changeset 28:4289d1b76d66 draft

Deleted selected files

Binary file model1600.sav has changed

Binary file model2400.sav has changed

--- a/phagepromoter.py	Mon Oct 28 11:57:47 2019 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,570 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Jul 19 13:45:05 2018
-
-@author: Marta
-"""
-
-from Bio import SeqIO
-import numpy as np
-import pandas as pd
-from auxiliar import free_energy,freq_base
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import IUPAC
-from auxiliar import get_bacteria, get_families, get_max_pssm, get_scores, get_lifecycle
-
-#division of the test genome in sequences of 65 bp
-def get_testseqs65(form,fic,both=False):
-    ALL = []
-    indexes = []
-    a = 0
-    rec = SeqIO.read(fic,form)
-    genome = rec.seq
-    i = 0
-    j = 65
-    while j < len(genome):
-        s = genome[i:j]
-        ALL.append([1,i,j,s])
-        i += 20
-        j += 20
-        a += 1
-        indexes.append(rec.name+":"+str(a))
-    if both:
-        comp = genome.reverse_complement()
-        size = len(rec.seq)
-        i = 0
-        j = 65
-        while j < len(comp):
-            s = comp[i:j]
-            ALL.append([-1,size-j,size-i,s])
-            i += 20
-            j += 20
-            a += 1
-            indexes.append(rec.name+":"+str(a))
-    df = pd.DataFrame(ALL, index=indexes, columns=['strand','iniprom','endprom','seq'])
-    return df
-
-#calculate the scores of all sequences (similar to get_posScores and get_negScores)
-def get_testScores(loc,test):
-    scores = []
-    posis = []
-    sizes = []
-    dic = {}
-    for ind,row in test.iterrows():
-        _,window = ind.split(':')
-        strand = row['strand']
-        ini = row['iniprom']
-        end = row['endprom']
-        seq = row['seq']
-        pos = [ini,end,strand]
-        dic[window] = pos
-        s = seq
-        score10_6,pos10_6 = get_scores(os.path.join(loc,'pssm10_6.txt'), s)
-        maxi10_6 = get_max_pssm(os.path.join(loc,'pssm10_6.txt'))
-        score10_8,pos10_8 = get_scores(os.path.join(loc,'pssm10_8.txt'), s)
-        maxi10_8 = get_max_pssm(os.path.join(loc,'pssm10_8.txt'))
-        scores23,pos23 = get_scores(os.path.join(loc,'pssm_23.txt'), s)
-        maxi23 = get_max_pssm(os.path.join(loc,'pssm_23.txt'))
-        scores21,pos21 = get_scores(os.path.join(loc,'pssm_21.txt'), s)
-        maxi21 = get_max_pssm(os.path.join(loc,'pssm_21.txt'))
-        scores27,pos27 = get_scores(os.path.join(loc,'pssm_27.txt'), s)
-        maxi27 = get_max_pssm(os.path.join(loc,'pssm_27.txt'))
-        scores32,pos32 = get_scores(os.path.join(loc,'pssm_32.txt'), s)
-        maxi32 = get_max_pssm(os.path.join(loc,'pssm_32.txt'))
-        score23 = max(scores23)
-        score21 = max(scores21)
-        score27 = max(scores27)
-        score32 = max(scores32)
-        maxiphage = max(score23,score21,score27,score32)
-        if maxiphage == score23: phage_max = score23*maxi23
-        elif maxiphage == score21: phage_max = score21*maxi21
-        elif maxiphage == score27: phage_max = score27*maxi27
-        elif maxiphage == score32: phage_max = score32*maxi32
-        score35_6,pos35_6 = get_scores(os.path.join(loc,'pssm35_6.txt'), s)
-        maxi35_6 = get_max_pssm(os.path.join(loc,'pssm35_6.txt'))
-        score35_9,pos35_9 = get_scores(os.path.join(loc,'pssm35_9.txt'), s)
-        maxi35_9 = get_max_pssm(os.path.join(loc,'pssm35_9.txt'))
-        score35_t4,pos35_t4 = get_scores(os.path.join(loc,'pssm35_t4.txt'), s)
-        maxi35_t4 = get_max_pssm(os.path.join(loc,'pssm35_t4.txt'))
-        score35_cbb,pos35_cbb = get_scores(os.path.join(loc,'pssm35_cbb.txt'), s)
-        maxi35_cbb = get_max_pssm(os.path.join(loc,'pssm35_cbb.txt'))
-        score35_lb,pos35_lb = get_scores(os.path.join(loc,'pssm35_lb.txt'),s)
