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Commit faafebd8 by Mustafa Tekpinar
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Added a timer function to sgemme.py to show total compute time.

parent a53a653e
Showing with 146 additions and 131 deletions
example/aliBLAT.fasta
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example/blat-af2.pdb
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example/example-gemme-script.sh
......@@ -9,27 +9,33 @@ then
# rm -rf BLAT.fasta
rm -rf BLAT*
rm -f default.conf caracTest.dat
rm -rf bin*.fasta
elif [ "$1" == "jetoff" ]
then
# If you have your own JET2 score file, you can turn off JET2 as follows:
# In ../tests folder, you can find a sample JET2 file called BLAT_jet.res
echo "Running GEMME with a user-provided alignment file."
echo "Running SGEMME with a user-provided alignment file."
echo "Using a previously produced prot_jet.res file to check reproducibility!"
cp ../tests/BLAT_jet.res .
python $GEMME_PATH/gemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --jetfile BLAT_jet.res
python $SGEMME_PATH/sgemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --jetfile BLAT_jet.res
elif [ "$1" == "withpdb" ]
then
#Please note that CV isa structural feature and it can not be calculated if you don't specify a pdb file.
echo "Using blat-curated.pdb for the structural feature calculations!"
python $GEMME_PATH/gemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --pdbfile blat-curated.pdb --normweightmode dfi
echo "Using blat-af2.pdb for the structural feature calculations!"
python $SGEMME_PATH/sgemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --pdbfile blat-af2.pdb --normweightmode maxhalftracepchalftracecvhalfcvpc
else
echo "Running GEMME with a user-provided alignment file."
python $GEMME_PATH/gemme.py aliBLAT.fasta -r input -f aliBLAT.fasta
elif [ "$1" == "withpdb-withmutfile" ]
then
fi
#Please note that CV isa structural feature and it can not be calculated if you don't specify a pdb file.
echo "Using blat-af2.pdb for the structural feature calculations!"
python $SGEMME_PATH/sgemme.py aliBLAT.fasta -r input -f aliBLAT.fasta --pdbfile blat-af2.pdb --normweightmode maxhalftracepchalftracecvhalfcvpc -m Stiffler_2015_BLAT_ECOLX.mut
else
echo "Running SGEMME with a user-provided alignment file."
python $SGEMME_PATH/sgemme.py aliBLAT.fasta -r input -f aliBLAT.fasta
fi
gemmeAnal.py
......@@ -43,8 +43,10 @@ def extractQuerySeq(filename):
def getNbSeq(filename):
#TODO: Remove bash dependency and count the sequences within Python!
"""
# Get the number of sequences in a multi-fasta file
"""
if filename!='':
proc=subprocess.Popen("grep -c '^>' "+filename,stdout=subprocess.PIPE,shell=True)
......@@ -131,7 +133,7 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
chainID = chains[0]
subprocess.call("cp $GEMME_PATH/default.conf .",shell=True)
subprocess.call("cp $SGEMME_PATH/default.conf .",shell=True)
if retMet=="input":
if bFile!='':
#TODO: I think these two lines must be here as well but I am not sure.
