| Previous changeset 3:65f235b5fe14 (2018-02-06) Next changeset 5:72f4afa46265 (2018-02-06) |
|
Commit message:
Uploaded |
|
added:
kinatestid_r/._kinatestid_r.xml kinatestid_r/._screener7-7.csv kinatestid_r/.shed.yml kinatestid_r/Kinatest-R_part1.R kinatestid_r/Kinatest-R_part2.R kinatestid_r/kinatestid_r.xml kinatestid_r/screener7-7.csv kinatestid_r/test-data/Characterization.csv kinatestid_r/test-data/EPM.csv kinatestid_r/test-data/SBF.csv kinatestid_r/test-data/SDtable.csv kinatestid_r/test-data/negatives.csv kinatestid_r/test-data/substrates.csv |
|
removed:
Kinatest-R_part1.R Kinatest-R_part2.R kinatestid_r.xml screener7-7.csv |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b Kinatest-R_part1.R --- a/Kinatest-R_part1.R Tue Feb 06 17:13:59 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,1113 +0,0 @@\n-this.dir <- dirname(parent.frame(2)$ofile)\r\n-setwd(this.dir)\r\n-\r\n-\r\n-ImportedSubstrateList<- read.csv(input1, stringsAsFactors=FALSE)\r\n-NegativeSubstrateList<- read.csv(input2, stringsAsFactors=FALSE)\r\n-SubstrateBackgroundFrequency<- read.csv(input3, stringsAsFactors=FALSE)\r\n-\r\n-ScreenerFilename<-screener\r\n-\r\n-\r\n-\r\n-FILENAME<-"output1"\r\n-FILENAME2<-"output2"\r\n-FILENAME3<-"output3"\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-OutputMatrix<-"KinaseMatrix.csv"\r\n-CharacterizationTable<-"CharacterizationTableForThisKinase.csv"\r\n-SDtable<-"SDtableforthisKinase"\r\n-SiteSelectivityTable<-"SiteSelectivityForThisKinase"\r\n-\r\n-\r\n-\r\n-substrates<-matrix(rep("A",times=((nrow(ImportedSubstrateList)-1)*15)),ncol = 15)\r\n-#SeqsToBeScored<-"asdasd"\r\n- \r\n-for (i in 2:nrow(ImportedSubstrateList))\r\n-{\r\n- substratemotif<-ImportedSubstrateList[i,4:18]\r\n- substratemotif[8]<-"Y"\r\n- #substratemotif<-paste(substratemotif,sep = "",collapse = "")\r\n- j=i-1\r\n- substratemotif<-unlist(substratemotif)\r\n- substrates[j,1:15]<-substratemotif\r\n-}\r\n-\r\n-# SpacesToOs<-c(""="O",)\r\n-# substrates<-SpacesToOs[substrates]\r\n-\r\n-SubstrateBackgroundFrequency[nrow(SubstrateBackgroundFrequency),2]\r\n-\r\n-if(2==2){\r\n-Amean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),2]), na.rm=TRUE)\r\n-Cmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),3]), na.rm=TRUE)\r\n-Dmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),4]), na.rm=TRUE)\r\n-Emean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),5]), na.rm=TRUE)\r\n-Fmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),6]), na.rm=TRUE)\r\n-Gmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),7]), na.rm=TRUE)\r\n-Hmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),8]), na.rm=TRUE)\r\n-Imean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),9]), na.rm=TRUE)\r\n-Kmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),10]), na.rm=TRUE)\r\n-Lmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),11]), na.rm=TRUE)\r\n-Mmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),12]), na.rm=TRUE)\r\n-Nmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),13]), na.rm=TRUE)\r\n-Pmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),14]), na.rm=TRUE)\r\n-Qmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),15]), na.rm=TRUE)\r\n-Rmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),16]), na.rm=TRUE)\r\n-Smean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),17]), na.rm=TRUE)\r\n-Tmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),18]), na.rm=TRUE)\r\n-Vmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),19]), na.rm=TRUE)\r\n-Wmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),20]), na.rm=TRUE)\r\n-Ymean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),21]), na.rm=TRUE)\r\n-\r\n-AllMeans<-c(Amean,Cmean,Dmean,Emean,Fmean,Gmean,Hmean,Imean,Kmean,Lmean,Mmean,Nmean,Pmean,Qmean,Rmean,Smean,Tmean,Vmean,Wmean,Ymean)\r\n-\r\n-Asd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),2]), na.rm=TRUE)\r\n-Csd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),3]), na.rm=TRUE)\r\n-Dsd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),4]), na.rm=TRUE)\r\n-Esd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),5]), '..b'/(PositionTable[13,]*.01*Pmean))\r\n-# EPMtable[14,]<-(PositionTable[14,]/(PositionTable[14,]*.01*Qmean))\r\n-# EPMtable[15,]<-(PositionTable[15,]/(PositionTable[15,]*.01*Rmean))\r\n-# EPMtable[16,]<-(PositionTable[16,]/(PositionTable[16,]*.01*Smean))\r\n-# EPMtable[17,]<-(PositionTable[17,]/(PositionTable[17,]*.01*Tmean))\r\n-# EPMtable[18,]<-(PositionTable[18,]/(PositionTable[18,]*.01*Vmean))\r\n-# EPMtable[19,]<-(PositionTable[19,]/(PositionTable[19,]*.01*Wmean))\r\n-# EPMtable[20,]<-(PositionTable[20,]/(PositionTable[20,]*.01*Ymean))\r\n-\r\n-columns<-c(length(Column1)-sum(Column1==""),\r\n- length(Column2)-sum(Column2==""),\r\n- length(Column3)-sum(Column3==""),\r\n- length(Column4)-sum(Column4==""),\r\n- length(Column5)-sum(Column5==""),\r\n- length(Column6)-sum(Column6==""),\r\n- length(Column7)-sum(Column7==""),\r\n- length(Column8)-sum(Column8==""),\r\n- length(Column9)-sum(Column9==""),\r\n- length(Column10)-sum(Column10==""),\r\n- length(Column11)-sum(Column11==""),\r\n- length(Column12)-sum(Column12==""),\r\n- length(Column13)-sum(Column13==""),\r\n- length(Column14)-sum(Column14==""),\r\n- length(Column15)-sum(Column15==""))\r\n-\r\n-for (z in 1:15) {\r\n- for (y in 1:20) {\r\n- if (PositionTable[y,z]>0){\r\n- EPMtable[y,z]<-PositionTable[y,z]/((columns[z]*.01*AllMeans[y]))\r\n- }\r\n- if (PositionTable[y,z]==0){\r\n- EPMtable[y,z]<-(1/columns[z])/((columns[z]*.01*AllMeans[y]))\r\n- }\r\n- }\r\n-}\r\n-#here I created the endogenous probability matrix\r\n-#now all I need to do is make the program automatically determine which SDs are >2, and then make it perform screener and sorter on those SDs\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-# write.xlsx(SDtable,file=FILENAME, sheetName = "Standard Deviation Table",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(PercentTable,file = FILENAME,sheetName = "Percent Table",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(SelectivitySheet,file = FILENAME,sheetName = "Site Selectivity",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(EPMtable,file=FILENAME,sheetName = "Endogenous Probability Matrix",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(NormalizationScore,file = FILENAME,sheetName = "Normalization Score",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-\r\n-NormalizationScore<-c("Normalization Score",NormalizationScore)\r\n-\r\n-write.table(x=c("SD Table"),file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n-write.table(SDtable,file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n-write.table(x=c("Percent Table"),file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n-write.table(PercentTable,file=FILENAME, append = TRUE,sep=",",row.names = FALSE, col.names = FALSE)\r\n-\r\n-EPMtableu<-EPMtable\r\n-HeaderSD<-c(-7:7)\r\n-EPMtableu<-rbind(HeaderSD,EPMtableu)\r\n-EPMtableu<-data.frame(SetOfAAs,EPMtableu)\r\n-\r\n-write.table("Site Selectivity Matrix", file = FILENAME2, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE)\r\n-SelectivityHeader=matrix(data = c("Position",-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7),nrow = 1)\r\n-head<-matrix(data=rep(" ",times=16),nrow = 1)\r\n-SelectivityHeader<-rbind(head,SelectivityHeader)\r\n-\r\n-write.table(SelectivityHeader, file = FILENAME2, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE)\r\n-#colnames(SelectivitySheet)<-c("-7","-6","-5","-4","-3","-2","-1","0","1","2","3","4","5","6","7")\r\n-write.table(SelectivitySheet,file = FILENAME2, append = TRUE,sep = ",",row.names = TRUE, col.names = FALSE)\r\n-write.table(x=c("Endogenous Probability Matrix"),file=FILENAME2,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n-write.table(EPMtableu,file = FILENAME2, append = TRUE,sep = ",",row.names = FALSE, col.names = FALSE)\r\n-write.table(NormalizationScore, file = FILENAME2, append = TRUE,sep = ",",row.names = FALSE, col.names = FALSE)\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b Kinatest-R_part2.R --- a/Kinatest-R_part2.R Tue Feb 06 17:13:59 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,782 +0,0 @@\n-#test myself: this script should take in amino acids for each of the 9 positions and give out every single combination of those AAs\r\n-\r\n-#need to do following: fix it so that the accession numbers stay with the substrates,\r\n-#also the neg false constant is totaly unphos\'d Ys found by FASTA-2-CSV system# uniprot\r\n-\r\n-#HOW MANY: IF THERE\'S two aas in each position you get 2^9, so I assume the numbers are:\r\n-#(number in position-4)*(number in position -3)*(number in position -2)...