-        maxi35_lb = get_max_pssm(os.path.join(loc,'pssm35_lb.txt'))
-        score35_mu, pos35_mu = get_scores(os.path.join(loc,'pssm35_mu.txt'),s)
-        maxi35_mu = get_max_pssm(os.path.join(loc,'pssm35_mu.txt'))
-        
-        dists6 = []
-        score6 = []
-        for p in pos10_6:
-            for a in range(14,22):
-                d = p-a-6
-                if d >= 0: 
-                    s10 = score10_6[p]
-                    s35_6 = score35_6[d]
-                    score6.append([s35_6,s10])
-                    dists6.append([d,p])
-        dists9 = []            
-        score9 = []
-        for p in pos10_6:
-            for a in range(11,14):
-                d = p-a-9
-                if d >= 0: 
-                    s10 = score10_6[p]
-                    s35_9 = score35_9[d]
-                    score9.append([s35_9,s10])
-                    dists9.append([d,p])
-        distst4 = []
-        scoret4 = []
-        distscbb = []
-        scorecbb = []
-        for p in pos10_6:
-            for a in range(16,18):
-                d = p-a-7
-                if d >= 0: 
-                    s10 = score10_6[p]
-                    s35_t4 = score35_t4[d]
-                    s35_cbb = score35_cbb[d]
-                    scoret4.append([s35_t4,s10])
-                    distst4.append([d,p])
-                    scorecbb.append([s35_cbb,s10])
-                    distscbb.append([d,p])
-                    
-        
-        distsmu = []
-        scoremu = []
-        for p in pos10_6:
-            d = p-16-14
-            if d >= 0: 
-                s10 = score10_6[p]
-                s35_mu = score35_mu[d]
-                scoremu.append([s35_mu,s10])
-                distsmu.append([d,p])
-        
-        distslb = []
-        scorelb = []
-        for p in pos10_6:
-            d = p-13-14
-            if d >= 0: 
-                s10 = score10_6[p]
-                s35_lb = score35_lb[d]
-                scorelb.append([s35_lb,s10])
-                distslb.append([d,p])
-    
-        soma6 = [sum(x) for x in score6]
-        soma9 = [sum(x) for x in score9]
-        somat4 = [sum(x) for x in scoret4]
-        somacbb = [sum(x) for x in scorecbb]
-        somamu = [sum(x) for x in scoremu]
-        somalb = [sum(x) for x in scorelb]
-        
-        maxi6 = max(soma6)
-        maxi9 = max(soma9)
-        maxit4 = max(somat4)
-        maxicbb = max(somacbb)
-        maximu = max(somamu)
-        maxilb = max(somalb)
-        maxi_elems = max(maxi6,maxi9,maxit4,maxicbb,maxilb,maximu)
-
-        if maxi_elems == maxilb:
-            indmax = somalb.index(maxilb)
-            sc35 = scorelb[indmax][0]
-            sc10 = scorelb[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = distslb[indmax]
-            size35 = 14
-            elems_maxi = sc35*maxi35_lb+sc10*maxi10_6
-        elif maxi_elems == maximu:
-            indmax = somamu.index(maximu)
-            sc35 = scoremu[indmax][0]
-            sc10 = scoremu[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = distsmu[indmax]
-            size35 = 14
-            elems_maxi = sc35*maxi35_mu+sc10*maxi10_6
-        elif maxi_elems == maxi9:
-            indmax = soma9.index(maxi9)
-            sc35 = score9[indmax][0]
-            sc10 = score9[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = dists9[indmax]
-            size35 = 9
-            elems_maxi = sc35*maxi35_9+sc10*maxi10_6
-        elif maxi_elems == maxit4:
-            indmax = somat4.index(maxit4)
-            sc35 = scoret4[indmax][0]
-            sc10 = scoret4[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = distst4[indmax]
-            size35 = 7
-            elems_maxi = sc35*maxi35_t4+sc10*maxi10_6
-        elif maxi_elems == maxicbb:
-            indmax = somacbb.index(maxicbb)
-            sc35 = scorecbb[indmax][0]
