mv scripts in rules/ to scripts/ (issue #15)

rules/AMR/filter.pl

-#!/usr/bin/env perl
-
-use strict;
-use warnings;
-
-my $minlen = shift or die "Error: `minlen` parameter not provided\n";
-{
-    local $/=">";
-    while(<>) {
-        chomp;
-        next unless /\w/;
-        s/>$//gs;
-        my @chunk = split /\n/;
-        my $header = shift @chunk;
-        my $seqlen = length join "", @chunk;
-        print ">$_" if($seqlen >= $minlen);
-    }
-    local $/="\n";
-}

rules/Virulence/virulence_prediction.py

-#!/usr/bin/env python
-
-import Bio
-from Bio import SeqIO
-import re
-import pandas as pd
-#import argparse
-import numpy as np
-from sklearn import model_selection
-from sklearn.ensemble import RandomForestClassifier
-import pickle
-
-#from PyBioMed.PyProtein import AAComposition
-
-AALetter = ["A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"]
-
-def CalculateAAComposition(ProteinSequence):
-        LengthSequence = len(ProteinSequence)
-        Result={}
-        for i in AALetter:
-                Result[i] = round(float(ProteinSequence.seq.count(i))/ LengthSequence * 100, 2)
-        return Result
-
-def CalculateDipeptideComposition(ProteinSequence):
-        LengthSequence = len(ProteinSequence)
-        Result = {}
-        for i in AALetter:
-                for j in AALetter:
-                        Dipeptide = i + j
-                        Result[Dipeptide] = round(float(ProteinSequence.seq.count(Dipeptide)) / (LengthSequence -1) * 100, 2)
-
-        return Result
-
-def CalculateKmerComposition(ProteinSequence):
-        LengthSequence = len(ProteinSequence)
-        Result = {}
-        for i in AALetter:
-                for j in AALetter:
-                        for k in AALetter:
-                                Kmer = i + j + k
-                                Result[Kmer] = round(float(ProteinSequence.seq.count(Kmer)) / (LengthSequence -2) * 100, 2)
-        return Result
-
-def CalculateAADipeptideComposition(ProteinSequence):
-        Result = {}
-        Result.update(CalculateAAComposition(ProteinSequence))
-        Result.update(CalculateDipeptideComposition(ProteinSequence))
-        Result.update(CalculateKmerComposition(ProteinSequence))
-        return Result
-
-
-#if __name__ == "__main__":
-#	parser = argparse.ArgumentParser(description='Process fasta input with random forest virulence prediction model')
-#	parser.add_argument('infile', metavar='infile', type=str,
-#		help='The input file (FASTA format)')
-#	parser.add_argument('outfile', metavar='outfile', type=str,
-#		help='The outpufile (TSV format)')
-#	args = parser.parse_args()
-	
-df = pd.DataFrame([])
-	
-for Sequence in SeqIO.parse(snakemake.input[0], "fasta"):	
-	ProteinSequence = Sequence	
-	DIP = CalculateAADipeptideComposition(ProteinSequence)
-	data = DIP
-	data = pd.DataFrame(data.items())
-	data = data.transpose()
-
-	data.columns=data.iloc[0]
-	data = data.drop(data.index[[0]])
-	data['Sequence'] = Sequence.id
-	df =df.append(data)
-	rfc = pickle.load(open("finalized_model_3f.sav", 'rb'))
-
-X_predict = df.drop('Sequence', axis = 1)
-rfc_predict = rfc.predict(X_predict)	
-rfc_probs = rfc.predict_proba(X_predict)[:,1]
-	
-Prediction = pd.DataFrame([])
-dfToList = df['Sequence'].tolist()
-Prediction['Sequence'] = dfToList
-Prediction['Prediction'] = rfc_predict
-Prediction['Probability'] = rfc_probs
-Prediction.to_csv(snakemake.output[0], sep='\t')	
-
-
-

scripts/virulence_prediction.py

 #!/usr/bin/env python
 
+import Bio
+from Bio import SeqIO
 import re
 import pandas as pd
-import argparse
+#import argparse
 import numpy as np
+from sklearn import model_selection
 from sklearn.ensemble import RandomForestClassifier
 import pickle
 
