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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
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7 #$ -pe smp 1 # Parallel Environment (how many cores)
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1
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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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7
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14 # >python cpo_galaxy_tree.py -t /path/to/tree.ph -d /path/to/distance/matrix -m /path/to/metadata
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15
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16 # <requirements>
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17 # <requirement type="package" version="0.23.4">pandas</requirement>
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18 # <requirement type="package" version="3.6">python</requirement>
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19 # <requirement type="package" version="3.1.1">ete3</requirement>
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20 # <requirement type="package" version="5.9.3">pyqt</requirement>
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21 # </requirements>
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22
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23 import subprocess
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24 import pandas #conda pandas
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25 import optparse
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26 import os
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27 import datetime
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28 import sys
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29 import time
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30 import urllib.request
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31 import gzip
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32 import collections
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33 import json
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34 import numpy #conda numpy
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35 import ete3 as e #conda ete3 3.1.1**** >requires pyqt5
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36
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37
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38 #parses some parameters
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39 parser = optparse.OptionParser("Usage: %prog [options] arg1 arg2 ...")
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40 parser.add_option("-t", "--tree", dest="treePath", type="string", default="./pipelineTest/tree.txt", help="identifier of the isolate")
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41 parser.add_option("-d", "--distance", dest="distancePath", type="string", default="./pipelineTest/distance.tab", help="absolute file path forward read (R1)")
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42 parser.add_option("-m", "--metadata", dest="metadataPath", type="string", default="./pipelineTest/metadata.tsv",help="absolute file path to reverse read (R2)")
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43 (options,args) = parser.parse_args()
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44 treePath = str(options.treePath).lstrip().rstrip()
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45 distancePath = str(options.distancePath).lstrip().rstrip()
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46 metadataPath = str(options.metadataPath).lstrip().rstrip()
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47
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48
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49 #region result objects
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50 #define some objects to store values from results
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51 #//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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52 class workflowResult(object):
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53 def __init__(self):
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54 self.new = False
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55 self.ID = ""
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56 self.ExpectedSpecies = ""
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57 self.MLSTSpecies = ""
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58 self.SequenceType = ""
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59 self.MLSTScheme = ""
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60 self.CarbapenemResistanceGenes =""
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61 self.OtherAMRGenes=""
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62 self.TotalPlasmids = 0
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63 self.plasmids = []
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64 self.DefinitelyPlasmidContigs =""
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65 self.LikelyPlasmidContigs=""
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66 self.row = ""
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67 class plasmidObj(object):
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68 def __init__(self):
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69 self.PlasmidsID = 0
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70 self.Num_Contigs = 0
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71 self.PlasmidLength = 0
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72 self.PlasmidRepType = ""
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73 self.PlasmidMobility = ""
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74 self.NearestReference = ""
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75
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76 #endregion
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77
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78 #region useful functions
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79 def read(path): #read in a text file to a list
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80 return [line.rstrip('\n') for line in open(path)]
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81 def execute(command): #subprocess.popen call bash command
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82 process = subprocess.Popen(command, shell=False, cwd=curDir, universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
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83
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84 # Poll process for new output until finished
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85 while True:
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86 nextline = process.stdout.readline()
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87 if nextline == '' and process.poll() is not None:
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88 break
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89 sys.stdout.write(nextline)
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90 sys.stdout.flush()
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91
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92 output = process.communicate()[0]
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93 exitCode = process.returncode
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94
