annotate cpo_galaxy_prediction.py @ 4:bd6f5844d60e draft

planemo upload
author jjjjia
date Mon, 20 Aug 2018 19:31:34 -0400
parents e6027598a35c
children 698579246d0d
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1 #!/home/jjjjia/.conda/envs/py36/bin/python
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2
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3 #$ -S /home/jjjjia/.conda/envs/py36/bin/python
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4 #$ -V # Pass environment variables to the job
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5 #$ -N CPO_pipeline # Replace with a more specific job name
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6 #$ -wd /home/jjjjia/testCases # Use the current working dir
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7 #$ -pe smp 8 # Parallel Environment (how many cores)
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8 #$ -l h_vmem=11G # Memory (RAM) allocation *per core*
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9 #$ -e ./logs/$JOB_ID.err
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10 #$ -o ./logs/$JOB_ID.log
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11 #$ -m ea
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12 #$ -M bja20@sfu.ca
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13
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14 #./prediction.py -i ~/testCases/cpoResults/contigs/BC11-Kpn005_S2.fa -m ~/testCases/predictionResultsQsubTest/predictions/BC11-Kpn005_S2.mlst -c ~/testCases/predictionResultsQsubTest/predictions/BC11-Kpn005_S2.recon/contig_report.txt -f ~/testCases/predictionResultsQsubTest/predictions/BC11-Kpn005_S2.recon/mobtyper_aggregate_report.txt -a ~/testCases/predictionResultsQsubTest/predictions/BC11-Kpn005_S2.cp -r ~/testCases/predictionResultsQsubTest/predictions/BC11-Kpn005_S2.rgi.txt -e "Klebsiella"
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15 import subprocess
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16 import pandas
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17 import optparse
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18 import os
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19 import datetime
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20 import sys
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21 import time
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22 import urllib.request
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23 import gzip
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24 import collections
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25 import json
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26 import numpy
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28
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29 debug = False #True #debug skips the shell scripts and also dump out a ton of debugging messages
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30
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31 if not debug:
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32 #parses some parameters
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33 parser = optparse.OptionParser("Usage: %prog [options] arg1 arg2 ...")
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34 #required
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35 #MLSTHIT, mobsuite, resfinder, rgi, mlstscheme
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36 parser.add_option("-i", "--id", dest="id", type="string", help="identifier of the isolate")
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37 parser.add_option("-m", "--mlst", dest="mlst", type="string", help="absolute file path to mlst result")
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38 parser.add_option("-c", "--mobfinderContig", dest="mobfinderContig", type="string", help="absolute path to mobfinder aggregate result")
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39 parser.add_option("-f", "--mobfinderAggregate", dest="mobfinderAggregate", type="string", help="absolute path to mobfinder plasmid results")
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40 parser.add_option("-a", "--abricate", dest="abricate", type="string", help="absolute path to abricate results")
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41 parser.add_option("-r", "--rgi", dest="rgi", type="string", help="absolute path to rgi results")
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42 parser.add_option("-e", "--expected", dest="expectedSpecies", default="NA/NA/NA", type="string", help="expected species of the isolate")
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43 parser.add_option("-s", "--mlst-scheme", dest="mlstScheme", default= "./scheme_species_map.tab", type="string", help="absolute file path to mlst scheme")
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44 parser.add_option("-p", "--plasmidfinder", dest="plasmidfinder", type="string", help="absolute file path to plasmidfinder ")
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45
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46 #parallelization, useless, these are hard coded to 8cores/64G RAM
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47 #parser.add_option("-t", "--threads", dest="threads", default=8, type="int", help="number of cpu to use")
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48 #parser.add_option("-p", "--memory", dest="memory", default=64, type="int", help="memory to use in GB")
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49
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50 (options,args) = parser.parse_args()
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51 #if len(args) != 8:
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52 #parser.error("incorrect number of arguments, all 7 is required")
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53 curDir = os.getcwd()
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54 ID = str(options.id).lstrip().rstrip()
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55 mlst = str(options.mlst).lstrip().rstrip()
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56 mobfindercontig = str(options.mobfinderContig).lstrip().rstrip()
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57 mobfinderaggregate = str(options.mobfinderAggregate).lstrip().rstrip()
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58 abricate = str(options.abricate).lstrip().rstrip()
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59 rgi = str(options.rgi).lstrip().rstrip()
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60 expectedSpecies = str(options.expectedSpecies).lstrip().rstrip()
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61 mlstScheme = str(options.mlstScheme).lstrip().rstrip()
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62 plasmidfinder = str(options.plasmidfinder).lstrip().rstrip()
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63 outputDir = "./"
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64 print(mlst)
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65 print(mobfindercontig)
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66 print(mobfinderaggregate)
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67 print(abricate)
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68 print(rgi)
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69 print(expectedSpecies)
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70 print(mlstScheme)
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71 else:
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72 curDir = os.getcwd()
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73 ID = "BC11"
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74 mlst = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.mlst"
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75 mobfindercontig = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.recon\contig_report.txt"
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76 mobfinderaggregate = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.recon\mobtyper_aggregate_report.txt"
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77 abricate = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.cp"
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78 rgi = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.rgi.txt"
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79 expectedSpecies = "Escherichia coli"
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80 mlstScheme = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\scheme_species_map.tab"
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81 plasmidfinder = "D:\OneDrive\ProjectCDC\ProjectCDCInPython\ProjectCDCInPython\pipelineTest\predictions\BC11-Kpn005_S2.origins"
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82 outputDir = "./"
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83
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84 #region result objects
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85 #define some objects to store values from results
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86 #//TODO this is not the proper way of get/set private object variables. every value has manually assigned defaults intead of specified in init(). Also, use property(def getVar, def setVar).
