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0
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1
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2
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3 class GalaxyPrediction:
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4
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5 def __init__(self, phage_input_type='ID', bact_input_type='ID', phage='', bacteria='', ml_model='RandomForests', run_interpro=False):
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6 import pickle
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7 import os
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8 import re
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1
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9 with open('files/feature_dataset', 'rb') as f:
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0
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10 dataset = pickle.load(f)
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11 self.all_phages = []
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12 self.all_bacteria = []
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13 for ID in dataset.index:
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14 temp_phage = ID[:ID.find('--')]
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15 temp_bacteria = ID[ID.find('--')+2:]
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16 if temp_phage not in self.all_phages:
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17 self.all_phages.append(temp_phage)
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18 if temp_bacteria not in self.all_bacteria:
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19 self.all_bacteria.append(temp_bacteria)
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20 if phage_input_type == 'ID':
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21 phage = re.split('\W', phage.replace(' ', ''))
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22 len_phage_id = len(phage)
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23 phage_seqs = self._retrieve_from_phage_id(phage)
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24 elif phage_input_type == 'seq_file':
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25 phage_seqs = {}
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26 phage_seqs['PhageFasta'] = {}
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27 with open(phage, 'r') as f:
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28 temp = f.readlines()
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29 count_prot = 0
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30 prot = ''
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31 i=0
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32 while i < len(temp):
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33 if '>' in temp[i]:
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34 if prot:
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35 phage_seqs['PhageFasta']['Protein' + str(count_prot)] = ['Unknown', prot]
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36 count_prot += 1
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37 prot = ''
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38 i+=1
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39 else:
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40 prot += temp[i].strip()
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41 i+=1
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42 if bact_input_type == 'ID':
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43 bacteria = re.split('\W', bacteria.replace(' ', ''))
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44 if len(bacteria) > 1 and len_phage_id == 1 or len(bacteria) == 1:
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45 bact_seqs = self._retrieve_from_bact_id(bacteria)
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46 elif bact_input_type == 'seq_file':
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47 bact_seqs = {}
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48 bact_seqs['BacteriaFasta'] = {}
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49 with open(bacteria, 'r') as f:
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50 temp = f.readlines()
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51 count_prot = 0
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52 prot = ''
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53 i=0
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54 while i < len(temp):
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55 if '>' in temp[i]:
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56 if prot:
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57 bact_seqs['BacteriaFasta']['Protein' + str(count_prot)] = ['Unknown', prot]
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58 count_prot += 1
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59 prot = ''
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60 i+=1
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61 else:
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62 prot += temp[i].strip()
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63 i+=1
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64 phage_seqs = self._find_phage_functions(phage_seqs, run_interpro)
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65 phage_seqs = self._find_phage_tails(phage_seqs)
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66
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67 list_remove = []
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68 for org in phage_seqs:
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69 if not phage_seqs[org]:
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70 print('Could not find tails for phage ' + org + '. Deleting entry...')
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71 list_remove.append(org)
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72 for org in list_remove:
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73 del phage_seqs[org]
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74
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75 if phage_seqs:
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76 output = self.run_prediction(phage_seqs, bact_seqs, ml_model)
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77 self.create_output(output, phage_seqs, bact_seqs)
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78 else:
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79 with open(or_location + '/output.tsv', 'w') as f:
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80 f.write('No phage tails found in query')
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81 for file in os.listdir('files'):
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82 if file.startswith('temp'):
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83 os.remove('files/' + file)
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84
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85 def _retrieve_from_phage_id(self, phage):
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86 temp_phage = {}
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87 for ID in phage:
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88 temp_phage[ID] = {}
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89 if ID in self.all_phages:
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90 import json
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91 with open('files/phageTails.json', encoding='utf-8') as f:
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92 phage_tails = json.loads(f.read())
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93 temp_phage[ID] = phage_tails[ID]
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94 else:
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95 from Bio import Entrez
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96 from Bio import SeqIO
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97 phage = {}
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98 Entrez.email = 'insert@email.com'
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1
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99 try:
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100 with Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id=ID) as handle:
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101 genome = SeqIO.read(handle, "gb")
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102 for feat in genome.features:
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103 if feat.type == 'CDS':
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104 try: temp_phage[ID][feat.qualifiers['protein_id'][0]] = [feat.qualifiers['product'][0], feat.qualifiers['translation'][0]]
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105 except: pass
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106 except:
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107 print(ID, 'not found in GenBank')
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0
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108 return temp_phage
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109
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110 def _retrieve_from_bact_id(self, bacteria):
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111 temp_bacteria = {}
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112 for ID in bacteria:
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113 temp_bacteria[ID] = {}
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114 if '.' in ID:
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115 ID = ID[:ID.find('.')]
