Implemented --isjet2on option for reproducibility checks.

computePred.R

 # Copyright (c) 2018: Elodie Laine
+# Copyright (c) 2022: Mustafa Tekpinar
 # This code is part of the gemme package and governed by its license.
 # Please see the LICENSE.txt file included as part of this package.
 
@@ -43,12 +44,6 @@ write.table(res[[3]][[3]],paste0(prot,"_pssm80.txt"))
 jet=read.table(paste(prot,"_jet.res",sep=""),head=TRUE)
 if(sum(colnames(jet)=="traceMax")==1){trace=jet[,"traceMax"]}else{trace=jet[,"trace"]}
 
-#You should comment line 44 to use this functionality. Or maybe, it should go into normalize functions
-#That was what we originally decided with Alessandra. 
-#To get max values of PC, CV or Trace
-#trace = c()
-#for (row in 1:nrow(jet)) { trace<-append(trace, max(jet[row, "trace"], jet[row, "pc"])) }
-
 #traceAli = sweep(binAli, MARGIN=2, trace, `*`)
 # compute evolutionary distances of all sequences with respect to the query
 distTrace = binAli[2:N[1],] %*% trace^2
@@ -75,44 +70,56 @@ nbGaps = N[1] - apply(nbSeqs,2,sum)
 # output the conservation values
 dat=rbind(trace,KL=res[[1]][[2]],SE=res[[1]][[1]],gap=nbGaps/N[1],KL60=res[[2]][[2]],SE60=res[[2]][[1]],KL80=res[[3]][[2]],SE80=res[[3]][[1]])
 write.table(dat,paste0(prot,"_conservation.txt"))
-
 # compute log-odd ratios between mutated and wt sequence counts
 predInd = computePredNbSeqs(wt,nbSeqs)
-# output the sequence counts log-odd ratios
-write.table(predInd,paste0(prot,"_pred_evolInd.txt"))
-
-# Do the normalization for the independent component here!
 rownames(predInd)=aa
-if(simple){
-  normPredInd = normalizePredWithNbSeqsZero(predInd,trace,wt)
-  rownames(normPredInd)=aa
-}else{
-  normPredInd = normalizePredWithNbSeqsZeroSelMult(predInd, trace, wt, list(pos,subsaa))
-  names(normPredInd)=rawMut
-}
 
-# output the predicted mutational effects based on sequence counts (conservation at the bottom)
-write.table(normPredInd,paste0(prot,"_normPred_evolInd.txt"))
+# output the sequence counts log-odd ratios
+write.table(predInd,paste0(prot,"_pred_evolInd.txt"))
 print("done")
 
 print("running global epistatic model...")
 pred=computePredSimple(ali,distTrace,wt,5)
+rownames(pred)=aa
 
 # output the evolutionary distances between the query and the closest variants
 evolDist = pred/sum(trace^2)
 evolDist[is.na(evolDist)] = 1
 write.table(evolDist,paste0(prot,"_pred_evolEpi.txt"))
+print("done")
+
+print("running normalization...")
+#You should comment line 44 to use this functionality. Or maybe, it should go into normalize functions
+#That was what we originally decided with Alessandra. 
+#To get max values of PC, or Trace
+# trace = c()
+# print(trace)
+# for (row in 1:nrow(jet)) { trace<-append(trace, max(jet[row, "trace"], jet[row, "pc"])) }
+# print(trace)
 
-# Do the normalization for the epistatic component here!
-rownames(pred)=aa
 if(simple){
+  #Independent model normalization
+  normPredInd = normalizePredWithNbSeqsZero(predInd,trace,wt)
+  rownames(normPredInd)=aa
+  
+  # Epistatic model normalization
   normPred=normalizePred(pred, trace, wt)
   rownames(normPred)=aa
+
 }else{
+  #Independent model normalization
+  normPredInd = normalizePredWithNbSeqsZeroSelMult(predInd, trace, wt, list(pos,subsaa))
+  names(normPredInd)=rawMut
+
+  # Epistatic model normalization
   normPred=normalizePredSelMult(pred, trace, wt, list(pos,subsaa))
   names(normPred)=rawMut
 }
-# output the predicted mutational effects based on evolutionary distance (conservation at the bottom)
+
+# output the normalized predicted mutational effects based on sequence counts (conservation at the bottom)
+write.table(normPredInd,paste0(prot,"_normPred_evolInd.txt"))
+
+# output the predicted normalized mutational effects based on evolutionary distance (conservation at the bottom)
 write.table(normPred,paste0(prot,"_normPred_evolEpi.txt"))
 print("done")
 

