ALT': alt_noMatter} |
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568 |
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569 if alt_correct is None: |
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570 if counter - len(correct_last_absent_position) in correct_absent_positions: |
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571 correct_absent_positions[counter - len(correct_last_absent_position)]['REF'] += ref |
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572 else: |
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573 correct_absent_positions[counter] = {'REF': ref, 'ALT': ''} |
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574 correct_last_absent_position += ref |
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575 else: |
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576 if alt_correct != '.': |
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577 if len(alt_correct) < len(ref): |
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578 if len(alt_correct) == 1: |
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579 correct_absent_positions[counter + 1] = {'REF': ref[1:], 'ALT': ''} |
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580 else: |
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581 correct_absent_positions[counter + 1] = {'REF': ref[1:], 'ALT': alt_correct[1:]} |
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582 |
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583 correct_last_absent_position = ref[1:] |
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584 else: |
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585 variants_correct[counter] = {'REF': ref, 'ALT': alt_correct} |
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586 correct_last_absent_position = '' |
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587 else: |
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588 correct_last_absent_position = '' |
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589 |
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590 if multiple_alleles: |
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591 multiple_alleles_found.append(counter) |
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592 |
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593 counter += len(ref) |
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594 else: |
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595 variants_alignment[counter] = {'REF': sequence[counter - 1], 'ALT': 'N'} |
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596 |
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597 if counter - len(correct_last_absent_position) in correct_absent_positions: |
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598 correct_absent_positions[counter - len(correct_last_absent_position)]['REF'] += sequence[counter - 1] |
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599 else: |
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600 correct_absent_positions[counter] = {'REF': sequence[counter - 1], 'ALT': ''} |
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601 correct_last_absent_position += sequence[counter - 1] |
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602 |
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603 if counter - len(noMatter_last_absent_position) in noMatter_absent_positions: |
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604 noMatter_absent_positions[counter - len(noMatter_last_absent_position)]['REF'] += sequence[counter - 1] |
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605 else: |
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606 noMatter_absent_positions[counter] = {'REF': sequence[counter - 1], 'ALT': ''} |
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607 noMatter_last_absent_position += sequence[counter - 1] |
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608 |
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609 counter += 1 |
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610 |
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611 for position in correct_absent_positions: |
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612 if position == 1: |
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613 variants_correct[position] = {'REF': correct_absent_positions[position]['REF'], 'ALT': 'N'} |
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614 else: |
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615 if position - 1 not in variants_correct: |
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616 variants_correct[position - 1] = {'REF': sequence[position - 2] + correct_absent_positions[position]['REF'], 'ALT': sequence[position - 2] + correct_absent_positions[position]['ALT']} |
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617 else: |
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618 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:]} |
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619 |
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620 for position in noMatter_absent_positions: |
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621 if position == 1: |
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622 variants_noMatter[position] = {'REF': noMatter_absent_positions[position]['REF'], 'ALT': 'N'} |
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623 else: |
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624 if position - 1 not in variants_noMatter: |
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625 variants_noMatter[position - 1] = {'REF': sequence[position - 2] + noMatter_absent_positions[position]['REF'], 'ALT': sequence[position - 2] + noMatter_absent_positions[position]['ALT']} |
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626 else: |
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627 variants_noMatter[position - 1] = {'REF': variants_noMatter[position - 1]['REF'] + noMatter_absent_positions[position]['REF'][len(variants_noMatter[position - 1]['REF']) - 1:], 'ALT': variants_noMatter[position - 1]['ALT'] + noMatter_absent_positions[position]['ALT'][len(variants_noMatter[position - 1]['ALT']) - 1 if len(variants_noMatter[position - 1]['ALT']) > 0 else 0:]} |
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628 |
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629 return variants_correct, variants_noMatter, variants_alignment, multiple_alleles_found |
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630 |
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631 |
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632 def clean_variant_in_extra_seq_left(variant_dict, position, length_extra_seq, multiple_alleles_found, number_multi_alleles): |
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633 number_diferences = 0 |
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634 |
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635 if position + len(variant_dict[position]['REF']) - 1 > length_extra_seq: |
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636 if multiple_alleles_found is not None and position in multiple_alleles_found: |
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637 number_multi_alleles += 1 |
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638 |
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639 temp_variant = variant_dict[position] |
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640 del variant_dict[position] |
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641 variant_dict[length_extra_seq] = {} |
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642 variant_dict[length_extra_seq]['REF'] = temp_variant['REF'][length_extra_seq - position:] |
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643 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] |