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87 class starFinders(object):
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88 def __init__(self):
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89 self.file = ""
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90 self.sequence = ""
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91 self.start = 0
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92 self.end = 0
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93 self.gene = ""
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94 self.shortGene = ""
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95 self.coverage = ""
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96 self.coverage_map = ""
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97 self.gaps = ""
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98 self.pCoverage = 100.00
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99 self.pIdentity = 100.00
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100 self.database = ""
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101 self.accession = ""
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102 self.product = ""
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103 self.source = "chromosome"
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104 self.row = ""
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105
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106 class PlasFlowResult(object):
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107 def __init__(self):
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108 self.sequence = ""
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109 self.length = 0
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110 self.label = ""
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111 self.confidence = 0
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112 self.usefulRow = ""
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113 self.row = ""
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114
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115 class MlstResult(object):
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116 def __init__(self):
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117 self.file = ""
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118 self.speciesID = ""
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119 self.seqType = 0
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120 self.scheme = ""
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121 self.species = ""
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122 self.row=""
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123
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124 class mobsuiteResult(object):
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125 def __init__(self):
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126 self.file_id = ""
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127 self.cluster_id = ""
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128 self.contig_id = ""
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129 self.contig_num = 0
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130 self.contig_length = 0
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131 self.circularity_status = ""
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132 self.rep_type = ""
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133 self.rep_type_accession = ""
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134 self.relaxase_type = ""
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135 self.relaxase_type_accession = ""
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136 self.mash_nearest_neighbor = ""
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137 self.mash_neighbor_distance = 0.00
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138 self.repetitive_dna_id = ""
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139 self.match_type = ""
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140 self.score = 0
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141 self.contig_match_start = 0
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142 self.contig_match_end = 0
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143 self.row = ""
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144
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145 class mobsuitePlasmids(object):
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146 def __init__(self):
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147 self.file_id = ""
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148 self.num_contigs = 0
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149 self.total_length = 0
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150 self.gc = ""
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151 self.rep_types = ""
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152 self.rep_typeAccession = ""
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153 self.relaxase_type= ""
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154 self.relaxase_type_accession = ""
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155 self.mpf_type = ""
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156 self.mpf_type_accession= ""
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157 self.orit_type = ""
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158 self.orit_accession = ""
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159 self.PredictedMobility = ""
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160 self.mash_nearest_neighbor = ""
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161 self.mash_neighbor_distance = 0.00
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162 self.mash_neighbor_cluster= 0
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163 self.row = ""
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164
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165 class RGIResult(object):
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166 def __init__(self):
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167 self.ORF_ID = ""
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168 self.Contig = ""
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169 self.Start = -1
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170 self.Stop = -1
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171 self.Orientation = ""
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172 self.Cut_Off = ""
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173 self.Pass_Bitscore = 100000
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174 self.Best_Hit_Bitscore = 0.00
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175 self.Best_Hit_ARO = ""
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176 self.Best_Identities = 0.00
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177 self.ARO = 0
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178 self.Model_type = ""
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179 self.SNPs_in_Best_Hit_ARO = ""
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180 self.Other_SNPs = ""
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181 self.Drug_Class = ""
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182 self.Resistance_Mechanism = ""
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183 self.AMR_Gene_Family = ""
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184 self.Predicted_DNA = ""
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185 self.Predicted_Protein = ""
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186 self.CARD_Protein_Sequence = ""
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187 self.Percentage_Length_of_Reference_Sequence = 0.00
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188 self.ID = ""
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189 self.Model_ID = 0
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190 self.source = ""
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191 self.row = ""
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192
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193 #endregion
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194
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195 #region useful functions
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196 def read(path):
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197 return [line.rstrip('\n') for line in open(path)]
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parents:
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198 def execute(command):