......@@ -184,42 +186,7 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
print("\nRunning for SC1:\n"+jetcmd)
reCode=subprocess.call(jetcmd,shell=True)
if os.path.isfile(prot+"/"+prot+"_jet.res"):
os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc1.res")
dir_name = prot+"/"
if os.path.isdir(dir_name):
for f in os.listdir(dir_name):
f_path = os.path.join(dir_name, f)
if os.path.isfile(f_path):
os.remove(f_path)
os.rmdir(dir_name)
# Calculate SC2
jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 4"+" > "+prot+".out"
#One can also add: -g 'trace,pc,cv,clusters,axs'
print("\nRunning for SC2:\n"+jetcmd)
reCode=subprocess.call(jetcmd,shell=True)
if os.path.isfile(prot+"/"+prot+"_jet.res"):
os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc2.res")
dir_name = prot+"/"
if os.path.isdir(dir_name):
for f in os.listdir(dir_name):
f_path = os.path.join(dir_name, f)
if os.path.isfile(f_path):
os.remove(f_path)
os.rmdir(dir_name)
# Calculate SC3
jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 5"+" > "+prot+".out"
#One can also add: -g 'trace,pc,cv,clusters,axs'
print("\nRunning for SC3:\n"+jetcmd)
reCode=subprocess.call(jetcmd,shell=True)
if os.path.isfile(prot+"/"+prot+"_jet.res"):
os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc3.res")
os.rename(prot+"/"+prot+"_jet.res",prot+"_jet.res")
dir_name = prot+"/"
if os.path.isdir(dir_name):
......@@ -228,118 +195,153 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
if os.path.isfile(f_path):
os.remove(f_path)
os.rmdir(dir_name)
# # Calculate SC2
# jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
# prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 4"+" > "+prot+".out"
# #One can also add: -g 'trace,pc,cv,clusters,axs'
dfSC1 = pd.read_table(prot+"_jet_sc1.res", delimiter=r"\s+")
print(dfSC1)
# print("\nRunning for SC2:\n"+jetcmd)
# reCode=subprocess.call(jetcmd,shell=True)
# if os.path.isfile(prot+"/"+prot+"_jet.res"):
# os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc2.res")
dfSC2 = pd.read_table(prot+"_jet_sc2.res", delimiter=r"\s+")
print(dfSC2)
# dir_name = prot+"/"
# if os.path.isdir(dir_name):
# for f in os.listdir(dir_name):
# f_path = os.path.join(dir_name, f)
# if os.path.isfile(f_path):
# os.remove(f_path)
# os.rmdir(dir_name)
dfSC3 = pd.read_table(prot+"_jet_sc3.res", delimiter=r"\s+")
print(dfSC3)
dfSC1['sc1'] =dfSC1['clusters']
dfSC1['sc2'] =dfSC2['clusters']
dfSC1['sc3'] =dfSC3['clusters']
# # Calculate SC3
# jetcmd = "java -Xmx4096m -cp $JET2_PATH:$JET2_PATH/jet/extLibs/vecmath.jar jet.JET -c default.conf -i "+\
# prot+".pdb -o `pwd` -p AVJCG -r input -f "+prot+"_"+chainID+".fasta -d chain -n "+n+" -a 5"+" > "+prot+".out"
# #One can also add: -g 'trace,pc,cv,clusters,axs'
print(dfSC1)
# print("\nRunning for SC3:\n"+jetcmd)
# reCode=subprocess.call(jetcmd,shell=True)
# if os.path.isfile(prot+"/"+prot+"_jet.res"):
# os.rename(prot+"/"+prot+"_jet.res",prot+"_jet_sc3.res")
# dir_name = prot+"/"
# if os.path.isdir(dir_name):
# for f in os.listdir(dir_name):
# f_path = os.path.join(dir_name, f)
# if os.path.isfile(f_path):
# os.remove(f_path)
# os.rmdir(dir_name)
# dfSC1 = pd.read_table(prot+"_jet_sc1.res", delimiter=r"\s+")
# print(dfSC1)
# # dfSC2 = pd.read_table(prot+"_jet_sc2.res", delimiter=r"\s+")
# # print(dfSC2)
# # dfSC3 = pd.read_table(prot+"_jet_sc3.res", delimiter=r"\s+")
# # print(dfSC3)
# dfSC1['sc1'] =dfSC1['clusters']
# # dfSC1['sc2'] =dfSC2['clusters']
# # dfSC1['sc3'] =dfSC3['clusters']
# print(dfSC1)
# #Calculate combined values of SC1, SC2 and SC3 and normalize them.
# combinedValuesV1 = []
# combinedValuesV2 = []
# value1List = []
# value2List = []
# value3List = []
# value4List = []
# value5List = []
# value6List = []
# temp1List = []
# temp2List = []
# temp3List = []
#Calculate combined values of SC1, SC2 and SC3 and normalize them.
