changeset 35:a662eb3f87c2 draft

Uploaded

--- /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()
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