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644 number_diferences = count_number_diferences(variant_dict[length_extra_seq]['REF'], variant_dict[length_extra_seq]['ALT']) |
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645 else: |
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646 del variant_dict[position] |
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647 |
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648 return variant_dict, number_multi_alleles, number_diferences |
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649 |
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650 |
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651 def clean_variant_in_extra_seq_rigth(variant_dict, position, sequence_length, length_extra_seq): |
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652 if position + len(variant_dict[position]['REF']) - 1 > sequence_length - length_extra_seq: |
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653 variant_dict[position]['REF'] = variant_dict[position]['REF'][: - (position - (sequence_length - length_extra_seq)) + 1] |
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654 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'] |
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655 |
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656 number_diferences = count_number_diferences(variant_dict[position]['REF'], variant_dict[position]['ALT']) |
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657 |
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658 return variant_dict, number_diferences |
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659 |
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660 |
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661 def cleanning_variants_extra_seq(variants_correct, variants_noMatter, variants_alignment, multiple_alleles_found, length_extra_seq, sequence_length): |
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662 number_multi_alleles = 0 |
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663 number_diferences = 0 |
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664 |
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665 counter = 1 |
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666 while counter <= sequence_length: |
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667 if counter <= length_extra_seq: |
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668 if counter in variants_correct: |
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669 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) |
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670 if counter in variants_noMatter: |
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671 variants_noMatter, ignore, ignore = clean_variant_in_extra_seq_left(variants_noMatter, counter, length_extra_seq, None, None) |
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672 if counter in variants_alignment: |
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673 variants_alignment, ignore, ignore = clean_variant_in_extra_seq_left(variants_alignment, counter, length_extra_seq, None, None) |
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674 elif counter > length_extra_seq and counter <= sequence_length - length_extra_seq: |
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675 if counter in variants_correct: |
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676 if counter in multiple_alleles_found: |
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677 number_multi_alleles += 1 |
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678 variants_correct, number_diferences_found = clean_variant_in_extra_seq_rigth(variants_correct, counter, sequence_length, length_extra_seq) |
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679 number_diferences += number_diferences_found |
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680 if counter in variants_noMatter: |
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681 variants_noMatter, ignore = clean_variant_in_extra_seq_rigth(variants_noMatter, counter, sequence_length, length_extra_seq) |
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682 if counter in variants_alignment: |
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683 variants_alignment, ignore = clean_variant_in_extra_seq_rigth(variants_alignment, counter, sequence_length, length_extra_seq) |
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684 else: |
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685 if counter in variants_correct: |
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686 del variants_correct[counter] |
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687 if counter in variants_noMatter: |
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688 del variants_noMatter[counter] |
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689 if counter in variants_alignment: |
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690 del variants_alignment[counter] |
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691 |
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692 counter += 1 |
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693 |
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694 return variants_correct, variants_noMatter, variants_alignment, number_multi_alleles, number_diferences |
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695 |
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696 |
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697 def get_coverage(gene_coverage): |
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698 coverage = {} |
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699 |
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700 with open(gene_coverage, 'rtU') as reader: |
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701 for line in reader: |
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702 line = line.splitlines()[0] |
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703 if len(line) > 0: |
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704 line = line.split('\t') |
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705 coverage[int(line[1])] = int(line[2]) |
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706 |
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707 return coverage |
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708 |
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709 |
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710 def get_coverage_report(coverage, sequence_length, minimum_depth_presence, minimum_depth_call, length_extra_seq): |
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711 if len(coverage) == 0: |
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712 return sequence_length - 2 * length_extra_seq, 100.0, 0.0 |
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713 |
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714 count_absent = 0 |
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715 count_lowCoverage = 0 |
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716 sum_coverage = 0 |
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717 |
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718 counter = 1 |
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719 while counter <= sequence_length: |
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720 if counter > length_extra_seq and counter <= sequence_length - length_extra_seq: |
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721 if coverage[counter] < minimum_depth_presence: |
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722 count_absent += 1 |
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723 else: |
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724 if coverage[counter] < minimum_depth_call: |
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725 count_lowCoverage += 1 |
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726 sum_coverage += coverage[counter] |
