Mercurial > repos > cstrittmatter > ss2v110
annotate bin/SeqSero2_package.py @ 14:047956dacae4 draft default tip
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author | cstrittmatter |
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date | Thu, 21 May 2020 22:01:02 -0400 |
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1 #!/usr/bin/env python3 |
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2 |
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3 import sys |
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4 import time |
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5 import random |
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6 import os |
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7 import subprocess |
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8 import gzip |
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9 import io |
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10 import pickle |
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11 import argparse |
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12 import itertools |
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13 from distutils.version import LooseVersion |
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14 from distutils.spawn import find_executable |
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15 sys.path.insert(1,sys.path[0]+'/..') |
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16 |
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17 try: |
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18 from .version import SeqSero2_version |
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19 except Exception: #ImportError |
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20 from version import SeqSero2_version |
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21 |
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22 ### SeqSero Kmer |
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23 def parse_args(): |
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24 "Parse the input arguments, use '-h' for help." |
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25 parser = argparse.ArgumentParser(usage='SeqSero2_package.py -t <data_type> -m <mode> -i <input_data> [-d <output_directory>] [-p <number of threads>] [-b <BWA_algorithm>]\n\nDevelopper: Shaokang Zhang (zskzsk@uga.edu), Hendrik C Den-Bakker (Hendrik.DenBakker@uga.edu) and Xiangyu Deng (xdeng@uga.edu)\n\nContact email:seqsero@gmail.com\n\nVersion: v1.1.1')#add "-m <data_type>" in future |
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26 parser.add_argument("-i",nargs="+",help="<string>: path/to/input_data",type=os.path.abspath) ### add 'type=os.path.abspath' to generate absolute path of input data. |
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27 parser.add_argument("-t",choices=['1','2','3','4','5','6'],help="<int>: '1' for interleaved paired-end reads, '2' for separated paired-end reads, '3' for single reads, '4' for genome assembly, '5' for nanopore fasta, '6' for nanopore fastq") |
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28 parser.add_argument("-b",choices=['sam','mem'],default="mem",help="<string>: algorithms for bwa mapping for allele mode; 'mem' for mem, 'sam' for samse/sampe; default=mem; optional; for now we only optimized for default 'mem' mode") |
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29 parser.add_argument("-p",default="1",help="<int>: number of threads for allele mode, if p >4, only 4 threads will be used for assembly since the amount of extracted reads is small, default=1") |
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30 parser.add_argument("-m",choices=['k','a'],default="a",help="<string>: which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a") |
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31 parser.add_argument("-n",help="<string>: optional, to specify a sample name in the report output") |
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32 parser.add_argument("-d",help="<string>: optional, to specify an output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number") |
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33 parser.add_argument("-c",action="store_true",help="<flag>: if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files") |
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34 parser.add_argument("-s",action="store_true",help="<flag>: if '-s' was flagged, SeqSero2 will not output header in SeqSero_result.tsv") |
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35 parser.add_argument("--check",action="store_true",help="<flag>: use '--check' flag to check the required dependencies") |
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36 parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + SeqSero2_version) |
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37 return parser.parse_args() |
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38 |
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39 ### check paths of dependencies |
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40 check_dependencies = parse_args().check |
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41 dependencies = ['bwa','samtools','blastn','fastq-dump','spades.py','bedtools','SalmID.py'] |
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42 if check_dependencies: |
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43 for item in dependencies: |
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44 ext_path = find_executable(item) |
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45 if ext_path is not None: |
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46 print ("Using "+item+" - "+ext_path) |
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47 else: |
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48 print ("ERROR: can not find "+item+" in PATH") |
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49 sys.exit() |
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50 ### end of --check |
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51 |
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52 def reverse_complement(sequence): |
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53 complement = { |
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54 'A': 'T', |
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55 'C': 'G', |
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56 'G': 'C', |
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57 'T': 'A', |
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58 'N': 'N', |
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59 'M': 'K', |
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60 'R': 'Y', |
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61 'W': 'W', |
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62 'S': 'S', |
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63 'Y': 'R', |
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64 'K': 'M', |
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65 'V': 'B', |
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66 'H': 'D', |
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67 'D': 'H', |
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68 'B': 'V' |
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69 } |
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70 return "".join(complement[base] for base in reversed(sequence)) |
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71 |
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72 |
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73 def createKmerDict_reads(list_of_strings, kmer): |
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74 kmer_table = {} |
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75 for string in list_of_strings: |
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76 sequence = string.strip('\n') |
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77 for i in range(len(sequence) - kmer + 1): |
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78 new_mer = sequence[i:i + kmer].upper() |
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79 new_mer_rc = reverse_complement(new_mer) |
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80 if new_mer in kmer_table: |
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81 kmer_table[new_mer.upper()] += 1 |
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82 else: |
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83 kmer_table[new_mer.upper()] = 1 |
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84 if new_mer_rc in kmer_table: |
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85 kmer_table[new_mer_rc.upper()] += 1 |
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86 else: |
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87 kmer_table[new_mer_rc.upper()] = 1 |
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88 return kmer_table |
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89 |
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90 |
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91 def multifasta_dict(multifasta): |
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92 multifasta_list = [ |
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93 line.strip() for line in open(multifasta, 'r') if len(line.strip()) > 0 |
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94 ] |
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95 headers = [i for i in multifasta_list if i[0] == '>'] |
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96 multifasta_dict = {} |
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97 for h in headers: |
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98 start = multifasta_list.index(h) |
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99 for element in multifasta_list[start + 1:]: |
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100 if element[0] == '>': |
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101 break |
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102 else: |
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103 if h[1:] in multifasta_dict: |
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104 multifasta_dict[h[1:]] += element |
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105 else: |
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106 multifasta_dict[h[1:]] = element |
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107 return multifasta_dict |
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108 |
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109 |
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110 def multifasta_single_string(multifasta): |
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111 multifasta_list = [ |
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112 line.strip() for line in open(multifasta, 'r') |
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113 if (len(line.strip()) > 0) and (line.strip()[0] != '>') |
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114 ] |
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115 return ''.join(multifasta_list) |
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116 |
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117 |
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118 def chunk_a_long_sequence(long_sequence, chunk_size=60): |
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119 chunk_list = [] |
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120 steps = len(long_sequence) // 60 #how many chunks |
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121 for i in range(steps): |
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122 chunk_list.append(long_sequence[i * chunk_size:(i + 1) * chunk_size]) |
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123 chunk_list.append(long_sequence[steps * chunk_size:len(long_sequence)]) |
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124 return chunk_list |
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125 |
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126 |
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127 def target_multifasta_kmerizer(multifasta, k, kmerDict): |
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128 forward_length = 300 #if find the target, put forward 300 bases |
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129 reverse_length = 2200 #if find the target, put backward 2200 bases |
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130 chunk_size = 60 #it will firstly chunk the single long sequence to multiple smaller sequences, it controls the size of those smaller sequences |
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131 target_mers = [] |
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132 long_single_string = multifasta_single_string(multifasta) |
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133 multifasta_list = chunk_a_long_sequence(long_single_string, chunk_size) |
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134 unit_length = len(multifasta_list[0]) |
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135 forward_lines = int(forward_length / unit_length) + 1 |
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136 reverse_lines = int(forward_length / unit_length) + 1 |
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137 start_num = 0 |
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138 end_num = 0 |
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139 for i in range(len(multifasta_list)): |
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140 if i not in range(start_num, end_num): #avoid computational repetition |
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141 line = multifasta_list[i] |
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142 start = int((len(line) - k) // 2) |
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143 s1 = line[start:k + start] |
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144 if s1 in kmerDict: #detect it is a potential read or not (use the middle part) |
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145 if i - forward_lines >= 0: |
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146 start_num = i - forward_lines |
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147 else: |
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148 start_num = 0 |
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149 if i + reverse_lines <= len(multifasta_list) - 1: |
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150 end_num = i + reverse_lines |
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151 else: |
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152 end_num = len(multifasta_list) - 1 |
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153 target_list = [ |
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154 x.strip() for x in multifasta_list[start_num:end_num] |
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155 ] |
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156 target_line = "".join(target_list) |
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157 target_mers += [ |
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158 k1 for k1 in createKmerDict_reads([str(target_line)], k) |
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159 ] ##changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length) |
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160 else: |
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161 pass |
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162 return set(target_mers) |
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163 |
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164 |
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165 def target_read_kmerizer(file, k, kmerDict): |
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166 i = 1 |
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167 n_reads = 0 |
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168 total_coverage = 0 |
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169 target_mers = [] |
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170 if file.endswith(".gz"): |
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171 file_content = io.BufferedReader(gzip.open(file)) |
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172 else: |
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173 file_content = open(file, "r").readlines() |
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174 for line in file_content: |
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175 start = int((len(line) - k) // 2) |
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176 if i % 4 == 2: |
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177 if file.endswith(".gz"): |
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178 s1 = line[start:k + start].decode() |
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179 line = line.decode() |
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180 else: |
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181 s1 = line[start:k + start] |
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182 if s1 in kmerDict: #detect it is a potential read or not (use the middle part) |
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183 n_reads += 1 |
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184 total_coverage += len(line) |
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185 target_mers += [ |
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186 k1 for k1 in createKmerDict_reads([str(line)], k) |
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187 ] #changed k to k1, just want to avoid the mixes of this "k" (kmer) to the "k" above (kmer length) |
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188 i += 1 |
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189 if total_coverage >= 4000000: |
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190 break |
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191 return set(target_mers) |
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192 |
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193 |
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194 def minion_fasta_kmerizer(file, k, kmerDict): |
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195 i = 1 |
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196 n_reads = 0 |
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197 total_coverage = 0 |
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198 target_mers = {} |
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199 for line in open(file): |
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200 if i % 2 == 0: |
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201 for kmer, rc_kmer in kmers(line.strip().upper(), k): |
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202 if (kmer in kmerDict) or (rc_kmer in kmerDict): |
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203 if kmer in target_mers: |
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204 target_mers[kmer] += 1 |
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205 else: |
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206 target_mers[kmer] = 1 |
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207 if rc_kmer in target_mers: |
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208 target_mers[rc_kmer] += 1 |
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209 else: |
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210 target_mers[rc_kmer] = 1 |
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211 i += 1 |
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212 return set([h for h in target_mers]) |
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213 |
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214 |
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215 def minion_fastq_kmerizer(file, k, kmerDict): |
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216 i = 1 |
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217 n_reads = 0 |
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218 total_coverage = 0 |
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219 target_mers = {} |
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220 for line in open(file): |
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221 if i % 4 == 2: |
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222 for kmer, rc_kmer in kmers(line.strip().upper(), k): |