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199 process = subprocess.Popen(command, shell=False, cwd=curDir, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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200
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parents:
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201 # Poll process for new output until finished
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parents:
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202 while True:
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parents:
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203 nextline = process.stdout.readline()
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parents:
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204 if nextline == '' and process.poll() is not None:
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parents:
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205 break
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206 sys.stdout.write(nextline)
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parents:
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207 sys.stdout.flush()
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parents:
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208
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parents:
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209 output = process.communicate()[0]
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parents:
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210 exitCode = process.returncode
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parents:
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211
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parents:
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212 if (exitCode == 0):
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213 return output
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parents:
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214 else:
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parents:
diff changeset
215 raise subprocess.CalledProcessError(exitCode, command)
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parents:
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216 def httpGetFile(url, filepath=""):
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parents:
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217 if (filepath == ""):
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parents:
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218 return urllib.request.urlretrieve(url)
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parents:
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219 else:
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parents:
diff changeset
220 urllib.request.urlretrieve(url, filepath)
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parents:
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221 return True
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parents:
diff changeset
222 def gunzip(inputpath="", outputpath=""):
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parents:
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223 if (outputpath == ""):
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parents:
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224 with gzip.open(inputpath, 'rb') as f:
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parents:
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225 gzContent = f.read()
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parents:
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226 return gzContent
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parents:
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227 else:
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parents:
diff changeset
228 with gzip.open(inputpath, 'rb') as f:
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229 gzContent = f.read()
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parents:
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230 with open(outputpath, 'wb') as out:
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parents:
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231 out.write(gzContent)
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parents:
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232 return True
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parents:
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233 def ToJson(dictObject, outputPath):
3
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parents: 2
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234 #outDir = outputDir + '/summary/' + ID + ".json/"
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parents: 2
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235 #if not (os.path.exists(outDir)):
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236 #os.makedirs(outDir)
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237 #with open(outputPath, 'w') as f:
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238 #json.dump([ob.__dict__ for ob in dictObject.values()], f, ensure_ascii=False)
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239 return ""
1
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parents:
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240 #endregion
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241
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parents:
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242 #region functions to parse result files
3
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243 def ParseMLSTResult(pathToMLSTResult, scheme):
1
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244 _mlstResult = {}
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245 scheme = pandas.read_csv(scheme, delimiter='\t', header=0)
1
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246 scheme = scheme.replace(numpy.nan, '', regex=True)
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247
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248 taxon = {}
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249 #record the scheme as a dictionary
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250 taxon["-"] = "No MLST Match"
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251 for i in range(len(scheme.index)):
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parents:
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252 key = scheme.iloc[i,0]
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parents:
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253 if (str(scheme.iloc[i,2]) == "nan"):
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parents:
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254 value = str(scheme.iloc[i,1])
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parents:
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255 else:
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parents:
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256 value = str(scheme.iloc[i,1]) + " " + str(scheme.iloc[i,2])
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257
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parents:
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258 if (key in taxon.keys()):
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parents:
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259 taxon[key] = taxon.get(key) + ";" + value
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parents:
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260 else:
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parents:
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261 taxon[key] = value
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parents:
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262 #read in the mlst result
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parents:
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263 mlst = pandas.read_csv(pathToMLSTResult, delimiter='\t', header=None)
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264 _mlstHit = MlstResult()
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265
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parents:
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266 _mlstHit.file = mlst.iloc[0,0]
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parents:
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267 _mlstHit.speciesID = (mlst.iloc[0,1])
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parents:
