Mercurial > repos > jose_duarte > phagedpo
changeset 3:d4853259dec7 draft
Deleted selected files
| author | jose_duarte |
|---|---|
| date | Wed, 24 Nov 2021 17:30:46 +0000 |
| parents | 525fe9bb114b |
| children | 2152b92c19a1 |
| files | DPOGALAXY.py |
| diffstat | 1 files changed, 0 insertions(+), 164 deletions(-) [+] |
line wrap: on
line diff
--- a/DPOGALAXY.py Wed Nov 24 17:29:49 2021 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,164 +0,0 @@ -#print('Hello world') -#PS C:\Users\joseduarte\Documents\pythonfiles\phage> python pdpo_test.py -#Hello world - -class PDPOPrediction: - def __init__(self, Folder = 'location', mdl='',seq_file = 'fasta_file.fasta',ttable=11): - import pickle - import pandas as pd - from Bio import SeqIO - import os - from pathlib import Path - self.data = {} - self.df_output = None - self.seqfile = seq_file - self.__location__ = os.path.realpath(os.path.join(os.getcwd(), Folder)) - - with open(os.path.join(self.__location__,mdl), 'rb') as m: - self.model = pickle.load(m) - if mdl == 'SVM4311': - with open(os.path.join(__location__,'d4311_SCALER'),'rb') as sl: - self.scaler = pickle.load(sl) - self.name = mdl - elif mdl == 'RF5748': - with open(os.path.join(__location__,'d5748_SCALER'),'rb') as sc: - self.scaler = pickle.load(sc) - self.name = mdl - elif mdl == 'ANN4311': - with open(os.path.join(__location__,'d4311_SCALER'),'rb') as sl: - self.scaler = pickle.load(sl) - self.name = mdl - - for seq in SeqIO.parse(os.path.join(self.__location__,self.seqfile), 'fasta'): - #name_seq = seq.id - DNA_seq = seq.seq - AA_seq = DNA_seq.translate(table=ttable) - descr_seq = seq.description.replace(' ','') - self.data[descr_seq]=[DNA_seq._data,AA_seq._data] - self.df = pd.DataFrame({'ID':list(self.data.keys()), - 'DNAseq':[elem[0] for elem in self.data.values()], - 'AAseq':[elem[1] for elem in self.data.values()]}) - self.df = self.df.set_index('ID') - - def Datastructure(self): - import pandas as pd - import pickle - from Bio.SeqUtils.ProtParam import ProteinAnalysis - from propy import CTD - from propy import AAComposition - - def count_orf(orf_seq): - dic = {'DNA-A': 0, 'DNA-C': 0, 'DNA-T': 0, 'DNA-G': 0, 'DNA-GC': 0} - for letter in range(len(orf_seq)): - for k in range(0, 4): - if orf_seq[letter] in list(dic.keys())[k][-1]: - dic[list(dic.keys())[k]] += 1 - dic['DNA-GC'] = ((dic['DNA-C'] + dic['DNA-G']) / ( - dic['DNA-A'] + dic['DNA-C'] + dic['DNA-T'] + dic['DNA-G'])) * 100 - return dic - - def count_aa(aa_seq): - dic = {'G': 0, 'A': 0, 'L': 0, 'V': 0, 'I': 0, 'P': 0, 'F': 0, 'S': 0, 'T': 0, 'C': 0, - 'Y': 0, 'N': 0, 'Q': 0, 'D': 0, 'E': 0, 'R': 0, 'K': 0, 'H': 0, 'W': 0, 'M': 0} - for letter in range(len(aa_seq)): - if aa_seq[letter] in dic.keys(): - dic[aa_seq[letter]] += 1 - return dic - - def sec_st_fr(aa_seq): - from Bio.SeqUtils.ProtParam import ProteinAnalysis - st_dic = {'Helix': 0, 'Turn': 0, 'Sheet': 0} - stu = ProteinAnalysis(aa_seq).secondary_structure_fraction() - st_dic['Helix'] = stu[0] - st_dic['Turn'] = stu[1] - st_dic['Sheet'] = stu[2] - return st_dic - - self.feat={"SVM4311": ["DNA-A", "DNA-T", "DNA-G", "DNA-GC", "AA_Len", "G", "A", "S", "T", "N", "Turn", "Sheet", - "_PolarizabilityC1", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SecondaryStrC1", - "_SecondaryStrC2", "_SecondaryStrC3", "_ChargeC2", "_ChargeC3", "_PolarityC1", "_NormalizedVDWVC1", - "_NormalizedVDWVC3", "_HydrophobicityC2", "_HydrophobicityC3", "_SecondaryStrT23", - "_NormalizedVDWVT13", "_PolarizabilityD1001", "_SolventAccessibilityD1001", - "_SolventAccessibilityD2001", "_SolventAccessibilityD3001", "_SecondaryStrD1025", "_ChargeD1075", - "_ChargeD2001", "_ChargeD2025", "_ChargeD3025", "_ChargeD3050", "_PolarityD1075", "_PolarityD3025", - "_NormalizedVDWVD1001", "_NormalizedVDWVD3050", "_HydrophobicityD2001", "DG", "DT", "GD"], - "RF5748": ["DNA-A", "DNA-T", "DNA-G", "DNA-GC", "AA_Len", "G", "A", "S", "T", "N", "Turn", "Sheet", - "_PolarizabilityC1", "_PolarizabilityC3", "_SecondaryStrC1", "