=total\r\n-# require(rJava)\r\n-# require(xlsxjars)\r\n-# require(xlsx)\r\n-# # require(readxl)\r\n-\r\n-View(SDtable)\r\n-bareSDs<-SDtable[2:21,2:16]\r\n-goodones<-bareSDs>2\r\n-\r\n-Positionm7<-which(goodones[,1] %in% TRUE)\r\n-if (length(Positionm7)<1){Positionm7<-which(bareSDs[,1]==max(bareSDs[,1]))}\r\n-Positionm6<-which(goodones[,2] %in% TRUE)\r\n-if (length(Positionm6)<1){Positionm6<-which(bareSDs[,2]==max(bareSDs[,2]))}\r\n-Positionm5<-which(goodones[,3] %in% TRUE)\r\n-if (length(Positionm5)<1){Positionm5<-which(bareSDs[,3]==max(bareSDs[,3]))}\r\n-Positionm4<-which(goodones[,4] %in% TRUE)\r\n-if (length(Positionm4)<1){Positionm4<-which(bareSDs[,4]==max(bareSDs[,4]))}\r\n-Positionm3<-which(goodones[,5] %in% TRUE)\r\n-if (length(Positionm3)<1){Positionm3<-which(bareSDs[,5]==max(bareSDs[,5]))}\r\n-Positionm2<-which(goodones[,6] %in% TRUE)\r\n-if (length(Positionm2)<1){Positionm2<-which(bareSDs[,6]==max(bareSDs[,6]))}\r\n-Positionm1<-which(goodones[,7] %in% TRUE)\r\n-if (length(Positionm1)<1){Positionm1<-which(bareSDs[,7]==max(bareSDs[,7]))}\r\n-\r\n-Positiond0<-which(goodones[,8] %in% TRUE)\r\n-if (length(Positiond0)<1){Positiond0<-which(bareSDs[,8]==max(bareSDs[,8]))}\r\n-\r\n-Positionp1<-which(goodones[,9] %in% TRUE)\r\n-if (length(Positionp1)<1){Positionp1<-which(bareSDs[,9]==max(bareSDs[,9]))}\r\n-Positionp2<-which(goodones[,10] %in% TRUE)\r\n-if (length(Positionp2)<1){Positionp2<-which(bareSDs[,10]==max(bareSDs[,10]))}\r\n-Positionp3<-which(goodones[,11] %in% TRUE)\r\n-if (length(Positionp3)<1){Positionp3<-which(bareSDs[,11]==max(bareSDs[,11]))}\r\n-Positionp4<-which(goodones[,12] %in% TRUE)\r\n-if (length(Positionp4)<1){Positionp4<-which(bareSDs[,12]==max(bareSDs[,12]))}\r\n-Positionp5<-which(goodones[,13] %in% TRUE)\r\n-if (length(Positionp5)<1){Positionp5<-which(bareSDs[,13]==max(bareSDs[,13]))}\r\n-Positionp6<-which(goodones[,14] %in% TRUE)\r\n-if (length(Positionp6)<1){Positionp6<-which(bareSDs[,14]==max(bareSDs[,14]))}\r\n-Positionp7<-which(goodones[,15] %in% TRUE)\r\n-if (length(Positionp7)<1){Positionp7<-which(bareSDs[,15]==max(bareSDs[,15]))}\r\n-\r\n-aa_props2 <- c("1"="A", "2"="C", "3"="D", "4"="E", "5"="F", "6"="G", "7"="H", "8"="I", "9"="K", "10"="L", "11"="M", "12"="N",\r\n- "13"="P", "14"="Q", "15"="R", "16"="S", "17"="T", "18"="V", "19"="W", "20"="Y")\r\n-aa_props2<-c(1="A")\r\n-\r\n-Positionm7<-sapply(Positionm7, function (x) aa_props2[x])\r\n-Positionm6<-sapply(Positionm6, function (x) aa_props2[x])\r\n-Positionm5<-sapply(Positionm5, function (x) aa_props2[x])\r\n-Positionm4<-sapply(Positionm4, function (x) aa_props2[x])\r\n-Positionm3<-sapply(Positionm3, function (x) aa_props2[x])\r\n-Positionm2<-sapply(Positionm2, function (x) aa_props2[x])\r\n-Positionm1<-sapply(Positionm1, function (x) aa_props2[x])\r\n-Positiond0<-sapply(Positiond0, function (x) aa_props2[x])\r\n-Positionp1<-sapply(Positionp1, function (x) aa_props2[x])\r\n-Positionp2<-sapply(Positionp2, function (x) aa_props2[x])\r\n-Positionp3<-sapply(Positionp3, function (x) aa_props2[x])\r\n-Positionp4<-sapply(Positionp4, function (x) aa_props2[x])\r\n-Positionp5<-sapply(Positionp5, function (x) aa_props2[x])\r\n-Positionp6<-sapply(Positionp6, function (x) aa_props2[x])\r\n-Positionp7<-sapply(Positionp7, function (x) aa_props2[x])\r\n-\r\n-\r\n-# Positionm7<-c("D","H","N","V")\r\n-# Positionm6<-c("E","V")\r\n-# Positionm5<-c("D","H")\r\n-# Positionm4<-c("D","N")\r\n-# Positionm3<-c("D","E","F","Q")\r\n-# Positionm2<-c("D","N","Q","S")\r\n-# Positionm1<-c("F","I","L")\r\n-# Positiond0<-c("Y")\r\n-# Positionp1<-c("A","E")\r\n-# Positionp2<-c("T","S","Q","E")\r\n-# Positionp3<-c("'..b'tide[7]),9]*\r\n- #ThisKinTable[as.numeric(Scoringpeptide[8]),10]*\r\n- ThisKinTable[as.numeric(Scoringpeptide[9]),11]*ThisKinTable[as.numeric(Scoringpeptide[10]),12]*ThisKinTable[as.numeric(Scoringpeptide[11]),13]*\r\n- ThisKinTable[as.numeric(Scoringpeptide[12]),14]*ThisKinTable[as.numeric(Scoringpeptide[13]),15]*ThisKinTable[as.numeric(Scoringpeptide[14]),16]*ThisKinTable[as.numeric(Scoringpeptide[15]),17]\r\n- \r\n- PositiveScores[v]<-ThisKinTableScore\r\n- ThisKinTableScore<-(ThisKinTableScore/(ThisKinTableScore+1/as.numeric(NormalizationScore[2])))\r\n- PositiveWeirdScores[v]<-ThisKinTableScore*100\r\n-}\r\n-\r\n-positivesubstrates<-ImportedSubstrateList[,4:18]\r\n-positivewithscores<-cbind.data.frame(positivesubstrates,PositiveScores,PositiveWeirdScores)\r\n-\r\n-\r\n-#write down the transient transfection SOP and what we will be doing with them\r\n-#write down the vector names I will be using\r\n-#write down something about transforming bacteria and with what\r\n-\r\n-#90% whatevernness\r\n-# TPninetyone<-length(PositiveWeirdScores[PositiveWeirdScores>=0.91])\r\n-# Senseninetyone<-TPninetyone/nrow(positivesubstrates)\r\n-# \r\n-# TNninetyone<-length(NegativeWeirdScores[NegativeWeirdScores<91])\r\n-# Specninetyone<-TNninetyone/100\r\n-\r\n-#create the MCC table\r\n-\r\n-threshold<-c(1:100)\r\n-threshold<-order(threshold,decreasing = TRUE)\r\n-\r\n-Truepositives<-c(1:100)\r\n-Falsenegatives<-c(1:100)\r\n-Sensitivity<-c(1:100)\r\n-TrueNegatives<-c(1:100)\r\n-FalsePositives<-c(1:100)\r\n-Specificity<-c(1:100)\r\n-Accuracy<-c(1:100)\r\n-MCC<-c(1:100)\r\n-EER<-c(1:100)\r\n-\r\n-#MAKE DAMN SURE THAT THE ACCESSION NUMBERS FOLLOW THE MOTIFS\r\n-\r\n-for (z in 1:100) {\r\n- thres<-101-z\r\n- Truepositives[z]<-length(PositiveWeirdScores[PositiveWeirdScores>=(thres)])\r\n- Falsenegatives[z]<-nrow(positivesubstrates)-Truepositives[z]\r\n- Sensitivity[z]<-Truepositives[z]/(Falsenegatives[z]+Truepositives[z])\r\n- TrueNegatives[z]<-length(NegativeWeirdScores[NegativeWeirdScores<(thres)])\r\n-# at thresh 100 this should be 0, because it is total minus true negatives\r\n- FalsePositives[z]<-nrow(NegativeSubstrateList)-TrueNegatives[z]\r\n- Specificity[z]<-1-(TrueNegatives[z]/(FalsePositives[z]+TrueNegatives[z]))\r\n- Accuracy[z]<-100*(Truepositives[z]+TrueNegatives[z])/(Falsenegatives[z]+FalsePositives[z]+TrueNegatives[z]+Truepositives[z])\r\n- MCC[z]<-((Truepositives[z]+TrueNegatives[z])-(Falsenegatives[z]+FalsePositives[z]))/sqrt(round(round(Truepositives[z]+Falsenegatives[z])*round(TrueNegatives[z]+FalsePositives[z])*round(Truepositives[z]+FalsePositives[z])*round(TrueNegatives[z]+Falsenegatives[z])))\r\n- EER[z]<-.01*(((1-(Sensitivity[z]))*(Truepositives[z]+Falsenegatives[z]))+(Specificity[z]*(1-(Truepositives[z]+Falsenegatives[z]))))\r\n-}\r\n-Characterization<-cbind.data.frame(threshold,Truepositives,Falsenegatives,Sensitivity,TrueNegatives,FalsePositives,Specificity,Accuracy,MCC,EER)\r\n-\r\n-positiveheader<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,"RPMS","PMS")\r\n-positivewithscores<-rbind.data.frame(positiveheader,positivewithscores)\r\n-\r\n-negativeheader<-c("Substrate","RPMS","PMS")\r\n-colnames(NegativeWithScores)<-negativeheader\r\n-\r\n-# write.xlsx(NegativeWithScores,file = FILENAME, sheetName = "Negative Sequences Scored",col.names = TRUE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(Characterization,file = FILENAME,sheetName = "Characterization Table",col.names = TRUE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(RanksPeptides,file = FILENAME,sheetName = "Ranked Generated Peptides",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-# write.xlsx(positivewithscores,file = FILENAME, sheetName = "Positive Sequences Scored",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n-write.table(x=c("Characterzation Table"),file = FILENAME2, col.names = FALSE,row.names = FALSE, append = TRUE,sep = ",")\r\n-write.table(Characterization,file = FILENAME2, col.names = TRUE,row.names = FALSE, append = TRUE,sep = ",")\r\n-\r\n-\r\n-write.table(RanksPeptides,file = FILENAME3,append = TRUE,row.names = FALSE,col.names = TRUE,sep = ",")\r\n-\r\n-\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r.xml --- a/kinatestid_r.xml Tue Feb 06 17:13:59 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| @@ -1,46 +0,0 @@ -<tool id="kinatestid_r" name="Kinatest-ID 7_to_7" version="0.1.0"> - <description>calculate optimal substrates</description> - <requirements> - <requirement type="package">R</requirement> - </requirements> - <command><![CDATA[ - ln -s '$__tool_directory__/screener7-7.csv' screener && - ln -s '$substrates' input1 && - ln -s '$negatives' input2 && - ln -s '$SBF' input3 && - Rscript '$__tool_directory__/Kinatest-R_part1.R' && - Rscript '$__tool_directory__/Kinatest-R_part2.R' && - mv output1 output1.csv && - mv output2 output2.csv && - mv output3 output3.csv - ]]></command> - <inputs> - <param format="csv" name="substrates" type="data" label="Positive/Phosphorylated Substrate List"/> - <param format="csv" name="negatives" type="data" label="Negative/unPhosphorylated Substrate List"/> - <param format="csv" name="SBF" type="data" label="Substrate Background Frequency List"/> - </inputs> - <outputs> - <data format="csv" name="SDtable" from_work_dir="output1.csv" label="Standard Deviation Table"/> - <data format="csv" name="EPM" from_work_dir="output2.csv" label="Endogenous Probability Matrix"/> - <data format="csv" name="Characterization" from_work_dir="output3.csv" label="Characterization Table"/> - </outputs> - <tests> - <test> - <param name="substrates" ftype="tabular" value="substrates.tsv"/> - <param name="negatives" ftype="tabular" value="negatives.tsv"/> - <param name="SBF" ftype="tabular" value="SBF.tsv"/> - <output name="SDtable" file="SDtable.csv"/> - <output name="EPM" file="EPM.csv"/> - <output name="Characterization" file="Characterization.csv"/> - </test> - </tests> - - - <help><![CDATA[ -This tool is intended for use in conjunction with KinaMine.jar and Negative Motif Finder. Using the outputs from those two functions (The Positive and Negative substrates as well as the Substrate Background Frequency) this tool calculates optimal substrates - ]]></help> - <citations> - <citation type="doi">10.1021/ja507164a</citation> - </citations> -</tool> - |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/._kinatestid_r.xml |
| b |
| Binary file kinatestid_r/._kinatestid_r.xml has changed |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/._screener7-7.csv |
| b |
| Binary file kinatestid_r/._screener7-7.csv has changed |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/.shed.yml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/.shed.yml Tue Feb 06 17:16:05 2018 -0500 |
| [ |