diff changeset
268 _mlstHit.seqType = str(mlst.iloc[0,2])
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parents:
diff changeset
269 for i in range(3, len(mlst.columns)):
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270 _mlstHit.scheme += mlst.iloc[0,i] + ";"
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diff changeset
271 _mlstHit.species = taxon[_mlstHit.speciesID]
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272 _mlstHit.row = "\t".join(str(x) for x in mlst.ix[0].tolist())
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diff changeset
273 _mlstResult[_mlstHit.speciesID]=_mlstHit
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parents:
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274
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parents:
diff changeset
275 return _mlstResult
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parents:
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276
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parents:
diff changeset
277 def ParsePlasmidFinderResult(pathToPlasmidFinderResult):
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parents:
diff changeset
278 #pipelineTest/contigs/BC110-Kpn005.fa contig00019 45455 45758 IncFIC(FII)_1 8-308/499 ========/=..... 8/11 59.52 75.65 plasmidfinder AP001918 IncFIC(FII)_1__AP001918
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parents:
diff changeset
279 #example resfinder:
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parents:
diff changeset
280 #pipelineTest/contigs/BC110-Kpn005.fa contig00038 256 1053 OXA-181 1-798/798 =============== 0/0 100.00 100.00 bccdc AEP16366.1 OXA-48 family carbapenem-hydrolyzing class D beta-lactamase OXA-181
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parents:
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281
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parents:
diff changeset
282 _pFinder = {} #***********************
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parents:
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283 plasmidFinder = pandas.read_csv(pathToPlasmidFinderResult, delimiter='\t', header=0)
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parents:
diff changeset
284 plasmidFinder = plasmidFinder.replace(numpy.nan, '', regex=True)
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parents:
diff changeset
285
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parents:
diff changeset
286
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parents:
diff changeset
287 for i in range(len(plasmidFinder.index)):
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parents:
diff changeset
288 pf = starFinders()
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parents:
diff changeset
289 pf.file = str(plasmidFinder.iloc[i,0])
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parents:
diff changeset
290 pf.sequence = str(plasmidFinder.iloc[i,1])
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parents:
diff changeset
291 pf.start = int(plasmidFinder.iloc[i,2])
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parents:
diff changeset
292 pf.end = int(plasmidFinder.iloc[i,3])
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parents:
diff changeset
293 pf.gene = str(plasmidFinder.iloc[i,4])
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parents:
diff changeset
294 pf.shortGene = pf.gene[:pf.gene.index("_")]
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parents:
diff changeset
295 pf.coverage = str(plasmidFinder.iloc[i,5])
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jjjjia
parents:
diff changeset
296 pf.coverage_map = str(plasmidFinder.iloc[i,6])
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parents:
diff changeset
297 pf.gaps = str(plasmidFinder.iloc[i,7])
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parents:
diff changeset
298 pf.pCoverage = float(plasmidFinder.iloc[i,8])
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parents:
diff changeset
299 pf.pIdentity = float(plasmidFinder.iloc[i,9])
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parents:
diff changeset
300 pf.database = str(plasmidFinder.iloc[i,10])
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parents:
diff changeset
301 pf.accession = str(plasmidFinder.iloc[i,11])
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parents:
diff changeset
302 pf.product = str(plasmidFinder.iloc[i,12])
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parents:
diff changeset
303 pf.source = "plasmid"
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parents:
diff changeset
304 pf.row = "\t".join(str(x) for x in plasmidFinder.ix[i].tolist())
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parents:
diff changeset
305 _pFinder[pf.gene]=pf
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parents:
diff changeset
306 #row = "\t".join(str(x) for x in plasmidFinder.ix[i].tolist())
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parents:
diff changeset
307 #plasmidFinderContigs.append(str(plasmidFinder.iloc[i,1]))
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parents:
diff changeset
308 #origins.append(str(plasmidFinder.iloc[i,4][:plasmidFinder.iloc[i,4].index("_")]))
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parents:
diff changeset
309 return _pFinder
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parents:
diff changeset
310
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jjjjia
parents:
diff changeset
311 def ParseMobsuiteResult(pathToMobsuiteResult):
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jjjjia
parents:
diff changeset
312 _mobsuite = {}
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jjjjia
parents:
diff changeset
313 mResult = pandas.read_csv(pathToMobsuiteResult, delimiter='\t', header=0)
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jjjjia
parents:
diff changeset
314 mResult = mResult.replace(numpy.nan, '', regex=True)
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jjjjia
parents:
diff changeset
315
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jjjjia
parents:
diff changeset
316 for i in range(len(mResult.index)):
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jjjjia
parents:
diff changeset
317 mr = mobsuiteResult()
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jjjjia
parents:
diff changeset
318 mr.file_id = str(mResult.iloc[i,0])
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jjjjia
parents:
diff changeset
319 mr.cluster_id = str(mResult.iloc[i,1])
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jjjjia
parents:
diff changeset
320 if (mr.cluster_id == "chromosome"):
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jjjjia
parents:
diff changeset
321 break
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jjjjia
parents:
diff changeset
322 mr.contig_id = str(mResult.iloc[i,2])
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jjjjia
parents:
diff changeset
323 mr.contig_num = mr.contig_id[(mr.contig_id.find("contig")+6):mr.contig_id.find("_len=")]
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jjjjia
parents:
diff changeset
324 mr.contig_length = int(mResult.iloc[i,3])
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jjjjia
parents:
diff changeset
325 mr.circularity_status = str(mResult.iloc[i,4])
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jjjjia
parents:
diff changeset
326 mr.rep_type = str(mResult.iloc[i,5])
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jjjjia
parents:
diff changeset
327 mr.rep_type_accession = str(mResult.iloc[i,6])
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jjjjia
parents:
diff changeset
328 mr.relaxase_type = str(mResult.iloc[i,7])
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jjjjia
parents:
diff changeset
329 mr.relaxase_type_accession = str(mResult.iloc[i,8])
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jjjjia
parents:
diff changeset
330 mr.mash_nearest_neighbor = str(mResult.iloc[i,9])
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jjjjia
parents:
diff changeset
331 mr.mash_neighbor_distance = float(mResult.iloc[i,10])
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jjjjia
parents:
diff changeset
332 mr.repetitive_dna_id = str(mResult.iloc[i,11])
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jjjjia
parents:
diff changeset
333 mr.match_type = str(mResult.iloc[i,12])
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jjjjia
parents:
diff changeset
334 if (mr.match_type == ""):
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jjjjia
parents:
diff changeset
335 mr.score = -1
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jjjjia
parents:
diff changeset
336 mr.contig_match_start = -1