_SecondaryStrC2", "_SecondaryStrC3", - "_ChargeC1", "_ChargeC2", "_ChargeC3", "_NormalizedVDWVC1", "_NormalizedVDWVC3", "_HydrophobicityC2", - "_HydrophobicityC3", "_SolventAccessibilityT12", "_SolventAccessibilityT13", "_SecondaryStrT23", - "_NormalizedVDWVT23", "_HydrophobicityT12", "_PolarizabilityD1001", "_SolventAccessibilityD1001", - "_SolventAccessibilityD2001", "_SolventAccessibilityD3001", "_SecondaryStrD1001", - "_SecondaryStrD1025", "_ChargeD1025", "_ChargeD1075", "_ChargeD2001", "_ChargeD2025", "_ChargeD3025", - "_ChargeD3050", "_PolarityD1001", "_PolarityD1050", "_PolarityD1075", "_PolarityD3025", - "_NormalizedVDWVD1001", "_NormalizedVDWVD3001", "_HydrophobicityD1001", "_HydrophobicityD2001", "NG", - "DG", "DT", "GD", "GT"], - "ANN4311": ["DNA-A", "DNA-T", "DNA-G", "DNA-GC", "AA_Len", "G", "A", "S", "T", "N", "Turn", "Sheet", - "_PolarizabilityC1", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SecondaryStrC1", - "_SecondaryStrC2", "_SecondaryStrC3", "_ChargeC2", "_ChargeC3", "_PolarityC1", "_NormalizedVDWVC1", - "_NormalizedVDWVC3", "_HydrophobicityC2", "_HydrophobicityC3", "_SecondaryStrT23", - "_NormalizedVDWVT13", "_PolarizabilityD1001", "_SolventAccessibilityD1001", - "_SolventAccessibilityD2001", "_SolventAccessibilityD3001", "_SecondaryStrD1025", "_ChargeD1075", - "_ChargeD2001", "_ChargeD2025", "_ChargeD3025", "_ChargeD3050", "_PolarityD1075", "_PolarityD3025", - "_NormalizedVDWVD1001", "_NormalizedVDWVD3050", "_HydrophobicityD2001", "DG", "DT", "GD"]} - - self.df_output = self.df.copy() - self.df_output.drop(['DNAseq','AAseq'],axis=1,inplace=True) - dna_feat = {} - aa_len = {} - aroma_dic = {} - iso_dic = {} - aa_content = {} - st_dic_master = {} - CTD_dic = {} - dp = {} - for i in range(len(self.df)): - i_name = self.df.index[i] - dna_feat[i_name] = count_orf(self.df.iloc[i]['DNAseq']) - aa_len[i_name] = len(self.df.iloc[i]['AAseq']) - aroma_dic[i_name] = ProteinAnalysis(self.df.iloc[i]['AAseq']).aromaticity() - iso_dic[i_name] = ProteinAnalysis(self.df.iloc[i]['AAseq']).isoelectric_point() - aa_content[i_name] = count_aa(self.df.iloc[i]['AAseq']) - st_dic_master[i_name] = sec_st_fr(self.df.iloc[i]['AAseq']) - CTD_dic[i_name] = CTD.CalculateCTD(self.df.iloc[i]['AAseq']) - dp[i_name] = AAComposition.CalculateDipeptideComposition(self.df.iloc[i]['AAseq']) - for j in self.df.index: - self.df.loc[j, dna_feat[j].keys()] = dna_feat[j].values() #dic with multiple values - self.df.loc[j, 'AA_Len'] = int(aa_len[j]) #dic with one value - self.df.loc[j, 'Aromaticity'] = aroma_dic[j] - self.df.loc[j, 'IsoelectricPoint'] = iso_dic[j] - self.df.loc[j, aa_content[j].keys()] = aa_content[j].values() - self.df.loc[j, st_dic_master[j].keys()] = st_dic_master[j].values() - self.df.loc[j, CTD_dic[j].keys()] = CTD_dic[j].values() - self.df.loc[j, dp[j].keys()] = dp[j].values() - self.df.drop(['DNAseq','AAseq'],axis=1,inplace=True) - - def Prediction(self): - import os - import pickle - import json - import pandas as pd - import numpy as np - from pathlib import Path - ft_scaler = pd.DataFrame(self.scaler.transform(self.df.iloc[:, :]), index=self.df.index,columns=self.df.columns) - ft_scaler = ft_scaler.drop(columns=[col for col in self.df if col not in self.feat[self.name]], axis=1) - scores = self.model.predict_proba(ft_scaler) - pos_scores = np.empty((self.df.shape[0], 0), float) - for x in scores: - pos_scores = np.append(pos_scores, round(x[1]*100)) - self.df_output.reset_index(inplace=True) - self.df_output['{} DPO Prediction (%)'.format(self.name)]= pos_scores - self.df_output = self.df_output.sort_values(by='{} DPO Prediction (%)'.format(self.name), ascending=False) - self.df_output.to_html('output.html', index=False, justify='center') - -if __name__ == '__main__': - import os - import sys - __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) - - model = sys.argv[1] - fasta_file = sys.argv[2] - - PDPO = PDPOPrediction(__location__,model,fasta_file) - PDPO.Datastructure() - PDPO.Prediction() -