| @@ -0,0 +1,10 @@ +categories: [Computational chemistry] +description: Kinatest-ID 7_to_7 calculate optimal substrates +homepage_url: https://pubs.acs.org/doi/abs/10.1021/ja507164a +long_description: This tool is intended for use in conjunction with KinaMine.jar and + Negative Motif Finder. Using the outputs from those two functions (The Positive + and Negative substrates as well as the Substrate Background Frequency) this tool + calculates optimal substrate +name: kinatestid_r +owner: blank121 +remote_repository_url: https://github.umn.edu/blank121/Kinatest.R/tree/wo |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/Kinatest-R_part1.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/Kinatest-R_part1.R Tue Feb 06 17:16:05 2018 -0500 |
| [ |
| b'@@ -0,0 +1,1113 @@\n+this.dir <- dirname(parent.frame(2)$ofile)\r\n+setwd(this.dir)\r\n+\r\n+\r\n+ImportedSubstrateList<- read.csv(input1, stringsAsFactors=FALSE)\r\n+NegativeSubstrateList<- read.csv(input2, stringsAsFactors=FALSE)\r\n+SubstrateBackgroundFrequency<- read.csv(input3, stringsAsFactors=FALSE)\r\n+\r\n+ScreenerFilename<-screener\r\n+\r\n+\r\n+\r\n+FILENAME<-"output1"\r\n+FILENAME2<-"output2"\r\n+FILENAME3<-"output3"\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+OutputMatrix<-"KinaseMatrix.csv"\r\n+CharacterizationTable<-"CharacterizationTableForThisKinase.csv"\r\n+SDtable<-"SDtableforthisKinase"\r\n+SiteSelectivityTable<-"SiteSelectivityForThisKinase"\r\n+\r\n+\r\n+\r\n+substrates<-matrix(rep("A",times=((nrow(ImportedSubstrateList)-1)*15)),ncol = 15)\r\n+#SeqsToBeScored<-"asdasd"\r\n+ \r\n+for (i in 2:nrow(ImportedSubstrateList))\r\n+{\r\n+ substratemotif<-ImportedSubstrateList[i,4:18]\r\n+ substratemotif[8]<-"Y"\r\n+ #substratemotif<-paste(substratemotif,sep = "",collapse = "")\r\n+ j=i-1\r\n+ substratemotif<-unlist(substratemotif)\r\n+ substrates[j,1:15]<-substratemotif\r\n+}\r\n+\r\n+# SpacesToOs<-c(""="O",)\r\n+# substrates<-SpacesToOs[substrates]\r\n+\r\n+SubstrateBackgroundFrequency[nrow(SubstrateBackgroundFrequency),2]\r\n+\r\n+if(2==2){\r\n+Amean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),2]), na.rm=TRUE)\r\n+Cmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),3]), na.rm=TRUE)\r\n+Dmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),4]), na.rm=TRUE)\r\n+Emean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),5]), na.rm=TRUE)\r\n+Fmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),6]), na.rm=TRUE)\r\n+Gmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),7]), na.rm=TRUE)\r\n+Hmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),8]), na.rm=TRUE)\r\n+Imean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),9]), na.rm=TRUE)\r\n+Kmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),10]), na.rm=TRUE)\r\n+Lmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),11]), na.rm=TRUE)\r\n+Mmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),12]), na.rm=TRUE)\r\n+Nmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),13]), na.rm=TRUE)\r\n+Pmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),14]), na.rm=TRUE)\r\n+Qmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),15]), na.rm=TRUE)\r\n+Rmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),16]), na.rm=TRUE)\r\n+Smean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),17]), na.rm=TRUE)\r\n+Tmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),18]), na.rm=TRUE)\r\n+Vmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),19]), na.rm=TRUE)\r\n+Wmean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),20]), na.rm=TRUE)\r\n+Ymean<-mean(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),21]), na.rm=TRUE)\r\n+\r\n+AllMeans<-c(Amean,Cmean,Dmean,Emean,Fmean,Gmean,Hmean,Imean,Kmean,Lmean,Mmean,Nmean,Pmean,Qmean,Rmean,Smean,Tmean,Vmean,Wmean,Ymean)\r\n+\r\n+Asd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),2]), na.rm=TRUE)\r\n+Csd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),3]), na.rm=TRUE)\r\n+Dsd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),4]), na.rm=TRUE)\r\n+Esd<-sd(as.numeric(SubstrateBackgroundFrequency[1:(nrow(SubstrateBackgroundFrequency)),5]), '..b'/(PositionTable[13,]*.01*Pmean))\r\n+# EPMtable[14,]<-(PositionTable[14,]/(PositionTable[14,]*.01*Qmean))\r\n+# EPMtable[15,]<-(PositionTable[15,]/(PositionTable[15,]*.01*Rmean))\r\n+# EPMtable[16,]<-(PositionTable[16,]/(PositionTable[16,]*.01*Smean))\r\n+# EPMtable[17,]<-(PositionTable[17,]/(PositionTable[17,]*.01*Tmean))\r\n+# EPMtable[18,]<-(PositionTable[18,]/(PositionTable[18,]*.01*Vmean))\r\n+# EPMtable[19,]<-(PositionTable[19,]/(PositionTable[19,]*.01*Wmean))\r\n+# EPMtable[20,]<-(PositionTable[20,]/(PositionTable[20,]*.01*Ymean))\r\n+\r\n+columns<-c(length(Column1)-sum(Column1==""),\r\n+ length(Column2)-sum(Column2==""),\r\n+ length(Column3)-sum(Column3==""),\r\n+ length(Column4)-sum(Column4==""),\r\n+ length(Column5)-sum(Column5==""),\r\n+ length(Column6)-sum(Column6==""),\r\n+ length(Column7)-sum(Column7==""),\r\n+ length(Column8)-sum(Column8==""),\r\n+ length(Column9)-sum(Column9==""),\r\n+ length(Column10)-sum(Column10==""),\r\n+ length(Column11)-sum(Column11==""),\r\n+ length(Column12)-sum(Column12==""),\r\n+ length(Column13)-sum(Column13==""),\r\n+ length(Column14)-sum(Column14==""),\r\n+ length(Column15)-sum(Column15==""))\r\n+\r\n+for (z in 1:15) {\r\n+ for (y in 1:20) {\r\n+ if (PositionTable[y,z]>0){\r\n+ EPMtable[y,z]<-PositionTable[y,z]/((columns[z]*.01*AllMeans[y]))\r\n+ }\r\n+ if (PositionTable[y,z]==0){\r\n+ EPMtable[y,z]<-(1/columns[z])/((columns[z]*.01*AllMeans[y]))\r\n+ }\r\n+ }\r\n+}\r\n+#here I created the endogenous probability matrix\r\n+#now all I need to do is make the program automatically determine which SDs are >2, and then make it perform screener and sorter on those SDs\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+# write.xlsx(SDtable,file=FILENAME, sheetName = "Standard Deviation Table",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(PercentTable,file = FILENAME,sheetName = "Percent Table",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(SelectivitySheet,file = FILENAME,sheetName = "Site Selectivity",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(EPMtable,file=FILENAME,sheetName = "Endogenous Probability Matrix",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(NormalizationScore,file = FILENAME,sheetName = "Normalization Score",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+\r\n+NormalizationScore<-c("Normalization Score",NormalizationScore)\r\n+\r\n+write.table(x=c("SD Table"),file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n+write.table(SDtable,file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n+write.table(x=c("Percent Table"),file=FILENAME,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n+write.table(PercentTable,file=FILENAME, append = TRUE,sep=",",row.names = FALSE, col.names = FALSE)\r\n+\r\n+EPMtableu<-EPMtable\r\n+HeaderSD<-c(-7:7)\r\n+EPMtableu<-rbind(HeaderSD,EPMtableu)\r\n+EPMtableu<-data.frame(SetOfAAs,EPMtableu)\r\n+\r\n+write.table("Site Selectivity Matrix", file = FILENAME2, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE)\r\n+SelectivityHeader=matrix(data = c("Position",-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7),nrow = 1)\r\n+head<-matrix(data=rep(" ",times=16),nrow = 1)\r\n+SelectivityHeader<-rbind(head,SelectivityHeader)\r\n+\r\n+write.table(SelectivityHeader, file = FILENAME2, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE)\r\n+#colnames(SelectivitySheet)<-c("-7","-6","-5","-4","-3","-2","-1","0","1","2","3","4","5","6","7")\r\n+write.table(SelectivitySheet,file = FILENAME2, append = TRUE,sep = ",",row.names = TRUE, col.names = FALSE)\r\n+write.table(x=c("Endogenous Probability Matrix"),file=FILENAME2,append = TRUE,sep=",", row.names = FALSE, col.names = FALSE)\r\n+write.table(EPMtableu,file = FILENAME2, append = TRUE,sep = ",",row.names = FALSE, col.names = FALSE)\r\n+write.table(NormalizationScore, file = FILENAME2, append = TRUE,sep = ",",row.names = FALSE, col.names = FALSE)\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/Kinatest-R_part2.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/Kinatest-R_part2.R Tue Feb 06 17:16:05 2018 -0500 |
| [ |
| b'@@ -0,0 +1,782 @@\n+#test myself: this script should take in amino acids for each of the 9 positions and give out every single combination of those AAs\r\n+\r\n+#need to do following: fix it so that the accession numbers stay with the substrates,\r\n+#also the neg false constant is totaly unphos\'d Ys found by FASTA-2-CSV system# uniprot\r\n+\r\n+#HOW MANY: IF THERE\'S two aas in each position you get 2^9, so I assume the numbers are:\r\n+#(number in position-4)*(number in position -3)*(number in position -2)...