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223 if (kmer in kmerDict) or (rc_kmer in kmerDict): |
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224 if kmer in target_mers: |
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225 target_mers[kmer] += 1 |
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226 else: |
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227 target_mers[kmer] = 1 |
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228 if rc_kmer in target_mers: |
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229 target_mers[rc_kmer] += 1 |
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230 else: |
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231 target_mers[rc_kmer] = 1 |
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232 i += 1 |
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233 return set([h for h in target_mers]) |
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234 |
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235 |
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236 def multifasta_single_string2(multifasta): |
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237 single_string = '' |
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238 with open(multifasta, 'r') as f: |
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239 for line in f: |
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240 if line.strip()[0] == '>': |
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241 pass |
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242 else: |
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243 single_string += line.strip() |
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244 return single_string |
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245 |
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246 |
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247 def kmers(seq, k): |
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248 rev_comp = reverse_complement(seq) |
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249 for start in range(1, len(seq) - k + 1): |
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250 yield seq[start:start + k], rev_comp[-(start + k):-start] |
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251 |
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252 |
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253 def multifasta_to_kmers_dict(multifasta,k_size):#used to create database kmer set |
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254 multi_seq_dict = multifasta_dict(multifasta) |
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255 lib_dict = {} |
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256 for h in multi_seq_dict: |
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257 lib_dict[h] = set( |
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258 [k for k in createKmerDict_reads([multi_seq_dict[h]], k_size)]) |
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259 return lib_dict |
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260 |
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261 |
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262 def Combine(b, c): |
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263 fliC_combinations = [] |
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264 fliC_combinations.append(",".join(c)) |
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265 temp_combinations = [] |
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266 for i in range(len(b)): |
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267 for x in itertools.combinations(b, i + 1): |
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268 temp_combinations.append(",".join(x)) |
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269 for x in temp_combinations: |
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270 temp = [] |
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271 for y in c: |
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272 temp.append(y) |
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273 temp.append(x) |
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274 temp = ",".join(temp) |
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275 temp = temp.split(",") |
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276 temp.sort() |
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277 temp = ",".join(temp) |
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278 fliC_combinations.append(temp) |
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279 return fliC_combinations |
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280 |
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281 |
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282 def seqsero_from_formula_to_serotypes(Otype, fliC, fljB, special_gene_list,subspecies): |
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283 #like test_output_06012017.txt |
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284 #can add more varialbles like sdf-type, sub-species-type in future (we can conclude it into a special-gene-list) |
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285 from Initial_Conditions import phase1,phase2,phaseO,sero,subs,remove_list,rename_dict |
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286 rename_dict_not_anymore=[rename_dict[x] for x in rename_dict] |
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287 rename_dict_all=rename_dict_not_anymore+list(rename_dict) #used for decide whether to |
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288 seronames = [] |
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289 seronames_none_subspecies=[] |
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290 for i in range(len(phase1)): |
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291 fliC_combine = [] |
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292 fljB_combine = [] |
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293 if phaseO[i] == Otype: # no VII in KW, but it's there |
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294 ### for fliC, detect every possible combinations to avoid the effect of "[" |
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295 if phase1[i].count("[") == 0: |
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296 fliC_combine.append(phase1[i]) |
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297 elif phase1[i].count("[") >= 1: |
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298 c = [] |
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299 b = [] |
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300 if phase1[i][0] == "[" and phase1[i][-1] == "]" and phase1[i].count( |
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301 "[") == 1: |
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302 content = phase1[i].replace("[", "").replace("]", "") |
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303 fliC_combine.append(content) |
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304 fliC_combine.append("-") |
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305 else: |
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306 for x in phase1[i].split(","): |
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307 if "[" in x: |
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308 b.append(x.replace("[", "").replace("]", "")) |
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309 else: |
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310 c.append(x) |
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311 fliC_combine = Combine( |
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312 b, c |
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313 ) #Combine will offer every possible combinations of the formula, like f,[g],t: f,t f,g,t |
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314 ### end of fliC "[" detect |
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315 ### for fljB, detect every possible combinations to avoid the effect of "[" |
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316 if phase2[i].count("[") == 0: |
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317 fljB_combine.append(phase2[i]) |
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318 elif phase2[i].count("[") >= 1: |
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319 d = [] |
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320 e = [] |
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321 if phase2[i][0] == "[" and phase2[i][-1] == "]" and phase2[i].count( |
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322 "[") == 1: |
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323 content = phase2[i].replace("[", "").replace("]", "") |
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324 fljB_combine.append(content) |
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325 fljB_combine.append("-") |
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326 else: |
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327 for x in phase2[i].split(","): |
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328 if "[" in x: |
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329 d.append(x.replace("[", "").replace("]", "")) |
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330 else: |
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331 e.append(x) |
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332 fljB_combine = Combine(d, e) |
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333 ### end of fljB "[" detect |
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334 new_fliC = fliC.split( |
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335 "," |
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336 ) #because some antigen like r,[i] not follow alphabetical order, so use this one to judge and can avoid missings |
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337 new_fliC.sort() |
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338 new_fliC = ",".join(new_fliC) |
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339 new_fljB = fljB.split(",") |
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340 new_fljB.sort() |
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341 new_fljB = ",".join(new_fljB) |
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342 if (new_fliC in fliC_combine |
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343 or fliC in fliC_combine) and (new_fljB in fljB_combine |
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344 or fljB in fljB_combine): |
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345 ######start, remove_list,rename_dict, added on 11/11/2018 |
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346 if sero[i] not in remove_list: |
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347 temp_sero=sero[i] |
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348 if temp_sero in rename_dict: |
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349 temp_sero=rename_dict[temp_sero] #rename if in the rename list |
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350 if temp_sero not in seronames:#the new sero may already included, if yes, then not consider |
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351 if subs[i] == subspecies: |
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352 seronames.append(temp_sero) |
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353 seronames_none_subspecies.append(temp_sero) |
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354 else: |
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355 pass |
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356 else: |
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357 pass |
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358 ######end, added on 11/11/2018 |
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359 #analyze seronames |
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360 subspecies_pointer="" |
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361 if len(seronames) == 0 and len(seronames_none_subspecies)!=0: |
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362 # ed_SL_12182019: modified to fix the subspecies output problem |
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363 #seronames=seronames_none_subspecies |
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364 seronames=["N/A"] |
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365 #subspecies_pointer="1" |
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366 subspecies_pointer="0" |
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367 if len(seronames) == 0: |
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368 seronames = [ |
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369 "N/A (The predicted antigenic profile does not exist in the White-Kauffmann-Le Minor scheme)" |
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370 ] |
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371 star = "" |
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372 star_line = "" |
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373 if len(seronames) > 1: #there are two possible predictions for serotypes |
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374 star = "*" |
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375 #changed 04072019 |
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376 #star_line = "The predicted serotypes share the same general formula:\t" + Otype + ":" + fliC + ":" + fljB + "\n" |
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377 if subspecies_pointer=="1" and len(seronames_none_subspecies)!=0: |
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378 star="*" |
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379 star_line="The predicted O and H antigens correspond to serotype '"+(" or ").join(seronames)+"' in the Kauffmann-White scheme. The predicted subspecies by SalmID (github.com/hcdenbakker/SalmID) may not be consistent with subspecies designation in the Kauffmann-White scheme. " + star_line |
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380 #star_line="The formula with this subspieces prediction can't get a serotype in KW manual, and the serotyping prediction was made without considering it."+star_line |
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381 if Otype=="": |
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382 Otype="-" |
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383 predict_form = Otype + ":" + fliC + ":" + fljB |
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384 predict_sero = (" or ").join(seronames) |
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385 ###special test for Enteritidis |
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386 if predict_form == "9:g,m:-": |
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387 sdf = "-" |
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388 for x in special_gene_list: |
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389 if x.startswith("sdf"): |
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390 sdf = "+" |
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391 #star_line="Detected sdf gene, a marker to differentiate Gallinarum and Enteritidis" |
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392 star_line="sdf gene detected. " |
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393 #predict_form = predict_form + " Sdf prediction:" + sdf |
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394 predict_form = predict_form #changed 04072019 |
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395 if sdf == "-": |
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396 star = "*" |
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397 #star_line="Didn't detected sdf gene, a marker to differentiate Gallinarum and Enteritidis" |
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398 star_line="sdf gene not detected. " |
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399 #changed in 04072019, for new output |
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400 #star_line = "Additional characterization is necessary to assign a serotype to this strain. Commonly circulating strains of serotype Enteritidis are sdf+, although sdf- strains of serotype Enteritidis are known to exist. Serotype Gallinarum is typically sdf- but should be quite rare. Sdf- strains of serotype Enteritidis and serotype Gallinarum can be differentiated by phenotypic profile or genetic criteria.\n" |
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401 #predict_sero = "Gallinarum/Enteritidis" #04132019, for new output requirement |
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402 predict_sero = "Gallinarum or Enteritidis" |
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403 ###end of special test for Enteritidis |
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404 elif predict_form == "4:i:-": |
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405 predict_sero = "I 4,[5],12:i:-" # change serotype name |
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406 elif predict_form == "4:r:-": |
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407 predict_sero = "N/A (4:r:-)" |
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408 elif predict_form == "4:b:-": |
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409 predict_sero = "N/A (4:b:-)" |
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410 #elif predict_form == "8:e,h:1,2": #removed after official merge of newport and bardo |
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411 #predict_sero = "Newport" |
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412 #star = "*" |
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413 #star_line = "Serotype Bardo shares the same antigenic profile with Newport, but Bardo is exceedingly rare." |
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414 claim = "The serotype(s) is/are the only serotype(s) with the indicated antigenic profile currently recognized in the Kauffmann White Scheme. New serotypes can emerge and the possibility exists that this antigenic profile may emerge in a different subspecies. Identification of strains to the subspecies level should accompany serotype determination; the same antigenic profile in different subspecies is considered different serotypes.\n" |
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415 if "N/A" in predict_sero: |
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416 claim = "" |
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417 #special test for Typhimurium |
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418 if "Typhimurium" in predict_sero or predict_form == "4:i:-": |
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419 normal = 0 |
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420 mutation = 0 |
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421 for x in special_gene_list: |
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422 if "oafA-O-4_full" in x: |
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423 normal = float(special_gene_list[x]) |
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424 elif "oafA-O-4_5-" in x: |
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425 mutation = float(special_gene_list[x]) |
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426 if normal > mutation: |
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427 pass |
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428 elif normal < mutation: |
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429 #predict_sero = predict_sero.strip() + "(O5-)" |
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430 predict_sero = predict_sero.strip() #diable special sero for new output requirement, 04132019 |
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431 star = "*" |
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432 #star_line = "Detected the deletion of O5-." |
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433 star_line = "Detected a deletion that causes O5- variant of Typhimurium. " |
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434 else: |
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435 pass |
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436 #special test for Paratyphi B |
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437 if "Paratyphi B" in predict_sero or predict_form == "4:b:-": |
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438 normal = 0 |
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439 mutation = 0 |