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jjjjia
parents:
diff changeset
337 mr.contig_match_end = -1
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jjjjia
parents:
diff changeset
338 else:
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jjjjia
parents:
diff changeset
339 mr.score = int(mResult.iloc[i,13])
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jjjjia
parents:
diff changeset
340 mr.contig_match_start = int(mResult.iloc[i,14])
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jjjjia
parents:
diff changeset
341 mr.contig_match_end = int(mResult.iloc[i,15])
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jjjjia
parents:
diff changeset
342 mr.row = "\t".join(str(x) for x in mResult.ix[i].tolist())
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jjjjia
parents:
diff changeset
343 _mobsuite[mr.contig_id]=(mr)
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jjjjia
parents:
diff changeset
344 return _mobsuite
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jjjjia
parents:
diff changeset
345
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jjjjia
parents:
diff changeset
346 def ParseMobsuitePlasmids(pathToMobsuiteResult):
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jjjjia
parents:
diff changeset
347 _mobsuite = {}
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jjjjia
parents:
diff changeset
348 mResults = pandas.read_csv(pathToMobsuiteResult, delimiter='\t', header=0)
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jjjjia
parents:
diff changeset
349 mResults = mResults.replace(numpy.nan, '', regex=True)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
350
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
351 for i in range(len(mResults.index)):
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jjjjia
parents:
diff changeset
352 mr = mobsuitePlasmids()
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jjjjia
parents:
diff changeset
353 mr.file_id = str(mResults.iloc[i,0])
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jjjjia
parents:
diff changeset
354 mr.num_contigs = int(mResults.iloc[i,1])
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jjjjia
parents:
diff changeset
355 mr.total_length = int(mResults.iloc[i,2])
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jjjjia
parents:
diff changeset
356 mr.gc = int(mResults.iloc[i,3])
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jjjjia
parents:
diff changeset
357 mr.rep_types = str(mResults.iloc[i,4])
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jjjjia
parents:
diff changeset
358 mr.rep_typeAccession = str(mResults.iloc[i,5])
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
359 mr.relaxase_type = str(mResults.iloc[i,6])
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jjjjia
parents:
diff changeset
360 mr.relaxase_type_accession = str(mResults.iloc[i,7])
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jjjjia
parents:
diff changeset
361 mr.mpf_type = str(mResults.iloc[i,8])
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jjjjia
parents:
diff changeset
362 mr.mpf_type_accession = str(mResults.iloc[i,9])
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jjjjia
parents:
diff changeset
363 mr.orit_type = str(mResults.iloc[i,10])
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jjjjia
parents:
diff changeset
364 mr.orit_accession = str(mResults.iloc[i,11])
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jjjjia
parents:
diff changeset
365 mr.PredictedMobility = str(mResults.iloc[i,12])
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jjjjia
parents:
diff changeset
366 mr.mash_nearest_neighbor = str(mResults.iloc[i,13])
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jjjjia
parents:
diff changeset
367 mr.mash_neighbor_distance = float(mResults.iloc[i,14])
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jjjjia
parents:
diff changeset
368 mr.mash_neighbor_cluster = int(mResults.iloc[i,15])
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jjjjia
parents:
diff changeset
369 mr.row = "\t".join(str(x) for x in mResults.ix[i].tolist())
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jjjjia
parents:
diff changeset
370 _mobsuite[mr.file_id] = mr
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jjjjia
parents:
diff changeset
371 return _mobsuite
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jjjjia
parents:
diff changeset
372
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
373 def ParseResFinderResult(pathToResFinderResults, plasmidContigs, likelyPlasmidContigs):
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jjjjia
parents:
diff changeset
374 _rFinder = {}
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jjjjia
parents:
diff changeset
375 resFinder = pandas.read_csv(pathToResFinderResults, delimiter='\t', header=0)
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jjjjia
parents:
diff changeset
376 resFinder = resFinder.replace(numpy.nan, '', regex=True)
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jjjjia
parents:
diff changeset
377
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jjjjia
parents:
diff changeset
378 for i in range(len(resFinder.index)):
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jjjjia
parents:
diff changeset
379 rf = starFinders()
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jjjjia
parents:
diff changeset
380 rf.file = str(resFinder.iloc[i,0])
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jjjjia
parents:
diff changeset
381 rf.sequence = str(resFinder.iloc[i,1])
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jjjjia
parents:
diff changeset
382 rf.start = int(resFinder.iloc[i,2])
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jjjjia
parents:
diff changeset
383 rf.end = int(resFinder.iloc[i,3])
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jjjjia
parents:
diff changeset
384 rf.gene = str(resFinder.iloc[i,4])
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
385 rf.shortGene = rf.gene
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jjjjia
parents:
diff changeset
386 rf.coverage = str(resFinder.iloc[i,5])
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jjjjia
parents:
diff changeset
387 rf.coverage_map = str(resFinder.iloc[i,6])
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jjjjia
parents:
diff changeset
388 rf.gaps = str(resFinder.iloc[i,7])
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jjjjia
parents:
diff changeset
389 rf.pCoverage = float(resFinder.iloc[i,8])
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jjjjia
parents:
diff changeset
390 rf.pIdentity = float(resFinder.iloc[i,9])
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jjjjia
parents:
diff changeset
391 rf.database = str(resFinder.iloc[i,10])
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jjjjia
parents:
diff changeset
392 rf.accession = str(resFinder.iloc[i,11])
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jjjjia
parents:
diff changeset
393 rf.product = str(resFinder.iloc[i,12])
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
394 rf.row = "\t".join(str(x) for x in resFinder.ix[i].tolist())
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jjjjia
parents:
diff changeset
395 if (rf.sequence[6:] in plasmidContigs):
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jjjjia
parents:
diff changeset
396 rf.source = "plasmid"
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jjjjia
parents:
diff changeset
397 elif (rf.sequence[6:] in likelyPlasmidContigs):
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jjjjia
parents:
diff changeset
398 rf.source = "likely plasmid"
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jjjjia
parents:
diff changeset
399 else:
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jjjjia
parents:
diff changeset
400 rf.source = "likely chromosome"
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jjjjia
parents:
diff changeset
401 _rFinder[rf.gene]=rf
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jjjjia
parents:
diff changeset
402 return _rFinder
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jjjjia
parents:
diff changeset
403
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jjjjia
parents:
diff changeset
404 def ParseRGIResult(pathToRGIResults, plasmidContigs, likelyPlasmidContigs):
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jjjjia
parents:
diff changeset
405 _rgiR = {}
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jjjjia
parents:
diff changeset
406 RGI = pandas.read_csv(pathToRGIResults, delimiter='\t', header=0)
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jjjjia
parents:
diff changeset
407 RGI = RGI.replace(numpy.nan, '', regex=True)
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jjjjia