=total\r\n+# require(rJava)\r\n+# require(xlsxjars)\r\n+# require(xlsx)\r\n+# # require(readxl)\r\n+\r\n+View(SDtable)\r\n+bareSDs<-SDtable[2:21,2:16]\r\n+goodones<-bareSDs>2\r\n+\r\n+Positionm7<-which(goodones[,1] %in% TRUE)\r\n+if (length(Positionm7)<1){Positionm7<-which(bareSDs[,1]==max(bareSDs[,1]))}\r\n+Positionm6<-which(goodones[,2] %in% TRUE)\r\n+if (length(Positionm6)<1){Positionm6<-which(bareSDs[,2]==max(bareSDs[,2]))}\r\n+Positionm5<-which(goodones[,3] %in% TRUE)\r\n+if (length(Positionm5)<1){Positionm5<-which(bareSDs[,3]==max(bareSDs[,3]))}\r\n+Positionm4<-which(goodones[,4] %in% TRUE)\r\n+if (length(Positionm4)<1){Positionm4<-which(bareSDs[,4]==max(bareSDs[,4]))}\r\n+Positionm3<-which(goodones[,5] %in% TRUE)\r\n+if (length(Positionm3)<1){Positionm3<-which(bareSDs[,5]==max(bareSDs[,5]))}\r\n+Positionm2<-which(goodones[,6] %in% TRUE)\r\n+if (length(Positionm2)<1){Positionm2<-which(bareSDs[,6]==max(bareSDs[,6]))}\r\n+Positionm1<-which(goodones[,7] %in% TRUE)\r\n+if (length(Positionm1)<1){Positionm1<-which(bareSDs[,7]==max(bareSDs[,7]))}\r\n+\r\n+Positiond0<-which(goodones[,8] %in% TRUE)\r\n+if (length(Positiond0)<1){Positiond0<-which(bareSDs[,8]==max(bareSDs[,8]))}\r\n+\r\n+Positionp1<-which(goodones[,9] %in% TRUE)\r\n+if (length(Positionp1)<1){Positionp1<-which(bareSDs[,9]==max(bareSDs[,9]))}\r\n+Positionp2<-which(goodones[,10] %in% TRUE)\r\n+if (length(Positionp2)<1){Positionp2<-which(bareSDs[,10]==max(bareSDs[,10]))}\r\n+Positionp3<-which(goodones[,11] %in% TRUE)\r\n+if (length(Positionp3)<1){Positionp3<-which(bareSDs[,11]==max(bareSDs[,11]))}\r\n+Positionp4<-which(goodones[,12] %in% TRUE)\r\n+if (length(Positionp4)<1){Positionp4<-which(bareSDs[,12]==max(bareSDs[,12]))}\r\n+Positionp5<-which(goodones[,13] %in% TRUE)\r\n+if (length(Positionp5)<1){Positionp5<-which(bareSDs[,13]==max(bareSDs[,13]))}\r\n+Positionp6<-which(goodones[,14] %in% TRUE)\r\n+if (length(Positionp6)<1){Positionp6<-which(bareSDs[,14]==max(bareSDs[,14]))}\r\n+Positionp7<-which(goodones[,15] %in% TRUE)\r\n+if (length(Positionp7)<1){Positionp7<-which(bareSDs[,15]==max(bareSDs[,15]))}\r\n+\r\n+aa_props2 <- c("1"="A", "2"="C", "3"="D", "4"="E", "5"="F", "6"="G", "7"="H", "8"="I", "9"="K", "10"="L", "11"="M", "12"="N",\r\n+ "13"="P", "14"="Q", "15"="R", "16"="S", "17"="T", "18"="V", "19"="W", "20"="Y")\r\n+aa_props2<-c(1="A")\r\n+\r\n+Positionm7<-sapply(Positionm7, function (x) aa_props2[x])\r\n+Positionm6<-sapply(Positionm6, function (x) aa_props2[x])\r\n+Positionm5<-sapply(Positionm5, function (x) aa_props2[x])\r\n+Positionm4<-sapply(Positionm4, function (x) aa_props2[x])\r\n+Positionm3<-sapply(Positionm3, function (x) aa_props2[x])\r\n+Positionm2<-sapply(Positionm2, function (x) aa_props2[x])\r\n+Positionm1<-sapply(Positionm1, function (x) aa_props2[x])\r\n+Positiond0<-sapply(Positiond0, function (x) aa_props2[x])\r\n+Positionp1<-sapply(Positionp1, function (x) aa_props2[x])\r\n+Positionp2<-sapply(Positionp2, function (x) aa_props2[x])\r\n+Positionp3<-sapply(Positionp3, function (x) aa_props2[x])\r\n+Positionp4<-sapply(Positionp4, function (x) aa_props2[x])\r\n+Positionp5<-sapply(Positionp5, function (x) aa_props2[x])\r\n+Positionp6<-sapply(Positionp6, function (x) aa_props2[x])\r\n+Positionp7<-sapply(Positionp7, function (x) aa_props2[x])\r\n+\r\n+\r\n+# Positionm7<-c("D","H","N","V")\r\n+# Positionm6<-c("E","V")\r\n+# Positionm5<-c("D","H")\r\n+# Positionm4<-c("D","N")\r\n+# Positionm3<-c("D","E","F","Q")\r\n+# Positionm2<-c("D","N","Q","S")\r\n+# Positionm1<-c("F","I","L")\r\n+# Positiond0<-c("Y")\r\n+# Positionp1<-c("A","E")\r\n+# Positionp2<-c("T","S","Q","E")\r\n+# Positionp3<-c("'..b'tide[7]),9]*\r\n+ #ThisKinTable[as.numeric(Scoringpeptide[8]),10]*\r\n+ ThisKinTable[as.numeric(Scoringpeptide[9]),11]*ThisKinTable[as.numeric(Scoringpeptide[10]),12]*ThisKinTable[as.numeric(Scoringpeptide[11]),13]*\r\n+ ThisKinTable[as.numeric(Scoringpeptide[12]),14]*ThisKinTable[as.numeric(Scoringpeptide[13]),15]*ThisKinTable[as.numeric(Scoringpeptide[14]),16]*ThisKinTable[as.numeric(Scoringpeptide[15]),17]\r\n+ \r\n+ PositiveScores[v]<-ThisKinTableScore\r\n+ ThisKinTableScore<-(ThisKinTableScore/(ThisKinTableScore+1/as.numeric(NormalizationScore[2])))\r\n+ PositiveWeirdScores[v]<-ThisKinTableScore*100\r\n+}\r\n+\r\n+positivesubstrates<-ImportedSubstrateList[,4:18]\r\n+positivewithscores<-cbind.data.frame(positivesubstrates,PositiveScores,PositiveWeirdScores)\r\n+\r\n+\r\n+#write down the transient transfection SOP and what we will be doing with them\r\n+#write down the vector names I will be using\r\n+#write down something about transforming bacteria and with what\r\n+\r\n+#90% whatevernness\r\n+# TPninetyone<-length(PositiveWeirdScores[PositiveWeirdScores>=0.91])\r\n+# Senseninetyone<-TPninetyone/nrow(positivesubstrates)\r\n+# \r\n+# TNninetyone<-length(NegativeWeirdScores[NegativeWeirdScores<91])\r\n+# Specninetyone<-TNninetyone/100\r\n+\r\n+#create the MCC table\r\n+\r\n+threshold<-c(1:100)\r\n+threshold<-order(threshold,decreasing = TRUE)\r\n+\r\n+Truepositives<-c(1:100)\r\n+Falsenegatives<-c(1:100)\r\n+Sensitivity<-c(1:100)\r\n+TrueNegatives<-c(1:100)\r\n+FalsePositives<-c(1:100)\r\n+Specificity<-c(1:100)\r\n+Accuracy<-c(1:100)\r\n+MCC<-c(1:100)\r\n+EER<-c(1:100)\r\n+\r\n+#MAKE DAMN SURE THAT THE ACCESSION NUMBERS FOLLOW THE MOTIFS\r\n+\r\n+for (z in 1:100) {\r\n+ thres<-101-z\r\n+ Truepositives[z]<-length(PositiveWeirdScores[PositiveWeirdScores>=(thres)])\r\n+ Falsenegatives[z]<-nrow(positivesubstrates)-Truepositives[z]\r\n+ Sensitivity[z]<-Truepositives[z]/(Falsenegatives[z]+Truepositives[z])\r\n+ TrueNegatives[z]<-length(NegativeWeirdScores[NegativeWeirdScores<(thres)])\r\n+# at thresh 100 this should be 0, because it is total minus true negatives\r\n+ FalsePositives[z]<-nrow(NegativeSubstrateList)-TrueNegatives[z]\r\n+ Specificity[z]<-1-(TrueNegatives[z]/(FalsePositives[z]+TrueNegatives[z]))\r\n+ Accuracy[z]<-100*(Truepositives[z]+TrueNegatives[z])/(Falsenegatives[z]+FalsePositives[z]+TrueNegatives[z]+Truepositives[z])\r\n+ MCC[z]<-((Truepositives[z]+TrueNegatives[z])-(Falsenegatives[z]+FalsePositives[z]))/sqrt(round(round(Truepositives[z]+Falsenegatives[z])*round(TrueNegatives[z]+FalsePositives[z])*round(Truepositives[z]+FalsePositives[z])*round(TrueNegatives[z]+Falsenegatives[z])))\r\n+ EER[z]<-.01*(((1-(Sensitivity[z]))*(Truepositives[z]+Falsenegatives[z]))+(Specificity[z]*(1-(Truepositives[z]+Falsenegatives[z]))))\r\n+}\r\n+Characterization<-cbind.data.frame(threshold,Truepositives,Falsenegatives,Sensitivity,TrueNegatives,FalsePositives,Specificity,Accuracy,MCC,EER)\r\n+\r\n+positiveheader<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,"RPMS","PMS")\r\n+positivewithscores<-rbind.data.frame(positiveheader,positivewithscores)\r\n+\r\n+negativeheader<-c("Substrate","RPMS","PMS")\r\n+colnames(NegativeWithScores)<-negativeheader\r\n+\r\n+# write.xlsx(NegativeWithScores,file = FILENAME, sheetName = "Negative Sequences Scored",col.names = TRUE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(Characterization,file = FILENAME,sheetName = "Characterization Table",col.names = TRUE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(RanksPeptides,file = FILENAME,sheetName = "Ranked Generated Peptides",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+# write.xlsx(positivewithscores,file = FILENAME, sheetName = "Positive Sequences Scored",col.names = FALSE,row.names = FALSE,append = TRUE)\r\n+write.table(x=c("Characterzation Table"),file = FILENAME2, col.names = FALSE,row.names = FALSE, append = TRUE,sep = ",")\r\n+write.table(Characterization,file = FILENAME2, col.names = TRUE,row.names = FALSE, append = TRUE,sep = ",")\r\n+\r\n+\r\n+write.table(RanksPeptides,file = FILENAME3,append = TRUE,row.names = FALSE,col.names = TRUE,sep = ",")\r\n+\r\n+\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/kinatestid_r.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/kinatestid_r.xml Tue Feb 06 17:16:05 2018 -0500 |
| [ |
| @@ -0,0 +1,46 @@ +<tool id="kinatestid_r" name="Kinatest-ID 7_to_7" version="0.1.0"> + <description>calculate optimal substrates</description> + <requirements> + <requirement type="package">R</requirement> + </requirements> + <command><![CDATA[ + ln -s '$__tool_directory__/screener7-7.csv' screener && + ln -s '$substrates' input1 && + ln -s '$negatives' input2 && + ln -s '$SBF' input3 && + Rscript '$__tool_directory__/Kinatest-R_part1.R' && + Rscript '$__tool_directory__/Kinatest-R_part2.R' && + mv output1 output1.csv && + mv output2 output2.csv && + mv output3 output3.csv + ]]></command> + <inputs> + <param format="csv" name="substrates" type="data" label="Positive/Phosphorylated Substrate List"/> + <param format="csv" name="negatives" type="data" label="Negative/unPhosphorylated Substrate List"/> + <param format="csv" name="SBF" type="data" label="Substrate Background Frequency List"/> + </inputs> + <outputs> + <data format="csv" name="SDtable" from_work_dir="output1.csv" label="Standard Deviation Table"/> + <data format="csv" name="EPM" from_work_dir="output2.csv" label="Endogenous Probability Matrix"/> + <data format="csv" name="Characterization" from_work_dir="output3.csv" label="Characterization Table"/> + </outputs> + <tests> + <test> + <param name="substrates" ftype="tabular" value="substrates.tsv"/> + <param name="negatives" ftype="tabular" value="negatives.tsv"/> + <param name="SBF" ftype="tabular" value="SBF.tsv"/> + <output name="SDtable" file="SDtable.csv"/> + <output name="EPM" file="EPM.csv"/> + <output name="Characterization" file="Characterization.csv"/> + </test> + </tests> + + + <help><![CDATA[ +This tool is intended for use in conjunction with KinaMine.jar and Negative Motif Finder. Using the outputs from those two functions (The Positive and Negative substrates as well as the Substrate Background Frequency) this tool calculates optimal substrates + ]]></help> + <citations> + <citation type="doi">10.1021/ja507164a</citation> + </citations> +</tool> + |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/screener7-7.