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440 for x in special_gene_list: |
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441 if "gntR-family-regulatory-protein_dt-positive" in x: |
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442 normal = float(special_gene_list[x]) |
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443 elif "gntR-family-regulatory-protein_dt-negative" in x: |
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444 mutation = float(special_gene_list[x]) |
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445 #print(normal,mutation) |
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446 if normal > mutation: |
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447 #predict_sero = predict_sero.strip() + "(dt+)" #diable special sero for new output requirement, 04132019 |
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448 predict_sero = predict_sero.strip()+' var. L(+) tartrate+' if "Paratyphi B" in predict_sero else predict_sero.strip() |
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449 star = "*" |
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450 #star_line = "Didn't detect the SNP for dt- which means this isolate is a Paratyphi B variant L(+) tartrate(+)." |
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451 star_line = "The SNP that causes d-Tartrate nonfermentating phenotype of Paratyphi B was not detected. " |
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452 elif normal < mutation: |
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453 #predict_sero = predict_sero.strip() + "(dt-)" #diable special sero for new output requirement, 04132019 |
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454 predict_sero = predict_sero.strip() |
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455 star = "*" |
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456 #star_line = "Detected the SNP for dt- which means this isolate is a systemic pathovar of Paratyphi B." |
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457 star_line = "Detected the SNP for d-Tartrate nonfermenting phenotype of Paratyphi B. " |
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458 else: |
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459 star = "*" |
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460 #star_line = " Failed to detect the SNP for dt-, can't decide it's a Paratyphi B variant L(+) tartrate(+) or not." |
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461 star_line = " " ## ed_SL_05152019: do not report this situation. |
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462 #special test for O13,22 and O13,23 |
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463 if Otype=="13": |
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464 #ex_dir = os.path.dirname(os.path.realpath(__file__)) |
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465 ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019 |
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466 f = open(ex_dir + '/special.pickle', 'rb') |
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467 special = pickle.load(f) |
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468 O22_O23=special['O22_O23'] |
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469 if predict_sero.split(" or ")[0] in O22_O23[-1] and predict_sero.split(" or ")[0] not in rename_dict_all:#if in rename_dict_all, then it means already merged, no need to analyze |
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470 O22_score=0 |
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471 O23_score=0 |
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472 for x in special_gene_list: |
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473 if "O:22" in x: |
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474 O22_score = O22_score+float(special_gene_list[x]) |
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475 elif "O:23" in x: |
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476 O23_score = O23_score+float(special_gene_list[x]) |
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477 #print(O22_score,O23_score) |
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478 for z in O22_O23[0]: |
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479 if predict_sero.split(" or ")[0] in z: |
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480 if O22_score > O23_score: |
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481 star = "*" |
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482 #star_line = "Detected O22 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019 |
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483 predict_sero = z[0] |
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484 elif O22_score < O23_score: |
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485 star = "*" |
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486 #star_line = "Detected O23 specific genes to further differenciate '"+predict_sero+"'." #diabled for new output requirement, 04132019 |
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487 predict_sero = z[1] |
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488 else: |
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489 star = "*" |
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490 #star_line = "Fail to detect O22 and O23 differences." #diabled for new output requirement, 04132019 |
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491 if " or " in predict_sero: |
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492 star_line = star_line + "The predicted serotypes share the same general formula: " + Otype + ":" + fliC + ":" + fljB + "." |
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493 #special test for O6,8 |
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494 #merge_O68_list=["Blockley","Bovismorbificans","Hadar","Litchfield","Manhattan","Muenchen"] #remove 11/11/2018, because already in merge list |
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495 #for x in merge_O68_list: |
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496 # if x in predict_sero: |
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497 # predict_sero=x |
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498 # star="" |
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499 # star_line="" |
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500 #special test for Montevideo; most of them are monophasic |
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501 #if "Montevideo" in predict_sero and "1,2,7" in predict_form: #remove 11/11/2018, because already in merge list |
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502 #star="*" |
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503 #star_line="Montevideo is almost always monophasic, having an antigen called for the fljB position may be a result of Salmonella-Salmonella contamination." |
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504 return predict_form, predict_sero, star, star_line, claim |
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505 ### End of SeqSero Kmer part |
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506 |
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507 ### Begin of SeqSero2 allele prediction and output |
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508 def xml_parse_score_comparision_seqsero(xmlfile): |
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509 #used to do seqsero xml analysis |
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510 from Bio.Blast import NCBIXML |
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511 handle=open(xmlfile) |
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512 print(handle) |
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513 handle=NCBIXML.parse(handle) |
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514 handle=list(handle) |
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515 List=[] |
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516 List_score=[] |
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517 List_ids=[] |
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518 List_query_region=[] |
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519 for i in range(len(handle)): |
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520 if len(handle[i].alignments)>0: |
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521 for j in range(len(handle[i].alignments)): |
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522 score=0 |
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523 ids=0 |
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524 cover_region=set() #fixed problem that repeated calculation leading percentage > 1 |
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525 List.append(handle[i].query.strip()+"___"+handle[i].alignments[j].hit_def) |
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526 for z in range(len(handle[i].alignments[j].hsps)): |
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527 hsp=handle[i].alignments[j].hsps[z] |
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528 temp=set(range(hsp.query_start,hsp.query_end)) |
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529 if len(cover_region)==0: |
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530 cover_region=cover_region|temp |
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531 fraction=1 |
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532 else: |
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533 fraction=1-len(cover_region&temp)/float(len(temp)) |
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534 cover_region=cover_region|temp |
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535 if "last" in handle[i].query or "first" in handle[i].query: |
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536 score+=hsp.bits*fraction |
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537 ids+=float(hsp.identities)/handle[i].query_length*fraction |
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538 else: |
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539 score+=hsp.bits*fraction |
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540 ids+=float(hsp.identities)/handle[i].query_length*fraction |
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541 List_score.append(score) |
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542 List_ids.append(ids) |
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543 List_query_region.append(cover_region) |
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544 temp=zip(List,List_score,List_ids,List_query_region) |
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545 Final_list=sorted(temp, key=lambda d:d[1], reverse = True) |
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546 return Final_list |
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547 |
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548 |
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549 def Uniq(L,sort_on_fre="none"): #return the uniq list and the count number |
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550 Old=L |
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551 L.sort() |
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552 L = [L[i] for i in range(len(L)) if L[i] not in L[:i]] |
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553 count=[] |
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554 for j in range(len(L)): |
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555 y=0 |
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556 for x in Old: |
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557 if L[j]==x: |
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558 y+=1 |
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559 count.append(y) |
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560 if sort_on_fre!="none": |
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561 d=zip(*sorted(zip(count, L))) |
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562 L=d[1] |
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563 count=d[0] |
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564 return (L,count) |
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565 |
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566 def judge_fliC_or_fljB_from_head_tail_for_one_contig(nodes_vs_score_list): |
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567 #used to predict it's fliC or fljB for one contig, based on tail and head score, but output the score difference,if it is very small, then not reliable, use blast score for whole contig to test |
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568 #this is mainly used for |
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569 a=nodes_vs_score_list |
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570 fliC_score=0 |
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571 fljB_score=0 |
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572 for z in a: |
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573 if "fliC" in z[0]: |
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574 fliC_score+=z[1] |
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575 elif "fljB" in z[0]: |
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576 fljB_score+=z[1] |
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577 if fliC_score>=fljB_score: |
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578 role="fliC" |
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579 else: |
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580 role="fljB" |
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581 return (role,abs(fliC_score-fljB_score)) |
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582 |
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583 def judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(node_name,Final_list,Final_list_passed): |
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584 #used to predict contig is fliC or fljB, if the differnce score value on above head_and_tail is less than 10 (quite small) |
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585 #also used when no head or tail got blasted score for the contig |
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586 role="" |
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587 for z in Final_list_passed: |
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588 if node_name in z[0]: |
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589 role=z[0].split("_")[0] |
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590 break |
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591 return role |
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592 |
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593 def fliC_or_fljB_judge_from_head_tail_sequence(nodes_list,tail_head_list,Final_list,Final_list_passed): |
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594 #nodes_list is the c created by c,d=Uniq(nodes) in below function |
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595 first_target="" |
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596 role_list=[] |
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597 for x in nodes_list: |
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598 a=[] |
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599 role="" |
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600 for y in tail_head_list: |
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601 if x in y[0]: |
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602 a.append(y) |
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603 if len(a)==4: |
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604 role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a) |
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605 if diff<20: |
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606 role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed) |
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607 elif len(a)==3: |
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608 ###however, if the one with highest score is the fewer one, compare their accumulation score |
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609 role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a) |
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610 if diff<20: |
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611 role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed) |
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612 ###end of above score comparison |
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613 elif len(a)==2: |
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614 #must on same node, if not, then decide with unit blast score, blast-score/length_of_special_sequence(30 or 37) |
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615 temp=[] |
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616 for z in a: |
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617 temp.append(z[0].split("_")[0]) |
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618 m,n=Uniq(temp)#should only have one choice, but weird situation might occur too |
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619 if len(m)==1: |
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620 pass |
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621 else: |
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622 pass |
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623 role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a) |
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624 if diff<20: |
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625 role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed) |
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626 ###need to desgin a algorithm to guess most possible situation for nodes_list, See the situations of test evaluation |
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627 elif len(a)==1: |
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628 #that one |
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629 role,diff=judge_fliC_or_fljB_from_head_tail_for_one_contig(a) |
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630 if diff<20: |
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631 role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed) |
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632 #need to evaluate, in future, may set up a cut-off, if not met, then just find Final_list_passed best match,like when "a==0" |
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633 else:#a==0 |
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634 #use Final_list_passed best match |
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635 for z in Final_list_passed: |
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636 if x in z[0]: |
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637 role=z[0].split("_")[0] |
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638 break |
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639 #print x,role,len(a) |
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640 role_list.append((role,x)) |
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641 if len(role_list)==2: |
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642 if role_list[0][0]==role_list[1][0]:#this is the most cocmmon error, two antigen were assigned to same phase |
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643 #just use score to do a final test |
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644 role_list=[] |
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645 for x in nodes_list: |
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646 role=judge_fliC_or_fljB_from_whole_contig_blast_score_ranking(x,Final_list,Final_list_passed) |
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647 role_list.append((role,x)) |
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648 return role_list |
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649 |
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650 def decide_contig_roles_for_H_antigen(Final_list,Final_list_passed): |
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651 #used to decide which contig is FliC and which one is fljB |
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652 contigs=[] |
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653 nodes=[] |
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654 for x in Final_list_passed: |