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1
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116 #if ID in self.all_bacteria:
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117 # import json
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118 # with open('files/bacteria/' + ID + '.json', encoding='utf-8') as f:
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119 # temp_bacteria[ID] = json.loads(f.read())
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120 #else:
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121 from Bio import Entrez
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122 from Bio import SeqIO
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123 bacteria = {}
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124 Entrez.email = 'insert@email.com'
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125 try:
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0
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126 with Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id=ID+'.1') as handle:
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127 genome = SeqIO.read(handle, "gb")
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128 for feat in genome.features:
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129 if feat.type == 'CDS':
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130 try: temp_bacteria[ID][feat.qualifiers['protein_id'][0]] = [feat.qualifiers['product'][0], feat.qualifiers['translation'][0]]
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131 except: pass
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132 if len(genome.features) <= 5:
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133 with Entrez.efetch(db="nucleotide", rettype="gbwithparts", retmode="text", id=ID) as handle:
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134 genome = handle.readlines()
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135 for i in range(len(genome)):
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136 if ' CDS ' in genome[i]:
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137 j = i
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138 protDone = False
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139 while j < len(genome):
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140 if protDone:
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141 break
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142 if '/product=' in genome[j]:
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143 product = genome[j].strip()[10:]
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144 j += 1
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145 elif '_id=' in genome[j]:
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146 protKey = genome[j].strip()[13:-1]
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147 j += 1
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148 elif '/translation=' in genome[j]:
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149 protSeq = genome[j].strip()[14:]
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150 j += 1
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151 for k in range(j, len(genome)):
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152 if genome[k].islower():
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153 j = k
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154 protDone = True
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155 break
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156 else:
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157 protSeq += genome[k].strip()
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158 else:
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159 j += 1
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160 temp_bacteria[ID][protKey] = [product, protSeq[:protSeq.find('"')]]
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1
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161 except:
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162 print(ID, 'not found in GenBank')
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0
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163 return temp_bacteria
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164
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165 def _find_phage_functions(self, phage_dict, run_interpro):
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166 import os
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167 import json
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168 with open('files/known_function.json', encoding='utf-8') as F:
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169 known_function = json.loads(F.read())
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170 with open('files/temp_database.fasta', 'w') as F:
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171 for phage in known_function:
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172 for prot in known_function[phage]:
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173 F.write('>' + phage + '-' + prot + '\n' + known_function[phage][prot][1] + '\n')
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174 os.system('makeblastdb -in files/temp_database.fasta -dbtype prot -title PhageProts -parse_seqids -out files/temp_database -logfile files/temp_log')
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175 for org in phage_dict:
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176 with open('files/temp.fasta', 'w') as F:
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177 for prot in phage_dict[org]:
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178 F.write('>' + prot + '\n' + phage_dict[org][prot][1] + '\n')
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179 os.system('blastp -db files/temp_database -query files/temp.fasta -out files/temp_blast -num_threads 2 -outfmt 6')
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180 phage_dict[org] = self.process_blast(phage_dict[org], known_function)
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181 if run_interpro: phage_dict[org] = self.interpro(phage_dict[org])
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182 return phage_dict