example/example-gemme-script.sh

@@ -9,3 +9,8 @@ else
     echo "Running GEMME with a user-provided alignment file."
     python $GEMME_PATH/gemme.py aliBLAT.fasta -r input -f aliBLAT.fasta
 fi
+
+
+# If you have your own JET2 score file, you can turn off JET2 as follows:
+# python $GEMME_PATH/gemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --isjet2on false
+# This option can be useful for testing reproducibility!

gemme.py

@@ -11,6 +11,7 @@ import argparse
 import re
 import subprocess
 import math
+from xmlrpc.client import boolean
 import numpy as np
 import matplotlib.pylab as plt
 
@@ -254,6 +255,9 @@ def parse_command_line():
         help='fasta file containing related sequences',
         default=''
     )
+    retMet_args.add_argument('--isjet2on', dest='isjet2on', type=str, \
+        help="If false, it will skip JET2 calculation and use a precalculated JET2 file. Default is true",
+        required=False, default="True")
 
     args = parser.parse_args()
 
@@ -268,22 +272,32 @@ def parse_command_line():
     return args
 
 
-def doit(inAli,mutFile,retMet,bFile,fFile,n,N):
+def doit(inAli,mutFile,retMet,bFile,fFile,n,N, isjet2on):
     """
-    doit(args.input,args.mutations,args.retrievingMethod,args.blastFile,args.fastaFile)
+    Fonksiyon aciklamasi ile taniminin ayni olmasi super olmus!
+    doit(args.input,args.mutations,args.retrievingMethod,args.blastFile,args.fastaFile, args.isjet2on)
     """
+    simple = True
+
     prot,seq,nl=extractQuerySeq(inAli)
 
     createPDB(prot,seq)
  
     print("query protein: "+prot)
-    print("computing conservation levels...")
     
+    if((isjet2on.lower()) == "true"):
         #I intend to run JET2 completely externally!!
         #It is too much buggy and it has too many dependencies. 
         #Using it with a Docker or Singularity may be the best solution!
+        print("computing conservation levels...")
         launchJET(prot,retMet,bFile,fFile,n,N,nl)
         print("done")
+    elif((isjet2on.lower()) == "false"):
+        print("using previously calculated JET2 conservation levels...")
+        print("done")
+    else:
+        print("ERROR: You can only use true or false after --isjet2on!")
+        sys.exit(-1)
     launchPred(prot,inAli,mutFile)
 
     #Do Python plotting here
@@ -294,7 +308,6 @@ def doit(inAli,mutFile,retMet,bFile,fFile,n,N):
     #TODO: You can even put letters on the top line like in EVmutation output. 
     
 
-    simple = True
     if(simple):
         print("generating the plots...")
         gemmeData = parseGEMMEoutput(prot+"_normPred_evolEpi.txt", verbose=False)
@@ -315,7 +328,8 @@ def doit(inAli,mutFile,retMet,bFile,fFile,n,N):
 
 def main():
     args = parse_command_line()
-    doit(args.input,args.mutations,args.retrievingMethod,args.blastFile,args.fastaFile,args.nIter,args.NSeqs)
+    doit(args.input,args.mutations,args.retrievingMethod,args.blastFile,\
+         args.fastaFile,args.nIter,args.NSeqs, args.isjet2on)
 
 if (__name__ == '__main__'):
     main()

pred.R

 # Copyright (c) 2018: Elodie Laine
+# Copyright (c) 2022: Mustafa Tekpinar
 # This code is part of the gemme package and governed by its license.
 # Please see the LICENSE.txt file included as part of this package.