combinedValuesV1 = []
combinedValuesV2 = []
value1List = []
value2List = []
value3List = []
value4List = []
value5List = []
value6List = []
temp1List = []
temp2List = []
temp3List = []
# for i in range(len(dfSC1)):
for i in range(len(dfSC1)):
# value1=(dfSC1.loc[i, "sc1"]*dfSC1.loc[i, "trace"]) + dfSC1.loc[i, "trace"]
# value1List.append(value1)
value1=(dfSC1.loc[i, "sc1"]*dfSC1.loc[i, "trace"]) + dfSC1.loc[i, "trace"]
value1List.append(value1)
# value2=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)
# value2List.append(value2)
value2=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)
value2List.append(value2)
# value3=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)
# value3List.append(value3)
value3=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2 + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)
value3List.append(value3)
# value4=(dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value4List.append(value4)
value4=(dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2 + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
value4List.append(value4)
# value5=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value5List.append(value5)
value5=(dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
value5List.append(value5)
# value6=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
# value6List.append(value6)
value6=(dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)
value6List.append(value6)
# temp1 = (dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2
# temp1List.append(temp1)
# temp2 = (dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2
# temp2List.append(temp2)
# temp3 = (dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2
# temp3List.append(temp3)
temp1 = (dfSC1.loc[i, "sc1"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "pc"])/2)**2
temp1List.append(temp1)
temp2 = (dfSC1.loc[i, "sc2"]*(dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "trace"]+dfSC1.loc[i, "cv"])/2)**2
temp2List.append(temp2)
temp3 = (dfSC1.loc[i, "sc3"]*(dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2) + ((dfSC1.loc[i, "pc"]+dfSC1.loc[i, "cv"])/2)**2
temp3List.append(temp3)
# combinedValuesV1.append(max([value1, value2, value3]))
# combinedValuesV2.append(max([value2, value3, value4]))
combinedValuesV1.append(max([value1, value2, value3]))
combinedValuesV2.append(max([value2, value3, value4]))
# #MinMax normalize the list
# combinedValuesV1MinMaxed = minMaxNormalization(combinedValuesV1)
# combinedValuesV2MinMaxed = minMaxNormalization(combinedValuesV2)
#MinMax normalize the list
combinedValuesV1MinMaxed = minMaxNormalization(combinedValuesV1)
combinedValuesV2MinMaxed = minMaxNormalization(combinedValuesV2)
# # value1ListMinMaxed = minMaxNormalization(value1List)
# # value2ListMinMaxed = minMaxNormalization(value2List)
# # value3ListMinMaxed = minMaxNormalization(value3List)
# # value4ListMinMaxed = minMaxNormalization(value4List)
# # combinedValuesV3MinMaxed = []
# # for i in range(len(dfSC1)):
# # combinedValuesV3MinMaxed.append(max([value2ListMinMaxed[i], value3ListMinMaxed[i], value4ListMinMaxed[i]]))
# value1ListMinMaxed = minMaxNormalization(value1List)
# value2ListMinMaxed = minMaxNormalization(value2List)
# value3ListMinMaxed = minMaxNormalization(value3List)
# value4ListMinMaxed = minMaxNormalization(value4List)
# #Let's skip that minmax normalization part
# combinedValuesV3MinMaxed = []
# for i in range(len(dfSC1)):
# combinedValuesV3MinMaxed.append(max([value2ListMinMaxed[i], value3ListMinMaxed[i], value4ListMinMaxed[i]]))
#Let's skip that minmax normalization part
combinedValuesV3MinMaxed = []
for i in range(len(dfSC1)):
maxTemp = max([value2List[i], value3List[i], value4List[i]])
if(maxTemp > 1.0):
combinedValuesV3MinMaxed.append(1.0)
else:
combinedValuesV3MinMaxed.append(maxTemp)