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183
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184 def process_blast(self, phage_dict, known_function):
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185 import pandas as pd
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186 import re
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187 blast_domains = pd.read_csv('files/temp_blast', sep='\t', header=None)
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188 for prot in phage_dict:
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189 func = phage_dict[prot][0]
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190 known = False
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191 if (not any(i in func.lower() for i in ['hypothetical', 'unknown', 'kda', 'uncharacterized', 'hyphothetical']) and len(func) > 3) and not ('gp' in func.lower() and len(func.split(' ')) < 2) and not (len(func.split(' ')) == 1 and len(func) < 5):
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192 known = True
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193 if not known:
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194 evalue = []
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195 bitscore = []
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196 pred = blast_domains[blast_domains[0] == prot]
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197 if pred.shape[0] == 0: break
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198 for i in pred[10]:
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199 evalue.append(float(i))
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200 for i in pred[11]:
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201 bitscore.append(float(i))
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202 if min(evalue) < 1.0 and max(bitscore) > 30.0:
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203 ind = evalue.index(min(evalue))
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204 if ind != bitscore.index(max(bitscore)):
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205 ind = bitscore.index(max(bitscore))
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206 temp = pred.iloc[ind, 1]
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207 known_phage = temp[:temp.find('-')]
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208 known_prot = temp[temp.find('-') + 1:]
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209 if known_function[known_phage][known_prot]:
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210 new_func = known_function[known_phage][known_prot][0]
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211 # for j in known_function.keys():
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212 # if pred.iloc[ind, 1] in known_function[j].keys():
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213 # new_func = known_function[j][pred.iloc[ind, 1]][0]
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214 # break
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215 x = re.findall('(Gp\d{2,}[^,\d -]|Gp\d{1}[^,\d -])', temp) # se tiver hits, remover
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216 if not any(z in new_func.lower() for z in ['unknown', 'ucp', 'uncharacterized', 'consensus']) and len(new_func) > 3 and not x:
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217 phage_dict[prot][0] = new_func
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218 return phage_dict
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219
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220 def interpro(self, phage_dict):
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221 import os
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222 import pandas as pd
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223 import re
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224 os.system('interproscan.sh -b ' + 'files/temp_interpro -i ' + 'files/temp.fasta -f tsv > files/temp_interpro_log')
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225 domains = pd.read_csv('files/temp_interpro.tsv', sep='\t', index_col=0, header=None, names=list(range(13)))
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226 domains = domains.fillna('-')
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227 domains = domains[domains.loc[:, 3] != 'Coils']
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228 domains = domains[domains.loc[:, 3] != 'MobiDBLite']
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229 for prot in phage_dict:
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230 func = phage_dict[prot][0]
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231 known = False
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232 if (not any(i in func.lower() for i in ['hypothetical', 'unknown', 'kda', 'uncharacterized', 'hyphothetical']) and len(func) > 3) and not ('gp' in func.lower() and len(func.split(' ')) < 2) and not (len(func.split(' ')) == 1 and len(func) < 5):
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233 known = True
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234 if prot in domains.index and not known:
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235 temp = '-'
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236 try:
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237 for i in range(domains.loc[prot, :].shape[0]):
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238 if '-' not in domains.loc[prot, 12].iloc[i].lower():
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239 if float(domains.loc[prot, 8].iloc[i]) < 1.0:
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240 temp = domains.loc[prot, 12].iloc[i]
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241 break
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242 except:
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243 if float(domains.loc[prot, 8]) < 1.0:
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244 temp = domains.loc[prot, 12]
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245 x = re.findall('(Gp\d{2,}[^,\d -]|Gp\d{1}[^,\d -])', temp) # se tiver hits, remover
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246 if temp != '-' and not any(z in temp.lower() for z in ['unknown', 'ucp', 'uncharacterized', 'consensus']) and len(temp) > 3 and not x:
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247 phage_dict[prot][0] = temp
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248 else:
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249 try:
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250 for i in range(domains.loc[prot, :].shape[0]):
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251 if '-' not in domains.loc[prot, 5].iloc[i].lower():
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252 temp = domains.loc[prot, 5].iloc[i]
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253 break
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254 except:
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255 temp = domains.loc[prot, 5]
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256 x = re.findall('(Gp\d{2,}[^,\d -]|Gp\d{1}[^,\d -])', temp)
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257 if temp != '-' and not any(z in temp.lower() for z in ['unknown', 'ucp', 'uncharacterized', 'consensus']) and len(temp) > 3 and not x:
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258 phage_dict[prot][0] = temp
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259 return phage_dict
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260
|
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261 def _find_phage_tails(self, phage_dict):
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262 for org in phage_dict:
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263 list_remove = []
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264 for protein in phage_dict[org]:
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265 if any(z in phage_dict[org][protein][0].lower() for z in ['fiber', 'fibre', 'spike', 'hydrolase', 'bind', 'depolymerase', 'peptidase', 'lyase', 'sialidase', 'dextranase', 'lipase', 'adhesin', 'baseplate', 'protein h', 'recognizing', 'protein j', 'protein g', 'gpe', 'duf4035', 'host specifity', 'cor protein', 'specificity', 'baseplate component', 'gp38', 'gp12 tail', 'receptor', 'recognition', 'tail']) \
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266 and not any(z in phage_dict[org][protein][0].lower() for z in ['nucle', 'dna', 'rna', 'ligase', 'transferase', 'inhibitor', 'assembly', 'connect', 'nudix', 'atp', 'nad', 'transpos', 'ntp', 'molybdenum', 'hns', 'gtp', 'riib', 'inhibitor', 'replicat', 'codon', 'pyruvate', 'catalyst', 'hinge', 'sheath completion', 'head', 'capsid', 'tape', 'tip', 'strand', 'matur', 'portal', 'terminase', 'nucl', 'promot', 'block', 'olfact', 'wedge', 'lysozyme', 'mur', 'sheat']):
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267 pass
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268 else:
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269 list_remove.append(protein)
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270 for protein in list_remove:
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271 del phage_dict[org][protein]
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272 return phage_dict
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273
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274 def run_prediction(self, phage_dict, bact_dict, ml_model):
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275 from feature_construction import FeatureConstruction
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276 import pickle
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277 from sklearn.preprocessing import LabelEncoder
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278 from sklearn.preprocessing import StandardScaler
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279 import numpy as np
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280
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|
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281 if ml_model == 'RandomForests':
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282 with open('files/dataset_reduced', 'rb') as f:
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283 dataset = pickle.load(f)
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284 columns_remove = [3, 7, 9, 11, 24, 28, 32, 34, 38, 42, 45, 52, 53, 61, 65, 73, 75, 79, 104, 122, 141, 151, 154, 155, 157, 159, 160, 161, 163, 165, 169, 170, 173, 176, 178, 180, 182, 183, 185, 186, 187, 190, 193, 194, 196, 197, 201, 202, 203, 206, 207, 209, 210, 212, 216, 217, 221, 223, 225, 226, 230, 233, 235, 236, 245, 251]
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285 elif ml_model == 'SVM':
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286 with open('files/feature_dataset', 'rb') as f:
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287 dataset = pickle.load(f)
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|
288 columns_remove = []
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289
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290 dataset = dataset.dropna()
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291 le = LabelEncoder()
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292 le.fit(['Yes', 'No'])
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293 output = le.transform(dataset['Infects'].values)
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294 dataset = dataset.drop('Infects', 1)
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295 scaler = StandardScaler()
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296 scaler.fit(dataset)
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297 data_z = scaler.transform(dataset)
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298
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299 fc = FeatureConstruction()
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|
300 solution = []
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|
301 for phage in phage_dict:
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|
302 for bacteria in bact_dict:
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303 temp_solution = np.array([])
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304 temp_solution = np.append(temp_solution, fc.get_grouping(phage_dict[phage], bact_dict[bacteria]))
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|
305 temp_solution = np.append(temp_solution, fc.get_composition(phage_dict[phage], bact_dict[bacteria]))