parents:
diff changeset
408
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jjjjia
parents:
diff changeset
409 for i in range(len(RGI.index)):
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jjjjia
parents:
diff changeset
410 r = RGIResult()
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jjjjia
parents:
diff changeset
411 r.ORF_ID = str(RGI.iloc[i,0])
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jjjjia
parents:
diff changeset
412 r.Contig = str(RGI.iloc[i,1])
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jjjjia
parents:
diff changeset
413 r.Contig_Num = r.Contig[6:r.Contig.find("_")]
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jjjjia
parents:
diff changeset
414 r.Start = int(RGI.iloc[i,2])
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jjjjia
parents:
diff changeset
415 r.Stop = int(RGI.iloc[i,3])
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jjjjia
parents:
diff changeset
416 r.Orientation = str(RGI.iloc[i,4])
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jjjjia
parents:
diff changeset
417 r.Cut_Off = str(RGI.iloc[i,5])
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jjjjia
parents:
diff changeset
418 r.Pass_Bitscore = int(RGI.iloc[i,6])
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jjjjia
parents:
diff changeset
419 r.Best_Hit_Bitscore = float(RGI.iloc[i,7])
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jjjjia
parents:
diff changeset
420 r.Best_Hit_ARO = str(RGI.iloc[i,8])
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jjjjia
parents:
diff changeset
421 r.Best_Identities = float(RGI.iloc[i,9])
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jjjjia
parents:
diff changeset
422 r.ARO = int(RGI.iloc[i,10])
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jjjjia
parents:
diff changeset
423 r.Model_type = str(RGI.iloc[i,11])
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jjjjia
parents:
diff changeset
424 r.SNPs_in_Best_Hit_ARO = str(RGI.iloc[i,12])
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jjjjia
parents:
diff changeset
425 r.Other_SNPs = str(RGI.iloc[i,13])
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jjjjia
parents:
diff changeset
426 r.Drug_Class = str(RGI.iloc[i,14])
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jjjjia
parents:
diff changeset
427 r.Resistance_Mechanism = str(RGI.iloc[i,15])
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jjjjia
parents:
diff changeset
428 r.AMR_Gene_Family = str(RGI.iloc[i,16])
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jjjjia
parents:
diff changeset
429 r.Predicted_DNA = str(RGI.iloc[i,17])
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jjjjia
parents:
diff changeset
430 r.Predicted_Protein = str(RGI.iloc[i,18])
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jjjjia
parents:
diff changeset
431 r.CARD_Protein_Sequence = str(RGI.iloc[i,19])
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jjjjia
parents:
diff changeset
432 r.Percentage_Length_of_Reference_Sequence = float(RGI.iloc[i,20])
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jjjjia
parents:
diff changeset
433 r.ID = str(RGI.iloc[i,21])
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jjjjia
parents:
diff changeset
434 r.Model_ID = int(RGI.iloc[i,22])
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jjjjia
parents:
diff changeset
435 r.row = "\t".join(str(x) for x in RGI.ix[i].tolist())
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
436 if (r.Contig_Num in plasmidContigs):
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jjjjia
parents:
diff changeset
437 r.source = "plasmid"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
438 elif (r.Contig_Num in likelyPlasmidContigs):
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jjjjia
parents:
diff changeset
439 r.source = "likely plasmid"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
440 else:
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jjjjia
parents:
diff changeset
441 r.source = "likely chromosome"
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jjjjia
parents:
diff changeset
442 _rgiR[r.Model_ID]=r
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jjjjia
parents:
diff changeset
443 return _rgiR
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
444
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
445 def ParsePlasmidFinderResult(pathToPlasmidFinderResult):
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jjjjia
parents: 2
diff changeset
446 #pipelineTest/contigs/BC110-Kpn005.fa contig00019 45455 45758 IncFIC(FII)_1 8-308/499 ========/=..... 8/11 59.52 75.65 plasmidfinder AP001918 IncFIC(FII)_1__AP001918
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jjjjia
parents: 2
diff changeset
447 #example resfinder:
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jjjjia
parents: 2
diff changeset
448 #pipelineTest/contigs/BC110-Kpn005.fa contig00038 256 1053 OXA-181 1-798/798 =============== 0/0 100.00 100.00 bccdc AEP16366.1 OXA-48 family carbapenem-hydrolyzing class D beta-lactamase OXA-181
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jjjjia
parents: 2
diff changeset
449
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jjjjia
parents: 2
diff changeset
450 _pFinder = {} #***********************
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jjjjia
parents: 2
diff changeset
451 plasmidFinder = pandas.read_csv(pathToPlasmidFinderResult, delimiter='\t', header=0)
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jjjjia
parents: 2
diff changeset
452
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
453 for i in range(len(plasmidFinder.index)):
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jjjjia
parents: 2
diff changeset
454 pf = starFinders()
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jjjjia
parents: 2
diff changeset
455 pf.file = str(plasmidFinder.iloc[i,0])
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jjjjia
parents: 2
diff changeset
456 pf.sequence = str(plasmidFinder.iloc[i,1])
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jjjjia
parents: 2
diff changeset
457 pf.start = int(plasmidFinder.iloc[i,2])
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jjjjia
parents: 2
diff changeset
458 pf.end = int(plasmidFinder.iloc[i,3])
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jjjjia
parents: 2
diff changeset
459 pf.gene = str(plasmidFinder.iloc[i,4])
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jjjjia
parents: 2
diff changeset
460 pf.shortGene = pf.gene[:pf.gene.index("_")]
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jjjjia
parents: 2
diff changeset
461 pf.coverage = str(plasmidFinder.iloc[i,5])
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
462 pf.coverage_map = str(plasmidFinder.iloc[i,6])
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jjjjia
parents: 2
diff changeset
463 pf.gaps = str(plasmidFinder.iloc[i,7])
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
464 pf.pCoverage = float(plasmidFinder.iloc[i,8])
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jjjjia
parents: 2
diff changeset
465 pf.pIdentity = float(plasmidFinder.iloc[i,9])
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jjjjia
parents: 2
diff changeset
466 pf.database = str(plasmidFinder.iloc[i,10])
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jjjjia
parents: 2
diff changeset
467 pf.accession = str(plasmidFinder.iloc[i,11])
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jjjjia
parents: 2
diff changeset
468 pf.product = str(plasmidFinder.iloc[i,12])
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
469 pf.source = "plasmid"
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jjjjia
parents: 2
diff changeset
470 pf.row = "\t".join(str(x) for x in plasmidFinder.ix[i].tolist())
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
471 _pFinder[pf.gene]=pf
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jjjjia
parents: 2
diff changeset
472 #row = "\t".join(str(x) for x in plasmidFinder.ix[i].tolist())
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jjjjia
parents: 2
diff changeset
473 #plasmidFinderContigs.append(str(plasmidFinder.iloc[i,1]))
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
474 #origins.append(str(plasmidFinder.iloc[i,4][:plasmidFinder.iloc[i,4].index("_")]))
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
475 return _pFinder
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
476 #endregion
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jjjjia
parents:
diff changeset
477
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
478 def Main():
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
479 outputDir = "./"
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