# maxTemp = max([value2List[i], value3List[i], value4List[i]])
# if(maxTemp > 1.0):
# combinedValuesV3MinMaxed.append(1.0)
# else:
# combinedValuesV3MinMaxed.append(maxTemp)
combinedValuesV4MinMaxed = []
for i in range(len(dfSC1)):
maxTemp = max([temp1List[i], temp2List[i], temp3List[i]])
if(maxTemp > 1.0):
combinedValuesV4MinMaxed.append(1.0)
else:
combinedValuesV4MinMaxed.append(maxTemp)
# combinedValuesV4MinMaxed = []
# for i in range(len(dfSC1)):
# maxTemp = max([temp1List[i], temp2List[i], temp3List[i]])
# if(maxTemp > 1.0):
# combinedValuesV4MinMaxed.append(1.0)
# else:
# combinedValuesV4MinMaxed.append(maxTemp)
dfSC1['combinedv1'] = combinedValuesV1MinMaxed.round(4)
dfSC1['combinedv2'] = combinedValuesV2MinMaxed.round(4)
dfSC1['combinedv3'] = list(np.around(np.array(combinedValuesV3MinMaxed), 4))
dfSC1['combinedv4'] = list(np.around(np.array(combinedValuesV4MinMaxed), 4))
#dfSC1['combinedv3'] = combinedValuesV3MinMaxed.round(4)
# dfSC1['combinedv1'] = combinedValuesV1MinMaxed.round(4)
# dfSC1['combinedv2'] = combinedValuesV2MinMaxed.round(4)
# dfSC1['combinedv3'] = list(np.around(np.array(combinedValuesV3MinMaxed), 4))
# dfSC1['combinedv4'] = list(np.around(np.array(combinedValuesV4MinMaxed), 4))
# #dfSC1['combinedv3'] = combinedValuesV3MinMaxed.round(4)
# dfSC1['cv'] = dfSC1['cv'].round(4)
# dfSC1['pc'] = dfSC1['pc'].round(4)
# dfSC1['tr'] = dfSC1['tr'].round(4)
# dfSC1['freq'] = dfSC1['freq'].round(1)
# dfSC1['trace'] = dfSC1['trace'].round(4)
# dfSC1['clusters'] = dfSC1['clusters'].round(4)
# dfSC1['clusnumber'] = dfSC1['clusnumber'].round(1)
# dfSC1['sc1'] = dfSC1['sc1'].round(4)
# dfSC1['sc2'] = dfSC1['sc2'].round(4)
# dfSC1['sc3'] = dfSC1['sc3'].round(4)
# # dfSC1['cv'] = dfSC1['cv'].round(4)
# # dfSC1['pc'] = dfSC1['pc'].round(4)
# # dfSC1['tr'] = dfSC1['tr'].round(4)
# # dfSC1['freq'] = dfSC1['freq'].round(1)
# # dfSC1['trace'] = dfSC1['trace'].round(4)
# # dfSC1['clusters'] = dfSC1['clusters'].round(4)
# # dfSC1['clusnumber'] = dfSC1['clusnumber'].round(1)
# # dfSC1['sc1'] = dfSC1['sc1'].round(4)
# # dfSC1['sc2'] = dfSC1['sc2'].round(4)
# # dfSC1['sc3'] = dfSC1['sc3'].round(4)
dfSC1.to_csv(prot+"_jet.res", header=True, index=None, sep='\t', mode='w')
# dfSC1.to_csv(prot+"_jet.res", header=True, index=None, sep='\t', mode='w')
#dfSC1.to_csv(prot+"_jet.res", header=True, index=None, mode='w')
#sys.exit(-1)
......@@ -363,9 +365,9 @@ def launchJET(prot, retMet, bFile, fFile, pdbfile, chains, n, N, nl):
def launchPred(prot,inAli,mutFile, normWeightMode, alphabet):
if mutFile!='':
rcmd="Rscript --save $GEMME_PATH/computePred.R "+prot+" "+inAli+" FALSE "+mutFile+" "+normWeightMode+" "+alphabet
rcmd="Rscript --save $SGEMME_PATH/computePred.R "+prot+" "+inAli+" FALSE "+mutFile+" "+normWeightMode+" "+alphabet
else:
rcmd="Rscript --save $GEMME_PATH/computePred.R "+prot+" "+inAli+" TRUE none "+normWeightMode+" "+alphabet
rcmd="Rscript --save $SGEMME_PATH/computePred.R "+prot+" "+inAli+" TRUE none "+normWeightMode+" "+alphabet
print("\nRunning: \n"+rcmd)
reCode=subprocess.call(rcmd,shell=True)
......
sgemme.py
# -*- coding: utf-8 -*-
# Copyright (c) 2018: Elodie Laine
# Copyright (c) 2022: Mustafa Tekpinar
# Copyright (c) 2022-2023: 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.
import sys
import os
import time
import argparse
import re
import subprocess
......@@ -672,11 +673,17 @@ def doit(inAli,mutFile,retMet,bFile,fFile,n,N, jetfile, pdbfile, normWeightMode,
def main():
tic = time.perf_counter()
args = parse_command_line()
doit(args.input,args.mutations,args.retrievingMethod,args.blastFile,\
args.fastaFile,args.nIter,args.NSeqs, args.jetfile, args.pdbfile,\
args.normweightmode, args.alphabet)
toc = time.perf_counter()
print(f"S-GEMME computation finished in {toc - tic:0.4f} seconds!")
if (__name__ == '__main__'):
main()
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