-            sc10 = scorecbb[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = distscbb[indmax]
-            size35 = 7
-            elems_maxi = sc35*maxi35_cbb+sc10*maxi10_6
-        else:
-            indmax = soma6.index(maxi6)
-            sc35 = score6[indmax][0]
-            sc10 = score6[indmax][1]
-            score_elems = [sc35,sc10]
-            posel = dists6[indmax]
-            size35 = 6
-            elems_maxi = sc35*maxi35_6+sc10*maxi10_6
-        
-        if score23 == maxiphage:
-            phage_score = score23
-            posiphage = scores23.index(score23)
-            sizephage = 23
-        elif score21 == maxiphage:
-            phage_score = score21
-            posiphage = scores21.index(score21)
-            sizephage = 21
-        elif score27 == maxiphage:
-            phage_score = score27
-            posiphage = scores27.index(score27)
-            sizephage = 27
-        else:
-            phage_score = score32
-            posiphage = scores32.index(score32)
-            sizephage = 32
-            
-        if elems_maxi >= max(score10_8)*maxi10_8:
-            i = posel[1]
-            ext = s[i-3:i-1]
-            if ext == 'TG': tg = 1
-            else: tg = 0
-            if elems_maxi > phage_max: host = 1
-            else: 
-                host = 0
-                tg = 0
-            sc = max(score10_8)
-            end35 = posel[0]+size35
-            dist = posel[1]-end35
-            scores.append([host, score_elems[1],sc,score_elems[0],phage_score,tg,dist,str(seq)])
-            posis.append([posel[1],posel[0],posiphage])
-            sizes.append([6,size35,sizephage])
-        else:
-            host = 1
-            sc = max(score10_8)
-            i = score10_8.index(sc)
-            ext = s[i-3:i-1]
-            if ext == 'TG': tg = 1
-            else: tg = 0
-            if max(score10_8)*maxi10_8 > phage_max: host = 1
-            else: 
-                host = 0
-                tg = 0
-            end35 = posel[0]+size35
-            dist = posel[1]-end35
-            scores.append([host,score_elems[1],sc,score_elems[0],phage_score,tg,dist,str(seq)])
-            posis.append([i,posel[0],posiphage])
-            sizes.append([8,size35,sizephage])
-    score = pd.DataFrame(scores, index=test.index, columns=['host','score10','score10_8','score35','score_phage','tg','dist','seq'])
-    posis = pd.DataFrame(posis, index=test.index, columns=['pos10','pos35','posphage'])
-    sizes = pd.DataFrame(sizes, index=test.index, columns=['size10','size35','size_phage'])
-    df_all = pd.concat([score,posis,sizes],axis=1)
-    return df_all,dic
-
-def create_dftest(scores_test,dic_window,family,bacteria,lifecycle):
-    tudo = []
-    tudo2 = []
-    for ind,row in scores_test.iterrows():
-        _,window = ind.split(':')
-        posis = dic_window[window]
-        strand=posis[2]
-        if strand == 1: ini=posis[0]
-        else: ini=posis[1]
-        seqprom = row['seq']
-        score10 = row['score10']
-        score10_8 = row['score10_8']
-        score35 = row['score35']
-        scorephage = row['score_phage']
-        size10 = row['size10']
-        size35 = row['size35']
-        sizephage = row['size_phage']
-        ini10 = row['pos10']
-        tg = row['tg']
-        host = row['host']
-        ini35 = row['pos35']
-        dist = row['dist']
-        end10=ini10+size10
-        iniphage = row['posphage']
-        endphage = iniphage+sizephage
-        if strand == 1:
-            if host == 0: 
-                new_seq = seqprom[iniphage:endphage]
-                new_ini = ini+iniphage+1
-                new_end = ini+endphage
-            else:
-                if size10 == 6: 
-                    new_seq = seqprom[ini35:end10]
-                    new_ini = ini+ini35+1
