Mercurial > repos > jose_duarte > phagedpo

changeset 34:a20338e6e58f draft

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author jose_duarte
date Tue, 13 Jun 2023 09:53:20 +0000
parents 269e43aa8721
children a662eb3f87c2
files ANN7185 DPOGALAXY.py SVM4311 d4311_SCALER d7185_SCALER
diffstat 5 files changed, 0 insertions(+), 193 deletions(-) [+]
line wrap: on
line diff
Binary file ANN7185 has changed
--- a/DPOGALAXY.py	Tue Jun 13 09:53:02 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,193 +0,0 @@
-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.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 == 'ANN7185':
-            with open(os.path.join(__location__, 'd7185_SCALER'), 'rb') as sc:
-                self.scaler = pickle.load(sc)
-                self.name = mdl
-
-        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.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"],
-                   "ANN7185": ["DNA-GC", "AA_Len", "Aromaticity", "IsoelectricPoint", "G", "A", "L", "V", "I", "P", "F",
-                               "S", "T", "C", "Y", "N", "Q", "D", "E", "R", "K", "H", "W", "M", "Turn", "Sheet", "_PolarizabilityC1",
-                               "_PolarizabilityC2", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SolventAccessibilityC2",
-                               "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_ChargeC2", "_ChargeC3", "_PolarityC2",
-                               "_NormalizedVDWVC2", "_NormalizedVDWVC3", "_HydrophobicityC1", "_HydrophobicityC2", "_SecondaryStrT13",
-                               "_SecondaryStrT23", "_ChargeT12", "_ChargeT13", "_HydrophobicityT12", "_PolarizabilityD1001",
-                               "_PolarizabilityD1025", "_PolarizabilityD1050", "_PolarizabilityD2001", "_PolarizabilityD3025",
-                               "_PolarizabilityD3050", "_PolarizabilityD3075", "_SolventAccessibilityD1050", "_SolventAccessibilityD2001",
-                               "_SolventAccessibilityD2025", "_SolventAccessibilityD2050", "_SolventAccessibilityD3025",
-                               "_SolventAccessibilityD3050", "_SolventAccessibilityD3100", "_SecondaryStrD1025", "_SecondaryStrD1050",
-                               "_SecondaryStrD1075", "_SecondaryStrD2001", "_SecondaryStrD2050", "_SecondaryStrD2075", "_ChargeD1050",
-                               "_ChargeD1075", "_ChargeD1100", "_ChargeD2025", "_ChargeD3025", "_ChargeD3050", "_PolarityD2050",
-                               "_PolarityD3050", "_NormalizedVDWVD1001", "_NormalizedVDWVD1050", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025",
-                               "_HydrophobicityD3001", "_HydrophobicityD3075", "AD", "AW", "AY", "RC", "RT", "NA", "NE",
-                               "NG", "NP", "DE", "DQ", "DG", "DT", "DY", "CG", "CL", "CY", "CV", "EN", "QA", "QR", "QE",
-                               "QI", "GA", "GR", "GD", "GQ", "GG", "GH", "GL", "GF", "GP", "GT", "GY", "HA", "HC", "HI",
-                               "HK", "HP", "IC", "IG", "IS", "IT", "IW", "LA", "LR", "LH", "LI", "LK", "LP", "KQ", "KH",
-                               "KS", "KT", "MQ", "MG", "MI", "FA", "FR", "FS", "FY", "PC", "PE", "PG", "PH", "PM", "PF",
-                               "PT", "SA", "SD", "SC", "SQ", "SW", "TA", "TC", "TM", "WL", "WV", "YE", "YG", "YH", "YI",
-                               "YL", "YK", "YM", "YS"]}
-
-        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: None
-        """
-        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)
-        print(self.df_output.columns)
-        self.df_output.rename(columns={'index': 'CDS'}, inplace=True)
-        self.df_output['CDS'] += 1
-        self.df_output['{} DPO Prediction (%)'.format(self.name)] = pos_scores
-        print(self.df_output)
-        #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__':
-    __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()
-
Binary file SVM4311 has changed
Binary file d4311_SCALER has changed
Binary file d7185_SCALER has changed