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model.sav phage_promoters.py phage_promoters.xml scaler.sav |
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| diff -r f391041379cc -r 4691c4418b00 model.sav |
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| Binary file model.sav has changed |
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| diff -r f391041379cc -r 4691c4418b00 phage_promoters.py --- a/phage_promoters.py Wed Aug 01 05:59:45 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| b'@@ -1,507 +0,0 @@\n-# -*- coding: utf-8 -*-\n-"""\n-Created on Thu Jul 19 13:45:05 2018\n-\n-@author: Marta\n-"""\n-\n-from Bio import SeqIO\n-import numpy as np\n-import pandas as pd\n-from auxiliar import free_energy,freq_base\n-from Bio.Seq import Seq\n-from Bio.SeqRecord import SeqRecord\n-from Bio.Alphabet import IUPAC\n-from auxiliar import get_bacteria, get_families, get_max_pssm, get_scores, get_lifecycle\n-\n-#division of the test genome in sequences of 65 bp\n-def get_testseqs65(form,fic,both=False):\n- ALL = []\n- indexes = []\n- a = 0\n- rec = SeqIO.read(fic,form)\n- genome = rec.seq\n- i = 0\n- j = 65\n- while j < len(genome):\n- s = genome[i:j]\n- ALL.append([1,i,j,s])\n- i += 20\n- j += 20\n- a += 1\n- indexes.append(rec.name+":"+str(a))\n- if both:\n- i = 0\n- j = 65\n- while j < len(genome):\n- s = genome[i:j].reverse_complement()\n- ALL.append([-1,i,j,s])\n- i += 20\n- j += 20\n- a += 1\n- indexes.append(rec.name+":"+str(a))\n- df = pd.DataFrame(ALL, index=indexes, columns=[\'strand\',\'iniprom\',\'endprom\',\'seq\'])\n- return df\n-\n-#calculate the scores of all sequences (similar to get_posScores and get_negScores)\n-def get_testScores(loc,test):\n- scores = []\n- posis = []\n- sizes = []\n- dic = {}\n- for ind,row in test.iterrows():\n- _,window = ind.split(\':\')\n- strand = row[\'strand\']\n- ini = row[\'iniprom\']\n- end = row[\'endprom\']\n- seq = row[\'seq\']\n- pos = [ini,end,strand]\n- dic[window] = pos\n- s = seq\n- score10_6,pos10_6 = get_scores(os.path.join(loc,\'pssm10_6.txt\'), s)\n- maxi10_6 = get_max_pssm(os.path.join(loc,\'pssm10_6.txt\'))\n- score10_8,pos10_8 = get_scores(os.path.join(loc,\'pssm10_8.txt\'), s)\n- maxi10_8 = get_max_pssm(os.path.join(loc,\'pssm10_8.txt\'))\n- scores23,pos23 = get_scores(os.path.join(loc,\'pssm_23.txt\'), s)\n- maxi23 = get_max_pssm(os.path.join(loc,\'pssm_23.txt\'))\n- scores21,pos21 = get_scores(os.path.join(loc,\'pssm_21.txt\'), s)\n- maxi21 = get_max_pssm(os.path.join(loc,\'pssm_21.txt\'))\n- scores27,pos27 = get_scores(os.path.join(loc,\'pssm_27.txt\'), s)\n- maxi27 = get_max_pssm(os.path.join(loc,\'pssm_27.txt\'))\n- scores32,pos32 = get_scores(os.path.join(loc,\'pssm_32.txt\'), s)\n- maxi32 = get_max_pssm(os.path.join(loc,\'pssm_32.txt\'))\n- score23 = max(scores23)\n- score21 = max(scores21)\n- score27 = max(scores27)\n- score32 = max(scores32)\n- maxiphage = max(score23,score21,score27,score32)\n- if maxiphage == score23: phage_max = score23*maxi23\n- elif maxiphage == score21: phage_max = score21*maxi21\n- elif maxiphage == score27: phage_max = score27*maxi27\n- elif maxiphage == score32: phage_max = score32*maxi32\n- score35_6,pos35_6 = get_scores(os.path.join(loc,\'pssm35_6.txt\'), s)\n- maxi35_6 = get_max_pssm(os.path.join(loc,\'pssm35_6.txt\'))\n- score35_9,pos35_9 = get_scores(os.path.join(loc,\'pssm35_9.txt\'), s)\n- maxi35_9 = get_max_pssm(os.path.join(loc,\'pssm35_9.txt\'))\n- score35_t4,pos35_t4 = get_scores(os.path.join(loc,\'pssm35_t4.txt\'), s)\n- maxi35_t4 = get_max_pssm(os.path.join(loc,\'pssm35_t4.txt\'))\n- score35_cbb,pos35_cbb = get_scores(os.path.join(loc,\'pssm35_cbb.txt\'), s)\n- maxi35_cbb = get_max_pssm(os.path.join(loc,\'pssm35_cbb.txt\'))\n- score35_lb,pos35_lb = get_scores(os.path.join(loc,\'pssm35_lb.txt\'),s)\n- maxi35_lb = get_max_pssm(os.path.join(loc,\'pssm35_lb.txt\'))\n- score35_mu, pos35_mu = get_scores(os.path.join(loc,\'pssm35_mu.txt\'),s)\n- maxi35_mu = get_max_pssm(os.path.join(loc,\'pssm35_mu.txt\'))\n- \n- dists6 = []\n- score6 = []\n- for p in pos10_6:\n- for a in range(14,22):\n- d = p-a-6\n- if d >= 0: \n- s10 = score10_6[p]\n- '..b'Pectobacterium\',\'Cronobacter\'])\n- return df_test,df_INFO\n-\n-\n-def get_finaldf(test):\n- new_df = test.groupby([\'positions\'])\n- groups = list(new_df.groups.keys())\n- for i in range(len(groups)-1):\n- for j in range(i, len(groups)):\n- inii = int(groups[i][1:].split(\'..