-                    new_end = ini+end10
-                else: 
-                    new_seq = seqprom[ini10:end10]
-                    new_ini = ini+ini10+1
-                    new_end = ini+end10
-            new_pos = '('+str(new_ini)+'..'+str(new_end)+')'
-        else:
-            if host == 0: 
-                new_seq = seqprom[iniphage:endphage]
-                new_ini = ini-endphage+1
-                new_end = ini-iniphage
-            else:
-                if size10 == 6: 
-                    new_seq = seqprom[ini35:end10]
-                    new_ini = ini-end10+1
-                    new_end = ini-ini35
-                else: 
-                    new_seq = seqprom[ini10:end10]
-                    new_ini = ini-end10+1
-                    new_end = ini-ini10
-            new_pos = 'complement('+str(new_ini)+'..'+str(new_end)+')'
-        if size10 == 6:
-            size10_6 = 1
-        else: 
-            size10_6 = 0
-        if size35 == 6:
-            size35_6 = 1
-            size35_7 = 0
-            size35_9 = 0
-            size35_14 = 0
-        elif size35 == 7:
-            size35_6 = 0
-            size35_7 = 1
-            size35_9 = 0
-            size35_14 = 0
-        elif size35 == 9:
-            size35_6 = 0
-            size35_7 = 0
-            size35_9 = 1
-            size35_14 = 0
-        else:
-            size35_6 = 0
-            size35_7 = 0
-            size35_9 = 0
-            size35_14 = 1
-        if sizephage == 23:
-            sizephage_23 = 1
-            sizephage_21 = 0
-            sizephage_32 = 0
-        elif sizephage == 21:
-            sizephage_23 = 0
-            sizephage_21 = 1
-            sizephage_32 = 0
-        elif sizephage == 32:
-            sizephage_23 = 0
-            sizephage_21 = 0
-            sizephage_32 = 1
-        else:
-            sizephage_23 = 0
-            sizephage_21 = 0
-            sizephage_32 = 0
-        if family == 'Podoviridae': 
-            Podo = 1
-            Sipho = 0
-            Myo = 0
-        elif family == 'Siphoviridae':
-            Podo = 0
-            Sipho = 1
-            Myo = 0
-        elif family == 'Myoviridae':
-            Podo = 0
-            Sipho = 0
-            Myo = 1
-        else: 
-            Podo = 0
-            Sipho = 0
-            Myo = 0
-        if bacteria == 'Bacillus':
-            bac = [1,0,0,0,0]
-        elif bacteria == 'Escherichia coli':
-            bac = [0,1,0,0,0]
-        elif bacteria == 'Klebsiella':
-            bac = [0,0,1,0,0]
-        elif bacteria == 'Pectobacterium':
-            bac = [0,0,0,1,0]
-        elif bacteria == 'Cronobacter':
-            bac = [0,0,0,0,1]
-        else:
-            bac = [0,0,0,0,0]
-        if lifecycle == 'virulent':  tp = 0
-        else: tp = 1
-        fe = free_energy(str(seqprom))
-        AT = freq_base(str(seqprom))
-        linha = [score10, score10_8, score35, dist, scorephage, fe, AT, host,size10_6,
-                 size35_6, size35_7, size35_9, size35_14,
-                 sizephage_23, sizephage_21,
-                 sizephage_32, tg, Podo, Sipho, Myo,tp]
-        linha.extend(bac)
-        tudo.append(linha)
-        linha2 = [new_pos,str(new_seq), host, size10_6,
-                 score10, score10_8, size35_6, size35_7, size35_9,size35_14, 
-                 score35, dist, sizephage_23, sizephage_21,
-                 sizephage_32, scorephage, tg, Podo, Sipho, Myo,tp, fe, AT]
-        linha2.extend(bac)
-        tudo2.append(linha2)
-    df_test = pd.DataFrame(tudo, index=scores_test.index, 
-                          columns = ['score10', 'score10_8','score35', 'dist35_10',
-                                     'scorephage','fe', 'freqAT',
-                                     'host','size10',
-                                     'size35_6', 'size35_7','size35_9','size35_14',
