# HG changeset patch # User jose_duarte # Date 1686650022 0 # Node ID a662eb3f87c263fa8eab30af5f92bdb4655729ee # Parent a20338e6e58f281d020d547d36b91795015f49ca Uploaded diff -r a20338e6e58f -r a662eb3f87c2 DPOGALAXY.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/DPOGALAXY.py Tue Jun 13 09:53:42 2023 +0000 @@ -0,0 +1,158 @@ +import pickle +from Bio import SeqIO +import os +import pandas as pd +import numpy as np +from local_ctd import CalculateCTD +from local_AAComposition import CalculateDipeptideComposition +import sys +from Bio.SeqUtils.ProtParam import ProteinAnalysis + + +class PDPOPrediction: + + def __init__(self, folder='location', mdl='', seq_file='fasta_file.fasta', ttable=11): + """ + Initialize PhageDPO prediction. + :param folder: data path + :param mdl: ml model, in this case ANN or SVM + :param seq_file: fasta file + :param ttable: Translational table. By default, The Bacterial, Archaeal and Plant Plastid Code Table 11 + """ + self.records = [] + 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.model0 = pickle.load(m) + self.model = self.model0.named_steps['clf'] + self.scaler = self.model0.named_steps['scl'] + self.selectk = self.model0.named_steps['selector'] + self.name = 'model' + + for seq in SeqIO.parse(os.path.join(self.__location__, self.seqfile), 'fasta'): + record = [] + 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] + record.append(seq.description) + record.append(DNA_seq._data) + record.append(AA_seq._data) + self.records.append(record) + + columns = ['ID', 'DNAseq', 'AAseq'] + self.df = pd.DataFrame(self.records, columns=columns) + #self.df = self.df.set_index('ID') + self.df.update(self.df.DNAseq[self.df.DNAseq.apply(type) == list].str[0]) + self.df.update(self.df.AAseq[self.df.AAseq.apply(type) == list].str[0]) + + def Datastructure(self): + """ + Create dataset with all features + """ + def count_orf(orf_seq): + """ + Function to count open reading frames + :param orf_seq: sequence to analyze + :return: dictionary with open reading frames + """ + 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 str(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): + """ + Function to count amino acids + :param aa_seq: sequence to analyze + :return: dictionary with amino acid composition + """ + 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): + """ + Function to analyze secondary structure. Helix, Turn and Sheet + :param aa_seq: sequence to analyze + :return: dictionary with composition of each secondary structure + """ + 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.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 = {} + self.df1 = self.df[['ID']].copy() + self.df.drop(['ID'], axis=1, inplace=True) + for i in range(len(self.df)): + i_name = self.df.index[i] + dna_feat[i] = count_orf(self.df.iloc[i]['DNAseq']) + aa_len[i] = len(self.df.iloc[i]['AAseq']) + aroma_dic[i] = ProteinAnalysis(self.df.iloc[i]['AAseq']).aromaticity() + iso_dic[i] = ProteinAnalysis(self.df.iloc[i]['AAseq']).isoelectric_point() + aa_content[i] = count_aa(self.df.iloc[i]['AAseq']) + st_dic_master[i] = sec_st_fr(self.df.iloc[i]['AAseq']) + CTD_dic[i] = CalculateCTD(self.df.iloc[i]['AAseq']) + dp[i] = 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): + """ + Predicts the percentage of each CDS being depolymerase. + :return: model prediction + """ + scores = self.model0.predict_proba(self.df.iloc[:, :]) + 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.rename(columns={'index': 'CDS'}, inplace=True) + self.df_output['CDS'] += 1 + self.df_output['{} DPO Prediction (%)'.format(self.name)] = pos_scores + self.df_output.to_html('output.html', index=False, justify='center') + + +if __name__ == '__main__': + __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) + + model = 'svm1495' + fasta_file = sys.argv[2] + + #model = "C:/Users/biosy/Desktop/phageDPO/DPO/svm1495" + #fasta_file = "C:/Users/biosy/Downloads/bacillus.fasta" + + PDPO = PDPOPrediction(__location__, model, fasta_file) + PDPO.Datastructure() + PDPO.Prediction() \ No newline at end of file