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655 if x[0].startswith("fl") and "last" not in x[0] and "first" not in x[0]: |
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656 nodes.append(x[0].split("___")[1].strip()) |
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657 c,d=Uniq(nodes)#c is node_list |
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658 #print c |
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659 tail_head_list=[x for x in Final_list if ("last" in x[0] or "first" in x[0])] |
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660 roles=fliC_or_fljB_judge_from_head_tail_sequence(c,tail_head_list,Final_list,Final_list_passed) |
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661 return roles |
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662 |
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663 def decide_O_type_and_get_special_genes(Final_list,Final_list_passed): |
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664 #decide O based on Final_list |
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665 O_choice="?" |
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666 O_list=[] |
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667 special_genes={} |
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668 nodes=[] |
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669 for x in Final_list_passed: |
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670 if x[0].startswith("O-"): |
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671 nodes.append(x[0].split("___")[1].strip()) |
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672 elif not x[0].startswith("fl"): |
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673 special_genes[x[0]]=x[2]#08172018, x[2] changed from x[-1] |
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674 #print "special_genes:",special_genes |
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675 c,d=Uniq(nodes) |
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676 #print "potential O antigen contig",c |
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677 final_O=[] |
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678 O_nodes_list=[] |
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679 for x in c:#c is the list for contigs |
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680 temp=0 |
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681 for y in Final_list_passed: |
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682 if x in y[0] and y[0].startswith("O-"): |
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683 final_O.append(y) |
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684 break |
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685 ### O contig has the problem of two genes on same contig, so do additional test |
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686 potenial_new_gene="" |
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687 for x in final_O: |
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688 pointer=0 #for genes merged or not |
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689 #not consider O-1,3,19_not_in_3,10, too short compared with others |
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690 if "O-1,3,19_not_in_3,10" not in x[0] and int(x[0].split("__")[1].split("___")[0])*x[2]+850 <= int(x[0].split("length_")[1].split("_")[0]):#gene length << contig length; for now give 300*2 (for secureity can use 400*2) as flank region |
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691 pointer=x[0].split("___")[1].strip()#store the contig name |
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692 print(pointer) |
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693 if pointer!=0:#it has potential merge event |
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694 for y in Final_list: |
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695 if pointer in y[0] and y not in final_O and (y[1]>=int(y[0].split("__")[1].split("___")[0])*1.5 or (y[1]>=int(y[0].split("__")[1].split("___")[0])*y[2] and y[1]>=400)):#that's a realtively strict filter now; if passed, it has merge event and add one more to final_O |
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696 potenial_new_gene=y |
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697 #print(potenial_new_gene) |
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698 break |
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699 if potenial_new_gene!="": |
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700 print("two differnt genes in same contig, fix it for O antigen") |
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701 print(potenial_new_gene[:3]) |
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702 pointer=0 |
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703 for y in final_O: |
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704 if y[0].split("___")[-1]==potenial_new_gene[0].split("___")[-1]: |
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705 pointer=1 |
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706 if pointer!=0: #changed to consider two genes in same contig |
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707 final_O.append(potenial_new_gene) |
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708 ### end of the two genes on same contig test |
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709 final_O=sorted(final_O,key=lambda x: x[2], reverse=True)#sorted |
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|
710 if len(final_O)==0 or (len(final_O)==1 and "O-1,3,19_not_in_3,10" in final_O[0][0]): |
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711 #print "$$$No Otype, due to no hit"#may need to be changed |
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712 O_choice="-" |
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|
713 else: |
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|
714 highest_O_coverage=max([float(x[0].split("_cov_")[-1].split("_")[0]) for x in final_O if "O-1,3,19_not_in_3,10" not in x[0]]) |
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715 O_list=[] |
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716 O_list_less_contamination=[] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
717 for x in final_O: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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718 if not "O-1,3,19_not_in_3,10__130" in x[0]:#O-1,3,19_not_in_3,10 is too small, which may affect further analysis; to avoid contamination affect, use 0.15 of highest coverage as cut-off |
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719 O_list.append(x[0].split("__")[0]) |
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720 O_nodes_list.append(x[0].split("___")[1]) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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721 if float(x[0].split("_cov_")[-1].split("_")[0])>highest_O_coverage*0.15: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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722 O_list_less_contamination.append(x[0].split("__")[0]) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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723 ### special test for O9,46 and O3,10 family |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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724 if ("O-9,46_wbaV" in O_list or "O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254" in O_list) and O_list_less_contamination[0].startswith("O-9,"):#not sure should use and float(O9_wbaV)/float(num_1) > 0.1 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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725 if "O-9,46_wzy" in O_list or "O-9,46_wzy_partial" in O_list:#and float(O946_wzy)/float(num_1) > 0.1 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
726 O_choice="O-9,46" |
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727 #print "$$$Most possilble Otype: O-9,46" |
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728 elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1 |
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729 O_choice="O-9,46,27" |
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730 #print "$$$Most possilble Otype: O-9,46,27" |
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731 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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732 O_choice="O-9"#next, detect O9 vs O2? |
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733 O2=0 |
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734 O9=0 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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735 for z in special_genes: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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736 if "tyr-O-9" in z: |
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737 O9=special_genes[z] |
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738 elif "tyr-O-2" in z: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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739 O2=special_genes[z] |
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740 if O2>O9: |
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741 O_choice="O-2" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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742 elif O2<O9: |
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743 pass |
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744 else: |
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|
745 pass |
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746 #print "$$$No suitable one, because can't distinct it's O-9 or O-2, but O-9 has a more possibility." |
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747 elif ("O-3,10_wzx" in O_list) and ("O-9,46_wzy" in O_list) and (O_list[0].startswith("O-3,10") or O_list_less_contamination[0].startswith("O-9,46_wzy")):#and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1 |
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748 if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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749 O_choice="O-3,10" |
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750 #print "$$$Most possilble Otype: O-3,10 (contain O-3,10_not_in_1,3,19)" |
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751 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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752 O_choice="O-1,3,19" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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753 #print "$$$Most possilble Otype: O-1,3,19 (not contain O-3,10_not_in_1,3,19)" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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754 ### end of special test for O9,46 and O3,10 family |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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755 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
756 try: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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757 max_score=0 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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758 for x in final_O: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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759 if x[2]>=max_score and float(x[0].split("_cov_")[-1].split("_")[0])>highest_O_coverage*0.15:#use x[2],08172018, the "coverage identity = cover_length * identity"; also meet coverage threshold |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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760 max_score=x[2]#change from x[-1] to x[2],08172018 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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761 O_choice=x[0].split("_")[0] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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762 if O_choice=="O-1,3,19": |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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763 O_choice=final_O[1][0].split("_")[0] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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764 #print "$$$Most possilble Otype: ",O_choice |
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|
765 except: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
766 pass |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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767 #print "$$$No suitable Otype, or failure of mapping (please check the quality of raw reads)" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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768 if O_choice=="O-9,46,27" and len(O_list)==2 and "O-4_wzx" in O_list: #special for very low chance sitatuion between O4 and O9,27,46, this is for serotypes like Bredeney and Schwarzengrund (normallly O-4 will have higher score, but sometimes sequencing quality may affect the prediction) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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769 O_choice="O-4" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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770 #print "O:",O_choice,O_nodes_list |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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771 Otypes=[] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
772 for x in O_list: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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773 if x!="O-1,3,19_not_in_3,10": |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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774 if "O-9,46_" not in x: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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775 Otypes.append(x.split("_")[0]) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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776 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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777 Otypes.append(x.split("-from")[0])#O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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778 #Otypes=[x.split("_")[0] for x in O_list if x!="O-1,3,19_not_in_3,10"] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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779 Otypes_uniq,Otypes_fre=Uniq(Otypes) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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780 contamination_O="" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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781 if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19": |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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782 if len(Otypes_uniq)>2: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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783 contamination_O="potential contamination from O antigen signals" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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784 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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785 if len(Otypes_uniq)>1: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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786 if O_choice=="O-4" and len(Otypes_uniq)==2 and "O-9,46,27" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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787 contamination_O="" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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788 elif O_choice=="O-9,46" and len(Otypes_uniq)==2 and "O-9,46_wbaV" in Otypes_uniq and "O-9,46_wzy" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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789 contamination_O="" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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790 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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791 contamination_O="potential contamination from O antigen signals" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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792 return O_choice,O_nodes_list,special_genes,final_O,contamination_O,Otypes_uniq |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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changeset
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793 ### End of SeqSero2 allele prediction and output |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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|
794 |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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795 def get_input_files(make_dir,input_file,data_type,dirpath): |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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796 #tell input files from datatype |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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797 #"<int>: '1'(pair-end reads, interleaved),'2'(pair-end reads, seperated),'3'(single-end reads), '4'(assembly),'5'(nanopore fasta),'6'(nanopore fastq)" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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798 for_fq="" |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
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799 rev_fq="" |
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|
800 os.chdir(make_dir) |
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801 if data_type=="1": |
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802 input_file=input_file[0].split("/")[-1] |
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|
803 if input_file.endswith(".sra"): |
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804 subprocess.check_call("fastq-dump --split-files "+input_file,shell=True) |
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|
805 for_fq=input_file.replace(".sra","_1.fastq") |
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|
806 rev_fq=input_file.replace(".sra","_2.fastq") |
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|
807 else: |
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|
808 core_id=input_file.split(".fastq")[0].split(".fq")[0] |
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|
809 for_fq=core_id+"_1.fastq" |
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|
810 rev_fq=core_id+"_2.fastq" |
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811 if input_file.endswith(".gz"): |
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812 subprocess.check_call("gzip -dc "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True) |
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813 else: |
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814 subprocess.check_call("cat "+input_file+" | "+dirpath+"/deinterleave_fastq.sh "+for_fq+" "+rev_fq,shell=True) |
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815 elif data_type=="2": |
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816 for_fq=input_file[0].split("/")[-1] |
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817 rev_fq=input_file[1].split("/")[-1] |
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818 elif data_type=="3": |
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819 input_file=input_file[0].split("/")[-1] |
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820 if input_file.endswith(".sra"): |
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821 subprocess.check_call("fastq-dump --split-files "+input_file,shell=True) |
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822 for_fq=input_file.replace(".sra","_1.fastq") |
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823 else: |
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824 for_fq=input_file |
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825 elif data_type in ["4","5","6"]: |
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826 for_fq=input_file[0].split("/")[-1] |
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827 os.chdir("..") |
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828 return for_fq,rev_fq |
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829 |
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830 def predict_O_and_H_types(Final_list,Final_list_passed,new_fasta): |
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831 #get O and H types from Final_list from blast parsing; allele mode |