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/screener7-7.csv Tue Feb 06 17:16:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,325 @@\n+Amino Acid,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,Abl\r\n+A,1,1,0.882322561,0.946431352,1.267758769,1.089980567,0.849406684,0,1.983180862,1.248128878,0.226122254,0.685178519,1,1,1,\r\n+C,1,1,0.009604626,0.009015706,0.742749396,0.997377969,0.007640366,0,0.63304489,1.858200501,0.012268107,1.92009365,1,1,1,\r\n+D,1,1,1.431703724,1.637464053,2.605079343,0.770520959,1.328600707,0,2.148130938,0.5957361,0.176594551,0.332519791,1,1,1,\r\n+E,1,1,0.643983264,1.989447239,2.094100346,1.439507752,1.245765716,0,1.24627458,0.468097263,0.000848801,0.725553316,1,1,1,\r\n+F,1,1,0.497001165,0.3379037,0.411572204,0.296542087,2.060804142,0,0.005820098,0.531995278,2.357742596,0.438745823,1,1,1,\r\n+G,1,1,0.893453251,1.528088747,0.67390472,1.762542109,0.335648512,0,1.013042344,0.800811003,0.003981392,1.313121648,1,1,1,\r\n+H,1,1,0.007832281,1.127242108,2.091972175,1.320938614,1.865005518,0,0.696245424,0.649606912,0.00730737,0.615276036,1,1,1,\r\n+I,1,1,0.881063052,0.167205137,1.184813256,2.035674575,9.807795409,0,1.654479752,0.950940898,1.507303461,0.722455635,1,1,1,\r\n+K,1,1,0.947921128,0.179766965,1.205417608,0.829787272,0.346855573,0,0.473066457,0.370489984,0.377025864,0.864435576,1,1,1,\r\n+L,1,1,0.500899691,0.262736557,0.121229505,0.303230844,1.077236813,0,0.809602792,0.382513407,0.718680068,1.105911199,1,1,1,\r\n+M,1,1,2.478037279,0.993521392,1.241173771,0.538634574,0.008436542,0,1.208456381,0.008168982,1.117390063,0.646024916,1,1,1,\r\n+N,1,1,1.615827052,1.179411378,0.744890739,1.439814286,0.551625197,0,0.7261286,1.289433753,0.196816516,0.004444734,1,1,1,\r\n+P,1,1,1.763222042,1.350550742,2.12505232,1.096484314,0.600847155,0,0.097493956,1.817626621,15.1017903,2.066452756,1,1,1,\r\n+Q,1,1,1.215052286,0.530735921,0.747268713,0.003547182,0.595958957,0,2.457274164,1.724106317,0.284965727,0.999253879,1,1,1,\r\n+R,1,1,0.927974325,0.678582936,0.212622438,0.67541443,0.422441115,0,1.268478795,1.252943286,0.002905196,1.263432811,1,1,1,\r\n+S,1,1,1.089114937,1.168382233,0.988930826,1.549280904,0.123768289,0,0.561910123,1.64939594,0.794937114,0.852167102,1,1,1,\r\n+T,1,1,1.318701224,1.079684538,0.783764874,0.324638108,0.646699246,0,0.934807562,2.570822141,0.732168201,0.828210681,1,1,1,\r\n+V,1,1,0.588885966,0.475177842,2.217365652,2.992336523,5.02600581,0,0.604585498,0.606232586,1.78752705,1.267711545,1,1,1,\r\n+W,1,1,1.389710835,0.015370202,0.020295574,0.018497672,0.846222832,0,0.024623679,0.014549452,2.690111916,1.570439269,1,1,1,\r\n+Y,1,1,0.006900535,0.342484705,0.417151943,0.004117292,1.253245644,677.8566817,0.430627113,1.078415219,0.010911903,0.444693959,1,1,1,\r\n+Normalize Factor,,,,,,,,,,,,,,,,\r\n+0.072093023,,,,,,,,,,,,,,,,\r\n+Threshold,,,,,,,,,,,,,,,,\r\n+56,,,,,,,,,,,,,,,,\r\n+Amino Acid,,,-5,-4,-3,-2,-1,0,1,2,3,4,,,,\r\n+A,1,1,0.943094978,1.501384107,1.95135657,1.880160203,0.073038604,0,1.338932042,1.229900149,0.075951271,0.063813178,1,1,1,Arg\r\n+C,1,1,0.218421841,2.659957114,0.134966466,3.894705692,0.204796443,0,3.601559506,0.265189109,0.297715434,0.236145846,1,1,1,\r\n+D,1,1,3.221368524,3.46570699,1.632661257,0.091767791,0.111076842,0,2.351303857,1.01810513,0.747194926,2.713123437,1,1,1,\r\n+E,1,1,0.074069921,2.323155165,3.38088485,1.190296131,0.095487211,0,0.90830603,0.882663695,0.506328757,0.915744205,1,1,1,\r\n+F,1,1,0.11911299,0.102411767,2.157136787,0.101526802,0.143188264,0,0.128628653,1.908695899,2.223303906,0.174281004,1,1,1,\r\n+G,1,1,1.685622529,0.694699429,0.03921412,3.013734957,0.800499624,0,1.041247934,0.737769895,0.070805422,6.003019282,1,1,1,\r\n+H,1,1,0.196014995,0.1697973,0.212485913,0.163795158,0.190485555,0,2.645948806,0.172270587,0.156995244,3.84815868,1,1,1,\r\n+I,1,1,1.508289663,0.065177198,1.559508344,2.933058204,2.764966898,0,0.054804971,0.108136152,1.645039272,0.144904016,1,1,1,\r\n+K,1,1,0.089214088,0.060880995,1.160673525,0.059748979,1.28191666,0,1.7991125,1.104219033,0.047129496,1.581213152,1,1,1,\r\n+L,1,1,0.766134776,0.550490812,0.042706744,0.521090197,0.922278598,0,0.488097183,0.515508197,0.838542183,2.525765703,1,1,1,\r\n+M,1,1,0.183007686,0.215571796,0.234'..b',1,1.759038759,0.008932852,0.637935052,0.008466463,1.768903208,0,0.53956636,1.594978452,2.117842234,2.131310757,1,1,1,\r\n+N,1,1,0.720373484,1.487226763,1.302177487,2.016367835,0.788875007,0,0.250460079,3.031683315,0.436896267,1.573194991,1,1,1,\r\n+P,1,1,1.833186802,0.777533261,0.420395865,0.567125516,0.238406257,0,0.002326649,0.771391218,7.270751957,1.103332241,1,1,1,\r\n+Q,1,1,0.235600141,0.004297437,0.500003046,0.994829208,0.379831149,0,0.695036596,0.004327428,0.80862803,1.575651985,1,1,1,\r\n+R,1,1,0.564009404,0.003525368,0.227098034,0.538917103,0.136536863,0,0.003591695,0.190792681,0.005422585,0.907984716,1,1,1,\r\n+S,1,1,0.818521276,0.978919963,1.689457578,0.915615462,1.150862322,0,0.237748244,1.331727596,1.19985019,1.121480305,1,1,1,\r\n+T,1,1,0.46747837,0.730560587,0.304249366,0.843021,0.524543798,0,0.779170053,1.166347147,0.960500715,0.623499655,1,1,1,\r\n+V,1,1,0.383039856,0.201074288,0.2478765,0.236550589,0.234975642,0,2.375954579,0.400179532,6.138746915,0.807238062,1,1,1,\r\n+W,1,1,0.01987174,1.145128323,0.021731187,0.016566653,0.010690007,0,0.015401536,0.01642969,0.021461124,0.017254333,1,1,1,\r\n+Y,1,1,0.459914329,0.006810884,0.438274355,0.36295333,1.308560175,1,1.124088698,0.94740785,0.54611071,1.415811189,1,1,1,\r\n+Normalize Factor,,,,,,,,,,,,,,,,\r\n+0.052738337,,,,,,,,,,,,,,,,\r\n+Threshold,,,,,,,,,,,,,,,,\r\n+54,,,,,,,,,,,,,,,,\r\n+Amino Acid,,,-5,-4,-3,-2,-1,0,1,2,3,4,,,,\r\n+A,1,1,1.222188853,1.486603226,0.919170896,0.793911917,0.409740076,0,0.638480016,2.56572863,0.846871309,0.004025467,1,1,1,Yes\r\n+C,1,1,0.014768454,0.612323227,0.01733631,0.01431502,0.013620919,0,0.014857804,0.020544494,0.751226491,0.018818585,1,1,1,\r\n+D,1,1,1.793680299,3.783436502,3.189929787,3.530731289,0.623287514,0,1.455301756,0.617179375,0.228255986,0.882795484,1,1,1,\r\n+E,1,1,2.707237165,3.262501671,7.509267488,2.840702315,2.078923247,0,3.449957711,0.750753948,0.154315698,0.92756723,1,1,1,\r\n+F,1,1,0.368930904,0.006177867,0.005884929,1.274931107,0.597875789,0,0.006948944,0.343687804,1.628488797,0.332278839,1,1,1,\r\n+G,1,1,0.580720277,0.563429661,0.747049717,2.644759528,0.002554867,0,1.803040115,0.616994899,0.557328091,2.704367259,1,1,1,\r\n+H,1,1,0.408655956,0.838993858,0.007945388,1.872850327,0.333955199,0,0.606798686,0.006818885,0.600277041,1.531470657,1,1,1,\r\n+I,1,1,1.883887869,0.004269998,0.00490079,0.007897733,8.056462413,0,0.516435014,0.768417292,1.555521832,0.005769512,1,1,1,\r\n+K,1,1,0.599781851,0.003541306,0.51458723,0.435001391,0.199251972,0,0.267884479,0.390721439,0.148200027,0.240965618,1,1,1,\r\n+L,1,1,0.10798419,0.661132019,0.004342008,0.140684195,3.313737494,0,0.305099752,0.354148407,2.895214199,1.176621256,1,1,1,\r\n+M,1,1,1.500924535,0.596326712,0.012758235,0.011809241,0.012062097,0,0.01204882,0.014416658,0.010516891,0.014744175,1,1,1,\r\n+N,1,1,1.06432095,0.575254989,0.729141869,1.147005116,0.234567107,0,0.240700296,0.876070748,0.003497148,1.834052819,1,1,1,\r\n+P,1,1,0.57948738,1.123761802,0.829041851,0.894233218,0.338547075,0,0.136095286,0.199795252,2.143300779,1.379206148,1,1,1,\r\n+Q,1,1,0.004051494,0.006305015,0.37225292,1.049473144,0.500540673,0,0.432684795,0.2766543,0.264150289,0.560941973,1,1,1,\r\n+R,1,1,0.004667408,0.003704843,0.153495971,0.004132201,0.002358508,0,0.278529882,0.307235643,0.705639999,0.905800168,1,1,1,\r\n+S,1,1,0.92627953,1.324310304,1.659124469,0.684990056,0.434517693,0,2.843851614,1.61069191,1.218543907,0.839440563,1,1,1,\r\n+T,1,1,1.049881524,1.654126719,0.399151336,1.801762488,2.298611276,0,2.815591798,2.506789582,0.32211978,0.218532251,1,1,1,\r\n+V,1,1,0.150932833,0.004020882,0.783755106,0.428687698,6.887781036,0,0.531349744,3.207681554,4.125351327,0.514811072,1,1,1,\r\n+W,1,1,0.02630224,0.01943939,0.029064252,0.022741147,0.023211485,0,0.023272909,1.344884776,0.021172292,1.540208353,1,1,1,\r\n+Y,1,1,0.690669121,1.006196468,0.325003658,1.193388161,1.678909279,471.2746858,2.302592406,0.643411952,0.762166379,1.244106857,1,1,1,\r\n+Normalize Factor,,,,,,,,,,,,,,,,\r\n+0.159680639,,,,,,,,,,,,,,,,\r\n+Threshold,,,,,,,,,,,,,,,,\r\n+40,,,,,,,,,,,,,,,,\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/test-data/SBF.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/test-data/SBF.csv Tue Feb 06 17:16:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,242 @@\n+Amino Acids,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,Properties,Hydrophobic,Polar,Small,Negative,Postive,Amide,Large Aliphatic,Small Aliphatic,Aromatic,Hydroxy,,Number of Y,Number of pY,Total AAs\r\n+sp|P06493|CDK1_HUMAN,4.04040404,0.336700337,6.397306397,6.060606061,3.703703704,6.060606061,2.693602694,7.407407407,8.080808081,12.12121212,2.693602694,3.367003367,5.050505051,3.367003367,5.050505051,7.070707071,4.377104377,6.060606061,1.346801347,4.713804714,,48.48484848,35.01683502,42.76094276,12.45791246,15.82491582,6.734006734,19.52861953,16.16161616,9.764309764,11.44781145,,14,1,297\r\n+sp|P13639|EF2_HUMAN,7.226107226,1.981351981,6.060606061,7.226107226,4.079254079,7.692307692,1.864801865,6.293706294,7.575757576,8.508158508,3.263403263,3.146853147,5.361305361,3.37995338,5.128205128,5.128205128,4.778554779,8.041958042,0.815850816,2.447552448,,50.34965035,34.38228438,49.41724942,13.28671329,14.56876457,6.526806527,14.8018648,22.96037296,7.342657343,9.906759907,,21,4,858\r\n+sp|P08621|RU17_HUMAN,4.576659039,0.228832952,9.610983982,12.12814645,1.601830664,10.06864989,2.745995423,2.059496568,5.034324943,4.576659039,2.059496568,2.059496568,7.551487414,1.372997712,21.05263158,5.263157895,2.288329519,2.288329519,0.457665904,2.974828375,,30.89244851,54.00457666,43.93592677,21.73913043,28.83295195,3.432494279,6.636155606,16.93363844,5.034324943,7.551487414,,13,2,437\r\n+sp|P62899|RL31_HUMAN,6.4,0,4,6.4,2.4,5.6,1.6,6.4,12.8,4.8,2.4,7.2,4.8,0.8,11.2,2.4,7.2,9.6,0.8,3.2,,41.6,44,47.2,10.4,25.6,8,11.2,21.6,6.4,9.6,,4,1,125\r\n+sp|P09874|PARP1_HUMAN,6.607495069,1.380670611,6.114398422,7.396449704,2.958579882,7.001972387,1.873767258,4.733727811,12.4260355,8.875739645,2.465483235,3.550295858,4.339250493,3.353057199,3.25443787,8.382642998,4.142011834,6.706114398,1.282051282,3.15581854,,45.16765286,37.96844181,48.22485207,13.51084813,17.55424063,6.903353057,13.60946746,20.31558185,7.396449704,12