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306 temp_solution = np.append(temp_solution, fc.get_kmers(phage_dict[phage], bact_dict[bacteria]))
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|
|
307 temp_solution = temp_solution.reshape(1, -1)
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|
|
308 if columns_remove:
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|
309 temp_solution = np.delete(temp_solution, columns_remove, 1)
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|
|
310 if phage in self.all_phages:
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|
311 for ID in dataset.index:
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|
|
312 if phage in ID:
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|
313 for i in range(len(dataset.loc[ID].index)):
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|
314 if 'phage' in dataset.loc[ID].index[i]:
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|
315 temp_solution[0][i] = dataset.loc[ID, dataset.loc[ID].index[i]]
|
|
|
316 break
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|
317 if bacteria in self.all_bacteria:
|
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|
318 for ID in dataset.index:
|
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|
319 if bacteria in ID:
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|
320 for i in range(len(dataset.loc[ID].index)):
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321 if 'bact' in dataset.loc[ID].index[i]:
|
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322 temp_solution[0][i] = dataset.loc[ID, dataset.loc[ID].index[i]]
|
|
|
323 break
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324 if type(solution) != np.ndarray:
|
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|
325 solution = temp_solution
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|
|
326 else:
|
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|
327 solution = np.append(solution, temp_solution, axis=0)
|
|
|
328 # solution = solution.reshape(1, -1)
|
|
|
329 solution = scaler.transform(solution)
|
|
|
330
|
|
|
331 if ml_model == 'RandomForests':
|
|
|
332 from sklearn.ensemble import RandomForestClassifier
|
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333 clf = RandomForestClassifier(n_estimators=200, bootstrap=False, criterion='gini', min_samples_leaf=2, min_samples_split=4, oob_score=False)
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334 clf = clf.fit(data_z, output)
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335 elif ml_model == 'SVM':
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336 from sklearn.svm import SVC
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337 clf = SVC(C=10, degree=2, gamma='auto', kernel='rbf')
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338 clf = clf.fit(data_z, output)
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339 pred = clf.predict(solution)
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340 pred = list(le.inverse_transform(pred))
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341 return pred
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342
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343 def create_output(self, output, phage_seqs, bact_seqs):
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344 import pandas as pd
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345 list_orgs = []
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346 for phage in phage_seqs:
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347 for bact in bact_seqs:
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348 list_orgs.append(phage + ' - ' + bact)
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349 file = pd.DataFrame({'Phage - Bacteria': list_orgs, 'Infects': output})
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350 file.to_csv('files/output.tsv', sep='\t', index=False, header=True)
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351 file.to_csv(or_location + '/output.tsv', sep='\t', index=False, header=True)
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352
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353
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354 if __name__ == '__main__':
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355 import sys
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356 import os
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357 global or_location
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358 or_location = os.getcwd()
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359 os.chdir(os.path.dirname(__file__))
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360
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361 phage_input_type = sys.argv[1]
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362 Phages = sys.argv[2]
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363 bact_input_type = sys.argv[3]
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364 Bacts = sys.argv[4]
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365 run_interpro = sys.argv[5]
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366 if run_interpro == 'True':
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367 run_interpro = True
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368 else:
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369 run_interpro = False
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370 model = sys.argv[6]
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371 GalaxyPrediction(phage_input_type=phage_input_type, bact_input_type=bact_input_type, phage=Phages, bacteria=Bacts, ml_model=model, run_interpro=run_interpro)
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1
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372 #rg = GalaxyPrediction(phage_input_type='ID', bact_input_type='ID', phage='NC_050154', bacteria='NC_007414,NZ_MK033499,NZ_CP031214')
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0
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373 # GalaxyPrediction(phage_input_type='ID', bact_input_type='ID', phage='NC_031087,NC_049833,NC_049838,NC_049444', bacteria='LR133964', ml_model='SVM')
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