-                                     'sizephage_23', 
-                                     'sizephage_21', 'sizephage_32',
-                                     'TG', 'Podo', 'Sipho', 'Myo', 'tp',
-                                     'Bacillus', 'EColi', 
-                                     'Pectobacterium','Klebsiella', 'Cronobacter'])
-    df_INFO = pd.DataFrame(tudo2, index=scores_test.index, 
-                          columns = ['Positions','Promoter Sequence','host','size10',
-                                     'score10', 'score10_8',
-                                     'size35_6', 'size35_7','size35_9','size35_14',
-                                     'score35', 'dist35_10','sizephage_23', 
-                                     'sizephage_21', 'sizephage_32',
-                                     'scorephage', 'TG', 'Podo', 'Sipho', 'Myo', 'tp','fe', 'freqAT',
-                                     'EColi', 'Salmonella', 
-                                     'Pectobacterium','Cronobacter', 'Streptococcus'])
-    return df_test,df_INFO
-
-
-def get_predictions(scaler_file,model_file,test,df_testinfo,threshold):
-    from sklearn.externals import joblib
-    scaler = joblib.load(scaler_file)
-    model = joblib.load(model_file)
-    feat_scaled = pd.DataFrame(scaler.transform(test.iloc[:,:7]),index =test.index, columns=test.columns[:7])
-    TEST_scaled = pd.concat([feat_scaled,test.iloc[:,7:]],axis=1)
-    scores = model.predict_proba(TEST_scaled)
-    pos_scores = np.empty((TEST_scaled.shape[0],0), float)
-    for x in scores: pos_scores = np.append(pos_scores,x[1])
-    try: positive_indexes = np.nonzero(pos_scores>float(threshold))[0] #escolher os positivos, podia ser escolher com score > x
-    except ValueError: return 'The threshold value is not a float'
-    else:
-        if len(positive_indexes) == 0: return None
-        else:
-            positive_windows = TEST_scaled.index[positive_indexes]
-            INFO = df_testinfo.loc[positive_windows,['Positions','Promoter Sequence']]
-            promoter_type = []
-            for x in df_testinfo.loc[positive_windows,'host'].tolist():
-                if x == 0: promoter_type.append('phage')
-                else: promoter_type.append('host')
-            INFO['Type'] = promoter_type
-            INFO['Scores'] = np.around(pos_scores[positive_indexes],decimals=3)
-            INFO.index = positive_windows
-            return INFO
-
-
-def get_finaldf(test,rec):
-    new_df = test.groupby(['Positions'])
-    groups = list(new_df.groups.keys())
-    for i in range(len(groups)-1):
-        for j in range(i, len(groups)):
-            if 'complement' in groups[i]: inii = int(groups[i][11:].split('..')[0])
-            else: inii = int(groups[i][1:].split('..')[0])
-            if 'complement' in groups[j]: inij = int(groups[j][11:].split('..')[0])
-            else: inij = int(groups[j][1:].split('..')[0])
-            if inij < inii:
-                temp = groups[i]
-                groups[i] = groups[j]
-                groups[j] = temp
-    new_inds = []
-    for g in groups:
-        inds = new_df.groups[g]
-        if len(inds) == 1: new_inds.append(inds[0])
-        else:
-            maxi = max(new_df.get_group(g)['Scores'])
-            i = new_df.groups[g][new_df.get_group(g)['Scores']==maxi][0]
-            new_inds.append(i)
-    
-    output = test.loc[new_inds,:]
-    strands = []
-    new_pos = []
-    old_pos = output['Positions'].tolist()
-    
-    from Bio.SeqFeature import SeqFeature, FeatureLocation
-    feats = rec.features
-    for ind, row in output.iterrows():
-        pos = row['Positions']
-        if 'complement' in pos: 
-            strands.append('-')
-            new_pos.append(pos[10:])
-            ini,end= pos[11:-1].split('..')
