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1 import pickle
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2 from Bio import SeqIO
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3 import os
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4 import pandas as pd
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5 import numpy as np
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6 from local_ctd import CalculateCTD
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7 from local_AAComposition import CalculateDipeptideComposition
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8 import sys
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9 from Bio.SeqUtils.ProtParam import ProteinAnalysis
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10
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11
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12 class PDPOPrediction:
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13
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14 def __init__(self, folder='location', mdl='', seq_file='fasta_file.fasta', ttable=11):
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15 """
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16 Initialize PhageDPO prediction.
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17 :param folder: data path
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18 :param mdl: ml model, in this case ANN or SVM
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19 :param seq_file: fasta file
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20 :param ttable: Translational table. By default, The Bacterial, Archaeal and Plant Plastid Code Table 11
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21 """
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22 self.records = []
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23 self.data = {}
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24 self.df_output = None
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25 self.seqfile = seq_file
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26 self.__location__ = os.path.realpath(os.path.join(os.getcwd(), folder))
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27
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28 with open(os.path.join(self.__location__, mdl), 'rb') as m:
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29 self.model0 = pickle.load(m)
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30 self.model = self.model0.named_steps['clf']
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31 self.scaler = self.model0.named_steps['scl']
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32 self.selectk = self.model0.named_steps['selector']
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33 self.name = 'model'
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34
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35 for seq in SeqIO.parse(os.path.join(self.__location__, self.seqfile), 'fasta'):
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36 record = []
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37 DNA_seq = seq.seq
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38 AA_seq = DNA_seq.translate(table=ttable)
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39 descr_seq = seq.description.replace(' ', '')
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40 self.data[descr_seq] = [DNA_seq._data, AA_seq._data]
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41 record.append(seq.description)
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42 record.append(DNA_seq._data)
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43 record.append(AA_seq._data)
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44 self.records.append(record)
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45
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46 columns = ['ID', 'DNAseq', 'AAseq']
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47 self.df = pd.DataFrame(self.records, columns=columns)
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48 #self.df = self.df.set_index('ID')
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49 self.df.update(self.df.DNAseq[self.df.DNAseq.apply(type) == list].str[0])
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50 self.df.update(self.df.AAseq[self.df.AAseq.apply(type) == list].str[0])
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51
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52 def Datastructure(self):
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53 """
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54 Create dataset with all features
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55 """
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56 def count_orf(orf_seq):
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57 """
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58 Function to count open reading frames
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59 :param orf_seq: sequence to analyze
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60 :return: dictionary with open reading frames
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61 """
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62 dic = {'DNA-A': 0, 'DNA-C': 0, 'DNA-T': 0, 'DNA-G': 0, 'DNA-GC': 0}
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63 for letter in range(len(orf_seq)):
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64 for k in range(0, 4):
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65 if str(orf_seq[letter]) in list(dic.keys())[k][-1]:
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66 dic[list(dic.keys())[k]] += 1
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67 dic['DNA-GC'] = ((dic['DNA-C'] + dic['DNA-G']) / (
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68 dic['DNA-A'] + dic['DNA-C'] + dic['DNA-T'] + dic['DNA-G'])) * 100
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69 return dic
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70
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71 def count_aa(aa_seq):
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72 """
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73 Function to count amino acids
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74 :param aa_seq: sequence to analyze
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75 :return: dictionary with amino acid composition
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76 """
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77 dic = {'G': 0, 'A': 0, 'L': 0, 'V': 0, 'I': 0, 'P': 0, 'F': 0, 'S': 0, 'T': 0, 'C': 0,
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78 'Y': 0, 'N': 0, 'Q': 0, 'D': 0, 'E': 0, 'R': 0, 'K': 0, 'H': 0, 'W': 0, 'M': 0}
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79 for letter in range(len(aa_seq)):