480 notes = []
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jjjjia
parents:
diff changeset
481 #init the output list
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
482 output = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
483 jsonOutput = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
484
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
485 print(str(datetime.datetime.now()) + "\n\nID: " + ID + "\nAssembly: " + ID)
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
486 output.append(str(datetime.datetime.now()) + "\n\nID: " + ID + "\nAssembly: " + ID)
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
487
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
488 #region parse the mlst results
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
489 print("step 3: parsing mlst, plasmid, and amr results")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
490
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
491 print("identifying MLST")
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
492 mlstHit = ParseMLSTResult(mlst, str(mlstScheme))#***********************
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
493 ToJson(mlstHit, "mlst.json") #write it to a json output
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
494 mlstHit = list(mlstHit.values())[0]
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
495
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
496 #endregion
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
497
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
498 #region parse mobsuite, resfinder and rgi results
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
499 print("identifying plasmid contigs and amr genes")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
500
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
501 plasmidContigs = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
502 likelyPlasmidContigs = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
503 origins = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
504
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
505 #parse mobsuite results
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
506 mSuite = ParseMobsuiteResult(mobfindercontig) #outputDir + "/predictions/" + ID + ".recon/contig_report.txt")#*************
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
507 ToJson(mSuite, "mobsuite.json") #*************
3
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jjjjia
parents: 2
diff changeset
508 mSuitePlasmids = ParseMobsuitePlasmids(mobfinderaggregate)#outputDir + "/predictions/" + ID + ".recon/mobtyper_aggregate_report.txt")#*************
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
509 ToJson(mSuitePlasmids, "mobsuitePlasmids.json") #*************
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
510
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
511 for key in mSuite:
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jjjjia
parents:
diff changeset
512 if mSuite[key].contig_num not in plasmidContigs and mSuite[key].contig_num not in likelyPlasmidContigs:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
513 if not (mSuite[key].rep_type == ''):
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
514 plasmidContigs.append(mSuite[key].contig_num)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
515 else:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
516 likelyPlasmidContigs.append(mSuite[key].contig_num)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
517 for key in mSuite:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
518 if mSuite[key].rep_type not in origins:
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jjjjia
parents:
diff changeset
519 origins.append(mSuite[key].rep_type)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
520
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
521 #parse resfinder AMR results
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
522 pFinder = ParsePlasmidFinderResult(plasmidfinder)
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
523 ToJson(pFinder, "origins.json")
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jjjjia
parents: 2
diff changeset
524
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
525 rFinder = ParseResFinderResult(abricate, plasmidContigs, likelyPlasmidContigs)#outputDir + "/predictions/" + ID + ".cp", plasmidContigs, likelyPlasmidContigs) #**********************
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
526 ToJson(rFinder, "resfinder.json") #*************
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
527
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
528 rgiAMR = ParseRGIResult(rgi, plasmidContigs, likelyPlasmidContigs) # outputDir + "/predictions/" + ID + ".rgi.txt", plasmidContigs, likelyPlasmidContigs)#***********************
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
529 ToJson(rgiAMR, "rgi.json") #*************
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
530
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
531 carbapenamases = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
532 amrGenes = []
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
533 for keys in rFinder:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
534 carbapenamases.append(rFinder[keys].shortGene + "(" + rFinder[keys].source + ")")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
535 for keys in rgiAMR:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
536 if (rgiAMR[keys].Drug_Class.find("carbapenem") > -1):
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
537 if (rgiAMR[keys].Best_Hit_ARO not in carbapenamases):
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
538 carbapenamases.append(rgiAMR[keys].Best_Hit_ARO+ "(" + rgiAMR[keys].source + ")")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
539 else:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
540 if (rgiAMR[keys].Best_Hit_ARO not in amrGenes):
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
541 amrGenes.append(rgiAMR[keys].Best_Hit_ARO+ "(" + rgiAMR[keys].source + ")")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
542 #endregion
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
543
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
544 #region output parsed mlst information
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jjjjia
parents:
diff changeset
545 print("formatting mlst outputs")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
546 output.append("\n\n\n~~~~~~~MLST summary~~~~~~~")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
547 output.append("MLST determined species: " + mlstHit.species)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
548 output.append("\nMLST Details: ")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
549 output.append(mlstHit.row)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
550
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
551 output.append("\nMLST information: ")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
552 if (mlstHit.species == expectedSpecies):
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
553 output.append("MLST determined species is the same as expected species")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
554 #notes.append("MLST determined species is the same as expected species")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
555 else:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
556 output.append("!!!MLST determined species is NOT the same as expected species, contamination? mislabeling?")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
557 notes.append("MLST: Not expected species. Possible contamination or mislabeling")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
558
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
559 #endregion
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jjjjia
parents:
diff changeset
560
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jjjjia
parents:
diff changeset
561 #region output the parsed plasmid/amr results
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jjjjia
parents:
diff changeset
562 output.append("\n\n\n~~~~~~~~Plasmids~~~~~~~~\n")
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jjjjia
parents:
diff changeset
563
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jjjjia
parents:
diff changeset
564 output.append("predicted plasmid origins: ")
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jjjjia
parents:
diff changeset
565 output.append(";".join(origins))
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jjjjia
parents:
diff changeset
566
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jjjjia
