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1 ################################################################################
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2 # This program will read in a SAINT 'list.txt' file and the interactions from
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3 # the consensus path db database and return all the interactions that we saw in
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4 # our experiment in a format suitable for cytoscape. This allows us to filter
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5 # before getting PPIs so that it doesn't affect our SAINT score or include
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6 # interactions that don't score well
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7 ################################################################################
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8 import urllib2
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9 import itertools
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10 import sys
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10
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11 import os
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4
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12 ################################################################################
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13 ## REQUIRED INPUT ##
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14
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15 # 1) listfile: SAINTexpress output
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16 # 2) SAINT_cutoff: Saint score cutoff for import (between 0 and 1)
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17 # 3) Int_conf: Confidence of PPI from CPDB to include
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18 # - low: no filtering
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19 # - medium: >0.5
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20 # - high: >0.7
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21 # - very high: >0.9
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22 # 4) Species: Human, Yeast, or Mouse
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23 ###############################################################################
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24 listfile = sys.argv[1]
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25 SAINT_cutoff = sys.argv[2]
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26 Int_conf = sys.argv[3]
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27 Species = sys.argv[4]
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28 cyto_file = sys.argv[5]
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9
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29 db_path = sys.argv[6]
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4
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30 ###############################################################################
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31 class ReturnValue1(object):
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32 def __init__(self, uniprot_acc, gene, swissprot):
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33 self.up = uniprot_acc
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34 self.gn = gene
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35 self.sp = swissprot
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36 class ReturnValue2(object):
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37 def __init__(self, getdata, getproteins, getheader):
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38 self.data = getdata
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39 self.proteins = getproteins
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40 self.header = getheader
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41 def main(listfile, SAINT_cutoff, Int_conf, Species):
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42 cytoscape(dd_network(listfile, SAINT_cutoff, Int_conf), listfile, SAINT_cutoff)
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43 def readtab(infile):
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44 with open(infile,'r') as x: # read in tab-delim text
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45 output = []
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46 for line in x:
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47 line = line.strip()
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48 temp = line.split('\t')
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49 output.append(temp)
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50 return output
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51 def read_listfile(listfile): # Get data, proteins and header from scaffold output
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52 dupes = readtab(listfile)
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53 header = dupes[0]
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54 prot_start = header.index("PreyGene")-1
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55 data = dupes[1:] # cut off blank line and END OF FILE
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56 proteins = []
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57 for protein in data:
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58 proteins.append(protein[prot_start])
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59 return ReturnValue2(data, proteins, header)
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60
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61 def get_info(uniprot_accession_in): #get aa lengths and gene name
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62 error = open('error proteins.txt', 'a+')
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63 i=0
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64 while i==0:
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65 try:
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66 data = urllib2.urlopen("http://www.uniprot.org/uniprot/" + uniprot_accession_in + ".fasta")
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67 break
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68 except urllib2.HTTPError, err:
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69 i = i + 1
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70 if i == 50:
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71 sys.exit("More than 50 errors. Check your file or try again later.")
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72 if err.code == 404:
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73 error.write(uniprot_accession_in + '\t' + "Invalid URL. Check protein" + '\n')
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74 seqlength = 'NA'
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75 genename = 'NA'
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76 return ReturnValue1(seqlength, genename)
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77 elif err.code == 302:
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78 sys.exit("Request timed out. Check connection and try again.")
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79 else:
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80 sys.exit("Uniprot had some other error")
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81 lines = data.readlines()
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82 header = lines[0]
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83 lst = header.split('|')
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84 lst2 = lst[2].split(' ')
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85 swissprot = lst2[0]
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86 uniprot_acc = lst[1]
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87 if lines == []:
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88 error.write(uniprot_accession_in + '\t' + "Blank Fasta" + '\n')
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89 error.close
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90 uniprot_acc = 'NA'
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91 genename = 'NA'
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92 return ReturnValue1(uniprot_acc, genename, swissprot)
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93 if lines != []:
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94 seqlength = 0
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95 header = lines[0]
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96 if 'GN=' in header:
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97 lst = header.split('GN=')
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98 lst2 = lst[1].split(' ')
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99 genename = lst2[0]
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100 error.close
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101 return ReturnValue1(uniprot_acc, genename, swissprot)
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102 if 'GN=' not in header:
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103 genename = 'NA'
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104 error.close
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105 return ReturnValue1(uniprot_acc, genename, swissprot)
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106
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107 def dd_network(listfile, SAINTscore, CPDB_filter): ## Filter by SS and CPDB
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108 data = read_listfile(listfile).data # change to filtered list
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109 SS = (read_listfile(listfile).header).index("SaintScore")
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110 filt_data = []
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111 for i in data:
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112 if i[SS] >= SAINTscore:
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113 filt_data.append(i)
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114 accessions = []
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115 for i in filt_data:
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116 accessions.append(get_info(i[1]).sp)
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117 GO=[]
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118 for i in CPDB[2:]:
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119 if i[3] >= CPDB_filter: # filter interaction confidence
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120 GO.append(i[2]) # all known interactions
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121 GO2 = []
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122 for i in GO:
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123 GO2.append(i.split(',')) # make interactions list friendly
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124 unfiltered_network = {}
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125 for i in accessions:
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126 interactions = []
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127 for j in GO2:
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128 if i in j: # find the interactions
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129 if j not in interactions:# dont add duplicate interactions
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130 interactions.append(j)
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131 merged = list(itertools.chain(*interactions)) # flatten list of lists
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132 unfiltered_network[i]=merged # assign all possible interactions to protein in a dictionary
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133 dd_network = {} #data dependent network
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134 for i in unfiltered_network:
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135 temp = []
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136 for j in unfiltered_network[i]:
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137 if j in accessions:
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138 if j not in temp:
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139 if j != i:
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140 temp.append(j)
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141 dd_network[i]=temp
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142 return dd_network
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143 def cytoscape(dd_network, listfile, SAINTscore):
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144 with open('network.sif','wt') as y:
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145 data = read_listfile(listfile).data
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146 SS = (read_listfile(listfile).header).index("SaintScore")
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147 filt_data = []
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148 for i in data:
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149 if i[SS] >= SAINTscore:
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150 filt_data.append(i)
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151 header = ["Prey", "Interactions"]
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152 header = '\t'.join(header)
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153 y.write(header + '\n')
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154 for i in filt_data:
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155 if dd_network[i[1]] != []:
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156 lst = []
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157 #x='\t'.join(i)
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158 for j in dd_network[i[1]]:
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159 lst.append(j)
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160 for j in lst:
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161 y.write(i[1]+'\t'+'pp'+'\t' + j+'\n')
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162 if Species == "Human":
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163 CPDB = readtab(str(db_path) + 'ConsensusPathDB_human_PPI.txt')
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164 if Species == "Yeast":
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165 CPDB = readtab(str(db_path) + 'ConsensusPathDB_yeast_PPI.txt')
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166 if Species == "Mouse":
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167 CPDB = readtab(str(db_path) +'ConsensusPathDB_mouse_PPI.txt')
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168 if __name__ == '__main__':
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169 main(listfile, SAINT_cutoff, Int_conf, Species)
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170 #main("Crizo_list.txt", 0.7, 0.7, 'Human')
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171 os.rename('network.sif', str(cyto_file))
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