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80 if aa_seq[letter] in dic.keys():
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81 dic[aa_seq[letter]] += 1
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82 return dic
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83
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84 def sec_st_fr(aa_seq):
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85 """
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86 Function to analyze secondary structure. Helix, Turn and Sheet
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87 :param aa_seq: sequence to analyze
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88 :return: dictionary with composition of each secondary structure
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89 """
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90 st_dic = {'Helix': 0, 'Turn': 0, 'Sheet': 0}
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91 stu = ProteinAnalysis(aa_seq).secondary_structure_fraction()
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92 st_dic['Helix'] = stu[0]
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93 st_dic['Turn'] = stu[1]
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94 st_dic['Sheet'] = stu[2]
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95 return st_dic
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96
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97
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98 self.df_output = self.df.copy()
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99 self.df_output.drop(['DNAseq', 'AAseq'], axis=1, inplace=True)
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100 dna_feat = {}
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101 aa_len = {}
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102 aroma_dic = {}
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103 iso_dic = {}
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104 aa_content = {}
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105 st_dic_master = {}
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106 CTD_dic = {}
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107 dp = {}
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108 self.df1 = self.df[['ID']].copy()
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109 self.df.drop(['ID'], axis=1, inplace=True)
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110 for i in range(len(self.df)):
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111 i_name = self.df.index[i]
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112 dna_feat[i] = count_orf(self.df.iloc[i]['DNAseq'])
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113 aa_len[i] = len(self.df.iloc[i]['AAseq'])
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114 aroma_dic[i] = ProteinAnalysis(self.df.iloc[i]['AAseq']).aromaticity()
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115 iso_dic[i] = ProteinAnalysis(self.df.iloc[i]['AAseq']).isoelectric_point()
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116 aa_content[i] = count_aa(self.df.iloc[i]['AAseq'])
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117 st_dic_master[i] = sec_st_fr(self.df.iloc[i]['AAseq'])
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118 CTD_dic[i] = CalculateCTD(self.df.iloc[i]['AAseq'])
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119 dp[i] = CalculateDipeptideComposition(self.df.iloc[i]['AAseq'])
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120 for j in self.df.index:
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121 self.df.loc[j, dna_feat[j].keys()] = dna_feat[j].values() #dic with multiple values
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122 self.df.loc[j, 'AA_Len'] = int(aa_len[j]) #dic with one value
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123 self.df.loc[j, 'Aromaticity'] = aroma_dic[j]
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124 self.df.loc[j, 'IsoelectricPoint'] = iso_dic[j]
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125 self.df.loc[j, aa_content[j].keys()] = aa_content[j].values()
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126 self.df.loc[j, st_dic_master[j].keys()] = st_dic_master[j].values()
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127 self.df.loc[j, CTD_dic[j].keys()] = CTD_dic[j].values()
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128 self.df.loc[j, dp[j].keys()] = dp[j].values()
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129 self.df.drop(['DNAseq', 'AAseq'], axis=1, inplace=True)
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130
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131 def Prediction(self):
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132 """
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133 Predicts the percentage of each CDS being depolymerase.
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134 :return: model prediction
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135 """
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136 scores = self.model0.predict_proba(self.df.iloc[:, :])
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137 pos_scores = np.empty((self.df.shape[0], 0), float)
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138 for x in scores:
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139 pos_scores = np.append(pos_scores, round(x[1]*100))
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140 self.df_output.reset_index(inplace=True)
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141 self.df_output.rename(columns={'index': 'CDS'}, inplace=True)
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142 self.df_output['CDS'] += 1
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143 self.df_output['{} DPO Prediction (%)'.format(self.name)] = pos_scores
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144 self.df_output.to_html('output.html', index=False, justify='center')
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145
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146
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147 if __name__ == '__main__':
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148 __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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149
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150 model = 'svm1495'
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38
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151 fasta_file = sys.argv[1]
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35
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152
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153 PDPO = PDPOPrediction(__location__, model, fasta_file)
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154 PDPO.Datastructure()
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155 PDPO.Prediction() |