\')[0])\n- inij = int(groups[j][1:].split(\'..\')[0])\n- if inij < inii:\n- temp = groups[i]\n- groups[i] = groups[j]\n- groups[j] = temp\n- new_inds = []\n- for g in groups:\n- inds = new_df.groups[g]\n- if len(inds) == 1: new_inds.append(inds[0])\n- else:\n- maxi = max(new_df.get_group(g)[\'scores\'])\n- i = new_df.groups[g][new_df.get_group(g)[\'scores\']==maxi][0]\n- new_inds.append(i)\n- \n- output = test.loc[new_inds,:]\n- output.to_html(\'output.html\',index=False)\n- recs = []\n- for ind,row in output.iterrows():\n- s = Seq(row[\'promoter_seq\'])\n- posis = row[\'positions\']\n- typ = row[\'promoter_type\']\n- score = row[\'scores\']\n- sq = SeqRecord(seq=s, id=ind, description=typ+\' \'+posis+\' score=\'+str(score))\n- recs.append(sq)\n- SeqIO.write(recs, \'output.fasta\',\'fasta\')\n- \n-def get_predictions(scaler_file,model_file,test,df_testinfo,threshold):\n- from sklearn.externals import joblib\n- scaler = joblib.load(scaler_file)\n- model = joblib.load(model_file)\n- feat_scaled = pd.DataFrame(scaler.transform(test.iloc[:,:7]),index =test.index, columns=test.columns[:7])\n- TEST_scaled = pd.concat([feat_scaled,test.iloc[:,7:]],axis=1)\n- scores = model.predict_proba(TEST_scaled)\n- pos_scores = np.empty((TEST_scaled.shape[0],0), float)\n- for x in scores: pos_scores = np.append(pos_scores,x[1])\n- try: positive_indexes = np.nonzero(pos_scores>=float(threshold))[0] #escolher os positivos, podia ser escolher com score > x\n- except ValueError: return \'The threshold value is not a float\'\n- else:\n- if len(positive_indexes) == 0: return None\n- else:\n- positive_windows = TEST_scaled.index[positive_indexes]\n- INFO = df_testinfo.loc[positive_windows,[\'positions\',\'promoter_seq\']]\n- promoter_type = []\n- for x in df_testinfo.loc[positive_windows,\'host\'].tolist():\n- if x == 0: promoter_type.append(\'phage\')\n- else: promoter_type.append(\'host\')\n- INFO[\'promoter_type\'] = promoter_type\n- INFO[\'scores\'] = np.around(pos_scores[positive_indexes],decimals=3)\n- INFO.index = positive_windows\n- return INFO\n-\n-if __name__== "__main__":\n- \n- import sys\n- import os\n- __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n- scaler_file = os.path.join(__location__, \'scaler.sav\')\n- model_file = os.path.join(__location__, \'model.sav\')\n- \n- gen_format = sys.argv[1]\n- genome_file = sys.argv[2]\n- both = sys.argv[3]\n- threshold = sys.argv[4]\n- family = sys.argv[5]\n- host = sys.argv[6]\n- phage_type = sys.argv[7]\n- \'\'\'\n- gen_format = \'gb\'\n- genome_file = \'test-data/NC_015264.gb\'\n- genbank_fasta = \'genbank\'\n- both = False\n- threshold = \'0.50\'\n- family = \'Podoviridae\'\n- host = \'Pseudomonas\'\n- phage_type = \'virulent\'\n- \'\'\'\n-\n- test_windows = get_testseqs65(gen_format, genome_file,both)\n- try: score_test,dic_window = get_testScores(__location__,test_windows)\n- \n- except IndexError: print(\'Error. Input sequence can only have A,C,G or T\')\n- \n- else:\n- df_test,df_testinfo = create_dftest(score_test,dic_window,family,host,phage_type)\n- \n- preds = get_predictions(scaler_file, model_file, df_test,df_testinfo,threshold)\n- if preds is None: print(\'There is no sequence with a score value higher or equal to the threshold \'+str(threshold))\n- elif type(preds) == str: print(preds)\n- else: output = get_finaldf(preds)\n- \n' |
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| diff -r f391041379cc -r 4691c4418b00 phage_promoters.xml --- a/phage_promoters.xml Wed Aug 01 05:59:45 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,104 +0,0 @@ -<tool id="get_proms" name="PhagePromoters" version="0.1.0"> - <description> -Get promoters of phage genomes - </description> - <requirements> - <requirement type="package">biopython</requirement> - <requirement type="package">scikit-learn</requirement> - <requirement type="package">numpy</requirement> - <requirement type="package">pandas</requirement> - </requirements> - <command detect_errors="exit_code" interpreter="python3"><![CDATA[ - phage_promoters.py "$input_type.genome_format" "$genome" "$both" "$threshold" "$family" "$bacteria" "$lifecycle" - ]]> - </command> - <inputs> - <conditional name="input_type"> - <param type="select" name="genome_format" label='file format'> - <option value="genbank" selected="yes">genbank</option> - <option value="fasta">fasta</option> - </param> - <when value="genbank"> - <param type="data" name="genome" format="genbank" label='genome'/> - </when> - <when value="fasta"> - <param type="data" name="genome" format="fasta" label='genome'/> - </when> - </conditional> - <param type="boolean" name="both" label='Search both strands' checked="false" truevalue="-both" falsevalue="" /> - <param name="threshold" type="float" value="0.50" label="Threshold" help="Probabilty of being a promoter (float between 0 and 1)" /> - <param type="select" name="family" label='Phage family'> - <option value="Podoviridae" selected="yes">Podoviridae</option> - <option value="Siphoviridae">Siphoviridae</option> - <option value="Myoviridae">Myoviridae</option> - </param> - <param type="select" name="bacteria" label='Host bacteria Genus'> - <option value="Escherichia coli" selected="yes">Escherichia coli</option> - <option value="Salmonella">Salmonella</option> - <option value="Pseudomonas">Pseudomonas</option> - <option value="Yersinia">Yersinia</option> - <option value="Morganella">Morganella</option> - <option value="Cronobacter">Cronobacter</option> - <option value="Staphylococcus">Staphylococcus</option> - <option value="Streptococcus">Streptococcus</option> - <option value="Lactococcus">Lactococcus</option> - <option value="Streptomyces">Streptomyces</option> - <option value="Klebsiella">Klebsiella</option> - <option value="Bacillus">Bacillus</option> - <option value="Pectobacterium">Pectobacterium</option> - <option value="other">other</option> - </param> - <param type="select" name="lifecycle" label='Phage type'> - <option value="virulent" selected="yes">virulent</option> - <option value="temperate">temperate</option> - </param> - </inputs> - <outputs> - <data name="output1" format="html" from_work_dir="output.html" /> - <data name="output2" format="fasta" from_work_dir="output.fasta" /> - </outputs> - <tests> - <test> - <param name="genome_format" value="genbank"/> - <param name="genome" value="NC_015264.gb"/> - <param name="both" value="False"/> - <param name="threshold" value="0.50"/> - <param name="family" value="Podoviridae"/> - <param name="bacteria" value="Pseudomonas"/> - <param name="lifecycle" value="virulent"/> - <output name="output1" file="output.html"/> - <output name="output2" file="output.fasta"/> - </test> - </tests> - <help><![CDATA[ - -=============== -PhagePromoters -=============== - -Get promoters of phage genomes - -PhagePromoters is a python script that predicts promoter sequences in phage genomes, using a machine learning SVM model. This model was built from a train dataset with 25 features and 3200 examples (800 positives and 2400 negatives), each representing a 65 bp sequence of a phage genome. The positive cases represent the phage sequences that are already identified as promoters. - -**Inputs:** - -* genome format: fasta vs genbank; -* genome file: acepts both genbank and fasta formats; -* both strands (yes or no): allows the search in both DNA strands; -* threshold: represents the probability of the test sequence be a promoter (float between 0 and 1)" -* family: The family of the testing phage - Podoviridae, Siphoviridae or Myoviridae; -* Bacteria: The host of the phage. The train dataset include the following hosts: Bacillus, EColi, Salmonella, Pseudomonas, Yersinia, Klebsiella, Pectobacterium, Morganella, Cronobacter, Staphylococcus, Streptococcus, Streptomyces, Lactococcus. If the testing phage has a different host, select the option 'other', and it is recommended the use of a higher threshold value for more accurate results. -* phage type: The type of the phage, according to its lifecycle: virulent or temperate; - -**Outputs:** -This tool outputs two files: a FASTA file and a table in HTML, with the locations, sequence, score and type (recognized by host or phage RNAP) of the predicted promoters. - -**Requirements:** - -* Biopython -* Sklearn -* Numpy -* Pandas - - ]]></help> -</tool> |
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