+#from PyBioMed.PyProtein import AAComposition
 
-if __name__ == "__main__":
-	parser = argparse.ArgumentParser(description='Process fasta input with random forest virulence prediction model')
-	parser.add_argument('infile', metavar='infile', type=str,
-		help='The input file (FASTA format)')
-	parser.add_argument('outfile', metavar='outfile', type=str,
-		help='The outpufile (TSV format)')
-	args = parser.parse_args()
+AALetter = ["A", "R", "N", "D", "C", "E", "Q", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"]
 
-	df = pd.read_csv(args.infile, sep='\t')
-	X = df.drop('#', axis=1)
+def CalculateAAComposition(ProteinSequence):
+        LengthSequence = len(ProteinSequence)
+        Result={}
+        for i in AALetter:
+                Result[i] = round(float(ProteinSequence.seq.count(i))/ LengthSequence * 100, 2)
+        return Result
+
+def CalculateDipeptideComposition(ProteinSequence):
+        LengthSequence = len(ProteinSequence)
+        Result = {}
+        for i in AALetter:
+                for j in AALetter:
+                        Dipeptide = i + j
+                        Result[Dipeptide] = round(float(ProteinSequence.seq.count(Dipeptide)) / (LengthSequence -1) * 100, 2)
+
+        return Result
+
+def CalculateKmerComposition(ProteinSequence):
+        LengthSequence = len(ProteinSequence)
+        Result = {}
+        for i in AALetter:
+                for j in AALetter:
+                        for k in AALetter:
+                                Kmer = i + j + k
+                                Result[Kmer] = round(float(ProteinSequence.seq.count(Kmer)) / (LengthSequence -2) * 100, 2)
+        return Result
+
+def CalculateAADipeptideComposition(ProteinSequence):
+        Result = {}
+        Result.update(CalculateAAComposition(ProteinSequence))
+        Result.update(CalculateDipeptideComposition(ProteinSequence))
+        Result.update(CalculateKmerComposition(ProteinSequence))
+        return Result
+
+
+#if __name__ == "__main__":
+#	parser = argparse.ArgumentParser(description='Process fasta input with random forest virulence prediction model')
+#	parser.add_argument('infile', metavar='infile', type=str,
+#		help='The input file (FASTA format)')
+#	parser.add_argument('outfile', metavar='outfile', type=str,
+#		help='The outpufile (TSV format)')
+#	args = parser.parse_args()
+	
+df = pd.DataFrame([])
 	
-	rfc = pickle.load(open("scripts/Virulence_factor_model.sav", 'rb'))
-	rfc_predict = rfc.predict(X)	
-	rfc_probs = rfc.predict_proba(X)[:,1]
+for Sequence in SeqIO.parse(snakemake.input[0], "fasta"):	
+	ProteinSequence = Sequence	
+	DIP = CalculateAADipeptideComposition(ProteinSequence)
+	data = DIP
+	data = pd.DataFrame(data.items())
+	data = data.transpose()
+
+	data.columns=data.iloc[0]
+	data = data.drop(data.index[[0]])
+	data['Sequence'] = Sequence.id
+	df =df.append(data)
+	rfc = pickle.load(open("finalized_model_3f.sav", 'rb'))
+
+X_predict = df.drop('Sequence', axis = 1)
+rfc_predict = rfc.predict(X_predict)	
+rfc_probs = rfc.predict_proba(X_predict)[:,1]
 	
-	Prediction = pd.DataFrame([])
-	dfToList = df['#'].tolist()
-	Prediction['Sequence'] = dfToList
-	Prediction['Prediction'] = rfc_predict
-	Prediction['Probability'] = rfc_probs
-	Prediction.to_csv(args.outfile, sep='\t', header=False)	
+Prediction = pd.DataFrame([])
+dfToList = df['Sequence'].tolist()
+Prediction['Sequence'] = dfToList
+Prediction['Prediction'] = rfc_predict
+Prediction['Probability'] = rfc_probs
+Prediction.to_csv(snakemake.output[0], sep='\t')