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95 if (exitCode == 0):
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96 return output
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97 else:
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98 raise subprocess.CalledProcessError(exitCode, command)
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99 def httpGetFile(url, filepath=""): #download a file from the web
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100 if (filepath == ""):
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101 return urllib.request.urlretrieve(url)
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102 else:
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103 urllib.request.urlretrieve(url, filepath)
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104 return True
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105 def gunzip(inputpath="", outputpath=""): #gunzip
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106 if (outputpath == ""):
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107 with gzip.open(inputpath, 'rb') as f:
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108 gzContent = f.read()
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109 return gzContent
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110 else:
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111 with gzip.open(inputpath, 'rb') as f:
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112 gzContent = f.read()
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113 with open(outputpath, 'wb') as out:
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114 out.write(gzContent)
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115 return True
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116 def addFace(name): #function to add a facet to a tree
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117 #if its the reference branch, populate the faces with column headers
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118 face = e.faces.TextFace(name,fsize=10,tight_text=True)
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119 face.border.margin = 5
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120 face.margin_right = 5
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121 face.margin_left = 5
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122 return face
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123 #endregion
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124
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125 #region functions to parse result files
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126 def ParseWorkflowResults(pathToResult):
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127 _worflowResult = {}
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128 r = pandas.read_csv(pathToResult, delimiter='\t', header=0)
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129 r = r.replace(numpy.nan, '', regex=True)
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130 for i in range(len(r.index)):
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131 _results = workflowResult()
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132 if(str(r.loc[r.index[i], 'new']).lower() == "new"):
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133 _results.new = True
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134 else:
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135 _results.new = False
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136 _results.ID = str(r.loc[r.index[i], 'ID'])
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137 _results.ExpectedSpecies = str(r.loc[r.index[i], 'Expected Species'])
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138 _results.MLSTSpecies = str(r.loc[r.index[i], 'MLST Species'])
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139 _results.SequenceType = str(r.loc[r.index[i], 'Sequence Type'])
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140 _results.MLSTScheme = (str(r.loc[r.index[i], 'MLST Scheme']))
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141 _results.CarbapenemResistanceGenes = (str(r.loc[r.index[i], 'Carbapenem Resistance Genes']))
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142 _results.OtherAMRGenes = (str(r.loc[r.index[i], 'Other AMR Genes']))
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143 _results.TotalPlasmids = int(r.loc[r.index[i], 'Total Plasmids'])
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144 for j in range(0,_results.TotalPlasmids):
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145 _plasmid = plasmidObj()
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146 _plasmid.PlasmidsID =(((str(r.loc[r.index[i], 'Plasmids ID'])).split(";"))[j])
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147 _plasmid.Num_Contigs = (((str(r.loc[r.index[i], 'Num_Contigs'])).split(";"))[j])
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148 _plasmid.PlasmidLength = (((str(r.loc[r.index[i], 'Plasmid Length'])).split(";"))[j])
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149 _plasmid.PlasmidRepType = (((str(r.loc[r.index[i], 'Plasmid RepType'])).split(";"))[j])
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150 _plasmid.PlasmidMobility = ((str(r.loc[r.index[i], 'Plasmid Mobility'])).split(";"))[j]
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151 _plasmid.NearestReference = ((str(r.loc[r.index[i], 'Nearest Reference'])).split(";"))[j]
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152 _results.plasmids.append(_plasmid)
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153 _results.DefinitelyPlasmidContigs = (str(r.loc[r.index[i], 'Definitely Plasmid Contigs']))
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154 _results.LikelyPlasmidContigs = (str(r.loc[r.index[i], 'Likely Plasmid Contigs']))
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155 _results.row = "\t".join(str(x) for x in r.ix[i].tolist())
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156 _worflowResult[_results.ID] = _results
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157 return _worflowResult
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158
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159 #endregion
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160
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161 def Main():
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162 metadata = ParseWorkflowResults(metadataPath)
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163 distance = read(distancePath)
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164 treeFile = "".join(read(treePath))
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165
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166 distanceDict = {} #store the distance matrix as rowname:list<string>
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167 for i in range(len(distance)):
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168 temp = distance[i].split("\t")
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169 distanceDict[temp[0]] = temp[1:]
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170 #region step5: tree construction
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171
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172 '''
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173 #region create detailed tree
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174