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727 counter += 1 |
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728 |
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729 mean_coverage = 0 |
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730 percentage_lowCoverage = 0 |
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731 if sequence_length - 2 * length_extra_seq - count_absent > 0: |
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732 mean_coverage = float(sum_coverage) / float(sequence_length - 2 * length_extra_seq - count_absent) |
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733 percentage_lowCoverage = float(count_lowCoverage) / float(sequence_length - 2 * length_extra_seq - count_absent) * 100 |
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734 |
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735 return count_absent, percentage_lowCoverage, mean_coverage |
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736 |
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737 |
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738 # Get genome coverage data |
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739 def compute_genome_coverage_data(alignment_file, sequence_to_analyse, outdir, counter): |
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740 genome_coverage_data_file = os.path.join(outdir, 'samtools_depth.sequence_' + str(counter) + '.tab') |
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741 command = ['samtools', 'depth', '-a', '-q', '0', '-r', sequence_to_analyse, alignment_file, '>', genome_coverage_data_file] |
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742 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, True, None, False) |
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743 return run_successfully, genome_coverage_data_file |
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744 |
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745 |
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746 def write_variants_vcf(variants, outdir, sequence_to_analyse, sufix): |
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747 vcf_file = os.path.join(outdir, str(sequence_to_analyse + '.' + sufix + '.vcf')) |
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748 with open(vcf_file, 'wt') as writer: |
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749 writer.write('##fileformat=VCFv4.2' + '\n') |
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750 writer.write('#' + '\t'.join(['SEQUENCE', 'POSITION', 'ID_unused', 'REFERENCE_sequence', 'ALTERNATIVE_sequence', 'QUALITY_unused', 'FILTER_unused', 'INFO_unused', 'FORMAT_unused']) + '\n') |
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751 for i in sorted(variants.keys()): |
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752 writer.write('\t'.join([sequence_to_analyse, str(i), '.', variants[i]['REF'], variants[i]['ALT'], '.', '.', '.', '.']) + '\n') |
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753 |
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754 compressed_vcf_file = vcf_file + '.gz' |
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755 command = ['bcftools', 'convert', '-o', compressed_vcf_file, '-O', 'z', vcf_file] |
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756 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, False) |
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757 if run_successfully: |
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758 command = ['bcftools', 'index', compressed_vcf_file] |
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759 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, False) |
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760 |
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761 if not run_successfully: |
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762 compressed_vcf_file = None |
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763 |
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764 return run_successfully, compressed_vcf_file |
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765 |
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766 |
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767 def parse_fasta_inMemory(fasta_memory): |
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768 fasta_memory = fasta_memory.splitlines() |
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769 sequence_dict = {} |
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770 for line in fasta_memory: |
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771 if len(line) > 0: |
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772 if line.startswith('>'): |
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773 sequence_dict = {'header': line[1:], 'sequence': ''} |
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774 else: |
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775 sequence_dict['sequence'] += line |
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776 |
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777 return sequence_dict |
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778 |
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779 |
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780 def compute_consensus_sequence(reference_file, sequence_to_analyse, compressed_vcf_file, outdir, sufix): |
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781 sequence_dict = None |
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782 |
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783 gene_fasta = os.path.join(outdir, str(sequence_to_analyse + '.fasta')) |
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784 |
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785 run_successfully, stdout = index_fasta_samtools(reference_file, sequence_to_analyse, gene_fasta, False) |
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786 if run_successfully: |
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787 command = ['bcftools', 'norm', '-c', 's', '-f', gene_fasta, '-Ov', compressed_vcf_file] |
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788 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, False) |
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789 if run_successfully: |
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790 command = ['bcftools', 'consensus', '-f', gene_fasta, compressed_vcf_file, '-H', '1'] |
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791 run_successfully, stdout, stderr = utils.runCommandPopenCommunicate(command, False, None, False) |
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792 if run_successfully: |
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793 sequence_dict = parse_fasta_inMemory(stdout) |
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794 |
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795 return run_successfully, sequence_dict |
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796 |
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797 |
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798 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): |
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799 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) |
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800 |
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801 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)) |
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802 |
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803 run_successfully = False |
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804 consensus = {'correct': {}, 'noMatter': {}, 'alignment': {}} |
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805 for variant_type in ['variants_correct', 'variants_noMatter', 'variants_alignment']: |