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832 from Bio import SeqIO |
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833 fliC_choice="-" |
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834 fljB_choice="-" |
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835 fliC_contig="NA" |
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836 fljB_contig="NA" |
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837 fliC_region=set([0]) |
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838 fljB_region=set([0,]) |
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839 fliC_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length |
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840 fljB_length=0 #can be changed to coverage in future; in 03292019, changed to ailgned length |
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841 O_choice="-"#no need to decide O contig for now, should be only one |
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842 O_choice,O_nodes,special_gene_list,O_nodes_roles,contamination_O,Otypes_uniq=decide_O_type_and_get_special_genes(Final_list,Final_list_passed)#decide the O antigen type and also return special-gene-list for further identification |
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843 O_choice=O_choice.split("-")[-1].strip() |
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844 if (O_choice=="1,3,19" and len(O_nodes_roles)==1 and "1,3,19" in O_nodes_roles[0][0]) or O_choice=="": |
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845 O_choice="-" |
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846 H_contig_roles=decide_contig_roles_for_H_antigen(Final_list,Final_list_passed)#decide the H antigen contig is fliC or fljB |
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847 #add alignment locations, used for further selection, 03312019 |
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848 for i in range(len(H_contig_roles)): |
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849 x=H_contig_roles[i] |
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850 for y in Final_list_passed: |
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851 if x[1] in y[0] and y[0].startswith(x[0]): |
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852 H_contig_roles[i]+=H_contig_roles[i]+(y[-1],) |
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853 break |
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854 log_file=open("SeqSero_log.txt","a") |
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855 extract_file=open("Extracted_antigen_alleles.fasta","a") |
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856 handle_fasta=list(SeqIO.parse(new_fasta,"fasta")) |
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857 |
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858 #print("O_contigs:") |
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859 log_file.write("O_contigs:\n") |
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860 extract_file.write("#Sequences with antigen signals (if the micro-assembled contig only covers the flanking region, it will not be used for contamination analysis)\n") |
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861 extract_file.write("#O_contigs:\n") |
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862 for x in O_nodes_roles: |
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863 if "O-1,3,19_not_in_3,10" not in x[0]:#O-1,3,19_not_in_3,10 is just a small size marker |
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864 #print(x[0].split("___")[-1],x[0].split("__")[0],"blast score:",x[1],"identity%:",str(round(x[2]*100,2))+"%",str(min(x[-1]))+" to "+str(max(x[-1]))) |
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865 log_file.write(x[0].split("___")[-1]+" "+x[0].split("__")[0]+"; "+"blast score: "+str(x[1])+" identity%: "+str(round(x[2]*100,2))+"%; alignment from "+str(min(x[-1]))+" to "+str(max(x[-1]))+" of antigen\n") |
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866 title=">"+x[0].split("___")[-1]+" "+x[0].split("__")[0]+"; "+"blast score: "+str(x[1])+" identity%: "+str(round(x[2]*100,2))+"%; alignment from "+str(min(x[-1]))+" to "+str(max(x[-1]))+" of antigen\n" |
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867 seqs="" |
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868 for z in handle_fasta: |
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869 if x[0].split("___")[-1]==z.description: |
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870 seqs=str(z.seq) |
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871 extract_file.write(title+seqs+"\n") |
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872 if len(H_contig_roles)!=0: |
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873 highest_H_coverage=max([float(x[1].split("_cov_")[-1].split("_")[0]) for x in H_contig_roles]) #less than highest*0.1 would be regarded as contamination and noises, they will still be considered in contamination detection and logs, but not used as final serotype output |
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874 else: |
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875 highest_H_coverage=0 |
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876 for x in H_contig_roles: |
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877 #if multiple choices, temporately select the one with longest length for now, will revise in further change |
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878 if "fliC" == x[0] and len(x[-1])>=fliC_length and x[1] not in O_nodes and float(x[1].split("_cov_")[-1].split("_")[0])>highest_H_coverage*0.13:#remember to avoid the effect of O-type contig, so should not in O_node list |
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879 fliC_contig=x[1] |
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880 fliC_length=len(x[-1]) |
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881 elif "fljB" == x[0] and len(x[-1])>=fljB_length and x[1] not in O_nodes and float(x[1].split("_cov_")[-1].split("_")[0])>highest_H_coverage*0.13: |
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882 fljB_contig=x[1] |
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883 fljB_length=len(x[-1]) |
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884 for x in Final_list_passed: |
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885 if fliC_choice=="-" and "fliC_" in x[0] and fliC_contig in x[0]: |
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886 fliC_choice=x[0].split("_")[1] |
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887 elif fljB_choice=="-" and "fljB_" in x[0] and fljB_contig in x[0]: |
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888 fljB_choice=x[0].split("_")[1] |
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889 elif fliC_choice!="-" and fljB_choice!="-": |
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890 break |
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891 #now remove contigs not in middle core part |
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892 first_allele="NA" |
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cstrittmatter
parents:
diff
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|
893 first_allele_percentage=0 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
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parents:
diff
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|
894 for x in Final_list: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
895 if x[0].startswith("fliC") or x[0].startswith("fljB"): |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
896 first_allele=x[0].split("__")[0] #used to filter those un-middle contigs |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
897 first_allele_percentage=x[2] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
898 break |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
899 additional_contigs=[] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
900 for x in Final_list: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
901 if first_allele in x[0]: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
902 if (fliC_contig == x[0].split("___")[-1]): |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
903 fliC_region=x[3] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
904 elif fljB_contig!="NA" and (fljB_contig == x[0].split("___")[-1]): |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
905 fljB_region=x[3] |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
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|
906 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
907 if x[1]*1.1>int(x[0].split("___")[1].split("_")[3]):#loose threshold by multiplying 1.1 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
908 additional_contigs.append(x) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
909 #else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
910 #print x[:3] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
911 #we can just use the fljB region (or fliC depends on size), no matter set() or contain a large locations (without middle part); however, if none of them is fully assembled, use 500 and 1200 as conservative cut-off |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
912 if first_allele_percentage>0.9: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
913 if len(fliC_region)>len(fljB_region) and (max(fljB_region)-min(fljB_region))>1000: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
914 target_region=fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region)))) #fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region)))) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
915 elif len(fliC_region)<len(fljB_region) and (max(fliC_region)-min(fliC_region))>1000: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
916 target_region=fliC_region|(fljB_region-set(range(min(fliC_region),max(fliC_region)))) #fljB_region|(fliC_region-set(range(min(fljB_region),max(fljB_region)))) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
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|
917 else: |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
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|
918 target_region=set()#doesn't do anything |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
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|
919 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
920 target_region=set()#doesn't do anything |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
921 #print(target_region) |
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planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
922 #print(additional_contigs) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
923 target_region2=set(list(range(0,525))+list(range(1200,1700)))#I found to use 500 to 1200 as special region would be best |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
924 target_region=target_region2|target_region |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
925 for x in additional_contigs: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
926 removal=0 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
927 contig_length=int(x[0].split("___")[1].split("length_")[-1].split("_")[0]) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
928 if fljB_contig not in x[0] and fliC_contig not in x[0] and len(target_region&x[3])/float(len(x[3]))>0.65 and contig_length*0.5<len(x[3])<contig_length*1.5: #consider length and alignment length for now, but very loose,0.5 and 1.5 as cut-off |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
929 removal=1 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
930 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
931 if first_allele_percentage > 0.9 and float(x[0].split("__")[1].split("___")[0])*x[2]/len(x[-1])>0.96:#if high similiarity with middle part of first allele (first allele >0.9, already cover middle part) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
932 removal=1 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
933 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
934 pass |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
935 if removal==1: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
936 for y in H_contig_roles: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
937 if y[1] in x[0]: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
938 H_contig_roles.remove(y) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
939 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
940 pass |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
941 #print(x[:3],contig_length,len(target_region&x[3])/float(len(x[3])),contig_length*0.5,len(x[3]),contig_length*1.5) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
942 #end of removing none-middle contigs |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
943 #print("H_contigs:") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
944 log_file.write("H_contigs:\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
945 extract_file.write("#H_contigs:\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
946 H_contig_stat=[] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
947 H1_cont_stat={} |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
948 H2_cont_stat={} |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
949 for i in range(len(H_contig_roles)): |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
950 x=H_contig_roles[i] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
951 a=0 |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
952 for y in Final_list_passed: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
953 if x[1] in y[0] and y[0].startswith(x[0]): |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
954 if "first" in y[0] or "last" in y[0]: #this is the final filter to decide it's fliC or fljB, if can't pass, then can't decide |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
955 for y in Final_list_passed: #it's impossible to has the "first" and "last" allele as prediction, so re-do it |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
956 if x[1] in y[0]:#it's very possible to be third phase allele, so no need to make it must be fliC or fljB |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
957 #print(x[1],"can't_decide_fliC_or_fljB",y[0].split("_")[1],"blast_score:",y[1],"identity%:",str(round(y[2]*100,2))+"%",str(min(y[-1]))+" to "+str(max(y[-1]))) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
958 log_file.write(x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
959 H_contig_roles[i]="can't decide fliC or fljB, may be third phase" |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
960 title=">"+x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antiten\n" |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
961 seqs="" |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
962 for z in handle_fasta: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
963 if x[1]==z.description: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
964 seqs=str(z.seq) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
965 extract_file.write(title+seqs+"\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
966 break |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
967 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
968 #print(x[1],x[0],y[0].split("_")[1],"blast_score:",y[1],"identity%:",str(round(y[2]*100,2))+"%",str(min(y[-1]))+" to "+str(max(y[-1]))) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
969 log_file.write(x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
970 title=">"+x[1]+" "+x[0]+" "+y[0].split("_")[1]+"; "+"blast score: "+str(y[1])+" identity%: "+str(round(y[2]*100,2))+"%; alignment from "+str(min(y[-1]))+" to "+str(max(y[-1]))+" of antigen\n" |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
971 seqs="" |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
972 for z in handle_fasta: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
973 if x[1]==z.description: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
974 seqs=str(z.seq) |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
975 extract_file.write(title+seqs+"\n") |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
976 if x[0]=="fliC": |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
977 if y[0].split("_")[1] not in H1_cont_stat: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
978 H1_cont_stat[y[0].split("_")[1]]=y[2] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
979 else: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
980 H1_cont_stat[y[0].split("_")[1]]+=y[2] |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
981 if x[0]=="fljB": |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
|
982 if y[0].split("_")[1] not in H2_cont_stat: |
fc22ec8e924e
planemo upload commit 6b0a9d0f0ef4bdb0c2e2c54070b510ff28125f7a
cstrittmatter
parents:
diff
changeset
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983 H2_cont_stat[y[0].split("_")[1]]=y[2] |
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984 else: |
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985 H2_cont_stat[y[0].split("_")[1]]+=y[2] |
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986 break |
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987 #detect contaminations |
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988 #print(H1_cont_stat) |
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989 #print(H2_cont_stat) |
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990 H1_cont_stat_list=[x for x in H1_cont_stat if H1_cont_stat[x]>0.2] |
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991 H2_cont_stat_list=[x for x in H2_cont_stat if H2_cont_stat[x]>0.2] |
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992 contamination_H="" |
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993 if len(H1_cont_stat_list)>1 or len(H2_cont_stat_list)>1: |
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994 contamination_H="potential contamination from H antigen signals" |
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995 elif len(H2_cont_stat_list)==1 and fljB_contig=="NA": |
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996 contamination_H="potential contamination from H antigen signals, uncommon weak fljB signals detected" |
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997 #get additional antigens |
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998 """ |
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999 if ("O-9,46_wbaV" in O_list or "O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254" in O_list) and O_list_less_contamination[0].startswith("O-9,"):#not sure should use and float(O9_wbaV)/float(num_1) > 0.1 |
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1000 if "O-9,46_wzy" in O_list:#and float(O946_wzy)/float(num_1) > 0.1 |
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1001 O_choice="O-9,46" |
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1002 #print "$$$Most possilble Otype: O-9,46" |
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1003 elif "O-9,46,27_partial_wzy" in O_list:#and float(O94627)/float(num_1) > 0.1 |
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1004 O_choice="O-9,46,27" |
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1005 #print "$$$Most possilble Otype: O-9,46,27" |
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1006 elif ("O-3,10_wzx" in O_list) and ("O-9,46_wzy" in O_list) and (O_list[0].startswith("O-3,10") or O_list_less_contamination[0].startswith("O-9,46_wzy")):#and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1 |
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1007 if "O-3,10_not_in_1,3,19" in O_list:#and float(O310_no_1319)/float(num_1) > 0.1 |
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1008 O_choice="O-3,10" |
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1009 #print "$$$Most possilble Otype: O-3,10 (contain O-3,10_not_in_1,3,19)" |
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1010 else: |
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1011 O_choice="O-1,3,19" |
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1012 #print "$$$Most possilble Otype: O-1,3,19 (not contain O-3,10_not_in_1,3,19)" |
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1013 ### end of special test for O9,46 and O3,10 family |
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1014 |
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1015 if O_choice=="O-9,46,27" or O_choice=="O-3,10" or O_choice=="O-1,3,19": |