.52465483,,32,2,1014\r\n+sp|P38159|RBMX_HUMAN,3.836317136,0,8.695652174,3.324808184,1.79028133,15.60102302,0.767263427,0.767263427,3.324808184,3.069053708,2.046035806,1.79028133,11.50895141,0.511508951,14.57800512,16.11253197,2.557544757,2.813299233,0,6.905370844,,36.8286445,32.99232737,62.91560102,12.02046036,18.67007673,2.301790281,3.836317136,22.25063939,8.695652174,18.67007673,,27,2,391\r\n+sp|P08238|HS90B_HUMAN,5.248618785,0.828729282,7.044198895,13.25966851,3.591160221,4.696132597,1.79558011,6.629834254,10.35911602,8.563535912,2.624309392,3.867403315,3.17679558,2.900552486,4.419889503,6.629834254,4.696132597,5.801104972,0.552486188,3.314917127,,41.85082873,43.64640884,41.98895028,20.3038674,16.57458564,6.767955801,15.19337017,15.74585635,7.458563536,11.32596685,,24,8,724\r\n+sp|P07900|HS90A_HUMAN,4.644808743,0.956284153,7.37704918,13.25136612,3.415300546,4.371584699,1.639344262,6.830601093,10.92896175,8.469945355,2.732240437,4.234972678,2.868852459,3.415300546,4.098360656,5.737704918,5.87431694,5.191256831,0.546448087,3.415300546,,40.57377049,44.94535519,41.2568306,20.6284153,16.66666667,7.650273224,15.30054645,14.20765027,7.37704918,11.61202186,,25,7,732\r\n+sp|P14866|HNRPL_HUMAN,6.791171477,1.867572156,5.093378608,5.093378608,3.565365025,12.05432937,3.056027165,3.056027165,4.923599321,5.942275042,2.376910017,5.602716469,8.319185059,4.41426146,6.112054329,7.300509338,2.886247878,6.451612903,0.339558574,4.753820034,,47.19864177,34.29541596,56.36672326,10.18675722,14.09168081,10.01697793,8.998302207,25.29711375,8.658743633,10.18675722,,28,3,589\r\n+sp|P31942|HNRH3_HUMAN,4.624277457,0.289017341,7.225433526,4.046242775,4.624277457,24.27745665,2.89017341,4.046242775,2.312138728,3.468208092,4.913294798,4.624277457,3.757225434,2.312138728,8.092485549,5.49132948,2.601156069,3.179190751,0.578034682,6.647398844,,56.64739884,31.50289017,56.06936416,11.2716763,13.29479769,6.936416185,7.514450867,32.08092486,11.84971098,8.092485549,,23,1,346\r\n+sp|P62750|RL23A_HUMAN,12.82051282,0,5.769230769,3.205128205,1.923076923,1.923076923,2.564102564,7.051282051,19.23076923'..b'31085353,1.05374078,5.374077977,6.533192835,12.4341412,2.634351949,4.531085353,5.900948367,4.214963119,3.161222339,6.533192835,5.268703899,8.219178082,0.632244468,2.845100105,,49.73656481,32.56059009,51.31717597,13.06638567,10.74815595,8.746048472,17.80821918,21.28556375,6.322444679,11.80189673,,27,1,949\r\n+sp|Q96FW1|OTUB1_HUMAN,5.904059041,1.47601476,6.642066421,10.33210332,5.166051661,4.79704797,2.95202952,5.535055351,6.273062731,9.225092251,1.84501845,2.58302583,3.32103321,7.749077491,4.05904059,7.380073801,3.6900369,5.535055351,0,5.535055351,,45.01845018,40.5904059,41.32841328,16.97416974,13.28413284,10.33210332,14.7601476,16.23616236,10.70110701,11.0701107,,15,1,271\r\n+sp|P62807|H2B1C_HUMAN,10.31746032,0,2.380952381,5.555555556,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,11.11111111,6.349206349,7.142857143,0,3.968253968,,40.47619048,37.3015873,50,7.936507937,24.6031746,4.761904762,9.523809524,23.01587302,5.555555556,17.46031746,,5,2,126\r\n+sp|O60814|H2B1K_HUMAN,11.11111111,0,2.380952381,5.555555556,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,10.31746032,6.349206349,7.142857143,0,3.968253968,,41.26984127,37.3015873,50,7.936507937,24.6031746,4.761904762,9.523809524,23.80952381,5.555555556,16.66666667,,5,2,126\r\n+sp|Q99880|H2B1L_HUMAN,10.31746032,0,2.380952381,5.555555556,1.587301587,4.761904762,2.380952381,4.761904762,15.87301587,5.555555556,2.380952381,2.380952381,3.968253968,2.380952381,6.349206349,11.9047619,6.349206349,7.142857143,0,3.968253968,,40.47619048,37.3015873,49.20634921,7.936507937,24.6031746,4.761904762,10.31746032,22.22222222,5.555555556,18.25396825,,5,2,126\r\n+sp|P58876|H2B1D_HUMAN,9.523809524,0,2.380952381,5.555555556,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,11.11111111,7.142857143,7.142857143,0,3.968253968,,39.68253968,37.3015873,50,7.936507937,24.6031746,4.761904762,9.523809524,22.22222222,5.555555556,18.25396825,,5,2,126\r\n+sp|Q93079|H2B1H_HUMAN,10.31746032,0,3.174603175,4.761904762,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,11.11111111,6.349206349,7.142857143,0,3.968253968,,40.47619048,37.3015873,50.79365079,7.936507937,24.6031746,4.761904762,9.523809524,23.01587302,5.555555556,17.46031746,,5,2,126\r\n+sp|Q5QNW6|H2B2F_HUMAN,9.523809524,0,3.174603175,4.761904762,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,11.11111111,6.349206349,7.936507937,0,3.968253968,,40.47619048,37.3015873,50.79365079,7.936507937,24.6031746,4.761904762,9.523809524,23.01587302,5.555555556,17.46031746,,5,2,126\r\n+sp|Q99877|H2B1N_HUMAN,9.523809524,0,2.380952381,5.555555556,1.587301587,5.555555556,2.380952381,4.761904762,15.87301587,4.761904762,2.380952381,2.380952381,4.761904762,2.380952381,6.349206349,11.9047619,6.349206349,7.142857143,0,3.968253968,,39.68253968,37.3015873,50,7.936507937,24.6031746,4.761904762,9.523809524,22.22222222,5.555555556,18.25396825,,5,2,126\r\n+sp|Q99879|H2B1M_HUMAN,8.73015873,0,2.380952381,5.555555556,1.587301587,5.555555556,2.380952381,5.555555556,15.87301587,4.761904762,2.380952381,3.174603175,4.761904762,2.380952381,6.349206349,11.11111111,5.555555556,7.936507937,0,3.968253968,,40.47619048,38.0952381,49.20634921,7.936507937,24.6031746,5.555555556,10.31746032,22.22222222,5.555555556,16.66666667,,5,2,126\r\n+sp|P62263|RS14_HUMAN,9.933774834,1.986754967,4.635761589,5.960264901,2.649006623,11.25827815,1.986754967,5.298013245,7.947019868,5.960264901,2.649006623,1.986754967,4.635761589,3.311258278,9.933774834,5.960264901,6.622516556,6.622516556,0,0.662251656,,47.01986755,35.7615894,53.64238411,10.59602649,19.86754967,5.298013245,11.25827815,27.81456954,3.311258278,12.58278146,,1,1,151\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/test-data/negatives.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/test-data/negatives.csv Tue Feb 06 17:16:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,2902 @@\n+AccessionNumbers,Motifs\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 , MEDYTKIEKIG\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,EKIGEGTYGVVYKGR\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,EGTYGVVYKGRHKTT\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,LMQDSRLYLIFEFLS\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,LSMDLKKYLDSIPPG\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,DSIPPGQYMDSSLVK\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,DSSLVKSYLYQILQG\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,SLVKSYLYQILQGIV\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,FGIPIRVYTHEVVTL\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,HEVVTLWYRSPEVLL\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,VLLGSARYSTPVDIW\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,EVESLQDYKNTFPKW\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,LLSKMLIYDPAKRIS\r\n+>sp|P06493|CDK1_HUMAN Cyclin-dependent kinase 1 OS=Homo sapiens GN=CDK1 PE=1 SV=3 ,KMALNHPYFNDLDNQ\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,STAISLFYELSENDL\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,QLEPEELYQTFQRIV\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,VNVIISTYGEGESGP\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,LKQFAEMYVAKFAAK\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,KKLWGDRYFDPANGK\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,SPVTAQKYRCELLYE\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,KYRCELLYEGPPDDE\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PKGPLMMYISKMVPT\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,TSDKGRFYAFGRVFS\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,VRIMGPNYTPGKKED\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PGKKEDLYLKPIQRT\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,TILMMGRYVEPIEDV\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,KSDPVVSYRETVSEE\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PNKHNRLYMKARPFP\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,ELKQRARYLAEKYEW\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,ARYLAEKYEWDVAEA\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,DITKGVQYLNEIKDS\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PTARRCLYASVLTAQ\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PRLMEPIYLVEIQCP\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,EQVVGGIYGVLNRKR\r\n+>sp|P13639|EF2_HUMAN Elongation factor 2 OS=Homo sapiens GN=EEF2 PE=1 SV=4,PMFVVKAYLPVNESF\r\n+>sp|P08621|RU17_HUMAN U1 small nuclear ribonucleoprotein 70 kDa OS=Homo sapiens GN=SNRNP70 PE=1 SV=2,APRDPIPYLPPLEKL\r\n+>sp|P08621|RU17_HUMAN U1 small nuclear ribonucleoprotein 70 kDa OS=Homo sapiens GN=SNRNP70 PE=1 SV=2,EKHHNQPYCGIAPYI\r\n+>sp|P08621|RU17_HUMAN U1 small nuclear ribonucleoprotein 70 kDa OS=Homo sapiens GN=SNRNP70 PE=1 SV=2,PYCGIAPYIREFEDP\r\n+>sp|P08621|RU17_HUMAN U1 small nuclear ribonucleoprotein 70 kDa OS=Homo sapiens GN=SNRNP70 PE=1 SV=2,LFVARVNYDTTESKL\r\n+>sp|P08621|RU17_HUMAN U1 small nuclear ribonucleoprotein 70 kDa OS=Homo sapiens GN=SNRNP70 PE=1 SV=2,LRREFEVYG'..b'EVSQYIYQVYDS\r\n+>sp|P63010|AP2B1_HUMAN AP-2 complex subunit beta OS=Homo sapiens GN=AP2B1 PE=1 SV=1,PEVSQYIYQVYDSIL\r\n+>sp|P63010|AP2B1_HUMAN AP-2 complex subunit beta OS=Homo sapiens GN=AP2B1 PE=1 SV=1,SQYIYQVYDSILKN \r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2, MTDSKYFTTTKKG\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,ILDCLANYMPKDDRE\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,MLSKDLDYYGTLLKK\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,LSKDLDYYGTLLKKL\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,SAEPELQYVALRNIN\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,MKVFFVKYNDPIYVK\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,IKDIFRKYPNKYESV\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,FRKYPNKYESVIATL\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,LLDELICYIGTLASV\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,IGTLASVYHKPPSAF\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,VGTLSGSYVAPKAVW\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,KNNIDVFYFSTLYPL\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,VFYFSTLYPLHILFV\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,PEVSQHVYQAYETIL\r\n+>sp|Q10567|AP1B1_HUMAN AP-1 complex subunit beta-1 OS=Homo sapiens GN=AP1B1 PE=1 SV=2,SQHVYQAYETILKN \r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,EGVNCLAYDEAIMAQ\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,RLELSVLYKEYAEDD\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,LSVLYKEYAEDDNIY\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,YAEDDNIYQQKIKDL\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,IKDLHKKYSYIRKTR\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,DLHKKYSYIRKTRPD\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,RPDGNCFYRAFGFSH\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,NDQSTSDYLVVYLRL\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,TSDYLVVYLRLLTSG\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,LRLLTSGYLQRESKF\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,SVSIQVEYMDRGEGG\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,EGSEPKVYLLYRPGH\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,EPKVYLLYRPGHYDI\r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,LLYRPGHYDILYK \r\n+>sp|Q96FW1|OTUB1_HUMAN Ubiquitin thioesterase OTUB1 OS=Homo sapiens GN=OTUB1 PE=1 SV=2,PGHYDILYK \r\n+>sp|P62807|H2B1C_HUMAN Histone H2B type 1-C/E/F/G/I OS=Homo sapiens GN=HIST1H2BC PE=1 SV=4,KRSRKESYSVYVYKV\r\n+>sp|P62807|H2B1C_HUMAN Histone H2B type 1-C/E/F/G/I OS=Homo sapiens GN=HIST1H2BC PE=1 SV=4,RKESYSVYVYKVLKQ\r\n+>sp|P62807|H2B1C_HUMAN Histone H2B type 1-C/E/F/G/I OS=Homo sapiens GN=HIST1H2BC PE=1 SV=4,ESYSVYVYKVLKQVH\r\n+>sp|P62807|H2B1C_HUMAN Histone H2B type 1-C/E/F/G/I OS=Homo sapiens GN=HIST1H2BC PE=1 SV=4,EASRLAHYNKRSTIT\r\n+>sp|P62807|H2B1C_HUMAN Histone H2B type 1-C/E/F/G/I OS=Homo sapiens GN=HIST1H2BC PE=1 SV=4,GTKAVTKYTSSK \r\n+>sp|O60814|H2B1K_HUMAN Histone H2B type 1-K OS=Homo sapiens GN=HIST1H2BK PE=1 SV=3,GTKAVTKYTSAK \r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b kinatestid_r/test-data/substrates.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kinatestid_r/test-data/substrates.csv Tue Feb 06 17:16:05 2018 -0500 |
| b |
| b'@@ -0,0 +1,258 @@\n+Substrates,Species,Reference,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7, , , ,Phosphite\r\n+,,sp|P06493|CDK1_HUMAN,,,I,G,E,G,T,xY,G,V,V,Y,K,,,,,,\r\n+,,sp|P13639|EF2_HUMAN,,,,K,E,D,L,xY,L,K,P,I,Q,R,,,,,\r\n+,,sp|P08621|RU17_HUMAN,,,,E,F,E,V,xY,G,P,I,K,,,,,,,\r\n+,,sp|P62899|RL31_HUMAN,,,,,,,L,xY,T,L,V,T,Y,V,P,,,,\r\n+,,sp|P09874|PARP1_HUMAN,,,,,S,D,A,xY,Y,C,T,G,D,V,T,,,,\r\n+,,sp|P38159|RBMX_HUMAN,,,,,S,D,L,xY,S,S,G,R,,,,,,,\r\n+,,sp|P07900|HS90A_HUMAN; sp|P08238|HS90B_HUMAN,S,L,I,I,N,T,F,xY,S,N,K,,,,,,,,\r\n+,,sp|P14866|HNRPL_HUMAN,,,,,,C,L,xY,G,N,V,E,K,,,,,,\r\n+,,sp|P31942|HNRH3_HUMAN,G,M,D,N,Q,G,G,xY,G,S,V,G,R,,,,,,\r\n+,,sp|P62750|RL23A_HUMAN,,,,,,D,H,xY,A,I,I,K,,,,,,,\r\n+,,sp|P63261|ACTG_HUMAN; sp|P60709|ACTB_HUMAN,,,,,K,D,L,xY,A,N,T,V,L,S,G,,,,\r\n+,,sp|P60660|MYL6_HUMAN,,,,,,I,L,xY,S,Q,C,G,D,V,M,,,,\r\n+,,sp|P40939|ECHA_HUMAN,,,D,G,P,G,F,xY,T,T,R,,,,,,,,\r\n+,,sp|O00571|DDX3X_HUMAN,,E,L,A,V,Q,I,xY,E,E,A,R,,,,,,,\r\n+,,sp|P19105|ML12A_HUMAN; sp|O14950|ML12B_HUMAN,D,E,E,V,D,E,L,xY,R,,,,,,,,,,\r\n+,,sp|Q08945|SSRP1_HUMAN,,N,M,S,G,S,L,xY,E,M,V,S,R,,,,,,\r\n+,,sp|Q9Y3I0|RTCB_HUMAN,G,M,A,A,A,G,N,xY,A,W,V,N,R,,,,,,\r\n+,,sp|P30086|PEBP1_HUMAN,,,,,,,L,xY,E,Q,L,S,G,K,,,,,\r\n+,,sp|P52272|HNRPM_HUMAN,N,E,C,G,H,V,L,xY,A,D,I,K,,,,,,,\r\n+,,sp|Q8NBS9|TXND5_HUMAN,,V,D,C,T,Q,H,xY,E,L,C,S,G,N,Q,,,,\r\n+,,sp|P18077|RL35A_HUMAN,,,D,E,T,E,F,xY,L,G,K,,,,,,,,\r\n+,,sp|P10809|CH60_HUMAN,,,G,Y,I,S,P,xY,F,I,N,T,S,K,,,,,\r\n+,,sp|P68363|TBA1B_HUMAN; sp|Q71U36|TBA1A_HUMAN; sp|Q9BQE3|TBA1C_HUMAN; sp|Q13748|TBA3C_HUMAN; sp|Q6PEY2|TBA3E_HUMAN; sp|Q9NY65|TBA8_HUMAN,M,V,D,N,E,A,I,xY,D,I,C,R,,,,,,,\r\n+,,sp|Q9HB71|CYBP_HUMAN,,,,,I,S,N,xY,G,W,D,Q,S,D,K,,,,\r\n+,,sp|P15531|NDKA_HUMAN,,,,,,E,H,xY,V,D,L,K,,,,,,,\r\n+,,sp|P08238|HS90B_HUMAN,Q,E,E,Y,G,E,F,xY,K,,,,,,,,,,\r\n+,,sp|P49327|FAS_HUMAN,,,,,,S,F,xY,G,S,T,L,F,L,C,,,,\r\n+,,sp|Q02543|RL18A_HUMAN,,S,S,G,E,I,V,xY,C,G,Q,V,F,E,K,,,,\r\n+,,sp|P04406|G3P_HUMAN,,,,L,I,S,W,xY,D,N,E,F,G,Y,S,,,,\r\n+,,sp|P12956|XRCC6_HUMAN,,,,,,N,I,xY,V,L,Q,E,L,D,N,,,,\r\n+,,sp|P23396|RS3_HUMAN,P,E,G,S,V,E,L,xY,A,E,K,,,,,,,,\r\n+,,sp|Q9BUJ2|HNRL1_HUMAN,I,L,D,Q,T,N,V,xY,G,S,A,Q,R,,,,,,\r\n+,,sp|O43390|HNRPR_HUMAN,,,,,S,T,A,xY,E,D,Y,Y,Y,H,P,,,,\r\n+,,sp|Q01105|SET_HUMAN; sp|P0DME0|SETLP_HUMAN,F,Y,F,D,E,N,P,xY,F,E,N,K,,,,,,,\r\n+,,sp|O95433|AHSA1_HUMAN,L,T,S,P,E,E,L,xY,R,,,,,,,,,,\r\n+,,sp|P07437|TBB5_HUMAN,,,,,I,S,V,xY,Y,N,E,A,T,G,G,,,,\r\n+,,sp|Q07955|SRSF1_HUMAN,,,,E,D,M,T,xY,A,V,R,,,,,,,,\r\n+,,sp|Q9NWQ8|PHAG1_HUMAN,N,M,V,E,D,C,L,xY,E,T,V,K,,,,,,,\r\n+,,sp|Q9Y230|RUVB2_HUMAN,T,T,E,M,E,T,I,xY,D,L,G,T,K,,,,,,\r\n+,,sp|P55884|EIF3B_HUMAN,,,,,,D,Q,xY,S,V,I,F,E,S,G,,,,\r\n+,,sp|O76094|SRP72_HUMAN,,,,,,E,L,xY,G,Q,V,L,Y,R,,,,,\r\n+,,sp|Q9Y2Z0|SGT1_HUMAN,,,L,F,Q,Q,I,xY,S,D,G,S,D,E,V,,,,\r\n+,,sp|Q9NVP1|DDX18_HUMAN,,,,,E,P,L,xY,V,G,V,D,D,D,K,,,,\r\n+,,sp|P36578|RL4_HUMAN,,,P,L,I,S,V,xY,S,E,K,,,,,,,,\r\n+,,sp|PPIA_HUMAN; sp|P62937|PPIA_HUMAN,,,,,,S,I,xY,G,E,K,,,,,,,,\r\n+,,sp|P83881|RL36A_HUMAN,,,,,D,S,L,xY,A,Q,G,K,,,,,,,\r\n+,,sp|O14979|HNRDL_HUMAN,,,,D,L,T,E,xY,L,S,R,,,,,,,,\r\n+,,sp|Q969Q0|RL36L_HUMAN,,,,,D,S,L,xY,A,Q,G,R,,,,,,,\r\n+,,sp|P63261|ACTG_HUMAN; sp|P60709|ACTB_HUMAN; sp|P68133|ACTS_HUMAN; sp|P68032|ACTC_HUMAN; sp|P62736|ACTA_HUMAN; sp|P63267|ACTH_HUMAN,,,,,,D,S,xY,V,G,D,E,A,Q,S,,,,\r\n+,,sp|P62318|SMD3_HUMAN,V,A,Q,L,E,Q,V,xY,I,R,,,,,,,,,\r\n+,,sp|P09651|ROA1_HUMAN,,,D,Y,F,E,Q,xY,G,K,,,,,,,,,\r\n+,,sp|Q13247|SRSF6_HUMAN; sp|Q08170|SRSF4_HUMAN,,D,A,D,D,A,V,xY,E,L,N,G,K,,,,,,\r\n+,,sp|Q9Y2W1|TR150_HUMAN,K,P,W,P,D,A,T,xY,G,T,G,S,A,S,R,,,,\r\n+,,sp|P07948|LYN_HUMAN,,,,,,,L,xY,A,V,V,T,R,,,,,,\r\n+,,sp|P17844|DDX5_HUMAN; sp|Q92841|DDX17_HUMAN,,,,S,T,C,I,xY,G,G,A,P,K,,,,,,\r\n+,,sp|Q9BVG4|PBDC1_HUMAN,,,V,D,D,Q,I,xY,S,E,F,R,,,,,,,\r\n+,,sp|O75643|U520_HUMAN,N,S,A,F,E,S,L,xY,Q,D,K,,,,,,,,\r\n+,,sp|P19105|ML12A_HUMAN; sp|O14950|ML12B_HUMAN,,,,G,N,F,N,xY,I,E,F,T,R,,,,,,\r\n+,,sp|P63220|RS21_HUMAN,,,,,,,T,xY,A,I,C,G,A,I,R,,,,\r\n+,,sp|Q04837|SSBP_HUMAN,,S,G,D,S,E,V,xY,Q,L,G,D,V,S,Q,,,,\r\n+,,sp|P07900|HS90A_HUMAN,,,,,,H,I,xY,Y,I,T,G,E,T,K,,,,\r\n+,,sp|P30086|PEBP1_HUMAN,,,,,,,L,xY,T,L,V,L,T,D,P,,,,\r\n+,,sp|P61254|RL26_HUMAN; sp|Q9UNX3|RL26L_'..b'AN; sp|P60709|ACTB_HUMAN; sp|P68133|ACTS_HUMAN; sp|P68032|ACTC_HUMAN; sp|P62736|ACTA_HUMAN; sp|P63267|ACTH_HUMAN,,,,D,L,T,D,xY,L,M,K,,,,,,,,\r\n+,,sp|Q16666|IF16_HUMAN,,,,,T,T,I,xY,E,I,Q,D,D,R,,,,,\r\n+,,sp|Q96CW1|AP2M1_HUMAN,,,,,S,G,I,xY,E,T,R,,,,,,,,\r\n+,,sp|P62273|RS29_HUMAN,,,G,H,Q,Q,L,xY,W,S,H,P,R,,,,,,\r\n+,,sp|P50995|ANX11_HUMAN,T,P,V,L,F,D,I,xY,E,I,K,,,,,,,,\r\n+,,sp|P47756|CAPZB_HUMAN,,S,T,L,N,E,I,xY,F,G,K,,,,,,,,\r\n+,,sp|P53396|ACLY_HUMAN,,,T,T,D,G,V,xY,E,G,V,A,I,G,G,,,,\r\n+,,sp|P06733|ENOA_HUMAN,,,,G,V,P,L,xY,R,,,,,,,,,,\r\n+,,sp|P14927|QCR7_HUMAN,,,L,P,E,N,L,xY,N,D,R,,,,,,,,\r\n+,,sp|P22626|ROA2_HUMAN,,,D,Y,F,E,E,xY,G,K,,,,,,,,,\r\n+,,sp|P61158|ARP3_HUMAN,,A,E,P,E,D,H,xY,F,L,L,T,E,P,P,,,,\r\n+,,sp|P62249|RS16_HUMAN,G,G,H,V,A,Q,I,xY,A,I,R,,,,,,,,\r\n+,,sp|P09211|GSTP1_HUMAN; sp|GSTP1_HUMAN,,P,P,Y,T,V,V,xY,F,P,V,R,,,,,,,\r\n+,,sp|P62847|RS24_HUMAN,T,T,G,F,G,M,I,xY,D,S,L,D,Y,A,K,,,,\r\n+,,sp|P09429|HMGB1_HUMAN; sp|P26583|HMGB2_HUMAN,,,,,,S,S,xY,A,F,F,V,Q,T,C,,,,\r\n+,,sp|P07814|SYEP_HUMAN,,,,,,S,L,xY,D,E,V,A,A,Q,G,,,,\r\n+,,sp|O43390|HNRPR_HUMAN,S,T,A,Y,E,D,Y,xY,Y,H,P,P,P,R,,,,,\r\n+,,sp|P62805|H4_HUMAN,,R,I,S,G,L,I,xY,E,E,T,R,,,,,,,\r\n+,,sp|Q9UGI8|TES_HUMAN,,E,G,D,P,A,I,xY,A,E,R,,,,,,,,\r\n+,,sp|P06748|NPM_HUMAN,V,E,A,E,A,M,N,xY,E,G,S,P,I,K,,,,,\r\n+,,sp|P08865|RSSA_HUMAN,,,K,S,D,G,I,xY,I,I,N,L,K,,,,,,\r\n+,,sp|O14602|IF1AY_HUMAN; sp|P47813|IF1AX_HUMAN,,,E,D,G,Q,E,xY,A,Q,V,I,K,,,,,,\r\n+,,sp|Q15691|MARE1_HUMAN,,,,A,V,N,V,xY,S,T,S,V,T,S,D,,,,\r\n+,,sp|Q9BZK7|TBL1R_HUMAN,,,H,Q,E,P,V,xY,S,V,A,F,S,P,D,,,,\r\n+,,sp|P14866|HNRPL_HUMAN,S,L,N,G,A,D,I,xY,S,G,C,C,T,L,K,,,,\r\n+,,sp|P04406|G3P_HUMAN,,,,,I,S,W,xY,D,N,E,F,G,Y,S,,,,\r\n+,,sp|P09211|GSTP1_HUMAN; sp|GSTP1_HUMAN,Q,D,G,D,L,T,L,xY,Q,S,N,,,,,,,,\r\n+,,sp|P00505|AATM_HUMAN,,,,E,F,S,I,xY,M,T,K,,,,,,,,\r\n+,,sp|Q9H8W4|PKHF2_HUMAN,,,,S,F,A,V,xY,A,A,T,A,T,E,K,,,,\r\n+,,sp|P08621|RU17_HUMAN,,,,E,F,E,V,xY,G,P,I,K,R,,,,,,\r\n+,,sp|P13796|PLSL_HUMAN,,,,,,,V,xY,A,L,P,E,D,L,V,,,,\r\n+,,sp|Q99426|TBCB_HUMAN,,,,,L,G,E,xY,E,D,V,S,R,,,,,,\r\n+,,sp|Q06187|BTK_HUMAN,,,,,,,,xY,T,V,S,V,F,A,K,,,,\r\n+,,sp|O15144|ARPC2_HUMAN,,,D,D,E,T,M,xY,V,E,S,K,,,,,,,\r\n+,,sp|P60174|TPIS_HUMAN,,,,,,I,I,xY,G,G,S,V,T,G,A,,,,\r\n+,,sp|P18124|RL7_HUMAN,,S,V,N,E,L,I,xY,K,,,,,,,,,,\r\n+,,sp|Q9Y5V0|ZN706_HUMAN,,,,A,A,L,I,xY,T,C,T,V,C,R,,,,,\r\n+,,sp|Q92598|HS105_HUMAN,N,A,V,E,E,Y,V,xY,E,F,R,,,,,,,,\r\n+,,sp|Q969Q0|RL36L_HUMAN,,,G,K,D,S,L,xY,A,Q,G,R,,,,,,,\r\n+,,sp|P60842|IF4A1_HUMAN; sp|Q14240|IF4A2_HUMAN; sp|P38919|IF4A3_HUMAN,,,,,,G,I,xY,A,Y,G,F,E,K,P,,,,\r\n+,,sp|Q13595|TRA2A_HUMAN,H,T,P,T,P,G,I,xY,M,G,R,,,,,,,,\r\n+,,sp|P07437|TBB5_HUMAN,,,,I,S,V,Y,xY,N,E,A,T,G,G,K,,,,\r\n+,,sp|O43865|SAHH2_HUMAN; sp|Q96HN2|SAHH3_HUMAN,,,,F,D,N,L,xY,C,C,R,,,,,,,,\r\n+,,sp|P11142|HSP7C_HUMAN,G,I,D,L,G,T,T,xY,S,C,V,G,V,F,Q,,,,\r\n+,,sp|E9PAV3|NACAM_HUMAN; sp|Q13765|NACA_HUMAN,,S,P,A,S,D,T,xY,I,V,F,G,E,A,K,,,,\r\n+,,sp|P23193|TCEA1_HUMAN,,,,,N,C,T,xY,T,Q,V,Q,T,R,,,,,\r\n+,,sp|P36578|RL4_HUMAN,,,K,L,D,E,L,xY,G,T,W,R,,,,,,,\r\n+,,sp|Q99497|PARK7_HUMAN,,,,,E,G,P,xY,D,V,V,V,L,P,G,,,,\r\n+,,sp|P51610|HCFC1_HUMAN,,,Y,S,N,D,L,xY,E,L,Q,A,S,R,,,,,\r\n+,,sp|P04843|RPN1_HUMAN,N,I,E,I,D,S,P,xY,E,I,S,R,,,,,,,\r\n+,,sp|Q07020|RL18_HUMAN,,,,S,Q,D,I,xY,L,R,,,,,,,,,\r\n+,,sp|P11142|HSP7C_HUMAN; sp|P11021|GRP78_HUMAN,P,T,A,A,A,I,A,xY,G,L,D,K,,,,,,,\r\n+,,sp|P32969|RL9_HUMAN,,,F,L,D,G,I,xY,V,S,E,K,,,,,,,\r\n+,,sp|P25685|DNJB1_HUMAN,,,,,,D,Y,xY,Q,T,L,G,L,A,R,,,,\r\n+,,sp|P14625|ENPL_HUMAN,,,G,L,F,D,E,xY,G,S,K,,,,,,,,\r\n+,,sp|P08238|HS90B_HUMAN,E,M,T,S,L,S,E,xY,V,S,R,,,,,,,,\r\n+,,sp|P54136|SYRC_HUMAN,,,,G,E,S,F,xY,Q,D,R,,,,,,,,\r\n+,,sp|Q8IV50|LYSM2_HUMAN,,,D,E,E,S,P,xY,A,T,S,L,Y,H,S,,,,\r\n+,,sp|Q10567|AP1B1_HUMAN; sp|P63010|AP2B1_HUMAN,V,E,G,Q,D,M,L,xY,Q,S,L,K,,,,,,,\r\n+,,sp|Q96FW1|OTUB1_HUMAN,Y,A,E,D,D,N,I,xY,Q,Q,K,,,,,,,,\r\n+,,sp|Q99880|H2B1L_HUMAN; sp|Q99879|H2B1M_HUMAN; sp|Q99877|H2B1N_HUMAN; sp|Q93079|H2B1H_HUMAN; sp|Q5QNW6|H2B2F_HUMAN; sp|P62807|H2B1C_HUMAN; sp|P58876|H2B1D_HUMAN; sp|O60814|H2B1K_HUMAN,,,,,,E,S,xY,S,V,Y,V,Y,K,,,,,\r\n+,,sp|P62263|RS14_HUMAN,D,R,D,E,S,S,P,xY,A,A,M,L,A,A,Q,,,,\r\n+,,sp|P09651|ROA1_HUMAN,,,,S,S,G,P,xY,G,G,G,G,Q,Y,F,,,,\r\n' |