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806 run_successfully, compressed_vcf_file = write_variants_vcf(eval(variant_type), outdir, sequence_to_analyse, variant_type.split('_', 1)[1]) |
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807 if run_successfully: |
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808 run_successfully, sequence_dict = compute_consensus_sequence(reference_file, sequence_to_analyse, compressed_vcf_file, outdir, variant_type.split('_', 1)[1]) |
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809 if run_successfully: |
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810 consensus[variant_type.split('_', 1)[1]] = {'header': sequence_dict['header'], 'sequence': sequence_dict['sequence'][length_extra_seq:len(sequence_dict['sequence']) - length_extra_seq]} |
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811 |
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812 return run_successfully, number_multi_alleles, consensus, number_diferences |
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813 |
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814 |
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815 @utils.trace_unhandled_exceptions |
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816 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, threads): |
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817 count_absent = None |
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818 percentage_lowCoverage = None |
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819 meanCoverage = None |
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820 number_diferences = 0 |
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821 |
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822 # Create vcf file (for multiple alleles check) |
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823 run_successfully, gene_vcf = create_vcf(bam_file, sequence_information['header'], outdir, counter, reference_file) |
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824 if run_successfully: |
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825 # Create coverage tab file |
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826 run_successfully, gene_coverage = compute_genome_coverage_data(bam_file, sequence_information['header'], outdir, counter) |
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827 |
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828 if run_successfully: |
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829 variants = get_variants(gene_vcf) |
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830 |
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831 coverage = get_coverage(gene_coverage) |
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832 |
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833 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) |
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834 |
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835 count_absent, percentage_lowCoverage, meanCoverage = get_coverage_report(coverage, sequence_information['length'], minimum_depth_presence, minimum_depth_call, length_extra_seq) |
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836 |
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837 utils.saveVariableToPickle([run_successfully, counter, number_multi_alleles, count_absent, percentage_lowCoverage, meanCoverage, consensus_sequence, number_diferences], outdir, str('coverage_info.' + str(counter))) |
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838 |
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839 |
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840 def clean_header(header): |
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841 problematic_characters = ["|", " ", ",", ".", "(", ")", "'", "/", ":"] |
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842 new_header = str(header) |
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843 if any(x in header for x in problematic_characters): |
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844 for x in problematic_characters: |
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845 new_header = new_header.replace(x, '_') |
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846 return header, new_header |
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847 |
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848 |
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849 def get_sequence_information(fasta_file, length_extra_seq): |
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850 sequence_dict = {} |
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851 headers = {} |
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852 headers_changed = False |
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853 |
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854 with open(fasta_file, 'rtU') as reader: |
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855 blank_line_found = False |
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856 sequence_counter = 0 |
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857 temp_sequence_dict = {} |
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858 for line in reader: |
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859 line = line.splitlines()[0] |
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860 if len(line) > 0: |
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861 if not blank_line_found: |
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862 if line.startswith('>'): |
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863 if len(temp_sequence_dict) > 0: |
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864 if temp_sequence_dict.values()[0]['length'] - 2 * length_extra_seq > 0: |
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865 sequence_dict[temp_sequence_dict.keys()[0]] = temp_sequence_dict.values()[0] |
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866 else: |
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867 print '{header} sequence ignored due to length <= 0'.format(header=temp_sequence_dict.values()[0]['header']) |
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868 del headers[temp_sequence_dict.values()[0]['header']] |
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869 temp_sequence_dict = {} |
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870 |
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871 original_header, new_header = clean_header(line[1:]) |
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872 if new_header in headers: |
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873 sys.exit('Found duplicated sequence headers: {original_header}'.format(original_header=original_header)) |
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874 |
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875 sequence_counter += 1 |
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876 temp_sequence_dict[sequence_counter] = {'header': new_header, 'sequence': '', 'length': 0} |
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877 headers[new_header] = str(original_header) |
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878 if new_header != original_header: |
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879 headers_changed = True |
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880 else: |
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881 temp_sequence_dict[sequence_counter]['sequence'] += line |
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882 temp_sequence_dict[sequence_counter]['length'] += len(line) |
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883 else: |
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884 sys.exit('It was found a blank line between the fasta file above line ' + line) |
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885 else: |