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175 plasmidCount = 0
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176 for n in t.traverse():
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177 if (n.is_leaf() and not n.name == "Reference"):
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178 mData = metadata[n.name.replace(".fa","")]
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179 face = faces.TextFace(mData.MLSTSpecies,fsize=10,tight_text=True)
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180 face.border.margin = 5
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181 face.margin_left = 10
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182 face.margin_right = 10
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183 n.add_face(face, 0, "aligned")
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184 face = faces.TextFace(mData.SequenceType,fsize=10,tight_text=True)
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185 face.border.margin = 5
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186 face.margin_right = 10
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187 n.add_face(face, 1, "aligned")
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188 face = faces.TextFace(mData.CarbapenemResistanceGenes,fsize=10,tight_text=True)
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189 face.border.margin = 5
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190 face.margin_right = 10
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191 n.add_face(face, 2, "aligned")
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192 index = 3
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193 if (mData.TotalPlasmids > plasmidCount):
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194 plasmidCount = mData.TotalPlasmids
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195 for i in range(0, mData.TotalPlasmids):
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196 face = faces.TextFace(mData.plasmids[i].PlasmidRepType,fsize=10,tight_text=True)
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197 face.border.margin = 5
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198 face.margin_right = 10
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199 n.add_face(face, index, "aligned")
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200 index+=1
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201 face = faces.TextFace(mData.plasmids[i].PlasmidMobility,fsize=10,tight_text=True)
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202 face.border.margin = 5
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203 face.margin_right = 10
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204 n.add_face(face, index, "aligned")
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205 index+=1
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206
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207 face = faces.TextFace("Species",fsize=10,tight_text=True)
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208 face.border.margin = 5
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209 face.margin_right = 10
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210 face.margin_left = 10
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211 (t&"Reference").add_face(face, 0, "aligned")
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212 face = faces.TextFace("Sequence Type",fsize=10,tight_text=True)
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213 face.border.margin = 5
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214 face.margin_right = 10
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215 (t&"Reference").add_face(face, 1, "aligned")
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216 face = faces.TextFace("Carbapenamases",fsize=10,tight_text=True)
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217 face.border.margin = 5
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218 face.margin_right = 10
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219 (t&"Reference").add_face(face, 2, "aligned")
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220 index = 3
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221 for i in range(0, plasmidCount):
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222 face = faces.TextFace("plasmid " + str(i) + " replicons",fsize=10,tight_text=True)
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223 face.border.margin = 5
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224 face.margin_right = 10
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225 (t&"Reference").add_face(face, index, "aligned")
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226 index+=1
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227 face = faces.TextFace("plasmid " + str(i) + " mobility",fsize=10,tight_text=True)
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228 face.border.margin = 5
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229 face.margin_right = 10
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230 (t&"Reference").add_face(face, index, "aligned")
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231 index+=1
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232
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233 t.render("./pipelineTest/tree.png", w=5000,units="mm", tree_style=ts)
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234
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235 #endregion
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236 '''
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237 #region create box tree
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238 #region step5: tree construction
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239 treeFile = "".join(read(treePath))
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240 t = e.Tree(treeFile)
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241 t.set_outgroup(t&"Reference")
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242
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243 #set the tree style
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244 ts = e.TreeStyle()
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245 ts.show_leaf_name = False
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246 ts.show_branch_length = True
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247 ts.scale = 2000 #pixel per branch length unit
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248 ts.branch_vertical_margin = 15 #pixel between branches
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249 style2 = e.NodeStyle()
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250 style2["fgcolor"] = "#000000"
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251 style2["shape"] = "circle"
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252 style2["vt_line_color"] = "#0000aa"
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253 style2["hz_line_color"] = "#0000aa"
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254 style2["vt_line_width"] = 2
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255 style2["hz_line_width"] = 2
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256 style2["vt_line_type"] = 0 # 0 solid, 1 dashed, 2 dotted
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257 style2["hz_line_type"] = 0
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258 for n in t.traverse():
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259 n.set_style(style2)
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260