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1016 if len(Otypes_uniq)>2: |
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1017 contamination_O="potential contamination from O antigen signals" |
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1018 else: |
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1019 if len(Otypes_uniq)>1: |
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1020 if O_choice=="O-4" and len(Otypes_uniq)==2 and "O-9,46,27" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund |
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1021 contamination_O="" |
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1022 elif O_choice=="O-9,46" and len(Otypes_uniq)==2 and "O-9,46_wbaV" in Otypes_uniq and "O-9,46_wzy" in Otypes_uniq: #for special 4,12,27 case such as Bredeney and Schwarzengrund |
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1023 contamination_O="" |
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1024 """ |
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1025 additonal_antigents=[] |
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1026 #print(contamination_O) |
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1027 #print(contamination_H) |
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1028 log_file.write(contamination_O+"\n") |
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1029 log_file.write(contamination_H+"\n") |
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1030 log_file.close() |
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1031 return O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H,Otypes_uniq,H1_cont_stat_list,H2_cont_stat_list |
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1032 |
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1033 def get_input_K(input_file,lib_dict,data_type,k_size): |
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1034 #kmer mode; get input_Ks from dict and data_type |
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1035 kmers = [] |
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1036 for h in lib_dict: |
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1037 kmers += lib_dict[h] |
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1038 if data_type == '4': |
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1039 input_Ks = target_multifasta_kmerizer(input_file, k_size, set(kmers)) |
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1040 elif data_type == '1' or data_type == '2' or data_type == '3':#set it for now, will change later |
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1041 input_Ks = target_read_kmerizer(input_file, k_size, set(kmers)) |
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1042 elif data_type == '5':#minion_2d_fasta |
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1043 input_Ks = minion_fasta_kmerizer(input_file, k_size, set(kmers)) |
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1044 if data_type == '6':#minion_2d_fastq |
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1045 input_Ks = minion_fastq_kmerizer(input_file, k_size, set(kmers)) |
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1046 return input_Ks |
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1047 |
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1048 def get_kmer_dict(lib_dict,input_Ks): |
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1049 #kmer mode; get predicted types |
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1050 O_dict = {} |
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1051 H_dict = {} |
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1052 Special_dict = {} |
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1053 for h in lib_dict: |
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1054 score = (len(lib_dict[h] & input_Ks) / len(lib_dict[h])) * 100 |
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1055 if score > 1: # Arbitrary cut-off for similarity score very low but seems necessary to detect O-3,10 in some cases |
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1056 if h.startswith('O-') and score > 25: |
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1057 O_dict[h] = score |
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1058 if h.startswith('fl') and score > 40: |
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1059 H_dict[h] = score |
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1060 if (h[:2] != 'fl') and (h[:2] != 'O-'): |
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1061 Special_dict[h] = score |
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1062 return O_dict,H_dict,Special_dict |
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1063 |
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1064 def call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir): |
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1065 log_file=open("SeqSero_log.txt","a") |
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1066 log_file.write("O_scores:\n") |
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1067 #call O: |
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1068 highest_O = '-' |
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1069 if len(O_dict) == 0: |
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1070 pass |
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1071 else: |
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1072 for x in O_dict: |
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1073 log_file.write(x+"\t"+str(O_dict[x])+"\n") |
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1074 if ('O-9,46_wbaV__1002' in O_dict and O_dict['O-9,46_wbaV__1002']>70) or ("O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002" in O_dict and O_dict['O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002']>70): # not sure should use and float(O9_wbaV)/float(num_1) > 0.1 |
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1075 #if 'O-9,46_wzy__1191' in O_dict or "O-9,46_wzy_partial__216" in O_dict: # and float(O946_wzy)/float(num_1) > 0.1 |
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1076 #modified to fix miscall of O-9,46 |
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1077 if ('O-9,46_wzy__1191' in O_dict and O_dict['O-9,46_wzy__1191']>40) or ("O-9,46_wzy_partial__216" in O_dict and O_dict["O-9,46_wzy_partial__216"]>40): # and float(O946_wzy)/float(num_1) > 0.1 |
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1078 highest_O = "O-9,46" |
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1079 elif "O-9,46,27_partial_wzy__1019" in O_dict: # and float(O94627)/float(num_1) > 0.1 |
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1080 highest_O = "O-9,46,27" |
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1081 else: |
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1082 highest_O = "O-9" # next, detect O9 vs O2? |
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1083 O2 = 0 |
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1084 O9 = 0 |
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1085 for z in Special_dict: |
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1086 if "tyr-O-9" in z: |
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1087 O9 = float(Special_dict[z]) |
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1088 if "tyr-O-2" in z: |
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1089 O2 = float(Special_dict[z]) |
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1090 if O2 > O9: |
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1091 highest_O = "O-2" |
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1092 elif ("O-3,10_wzx__1539" in O_dict) and ( |
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1093 "O-9,46_wzy__1191" in O_dict |
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1094 ): # and float(O310_wzx)/float(num_1) > 0.1 and float(O946_wzy)/float(num_1) > 0.1 |
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1095 if "O-3,10_not_in_1,3,19__1519" in O_dict: # and float(O310_no_1319)/float(num_1) > 0.1 |
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1096 highest_O = "O-3,10" |
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1097 else: |
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1098 highest_O = "O-1,3,19" |
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1099 ### end of special test for O9,46 and O3,10 family |
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1100 else: |
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1101 try: |
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1102 max_score = 0 |
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1103 for x in O_dict: |
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1104 if float(O_dict[x]) >= max_score: |
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1105 max_score = float(O_dict[x]) |
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1106 #highest_O = x.split("_")[0] |
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1107 # ed_SL_12182019: modified to fix the O-9,46 error example1 |
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1108 if (x == 'O-9,46_wbaV__1002' or x == 'O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002') and ('O-9,46_wzy__1191' not in O_dict and 'O-9,46_wzy_partial__216' not in O_dict): |
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1109 highest_O = "O-9" |
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1110 else: |
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1111 highest_O = x.split("_")[0] |
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1112 if highest_O == "O-1,3,19": |
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1113 highest_O = '-' |
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1114 max_score = 0 |
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1115 for x in O_dict: |
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1116 if x == 'O-1,3,19_not_in_3,10__130': |
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1117 pass |
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1118 else: |
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1119 if float(O_dict[x]) >= max_score: |
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1120 max_score = float(O_dict[x]) |
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1121 #highest_O = x.split("_")[0] |
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1122 # ed_SL_12182019: modified to fix the O-9,46 error example1 |
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1123 if (x == 'O-9,46_wbaV__1002' or x == 'O-9,46_wbaV-from-II-9,12:z29:1,5-SRR1346254__1002') and ('O-9,46_wzy__1191' not in O_dict and 'O-9,46_wzy_partial__216' not in O_dict): |
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1124 highest_O = "O-9" |
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1125 else: |
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1126 highest_O = x.split("_")[0] |
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1127 except: |
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1128 pass |
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1129 #call_fliC: |
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1130 if len(H_dict)!=0: |
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1131 highest_H_score_both_BC=H_dict[max(H_dict.keys(), key=(lambda k: H_dict[k]))] #used to detect whether fljB existed or not |
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1132 else: |
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1133 highest_H_score_both_BC=0 |
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1134 highest_fliC = '-' |
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1135 highest_fliC_raw = '-' |
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1136 highest_Score = 0 |
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1137 log_file.write("\nH_scores:\n") |
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1138 for s in H_dict: |
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1139 log_file.write(s+"\t"+str(H_dict[s])+"\n") |
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1140 if s.startswith('fliC'): |
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1141 if float(H_dict[s]) > highest_Score: |
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1142 highest_fliC = s.split('_')[1] |
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1143 highest_fliC_raw = s |
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1144 highest_Score = float(H_dict[s]) |
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1145 #call_fljB |
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1146 highest_fljB = '-' |
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1147 highest_fljB_raw = '-' |
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1148 highest_Score = 0 |
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1149 for s in H_dict: |
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1150 if s.startswith('fljB'): |
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1151 if float(H_dict[s]) > highest_Score and float(H_dict[s]) > highest_H_score_both_BC * 0.65: #fljB is special, so use highest_H_score_both_BC to give a general estimate of coverage, currently 0.65 seems pretty good; the reason use a high (0.65) is some fliC and fljB shared with each other |
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1152 #highest_fljB = s.split('_')[1] |
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1153 #highest_fljB_raw = s |
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1154 #highest_Score = float(H_dict[s]) |
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1155 if s.split('_')[1]!=highest_fliC: |
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1156 highest_fljB = s.split('_')[1] |
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1157 highest_fljB_raw = s |
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1158 highest_Score = float(H_dict[s]) |
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1159 log_file.write("\nSpecial_scores:\n") |
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1160 for s in Special_dict: |
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1161 log_file.write(s+"\t"+str(Special_dict[s])+"\n") |
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1162 log_file.close() |
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1163 return highest_O,highest_fliC,highest_fljB |
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1164 |
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1165 def get_temp_file_names(for_fq,rev_fq): |
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1166 #seqsero2 -a; get temp file names |
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1167 sam=for_fq+".sam" |
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1168 bam=for_fq+".bam" |
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1169 sorted_bam=for_fq+"_sorted.bam" |
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1170 mapped_fq1=for_fq+"_mapped.fq" |
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1171 mapped_fq2=rev_fq+"_mapped.fq" |
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1172 combined_fq=for_fq+"_combined.fq" |
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1173 for_sai=for_fq+".sai" |
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1174 rev_sai=rev_fq+".sai" |
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1175 return sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai |
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1176 |
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1177 def map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode): |
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1178 #seqsero2 -a; do mapping and sort |
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1179 print("building database...") |
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1180 subprocess.check_call("bwa index "+database+ " 2>> data_log.txt",shell=True) |
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1181 print("mapping...") |
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1182 if mapping_mode=="mem": |
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1183 subprocess.check_call("bwa mem -k 17 -t "+threads+" "+database+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True) |
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1184 elif mapping_mode=="sam": |
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1185 if fnameB!="": |
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1186 subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True) |
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1187 subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameB+" > "+rev_sai+ " 2>> data_log.txt",shell=True) |
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1188 subprocess.check_call("bwa sampe "+database+" "+for_sai+" "+ rev_sai+" "+fnameA+" "+fnameB+" > "+sam+ " 2>> data_log.txt",shell=True) |
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1189 else: |
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1190 subprocess.check_call("bwa aln -t "+threads+" "+database+" "+fnameA+" > "+for_sai+ " 2>> data_log.txt",shell=True) |
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1191 subprocess.check_call("bwa samse "+database+" "+for_sai+" "+for_fq+" > "+sam) |
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1192 subprocess.check_call("samtools view -@ "+threads+" -F 4 -Sh "+sam+" > "+bam,shell=True) |
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1193 ### check the version of samtools then use differnt commands |
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1194 samtools_version=subprocess.Popen(["samtools"],stdout=subprocess.PIPE,stderr=subprocess.PIPE) |
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1195 out, err = samtools_version.communicate() |
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1196 version = str(err).split("ersion:")[1].strip().split(" ")[0].strip() |
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1197 print("check samtools version:",version) |
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1198 ### end of samtools version check and its analysis |
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1199 if LooseVersion(version)<=LooseVersion("1.2"): |
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1200 subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" "+fnameA+"_sorted",shell=True) |
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1201 else: |
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1202 subprocess.check_call("samtools sort -@ "+threads+" -n "+bam+" >"+sorted_bam,shell=True) |
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1203 |
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1204 def extract_mapped_reads_and_do_assembly_and_blast(current_time,sorted_bam,combined_fq,mapped_fq1,mapped_fq2,threads,fnameA,fnameB,database,mapping_mode): |
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1205 #seqsero2 -a; extract, assembly and blast |
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1206 subprocess.check_call("bamToFastq -i "+sorted_bam+" -fq "+combined_fq,shell=True) |
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1207 #print("fnameA:",fnameA) |
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1208 #print("fnameB:",fnameB) |
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1209 if fnameB!="": |
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1210 subprocess.check_call("bamToFastq -i "+sorted_bam+" -fq "+mapped_fq1+" -fq2 "+mapped_fq2 + " 2>> data_log.txt",shell=True)#2> /dev/null if want no output |
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1211 else: |
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1212 pass |
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1213 outdir=current_time+"_temp" |
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1214 print("assembling...") |
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1215 if int(threads)>4: |
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1216 t="4" |
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1217 else: |
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1218 t=threads |
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1219 if os.path.getsize(combined_fq)>100 and (fnameB=="" or os.path.getsize(mapped_fq1)>100):#if not, then it's "-:-:-" |
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1220 if fnameB!="": |
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1221 subprocess.check_call("spades.py --careful --pe1-s "+combined_fq+" --pe1-1 "+mapped_fq1+" --pe1-2 "+mapped_fq2+" -t "+t+" -o "+outdir+ " >> data_log.txt 2>&1",shell=True) |