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886 blank_line_found = True |
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887 |
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888 if len(temp_sequence_dict) > 0: |
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889 if temp_sequence_dict.values()[0]['length'] - 2 * length_extra_seq > 0: |
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890 sequence_dict[temp_sequence_dict.keys()[0]] = temp_sequence_dict.values()[0] |
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891 else: |
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892 print '{header} sequence ignored due to length <= 0'.format(header=temp_sequence_dict.values()[0]['header']) |
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893 del headers[temp_sequence_dict.values()[0]['header']] |
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894 |
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895 return sequence_dict, headers, headers_changed |
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896 |
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897 |
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898 def determine_threads_2_use(number_sequences, threads): |
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899 if number_sequences >= threads: |
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900 return 1 |
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901 else: |
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902 return threads / number_sequences |
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903 |
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904 |
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905 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, notWriteConsensus): |
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906 sequence_data_outdir = os.path.join(outdir, 'sequence_data', '') |
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907 utils.removeDirectory(sequence_data_outdir) |
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908 os.mkdir(sequence_data_outdir) |
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909 |
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910 sequences, headers, headers_changed = get_sequence_information(reference_file, length_extra_seq) |
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911 |
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912 threads_2_use = determine_threads_2_use(len(sequences), threads) |
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913 |
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914 pool = multiprocessing.Pool(processes=threads) |
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915 for sequence_counter in sequences: |
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916 sequence_dir = os.path.join(sequence_data_outdir, str(sequence_counter), '') |
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917 utils.removeDirectory(sequence_dir) |
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918 os.makedirs(sequence_dir) |
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919 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, threads_2_use,)) |
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920 pool.close() |
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921 pool.join() |
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922 |
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923 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, notWriteConsensus) |
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924 |
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925 return run_successfully, sample_data, consensus_files, consensus_sequences |
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926 |
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927 |
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928 def chunkstring(string, length): |
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929 return (string[0 + i:length + i] for i in range(0, len(string), length)) |
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930 |
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931 |
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932 def write_consensus(outdir, sample, consensus_sequence): |
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933 consensus_files = {} |
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934 for consensus_type in ['correct', 'noMatter', 'alignment']: |
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935 consensus_files[consensus_type] = os.path.join(outdir, str(sample + '.' + consensus_type + '.fasta')) |
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936 with open(consensus_files[consensus_type], 'at') as writer: |
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937 writer.write('>' + consensus_sequence[consensus_type]['header'] + '\n') |
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938 fasta_sequence_lines = chunkstring(consensus_sequence[consensus_type]['sequence'], 80) |
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939 for line in fasta_sequence_lines: |
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940 writer.write(line + '\n') |
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941 return consensus_files |
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942 |
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943 |
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944 def gather_data_together(sample, data_directory, sequences_information, outdir, debug_mode_true, length_extra_seq, notWriteConsensus): |
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945 run_successfully = True |
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946 counter = 0 |
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947 sample_data = {} |
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948 |
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949 consensus_files = None |
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950 consensus_sequences_together = {'correct': {}, 'noMatter': {}, 'alignment': {}} |
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951 |
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952 write_consensus_first_time = True |
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953 |
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954 genes_directories = [d for d in os.listdir(data_directory) if not d.startswith('.') and os.path.isdir(os.path.join(data_directory, d, ''))] |
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955 for gene_dir in genes_directories: |
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956 gene_dir_path = os.path.join(data_directory, gene_dir, '') |
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957 |
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958 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))] |
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959 for file_found in files: |
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960 if file_found.startswith('coverage_info.') and file_found.endswith('.pkl'): |
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961 file_path = os.path.join(gene_dir_path, file_found) |
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962 |
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963 if run_successfully: |
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964 run_successfully, sequence_counter, multiple_alleles_found, count_absent, percentage_lowCoverage, meanCoverage, consensus_sequence, number_diferences = utils.extractVariableFromPickle(file_path) |
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965 |
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966 if not notWriteConsensus: |
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967 for consensus_type in consensus_sequence: |