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261 #find the plasmid origins
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262 plasmidIncs = {}
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263 for key in metadata:
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264 for plasmid in metadata[key].plasmids:
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265 for inc in plasmid.PlasmidRepType.split(","):
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266 if (inc.lower().find("inc") > -1):
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267 if not (inc in plasmidIncs):
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268 plasmidIncs[inc] = [metadata[key].ID]
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269 else:
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270 if metadata[key].ID not in plasmidIncs[inc]:
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271 plasmidIncs[inc].append(metadata[key].ID)
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272 #plasmidIncs = sorted(plasmidIncs)
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273 for n in t.traverse(): #loop through the nodes of a tree
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274 if (n.is_leaf() and n.name == "Reference"):
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275 #if its the reference branch, populate the faces with column headers
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276 index = 0
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277 (t&"Reference").add_face(addFace("SampleID"), index, "aligned")
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278 index = index + 1
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279 (t&"Reference").add_face(addFace("New?"), index, "aligned")
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280 index = index + 1
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281 for i in range(len(plasmidIncs)): #this loop adds the columns (aka the incs) to the reference node
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282 (t&"Reference").add_face(addFace(list(plasmidIncs.keys())[i]), i + index, "aligned")
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283 index = index + len(plasmidIncs)
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284 (t&"Reference").add_face(addFace("MLSTScheme"), index, "aligned")
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285 index = index + 1
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286 (t&"Reference").add_face(addFace("Sequence Type"), index, "aligned")
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287 index = index + 1
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288 (t&"Reference").add_face(addFace("Carbapenamases"), index, "aligned")
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289 index = index + 1
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290 for i in range(len(distanceDict[list(distanceDict.keys())[0]])): #this loop adds the distance matrix
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291 (t&"Reference").add_face(addFace(distanceDict[list(distanceDict.keys())[0]][i]), index + i, "aligned")
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292 index = index + len(distanceDict[list(distanceDict.keys())[0]])
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293 elif (n.is_leaf() and not n.name == "Reference"):
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294 #not reference branches, populate with metadata
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295 index = 0
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296 mData = metadata[n.name.replace(".fa","")]
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297 n.add_face(addFace(mData.ID), index, "aligned")
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298 index = index + 1
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299 if (metadata[n.name.replace(".fa","")].new == True): #new column
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300 face = e.RectFace(30,30,"green","green") # TextFace("Y",fsize=10,tight_text=True)
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301 face.border.margin = 5
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302 face.margin_right = 5
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303 face.margin_left = 5
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304 face.vt_align = 1
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305 face.ht_align = 1
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306 n.add_face(face, index, "aligned")
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307 index = index + 1
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308 for incs in plasmidIncs: #this loop adds presence/absence to the sample nodes
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309 if (n.name.replace(".fa","") in plasmidIncs[incs]):
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310 face = e.RectFace(30,30,"black","black") # TextFace("Y",fsize=10,tight_text=True)
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311 face.border.margin = 5
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312 face.margin_right = 5
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313 face.margin_left = 5
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314 face.vt_align = 1
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315 face.ht_align = 1
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316 n.add_face(face, list(plasmidIncs.keys()).index(incs) + index, "aligned")
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317 index = index + len(plasmidIncs)
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318 n.add_face(addFace(mData.MLSTSpecies), index, "aligned")
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319 index = index + 1
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320 n.add_face(addFace(mData.SequenceType), index, "aligned")
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321 index = index + 1
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322 n.add_face(addFace(mData.CarbapenemResistanceGenes), index, "aligned")
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323 index = index + 1
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324 for i in range(len(distanceDict[list(distanceDict.keys())[0]])): #this loop adds distance matrix
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325 n.add_face(addFace(list(distanceDict[n.name])[i]), index + i, "aligned")
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326
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327 t.render("./tree.png", w=5000,units="mm", tree_style=ts) #save it as a png. or an phyloxml
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328
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329 #endregion
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330 #endregion
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331
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332
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333 start = time.time()#time the analysis
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334
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335 #analysis time
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336 Main()
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337
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338 end = time.time()
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339 print("Finished!\nThe analysis used: " + str(end-start) + " seconds") |