-            new_loc = FeatureLocation(int(ini),int(end),strand=-1)
-        else: 
-            strands.append('+')
-            new_pos.append(pos)
-            ini,end= pos[1:-1].split('..')
-            new_loc = FeatureLocation(int(ini),int(end),strand=1)
-        feat = SeqFeature(new_loc, type='regulatory',qualifiers={'regulatory_class':['promoter'], 'note=':['predicted by PhagePromoter']})
-        feats.append(feat)            
-            
-    output.insert(loc=0, column='Strand', value=strands)
-    output['Positions'] = new_pos
-    output.to_html('output.html',index=False, justify='center')
-    recs = []
-    i = 0
-    for ind,row in output.iterrows():
-        s = Seq(row['Promoter Sequence'])
-        posis = old_pos[i]
-        typ = row['Type']
-        score = row['Scores']
-        sq = SeqRecord(seq=s, id=ind, description=typ+' '+posis+' score='+str(score))
-        recs.append(sq)
-        i += 1
-    SeqIO.write(recs, 'output.fasta','fasta')
-    new_rec = rec
-    new_rec.seq.alphabet = IUPAC.IUPACAmbiguousDNA()
-    new_feats = sorted(feats, key=lambda x: x.location.start)
-    new_rec.features = new_feats
-    SeqIO.write(new_rec,'output.gb','genbank')
-
-if __name__== "__main__":
-    
-    import sys
-    import os
-    __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
-    
-    gen_format = sys.argv[1]
-    genome_file = sys.argv[2]
-    both = sys.argv[3]
-    threshold = sys.argv[4]
-    family = sys.argv[5]
-    host = sys.argv[6]
-    phage_type = sys.argv[7]
-    model = sys.argv[8]
-    '''
-    gen_format = 'genbank'
-    genome_file = 'test-data/NC_015264.gb'
-    both = False
-    threshold = '0.50'
-    family = 'Podoviridae'
-    host = 'Pseudomonas'
-    phage_type = 'virulent'
-    model = 'SVM2400'
-    #model = 'ANN1600'
-    '''
-    
-    rec = SeqIO.read(genome_file, gen_format)
-    test_windows = get_testseqs65(gen_format, genome_file,both)
-    try: score_test,dic_window = get_testScores(__location__,test_windows)
-    except IndexError: print('Error. Input sequence can only have A,C,G or T')
-    else:
-        df_test,df_testinfo = create_dftest(score_test,dic_window,family,host,phage_type)
-        if model == 'ANN1600':
-            scaler_file = os.path.join(__location__, 'scaler1600.sav')
-            model_file = os.path.join(__location__, 'model1600.sav')
-            preds =  get_predictions(scaler_file, model_file, df_test,df_testinfo,threshold)
-            if preds is None: print('There is no sequence with a score value higher or equal to the threshold '+str(threshold))
-            elif type(preds) == str: print(preds)
-            else: output = get_finaldf(preds,rec)
-        else:
-            scaler_file = os.path.join(__location__, 'scaler2400.sav')
-            model_file = os.path.join(__location__, 'model2400.sav')
-            new_df_test = df_test.iloc[:,[0,1,2,3,4,5,6,7,8,9,13,14,16,17,19,20,22,24,25]]
-            preds =  get_predictions(scaler_file, model_file, new_df_test,df_testinfo,threshold)
-            if preds is None: print('There is no sequence with a score value higher or equal to the threshold '+str(threshold))
-            elif type(preds) == str: print(preds)
-            else: output = get_finaldf(preds,rec)
-        

Binary file scaler1600.sav has changed

Binary file scaler2400.sav has changed