| b |
| diff -r 65f235b5fe14 -r 2f3df9b1c05b screener7-7.csv --- a/screener7-7.csv Tue Feb 06 17:13:59 2018 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,325 +0,0 @@\n-Amino Acid,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,Abl\r\n-A,1,1,0.882322561,0.946431352,1.267758769,1.089980567,0.849406684,0,1.983180862,1.248128878,0.226122254,0.685178519,1,1,1,\r\n-C,1,1,0.009604626,0.009015706,0.742749396,0.997377969,0.007640366,0,0.63304489,1.858200501,0.012268107,1.92009365,1,1,1,\r\n-D,1,1,1.431703724,1.637464053,2.605079343,0.770520959,1.328600707,0,2.148130938,0.5957361,0.176594551,0.332519791,1,1,1,\r\n-E,1,1,0.643983264,1.989447239,2.094100346,1.439507752,1.245765716,0,1.24627458,0.468097263,0.000848801,0.725553316,1,1,1,\r\n-F,1,1,0.497001165,0.3379037,0.411572204,0.296542087,2.060804142,0,0.005820098,0.531995278,2.357742596,0.438745823,1,1,1,\r\n-G,1,1,0.893453251,1.528088747,0.67390472,1.762542109,0.335648512,0,1.013042344,0.800811003,0.003981392,1.313121648,1,1,1,\r\n-H,1,1,0.007832281,1.127242108,2.091972175,1.320938614,1.865005518,0,0.696245424,0.649606912,0.00730737,0.615276036,1,1,1,\r\n-I,1,1,0.881063052,0.167205137,1.184813256,2.035674575,9.807795409,0,1.654479752,0.950940898,1.507303461,0.722455635,1,1,1,\r\n-K,1,1,0.947921128,0.179766965,1.205417608,0.829787272,0.346855573,0,0.473066457,0.370489984,0.377025864,0.864435576,1,1,1,\r\n-L,1,1,0.500899691,0.262736557,0.121229505,0.303230844,1.077236813,0,0.809602792,0.382513407,0.718680068,1.105911199,1,1,1,\r\n-M,1,1,2.478037279,0.993521392,1.241173771,0.538634574,0.008436542,0,1.208456381,0.008168982,1.117390063,0.646024916,1,1,1,\r\n-N,1,1,1.615827052,1.179411378,0.744890739,1.439814286,0.551625197,0,0.7261286,1.289433753,0.196816516,0.004444734,1,1,1,\r\n-P,1,1,1.763222042,1.350550742,2.12505232,1.096484314,0.600847155,0,0.097493956,1.817626621,15.1017903,2.066452756,1,1,1,\r\n-Q,1,1,1.215052286,0.530735921,0.747268713,0.003547182,0.595958957,0,2.457274164,1.724106317,0.284965727,0.999253879,1,1,1,\r\n-R,1,1,0.927974325,0.678582936,0.212622438,0.67541443,0.422441115,0,1.268478795,1.252943286,0.002905196,1.263432811,1,1,1,\r\n-S,1,1,1.089114937,1.168382233,0.988930826,1.549280904,0.123768289,0,0.561910123,1.64939594,0.794937114,0.852167102,1,1,1,\r\n-T,1,1,1.318701224,1.079684538,0.783764874,0.324638108,0.646699246,0,0.934807562,2.570822141,0.732168201,0.828210681,1,1,1,\r\n-V,1,1,0.588885966,0.475177842,2.217365652,2.992336523,5.02600581,0,0.604585498,0.606232586,1.78752705,1.267711545,1,1,1,\r\n-W,1,1,1.389710835,0.015370202,0.020295574,0.018497672,0.846222832,0,0.024623679,0.014549452,2.690111916,1.570439269,1,1,1,\r\n-Y,1,1,0.006900535,0.342484705,0.417151943,0.004117292,1.253245644,677.8566817,0.430627113,1.078415219,0.010911903,0.444693959,1,1,1,\r\n-Normalize Factor,,,,,,,,,,,,,,,,\r\n-0.072093023,,,,,,,,,,,,,,,,\r\n-Threshold,,,,,,,,,,,,,,,,\r\n-56,,,,,,,,,,,,,,,,\r\n-Amino Acid,,,-5,-4,-3,-2,-1,0,1,2,3,4,,,,\r\n-A,1,1,0.943094978,1.501384107,1.95135657,1.880160203,0.073038604,0,1.338932042,1.229900149,0.075951271,0.063813178,1,1,1,Arg\r\n-C,1,1,0.218421841,2.659957114,0.134966466,3.894705692,0.204796443,0,3.601559506,0.265189109,0.297715434,0.236145846,1,1,1,\r\n-D,1,1,3.221368524,3.46570699,1.632661257,0.091767791,0.111076842,0,2.351303857,1.01810513,0.747194926,2.713123437,1,1,1,\r\n-E,1,1,0.074069921,2.323155165,3.38088485,1.190296131,0.095487211,0,0.90830603,0.882663695,0.506328757,0.915744205,1,1,1,\r\n-F,1,1,0.11911299,0.102411767,2.157136787,0.101526802,0.143188264,0,0.128628653,1.908695899,2.223303906,0.174281004,1,1,1,\r\n-G,1,1,1.685622529,0.694699429,0.03921412,3.013734957,0.800499624,0,1.041247934,0.737769895,0.070805422,6.003019282,1,1,1,\r\n-H,1,1,0.196014995,0.1697973,0.212485913,0.163795158,0.190485555,0,2.645948806,0.172270587,0.156995244,3.84815868,1,1,1,\r\n-I,1,1,1.508289663,0.065177198,1.559508344,2.933058204,2.764966898,0,0.054804971,0.108136152,1.645039272,0.144904016,1,1,1,\r\n-K,1,1,0.089214088,0.060880995,1.160673525,0.059748979,1.28191666,0,1.7991125,1.104219033,0.047129496,1.581213152,1,1,1,\r\n-L,1,1,0.766134776,0.550490812,0.042706744,0.521090197,0.922278598,0,0.488097183,0.515508197,0.838542183,2.525765703,1,1,1,\r\n-M,1,1,0.183007686,0.215571796,0.234'..b',1,1.759038759,0.008932852,0.637935052,0.008466463,1.768903208,0,0.53956636,1.594978452,2.117842234,2.131310757,1,1,1,\r\n-N,1,1,0.720373484,1.487226763,1.302177487,2.016367835,0.788875007,0,0.250460079,3.031683315,0.436896267,1.573194991,1,1,1,\r\n-P,1,1,1.833186802,0.777533261,0.420395865,0.567125516,0.238406257,0,0.002326649,0.771391218,7.270751957,1.103332241,1,1,1,\r\n-Q,1,1,0.235600141,0.004297437,0.500003046,0.994829208,0.379831149,0,0.695036596,0.004327428,0.80862803,1.575651985,1,1,1,\r\n-R,1,1,0.564009404,0.003525368,0.227098034,0.538917103,0.136536863,0,0.003591695,0.190792681,0.005422585,0.907984716,1,1,1,\r\n-S,1,1,0.818521276,0.978919963,1.689457578,0.915615462,1.150862322,0,0.237748244,1.331727596,1.19985019,1.121480305,1,1,1,\r\n-T,1,1,0.46747837,0.730560587,0.304249366,0.843021,0.524543798,0,0.779170053,1.166347147,0.960500715,0.623499655,1,1,1,\r\n-V,1,1,0.383039856,0.201074288,0.2478765,0.236550589,0.234975642,0,2.375954579,0.400179532,6.138746915,0.807238062,1,1,1,\r\n-W,1,1,0.01987174,1.145128323,0.021731187,0.016566653,0.010690007,0,0.015401536,0.01642969,0.021461124,0.017254333,1,1,1,\r\n-Y,1,1,0.459914329,0.006810884,0.438274355,0.36295333,1.308560175,1,1.124088698,0.94740785,0.54611071,1.415811189,1,1,1,\r\n-Normalize Factor,,,,,,,,,,,,,,,,\r\n-0.052738337,,,,,,,,,,,,,,,,\r\n-Threshold,,,,,,,,,,,,,,,,\r\n-54,,,,,,,,,,,,,,,,\r\n-Amino Acid,,,-5,-4,-3,-2,-1,0,1,2,3,4,,,,\r\n-A,1,1,1.222188853,1.486603226,0.919170896,0.793911917,0.409740076,0,0.638480016,2.56572863,0.846871309,0.004025467,1,1,1,Yes\r\n-C,1,1,0.014768454,0.612323227,0.01733631,0.01431502,0.013620919,0,0.014857804,0.020544494,0.751226491,0.018818585,1,1,1,\r\n-D,1,1,1.793680299,3.783436502,3.189929787,3.530731289,0.623287514,0,1.455301756,0.617179375,0.228255986,0.882795484,1,1,1,\r\n-E,1,1,2.707237165,3.262501671,7.509267488,2.840702315,2.078923247,0,3.449957711,0.750753948,0.154315698,0.92756723,1,1,1,\r\n-F,1,1,0.368930904,0.006177867,0.005884929,1.274931107,0.597875789,0,0.006948944,0.343687804,1.628488797,0.332278839,1,1,1,\r\n-G,1,1,0.580720277,0.563429661,0.747049717,2.644759528,0.002554867,0,1.803040115,0.616994899,0.557328091,2.704367259,1,1,1,\r\n-H,1,1,0.408655956,0.838993858,0.007945388,1.872850327,0.333955199,0,0.606798686,0.006818885,0.600277041,1.531470657,1,1,1,\r\n-I,1,1,1.883887869,0.004269998,0.00490079,0.007897733,8.056462413,0,0.516435014,0.768417292,1.555521832,0.005769512,1,1,1,\r\n-K,1,1,0.599781851,0.003541306,0.51458723,0.435001391,0.199251972,0,0.267884479,0.390721439,0.148200027,0.240965618,1,1,1,\r\n-L,1,1,0.10798419,0.661132019,0.004342008,0.140684195,3.313737494,0,0.305099752,0.354148407,2.895214199,1.176621256,1,1,1,\r\n-M,1,1,1.500924535,0.596326712,0.012758235,0.011809241,0.012062097,0,0.01204882,0.014416658,0.010516891,0.014744175,1,1,1,\r\n-N,1,1,1.06432095,0.575254989,0.729141869,1.147005116,0.234567107,0,0.240700296,0.876070748,0.003497148,1.834052819,1,1,1,\r\n-P,1,1,0.57948738,1.123761802,0.829041851,0.894233218,0.338547075,0,0.136095286,0.199795252,2.143300779,1.379206148,1,1,1,\r\n-Q,1,1,0.004051494,0.006305015,0.37225292,1.049473144,0.500540673,0,0.432684795,0.2766543,0.264150289,0.560941973,1,1,1,\r\n-R,1,1,0.004667408,0.003704843,0.153495971,0.004132201,0.002358508,0,0.278529882,0.307235643,0.705639999,0.905800168,1,1,1,\r\n-S,1,1,0.92627953,1.324310304,1.659124469,0.684990056,0.434517693,0,2.843851614,1.61069191,1.218543907,0.839440563,1,1,1,\r\n-T,1,1,1.049881524,1.654126719,0.399151336,1.801762488,2.298611276,0,2.815591798,2.506789582,0.32211978,0.218532251,1,1,1,\r\n-V,1,1,0.150932833,0.004020882,0.783755106,0.428687698,6.887781036,0,0.531349744,3.207681554,4.125351327,0.514811072,1,1,1,\r\n-W,1,1,0.02630224,0.01943939,0.029064252,0.022741147,0.023211485,0,0.023272909,1.344884776,0.021172292,1.540208353,1,1,1,\r\n-Y,1,1,0.690669121,1.006196468,0.325003658,1.193388161,1.678909279,471.2746858,2.302592406,0.643411952,0.762166379,1.244106857,1,1,1,\r\n-Normalize Factor,,,,,,,,,,,,,,,,\r\n-0.159680639,,,,,,,,,,,,,,,,\r\n-Threshold,,,,,,,,,,,,,,,,\r\n-40,,,,,,,,,,,,,,,,\r\n' |