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968 consensus_sequences_together[consensus_type][sequence_counter] = {'header': consensus_sequence[consensus_type]['header'], 'sequence': consensus_sequence[consensus_type]['sequence']} |
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969 |
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970 if write_consensus_first_time: |
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971 for consensus_type in ['correct', 'noMatter', 'alignment']: |
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972 file_to_remove = os.path.join(outdir, str(sample + '.' + consensus_type + '.fasta')) |
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973 if os.path.isfile(file_to_remove): |
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974 os.remove(file_to_remove) |
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975 write_consensus_first_time = False |
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976 consensus_files = write_consensus(outdir, sample, consensus_sequence) |
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977 |
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978 gene_identity = 0 |
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979 if sequences_information[sequence_counter]['length'] - 2 * length_extra_seq - count_absent > 0: |
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980 gene_identity = 100 - (float(number_diferences) / (sequences_information[sequence_counter]['length'] - 2 * length_extra_seq - count_absent)) * 100 |
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981 |
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982 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_lowCoverage, 'gene_number_positions_multiple_alleles': multiple_alleles_found, 'gene_mean_read_coverage': meanCoverage, 'gene_identity': gene_identity} |
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983 counter += 1 |
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984 |
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985 if not debug_mode_true: |
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986 utils.removeDirectory(gene_dir_path) |
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987 |
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988 if counter != len(sequences_information): |
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989 run_successfully = False |
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990 |
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991 return run_successfully, sample_data, consensus_files, consensus_sequences_together |
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992 |
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993 |
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994 rematch_timer = functools.partial(utils.timer, name='ReMatCh module') |
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995 |
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996 |
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997 @rematch_timer |
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998 def runRematchModule(sample, fastq_files, reference_file, threads, outdir, length_extra_seq, minimum_depth_presence, minimum_depth_call, minimum_depth_frequency_dominant_allele, minimum_gene_coverage, conserved_True, debug_mode_true, numMapLoc, minimum_gene_identity, rematch_run, softClip_baseQuality, softClip_recodeRun, reference_dict, softClip_cigarFlagRecode, bowtieOPT, gene_list_reference, notWriteConsensus): |
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999 rematch_folder = os.path.join(outdir, 'rematch_module', '') |
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1000 utils.removeDirectory(rematch_folder) |
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1001 os.mkdir(rematch_folder) |
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1002 |
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1003 # Map reads |
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1004 run_successfully, bam_file, reference_file = mapping_reads(fastq_files, reference_file, threads, rematch_folder, conserved_True, numMapLoc, rematch_run, softClip_baseQuality, softClip_recodeRun, reference_dict, softClip_cigarFlagRecode, bowtieOPT) |
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1005 |
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1006 if run_successfully: |
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1007 # Index reference file |
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1008 run_successfully, stdout = index_fasta_samtools(reference_file, None, None, True) |
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1009 if run_successfully: |
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1010 print 'Analysing alignment data' |
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1011 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, notWriteConsensus) |
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1012 |
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1013 if run_successfully: |
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1014 print 'Writing report file' |
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1015 number_absent_genes = 0 |
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1016 number_genes_multiple_alleles = 0 |
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1017 mean_sample_coverage = 0 |
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1018 with open(os.path.join(outdir, 'rematchModule_report.txt'), 'wt') as writer: |
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1019 writer.write('\t'.join(['#gene', 'percentage_gene_coverage', 'gene_mean_read_coverage', 'percentage_gene_low_coverage', 'number_positions_multiple_alleles', 'percentage_gene_identity']) + '\n') |
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1020 for i in range(1, len(sample_data) + 1): |
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1021 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') |
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1022 |
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1023 if sample_data[i]['gene_coverage'] < minimum_gene_coverage or sample_data[i]['gene_identity'] < minimum_gene_identity: |
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1024 number_absent_genes += 1 |
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1025 else: |
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1026 mean_sample_coverage += sample_data[i]['gene_mean_read_coverage'] |
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1027 if sample_data[i]['gene_number_positions_multiple_alleles'] > 0: |
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1028 number_genes_multiple_alleles += 1 |
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1029 |
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1030 if len(sample_data) - number_absent_genes > 0: |
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1031 mean_sample_coverage = float(mean_sample_coverage) / float(len(sample_data) - number_absent_genes) |
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1032 else: |
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1033 mean_sample_coverage = 0 |
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1034 |
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1035 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') |
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1036 |
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1037 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)))]) |
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1038 |
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1039 if not debug_mode_true: |
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1040 utils.removeDirectory(rematch_folder) |
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1041 |
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1042 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 |