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1222 else: |
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1223 subprocess.check_call("spades.py --careful --pe1-s "+combined_fq+" -t "+t+" -o "+outdir+ " >> data_log.txt 2>&1",shell=True) |
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1224 new_fasta=fnameA+"_"+database+"_"+mapping_mode+".fasta" |
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1225 #new_fasta=fnameA+"_"+database.split('/')[-1]+"_"+mapping_mode+".fasta" # change path to databse for packaging |
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1226 subprocess.check_call("mv "+outdir+"/contigs.fasta "+new_fasta+ " 2> /dev/null",shell=True) |
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1227 #os.system("mv "+outdir+"/scaffolds.fasta "+new_fasta+ " 2> /dev/null") contigs.fasta |
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1228 subprocess.check_call("rm -rf "+outdir+ " 2> /dev/null",shell=True) |
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1229 print("blasting...","\n") |
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1230 xmlfile="blasted_output.xml"#fnameA+"-extracted_vs_"+database+"_"+mapping_mode+".xml" |
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1231 subprocess.check_call('makeblastdb -in '+new_fasta+' -out '+new_fasta+'_db '+'-dbtype nucl >> data_log.txt 2>&1',shell=True) #temp.txt is to forbid the blast result interrupt the output of our program###1/27/2015 |
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1232 subprocess.check_call("blastn -query "+database+" -db "+new_fasta+"_db -out "+xmlfile+" -outfmt 5 >> data_log.txt 2>&1",shell=True)###1/27/2015; 08272018, remove "-word_size 10" |
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1233 else: |
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1234 xmlfile="NA" |
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1235 return xmlfile,new_fasta |
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1236 |
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1237 def judge_subspecies(fnameA,dirpath): |
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1238 #seqsero2 -a; judge subspecies on just forward raw reads fastq |
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1239 samid_strcmd = "python " + dirpath + "/../SalmID.py -i "+fnameA |
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1240 print(samid_strcmd) |
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1241 #seqsero2 -a; judge subspecies on just forward raw reads fastq |
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1242 #salmID_output=subprocess.Popen("SalmID.py -i "+fnameA,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) |
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1243 salmID_output=subprocess.Popen(samid_strcmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) |
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1244 out, err = salmID_output.communicate() |
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1245 out=out.decode("utf-8") |
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1246 print(out) |
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1247 print(err) |
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1248 file=open("data_log.txt","a") |
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1249 file.write(out) |
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1250 file.close() |
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1251 salm_species_scores=out.split("\n")[1].split("\t")[6:] |
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1252 salm_species_results=out.split("\n")[0].split("\t")[6:] |
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1253 max_score=0 |
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1254 max_score_index=1 #default is 1, means "I" |
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1255 for i in range(len(salm_species_scores)): |
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1256 if max_score<float(salm_species_scores[i]): |
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1257 max_score=float(salm_species_scores[i]) |
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1258 max_score_index=i |
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1259 prediction=salm_species_results[max_score_index].split(".")[1].strip().split(" ")[0] |
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1260 #if float(out.split("\n")[1].split("\t")[4]) > float(out.split("\n")[1].split("\t")[5]): #bongori and enterica compare |
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1261 if float(out.split("\n")[1].split("\t")[4]) > 10 and float(out.split("\n")[1].split("\t")[4]) > float(out.split("\n")[1].split("\t")[5]): ## ed_SL_0318: change SalmID_ssp_threshold |
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1262 prediction="bongori" #if not, the prediction would always be enterica, since they are located in the later part |
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1263 #if max_score<10: ## ed_SL_0318: change SalmID_ssp_threshold |
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1264 if max_score<60: |
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1265 prediction="-" |
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1266 return prediction |
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1267 |
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1268 def judge_subspecies_Kmer(Special_dict): |
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1269 #seqsero2 -k; |
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1270 max_score=0 |
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1271 prediction="-" #default should be I |
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1272 for x in Special_dict: |
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1273 #if "mer" in x: ## ed_SL_0318: change ssp_threshold |
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1274 if "mer" in x and float(Special_dict[x]) > 60: |
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1275 if max_score<float(Special_dict[x]): |
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1276 max_score=float(Special_dict[x]) |
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1277 prediction=x.split("_")[-1].strip() |
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1278 if x.split("_")[-1].strip()=="bongori" and float(Special_dict[x])>95:#if bongori already, then no need to test enterica |
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1279 prediction="bongori" |
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1280 break |
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1281 return prediction |
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1282 |
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1283 ## ed_SL_11232019: add notes for missing antigen |
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1284 def check_antigens(ssp,O_antigen,H1_antigen,H2_antigen,NA_note): |
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1285 antigen_note = '' |
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1286 if ssp != '-': |
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1287 if O_antigen != '-' and H1_antigen == '-' and H2_antigen == '-': # O:-:- |
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1288 antigen_note = 'H antigens were not detected. This is an atypical result that should be further investigated. Most Salmonella strains have at least fliC, encoding the Phase 1 H antigen, even if it is not expressed. ' |
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1289 NA_note = '' |
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1290 elif O_antigen != '-' and H1_antigen == '-' and H2_antigen != '-': # O:-:H2 |
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1291 antigen_note = 'fliC was not detected. This is an atypical result that should be further investigated. Most Salmonella strains have fliC, encoding the Phase 1 H antigen, even if it is not expressed. ' |
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1292 NA_note = '' |
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1293 elif O_antigen == '-' and H1_antigen != '-': # -:H1:X |
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1294 antigen_note = 'O antigen was not detected. This result may be due to a rough strain that has deleted the rfb region. For raw reads input, the k-mer workflow is sometimes more sensitive than the microassembly workflow in detecting O antigen. Caution should be used with this approach because the k-mer result may be due to low levels of contamination. ' |
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1295 NA_note = '' |
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1296 elif O_antigen == '-' and H1_antigen == '-' and H2_antigen == '-': # -:-:- |
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1297 antigen_note = 'No serotype antigens were detected. This is an atypical result that should be further investigated. ' |
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1298 NA_note = '' |
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1299 else: |
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1300 antigen_note = 'The input genome cannot be identified as Salmonella. Check the input for taxonomic ID, contamination, or sequencing quality. ' |
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1301 NA_note = '' |
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1302 # if [O_antigen, H1_antigen, H2_antigen].count('-') >= 2: |
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1303 # antigen_note = 'No subspecies marker was detected and less than 2 serotype antigens were detected; further, this genome was not identified as Salmonella. This is an atypical result that should be further investigated. ' |
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1304 # else: |
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1305 # antigen_note = 'No subspecies marker was detected. This genome may not be Salmonella. This is an atypical result that should be further investigated. ' |
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1306 return (antigen_note,NA_note) |
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1307 |
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1308 def main(): |
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1309 #combine SeqSeroK and SeqSero2, also with SalmID |
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1310 args = parse_args() |
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1311 input_file = args.i |
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1312 data_type = args.t |
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1313 analysis_mode = args.m |
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1314 mapping_mode=args.b |
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1315 threads=args.p |
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1316 make_dir=args.d |
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1317 clean_mode=args.c |
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1318 sample_name=args.n |
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1319 ingore_header=args.s |
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1320 k_size=27 #will change for bug fixing |
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1321 dirpath = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) |
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1322 ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019: add ex_dir for packaging |
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1323 seqsero2_db=ex_dir+"/H_and_O_and_specific_genes.fasta" # ed_SL_11092019: change path to database for packaging |
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1324 database="H_and_O_and_specific_genes.fasta" |
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1325 note="Note: " |
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1326 NA_note="This predicted serotype is not in the Kauffman-White scheme. " # ed_SL_09272019: add for new output format |
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1327 if len(sys.argv)==1: |
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1328 subprocess.check_call(dirpath+"/SeqSero2_package.py -h",shell=True)#change name of python file |
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1329 else: |
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1330 request_id = time.strftime("%m_%d_%Y_%H_%M_%S", time.localtime()) |
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1331 request_id += str(random.randint(1, 10000000)) |
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1332 if make_dir is None: |
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1333 make_dir="SeqSero_result_"+request_id |
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1334 make_dir=os.path.abspath(make_dir) |
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1335 if os.path.isdir(make_dir): |
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1336 pass |
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1337 else: |
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1338 subprocess.check_call("mkdir -p "+make_dir,shell=True) |
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1339 #subprocess.check_call("cp "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True) |
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1340 #subprocess.check_call("ln -sr "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True) |
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1341 subprocess.check_call("ln -f -s "+seqsero2_db+" "+" ".join(input_file)+" "+make_dir,shell=True) # ed_SL_11092019: change path to database for packaging |
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1342 #subprocess.check_call("ln -f -s "+dirpath+"/"+database+" "+" ".join(input_file)+" "+make_dir,shell=True) ### use -f option to force the replacement of links, remove -r and use absolute path instead to avoid link issue (use 'type=os.path.abspath' in -i argument). |
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1343 ############################begin the real analysis |
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1344 if analysis_mode=="a": |
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1345 if data_type in ["1","2","3"]:#use allele mode |
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1346 for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath) |
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1347 os.chdir(make_dir) |
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1348 ###add a function to tell input files |
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1349 fnameA=for_fq.split("/")[-1] |
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1350 fnameB=rev_fq.split("/")[-1] |
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1351 current_time=time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) |
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1352 sam,bam,sorted_bam,mapped_fq1,mapped_fq2,combined_fq,for_sai,rev_sai=get_temp_file_names(fnameA,fnameB) #get temp files id |
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1353 map_and_sort(threads,database,fnameA,fnameB,sam,bam,for_sai,rev_sai,sorted_bam,mapping_mode) #do mapping and sort |
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1354 ### avoid error out when micro assembly fails. ed_SL_03172020 |
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1355 try: |
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1356 xmlfile,new_fasta=extract_mapped_reads_and_do_assembly_and_blast(current_time,sorted_bam,combined_fq,mapped_fq1,mapped_fq2,threads,fnameA,fnameB,database,mapping_mode) #extract the mapped reads and do micro assembly and blast |
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1357 except (UnboundLocalError, subprocess.CalledProcessError): |
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1358 xmlfile="NA" |
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1359 H1_cont_stat_list=[] |
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1360 H2_cont_stat_list=[] |
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1361 ### |
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1362 if xmlfile=="NA": |
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1363 O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H=("-","-","-",[],"","") |
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1364 else: |
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1365 Final_list=xml_parse_score_comparision_seqsero(xmlfile) #analyze xml and get parsed results |
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1366 file=open("data_log.txt","a") |
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1367 for x in Final_list: |
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1368 file.write("\t".join(str(y) for y in x)+"\n") |
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1369 file.close() |
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1370 Final_list_passed=[x for x in Final_list if float(x[0].split("_cov_")[1].split("_")[0])>=0.9 and (x[1]>=int(x[0].split("__")[1]) or x[1]>=int(x[0].split("___")[1].split("_")[3]) or x[1]>1000)] |
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1371 O_choice,fliC_choice,fljB_choice,special_gene_list,contamination_O,contamination_H,Otypes_uniq,H1_cont_stat_list,H2_cont_stat_list=predict_O_and_H_types(Final_list,Final_list_passed,new_fasta) #predict O, fliC and fljB |
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1372 subspecies=judge_subspecies(fnameA,dirpath) #predict subspecies |
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1373 ###output |
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1374 predict_form,predict_sero,star,star_line,claim=seqsero_from_formula_to_serotypes(O_choice,fliC_choice,fljB_choice,special_gene_list,subspecies) |
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1375 claim="" #04132019, disable claim for new report requirement |
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1376 contamination_report="" |
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1377 H_list=["fliC_"+x for x in H1_cont_stat_list if len(x)>0]+["fljB_"+x for x in H2_cont_stat_list if len(x)>0] |
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1378 if contamination_O!="" and contamination_H=="": |
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1379 contamination_report="#Potential inter-serotype contamination detected from O antigen signals. All O-antigens detected:"+"\t".join(Otypes_uniq)+"." |
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1380 elif contamination_O=="" and contamination_H!="": |
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1381 contamination_report="#Potential inter-serotype contamination detected or potential thrid H phase from H antigen signals. All H-antigens detected:"+"\t".join(H_list)+"." |
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1382 elif contamination_O!="" and contamination_H!="": |
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1383 contamination_report="#Potential inter-serotype contamination detected from both O and H antigen signals.All O-antigens detected:"+"\t".join(Otypes_uniq)+". All H-antigens detected:"+"\t".join(H_list)+"." |
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1384 if contamination_report!="": |
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1385 #contamination_report="potential inter-serotype contamination detected (please refer below antigen signal report for details)." #above contamination_reports are for back-up and bug fixing #web-based mode need to be re-used, 04132019 |
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1386 contamination_report="Co-existence of multiple serotypes detected, indicating potential inter-serotype contamination. See 'Extracted_antigen_alleles.fasta' for detected serotype determinant alleles. " |
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1387 #claim="\n"+open("Extracted_antigen_alleles.fasta","r").read()#used to store H and O antigen sequeences #04132019, need to change if using web-version |
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1388 #if contamination_report+star_line+claim=="": #0413, new output style |
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1389 # note="" |
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1390 #else: |
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1391 # note="Note:" |
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1392 |