parents:
diff changeset
567 output.append("\ndefinitely plasmid contigs")
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jjjjia
parents:
diff changeset
568 output.append(";".join(plasmidContigs))
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
569
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jjjjia
parents:
diff changeset
570 output.append("\nlikely plasmid contigs")
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jjjjia
parents:
diff changeset
571 output.append(";".join(likelyPlasmidContigs))
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jjjjia
parents:
diff changeset
572
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jjjjia
parents:
diff changeset
573 output.append("\nmob-suite prediction details: ")
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jjjjia
parents:
diff changeset
574 for key in mSuite:
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jjjjia
parents:
diff changeset
575 output.append(mSuite[key].row)
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jjjjia
parents:
diff changeset
576
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jjjjia
parents:
diff changeset
577 output.append("\n\n\n~~~~~~~~AMR Genes~~~~~~~~\n")
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jjjjia
parents:
diff changeset
578 output.append("predicted carbapenamase Genes: ")
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jjjjia
parents:
diff changeset
579 output.append(",".join(carbapenamases))
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jjjjia
parents:
diff changeset
580 output.append("other RGI AMR Genes: ")
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jjjjia
parents:
diff changeset
581 for key in rgiAMR:
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jjjjia
parents:
diff changeset
582 output.append(rgiAMR[key].Best_Hit_ARO + "(" + rgiAMR[key].source + ")")
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jjjjia
parents:
diff changeset
583
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jjjjia
parents:
diff changeset
584 output.append("\nDetails about the carbapenamase Genes: ")
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jjjjia
parents:
diff changeset
585 for key in rFinder:
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jjjjia
parents:
diff changeset
586 output.append(rFinder[key].row)
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jjjjia
parents:
diff changeset
587 output.append("\nDetails about the RGI AMR Genes: ")
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jjjjia
parents:
diff changeset
588 for key in rgiAMR:
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jjjjia
parents:
diff changeset
589 output.append(rgiAMR[key].row)
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jjjjia
parents:
diff changeset
590
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jjjjia
parents:
diff changeset
591 #write summary to a file
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jjjjia
parents:
diff changeset
592 summaryDir = outputDir + "/summary/" + ID
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
593 out = open("summary.txt", 'w')
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
594 for item in output:
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jjjjia
parents:
diff changeset
595 out.write("%s\n" % item)
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jjjjia
parents:
diff changeset
596
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jjjjia
parents:
diff changeset
597
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jjjjia
parents:
diff changeset
598 #TSV output
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jjjjia
parents:
diff changeset
599 tsvOut = []
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jjjjia
parents:
diff changeset
600 tsvOut.append("ID\tExpected Species\tMLST Species\tSequence Type\tMLST Scheme\tCarbapenem Resistance Genes\tOther AMR Genes\tTotal Plasmids\tPlasmids ID\tNum_Contigs\tPlasmid Length\tPlasmid RepType\tPlasmid Mobility\tNearest Reference\tDefinitely Plasmid Contigs\tLikely Plasmid Contigs")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
601 #start with ID
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jjjjia
parents:
diff changeset
602 temp = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
603 temp += (ID + "\t")
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jjjjia
parents:
diff changeset
604 temp += expectedSpecies + "\t"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
605
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jjjjia
parents:
diff changeset
606 #move into MLST
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jjjjia
parents:
diff changeset
607 temp += mlstHit.species + "\t"
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jjjjia
parents:
diff changeset
608 temp += str(mlstHit.seqType) + "\t"
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jjjjia
parents:
diff changeset
609 temp += mlstHit.scheme + "\t"
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jjjjia
parents:
diff changeset
610
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jjjjia
parents:
diff changeset
611 #now onto AMR genes
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jjjjia
parents:
diff changeset
612 temp += ";".join(carbapenamases) + "\t"
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jjjjia
parents:
diff changeset
613 temp += ";".join(amrGenes) + "\t"
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jjjjia
parents:
diff changeset
614
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
615 #lastly plasmids
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jjjjia
parents:
diff changeset
616 temp+= str(len(mSuitePlasmids)) + "\t"
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jjjjia
parents:
diff changeset
617 plasmidID = ""
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jjjjia
parents:
diff changeset
618 contigs = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
619 lengths = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
620 rep_type = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
621 mobility = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
622 neighbour = ""
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
623 for keys in mSuitePlasmids:
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jjjjia
parents:
diff changeset
624 plasmidID += str(mSuitePlasmids[keys].mash_neighbor_cluster) + ";"
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jjjjia
parents:
diff changeset
625 contigs += str(mSuitePlasmids[keys].num_contigs) + ";"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
626 lengths += str(mSuitePlasmids[keys].total_length) + ";"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
627 rep_type += str(mSuitePlasmids[keys].rep_types) + ";"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
628 mobility += str(mSuitePlasmids[keys].PredictedMobility) + ";"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
629 neighbour += str(mSuitePlasmids[keys].mash_nearest_neighbor) + ";"
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jjjjia
parents:
diff changeset
630 temp += plasmidID + "\t" + contigs + "\t" + lengths + "\t" + rep_type + "\t" + mobility + "\t" + neighbour + "\t"
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
631 temp += ";".join(plasmidContigs) + "\t"
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jjjjia
parents:
diff changeset
632 temp += ";".join(likelyPlasmidContigs)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
633 tsvOut.append(temp)
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jjjjia
parents:
diff changeset
634
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
635 summaryDir = outputDir + "/summary/" + ID
3
e6027598a35c planemo upload
jjjjia
parents: 2
diff changeset
636 out = open("summary.tsv", 'w')
1
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
637 for item in tsvOut:
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
638 out.write("%s\n" % item)
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
639 #endregion
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
640
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
641
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
642 start = time.time()#time the analysis
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
643 print("Starting workflow...")
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
644 #analysis time
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
645 Main()
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
646
fea89c4d5227 Uploaded
jjjjia
parents:
diff changeset
647 end = time.time()
4
bd6f5844d60e planemo upload
jjjjia
parents: 3
diff changeset
648 print("Finished!\nThe analysis used: " + str(end-start) + " seconds")