diff --git a/rules/AMR/filter.pl b/rules/AMR/filter.pl
deleted file mode 100755
index 07f81706b31575963a9633f86e904cd40cb08431..0000000000000000000000000000000000000000
--- a/rules/AMR/filter.pl
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/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";
-}
diff --git a/rules/Virulence/virulence_prediction.py b/rules/Virulence/virulence_prediction.py
deleted file mode 100755
index ca90232ec9191e9e327a79c0d218ab6680d4261b..0000000000000000000000000000000000000000
--- a/rules/Virulence/virulence_prediction.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/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')
-
-
-
diff --git a/rules/AMR/AMR_MGE.R b/scripts/AMR_MGE.R
similarity index 100%
rename from rules/AMR/AMR_MGE.R
rename to scripts/AMR_MGE.R
diff --git a/rules/Universal/PathoFact.R b/scripts/PathoFact.R
similarity index 100%
rename from rules/Universal/PathoFact.R
rename to scripts/PathoFact.R
diff --git a/rules/Virulence/hmm.R b/scripts/hmm.R
similarity index 100%
rename from rules/Virulence/hmm.R
rename to scripts/hmm.R
diff --git a/rules/Toxin/ownHMM_library.R b/scripts/ownHMM_library.R
similarity index 100%
rename from rules/Toxin/ownHMM_library.R
rename to scripts/ownHMM_library.R
diff --git a/scripts/virulence_prediction.py b/scripts/virulence_prediction.py
index b92a099329d9f70fbe31b6f15436149a813c06f6..ca90232ec9191e9e327a79c0d218ab6680d4261b 100755
--- a/scripts/virulence_prediction.py
+++ b/scripts/virulence_prediction.py
@@ -1,34 +1,87 @@
#!/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')