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1393 ### ed_SL_11232019: add notes for missing antigen |
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1394 if O_choice=="": |
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1395 O_choice="-" |
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1396 antigen_note,NA_note=check_antigens(subspecies,O_choice,fliC_choice,fljB_choice,NA_note) |
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1397 if sample_name: |
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1398 print ("Sample name:\t"+sample_name) |
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1399 ### |
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1400 |
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1401 if clean_mode: |
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1402 subprocess.check_call("rm -rf ../"+make_dir,shell=True) |
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1403 make_dir="none-output-directory due to '-c' flag" |
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1404 else: |
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1405 new_file=open("SeqSero_result.txt","w") |
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1406 ### ed_SL_01152020: add new output |
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1407 conta_note="yes" if "inter-serotype contamination" in contamination_report else "no" |
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1408 tsv_file=open("SeqSero_result.tsv","w") |
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1409 if ingore_header: |
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1410 pass |
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1411 else: |
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1412 tsv_file.write("Sample name\tOutput directory\tInput files\tO antigen prediction\tH1 antigen prediction(fliC)\tH2 antigen prediction(fljB)\tPredicted subspecies\tPredicted antigenic profile\tPredicted serotype\tPotential inter-serotype contamination\tNote\n") |
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1413 if sample_name: |
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1414 new_file.write("Sample name:\t"+sample_name+"\n") |
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1415 tsv_file.write(sample_name+'\t') |
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1416 else: |
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1417 tsv_file.write(input_file[0].split('/')[-1]+'\t') |
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1418 ### |
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1419 if "N/A" not in predict_sero: |
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1420 new_file.write("Output directory:\t"+make_dir+"\n"+ |
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1421 "Input files:\t"+"\t".join(input_file)+"\n"+ |
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1422 "O antigen prediction:\t"+O_choice+"\n"+ |
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1423 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+ |
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1424 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+ |
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1425 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1426 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1427 "Predicted serotype:\t"+predict_sero+"\n"+ |
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1428 note+contamination_report+star_line+claim+antigen_note+"\n")#+## |
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1429 tsv_file.write(make_dir+"\t"+" ".join(input_file)+"\t"+O_choice+"\t"+fliC_choice+"\t"+fljB_choice+"\t"+subspecies+"\t"+predict_form+"\t"+predict_sero+"\t"+conta_note+"\t"+contamination_report+star_line+claim+antigen_note+"\n") |
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1430 else: |
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1431 #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n" |
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1432 star_line="" #04132019, for new output requirement, diable star_line if "NA" in output |
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1433 new_file.write("Output directory:\t"+make_dir+"\n"+ |
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1434 "Input files:\t"+"\t".join(input_file)+"\n"+ |
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1435 "O antigen prediction:\t"+O_choice+"\n"+ |
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1436 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+ |
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1437 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+ |
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1438 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1439 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1440 "Predicted serotype:\t"+subspecies+' '+predict_form+"\n"+ # add serotype output for "N/A" prediction, add subspecies |
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1441 note+NA_note+contamination_report+star_line+claim+antigen_note+"\n")#+## |
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1442 tsv_file.write(make_dir+"\t"+" ".join(input_file)+"\t"+O_choice+"\t"+fliC_choice+"\t"+fljB_choice+"\t"+subspecies+"\t"+predict_form+"\t"+subspecies+' '+predict_form+"\t"+conta_note+"\t"+NA_note+contamination_report+star_line+claim+antigen_note+"\n") |
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1443 new_file.close() |
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1444 tsv_file.close() |
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1445 #subprocess.check_call("cat Seqsero_result.txt",shell=True) |
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1446 #subprocess.call("rm H_and_O_and_specific_genes.fasta* *.sra *.bam *.sam *.fastq *.gz *.fq temp.txt *.xml "+fnameA+"*_db* 2> /dev/null",shell=True) |
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1447 subprocess.call("rm H_and_O_and_specific_genes.fasta* *.sra *.bam *.sam *.fastq *.gz *.fq temp.txt "+fnameA+"*_db* 2> /dev/null",shell=True) |
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1448 if "N/A" not in predict_sero: |
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1449 #print("Output_directory:"+make_dir+"\nInput files:\t"+for_fq+" "+rev_fq+"\n"+"O antigen prediction:\t"+O_choice+"\n"+"H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+"H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+"Predicted antigenic profile:\t"+predict_form+"\n"+"Predicted subspecies:\t"+subspecies+"\n"+"Predicted serotype(s):\t"+predict_sero+star+"\nNote:"+contamination_report+star+star_line+claim+"\n")#+## |
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1450 print("Output directory:\t"+make_dir+"\n"+ |
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1451 "Input files:\t"+"\t".join(input_file)+"\n"+ |
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1452 "O antigen prediction:\t"+O_choice+"\n"+ |
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1453 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+ |
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1454 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+ |
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1455 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1456 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1457 "Predicted serotype:\t"+predict_sero+"\n"+ |
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1458 note+contamination_report+star_line+claim+antigen_note+"\n")#+## |
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1459 else: |
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1460 print("Output directory:\t"+make_dir+"\n"+ |
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1461 "Input files:\t"+"\t".join(input_file)+"\n"+ |
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1462 "O antigen prediction:\t"+O_choice+"\n"+ |
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1463 "H1 antigen prediction(fliC):\t"+fliC_choice+"\n"+ |
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1464 "H2 antigen prediction(fljB):\t"+fljB_choice+"\n"+ |
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1465 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1466 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1467 "Predicted serotype:\t"+subspecies+' '+predict_form+"\n"+ # add serotype output for "N/A" prediction, subspecies |
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1468 note+NA_note+contamination_report+star_line+claim+antigen_note+"\n") |
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1469 else: |
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1470 print("Allele modes only support raw reads datatype, i.e. '-t 1 or 2 or 3'; please use '-m k'") |
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1471 elif analysis_mode=="k": |
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1472 #ex_dir = os.path.dirname(os.path.realpath(__file__)) |
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1473 ex_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)),'seqsero2_db')) # ed_SL_09152019: change ex_dir for packaging |
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1474 #output_mode = args.mode |
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1475 for_fq,rev_fq=get_input_files(make_dir,input_file,data_type,dirpath) |
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1476 input_file = for_fq #-k will just use forward because not all reads were used |
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1477 os.chdir(make_dir) |
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1478 f = open(ex_dir + '/antigens.pickle', 'rb') |
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1479 lib_dict = pickle.load(f) |
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1480 f.close |
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1481 input_Ks=get_input_K(input_file,lib_dict,data_type,k_size) |
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1482 O_dict,H_dict,Special_dict=get_kmer_dict(lib_dict,input_Ks) |
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1483 highest_O,highest_fliC,highest_fljB=call_O_and_H_type(O_dict,H_dict,Special_dict,make_dir) |
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1484 subspecies=judge_subspecies_Kmer(Special_dict) |
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1485 if subspecies=="IIb" or subspecies=="IIa": |
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1486 subspecies="II" |
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1487 predict_form,predict_sero,star,star_line,claim = seqsero_from_formula_to_serotypes( |
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1488 highest_O.split('-')[1], highest_fliC, highest_fljB, Special_dict,subspecies) |
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1489 claim="" #no claim any more based on new output requirement |
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1490 #if star_line+claim=="": #0413, new output style |
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1491 # note="" |
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1492 #else: |
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1493 # note="Note:" |
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1494 |
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1495 ### ed_SL_11232019: add notes for missing antigen |
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1496 if highest_O.split('-')[-1]=="": |
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1497 O_choice="-" |
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1498 else: |
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1499 O_choice=highest_O.split('-')[-1] |
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1500 antigen_note,NA_note=check_antigens(subspecies,O_choice,highest_fliC,highest_fljB,NA_note) |
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1501 if sample_name: |
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1502 print ("Sample name:\t"+sample_name) |
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1503 ### |
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1504 |
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1505 if clean_mode: |
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1506 subprocess.check_call("rm -rf ../"+make_dir,shell=True) |
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1507 make_dir="none-output-directory due to '-c' flag" |
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1508 # ### ed_SL_05282019, fix the assignment issue of variable 'O_choice' using "-m k -c" |
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1509 # if highest_O.split('-')[-1]=="": |
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1510 # O_choice="-" |
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1511 # else: |
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1512 # O_choice=highest_O.split('-')[-1] |
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1513 # ### |
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1514 else: |
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1515 # if highest_O.split('-')[-1]=="": |
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1516 # O_choice="-" |
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1517 # else: |
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1518 # O_choice=highest_O.split('-')[-1] |
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1519 #print("Output_directory:"+make_dir+"\tInput_file:"+input_file+"\tPredicted subpecies:"+subspecies + '\tPredicted antigenic profile:' + predict_form + '\tPredicted serotype(s):' + predict_sero) |
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1520 new_file=open("SeqSero_result.txt","w") |
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1521 #new_file.write("Output_directory:"+make_dir+"\nInput files:\t"+input_file+"\n"+"O antigen prediction:\t"+O_choice+"\n"+"H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+"H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+"Predicted antigenic profile:\t"+predict_form+"\n"+"Predicted subspecies:\t"+subspecies+"\n"+"Predicted serotype(s):\t"+predict_sero+star+"\n"+star+star_line+claim+"\n")#+## |
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1522 ### ed_SL_01152020: add new output |
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1523 tsv_file=open("SeqSero_result.tsv","w") |
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1524 if ingore_header: |
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1525 pass |
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1526 else: |
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1527 tsv_file.write("Sample name\tOutput directory\tInput files\tO antigen prediction\tH1 antigen prediction(fliC)\tH2 antigen prediction(fljB)\tPredicted subspecies\tPredicted antigenic profile\tPredicted serotype\tNote\n") |
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1528 if sample_name: |
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1529 new_file.write("Sample name:\t"+sample_name+"\n") |
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1530 tsv_file.write(sample_name+'\t') |
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1531 else: |
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1532 tsv_file.write(input_file.split('/')[-1]+'\t') |
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1533 ### |
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1534 if "N/A" not in predict_sero: |
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1535 new_file.write("Output directory:\t"+make_dir+"\n"+ |
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1536 "Input files:\t"+input_file+"\n"+ |
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1537 "O antigen prediction:\t"+O_choice+"\n"+ |
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1538 "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+ |
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1539 "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+ |
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1540 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1541 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1542 "Predicted serotype:\t"+predict_sero+"\n"+ |
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1543 note+star_line+claim+antigen_note+"\n")#+## |
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1544 tsv_file.write(make_dir+"\t"+input_file+"\t"+O_choice+"\t"+highest_fliC+"\t"+highest_fljB+"\t"+subspecies+"\t"+predict_form+"\t"+predict_sero+"\t"+star_line+claim+antigen_note+"\n") |
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1545 else: |
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1546 #star_line=star_line.strip()+"\tNone such antigenic formula in KW.\n" |
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1547 star_line = "" #changed for new output requirement, 04132019 |
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1548 new_file.write("Output directory:\t"+make_dir+"\n"+ |
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1549 "Input files:\t"+input_file+"\n"+ |
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1550 "O antigen prediction:\t"+O_choice+"\n"+ |
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1551 "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+ |
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1552 "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+ |
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1553 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1554 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1555 "Predicted serotype:\t"+subspecies+' '+predict_form+"\n"+ # add serotype output for "N/A" prediction, subspecies |
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1556 note+NA_note+star_line+claim+antigen_note+"\n")#+## |
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1557 tsv_file.write(make_dir+"\t"+input_file+"\t"+O_choice+"\t"+highest_fliC+"\t"+highest_fljB+"\t"+subspecies+"\t"+predict_form+"\t"+subspecies+' '+predict_form+"\t"+NA_note+star_line+claim+antigen_note+"\n") |
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1558 new_file.close() |
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1559 tsv_file.close() |
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1560 subprocess.call("rm *.fasta* *.fastq *.gz *.fq temp.txt *.sra 2> /dev/null",shell=True) |
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1561 if "N/A" not in predict_sero: |
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1562 print("Output directory:\t"+make_dir+"\n"+ |
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1563 "Input files:\t"+input_file+"\n"+ |
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1564 "O antigen prediction:\t"+O_choice+"\n"+ |
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1565 "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+ |
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1566 "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+ |
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1567 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1568 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1569 "Predicted serotype:\t"+predict_sero+"\n"+ |
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1570 note+star_line+claim+antigen_note+"\n")#+## |
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1571 else: |
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1572 print("Output directory:\t"+make_dir+"\n"+ |
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1573 "Input files:\t"+input_file+"\n"+ |
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1574 "O antigen prediction:\t"+O_choice+"\n"+ |
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1575 "H1 antigen prediction(fliC):\t"+highest_fliC+"\n"+ |
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1576 "H2 antigen prediction(fljB):\t"+highest_fljB+"\n"+ |
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1577 "Predicted subspecies:\t"+subspecies+"\n"+ |
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1578 "Predicted antigenic profile:\t"+predict_form+"\n"+ |
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1579 "Predicted serotype:\t"+subspecies+' '+predict_form+"\n"+ # add serotype output for "N/A" prediction, subspecies |
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1580 note+NA_note+star_line+claim+antigen_note+"\n")#+## |
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1581 |
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1582 if __name__ == '__main__': |
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1583 main() |