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kinatestid_r/Kinatest-R.R kinatestid_r/kinatestid_r.xml kinatestid_r/screener7-7.csv |
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| diff -r 15b5d4ae4480 -r 26ef4add9f7b kinatestid_r/Kinatest-R.R --- a/kinatestid_r/Kinatest-R.R Thu Feb 08 15:49:05 2018 -0500 +++ b/kinatestid_r/Kinatest-R.R Wed Feb 28 14:09:19 2018 -0500 |
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| b'@@ -1,7 +1,6 @@\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+SubstrateBackgroundFrequency<- read.csv("input3", stringsAsFactors=FALSE, header = FALSE)\r\n \r\n ScreenerFilename<-"screener"\r\n \r\n@@ -13,21 +12,14 @@\n \r\n \r\n \r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n+SubstrateBackgroundFrequency<-t(SubstrateBackgroundFrequency)\r\n+# number<-nrow(SubstrateBackgroundFrequency)-1\r\n+SubstrateBackgroundFrequency<-SubstrateBackgroundFrequency[2:nrow(SubstrateBackgroundFrequency),]\r\n+Sub<-na.omit(SubstrateBackgroundFrequency)\r\n+SubstrateBackgroundFrequency<-Sub\r\n \r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n-\r\n+args = commandArgs(trailingOnly=TRUE)\r\n+TodaysKinase<-args[1]\r\n \r\n \r\n \r\n@@ -594,10 +586,10 @@\n PercentTable<-rbind(HeaderSD,PercentTable)\r\n row.names(PercentTable)<-NULL\r\n PercentTable<-data.frame(SetOfAAs,PercentTable)\r\n-numberofY<-as.numeric(SubstrateBackgroundFrequency$Number.of.Y)\r\n+numberofY<-as.numeric(SubstrateBackgroundFrequency[,34])\r\n numberofY<-numberofY[!is.na(numberofY)]\r\n \r\n-numberofPY<-as.numeric(SubstrateBackgroundFrequency$Number.of.pY)\r\n+numberofPY<-as.numeric(SubstrateBackgroundFrequency[,35])\r\n numberofPY<-numberofPY[!is.na(numberofPY)]\r\n \r\n NormalizationScore<-sum(numberofPY)/sum(numberofY)\r\n@@ -1161,38 +1153,110 @@\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)<2){Positionm4<-bareSDs[,4][order(bareSDs[,4])[1:2]]}\r\n+# Positionm3<-which(goodones[,5] %in% TRUE)\r\n+# if (length(Positionm3)<2){Positionm3<-bareSDs[,5][order(bareSDs[,5])[1:2]]}\r\n+# Positionm2<-which(goodones[,6] %in% TRUE)\r\n+# if (length(Positionm2)<2){Positionm2<-bareSDs[,6][order(bareSDs[,6])[1:2]]}\r\n+# Positionm1<-which(goodones[,7] %in% TRUE)\r\n+# if (length(Positionm1)<2){Positionm1<-bareSDs[,7][order(bareSDs[,7])[1:2]]}\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)<2){Positionp1<-bareSDs[,9][order(bareSDs[,9])[1:2]]}\r\n+# Positionp2<-which(goodones[,10] %in% TRUE)\r\n+# if (length(Positionp2)<2){Positionp2<-bareSDs[,10][order(bareSDs[,10])[1:2]]}\r\n+# Positionp3<-which(goodones[,11] %in% TRUE)\r\n+# if (length(Positionp3)<2){Positionp3<-bareSDs[,11][order(bareSDs[,11])[1:2]]}\r\n+# Positionp4<-which(goodones[,12] %in% TRUE)\r\n+# if (length(Positionp4)<2){Positionp4<-bareSDs[,12][order(bareSDs[,12])[1:2]]}\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+\r\n+\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+# Posit'..b'[24,1])\r\n Pyk2TrueThresh<-((Pyk2Thresh*Pyk2Norm)/(100-Pyk2Thresh))\r\n Pyk2Active<-unlist(Pyk2GeneratedScores)>Pyk2TrueThresh\r\n+if (TodaysKinase=="PYK2"){Pyk2Active<-rep(0,times=nrow(GeneratedPeptides))}\r\n \r\n SrcNorm<-1/as.numeric(Src[22,1])\r\n SrcThresh<-as.numeric(Src[24,1])\r\n SrcTrueThresh<-((SrcThresh*SrcNorm)/(100-SrcThresh))\r\n SrcActive<-unlist(SrcGeneratedScores)>SrcTrueThresh\r\n+if (TodaysKinase=="SRC"){SrcActive<-rep(0,times=nrow(GeneratedPeptides))}\r\n \r\n SykNorm<-1/as.numeric(Syk[22,1])\r\n SykThresh<-as.numeric(Syk[24,1])\r\n SykTrueThresh<-((SykThresh*SykNorm)/(100-SykThresh))\r\n SykActive<-unlist(SykGeneratedScores)>SykTrueThresh\r\n+if (TodaysKinase=="SYK"){SykActive<-rep(0,times=nrow(GeneratedPeptides))}\r\n \r\n YesNorm<-1/as.numeric(Yes[22,1])\r\n YesThresh<-as.numeric(Yes[24,1])\r\n YesTrueThresh<-((YesThresh*YesNorm)/(100-YesThresh))\r\n YesActive<-unlist(YesGeneratedScores)>YesTrueThresh\r\n+if (TodaysKinase=="YES"){YesActive<-rep(0,times=nrow(GeneratedPeptides))}\r\n \r\n AllActive<-AblActive+ArgActive+BtkActive+CskActive+FynActive+HckActive+JAk2Active+LckActive+LynActive+Pyk2Active+SrcActive+SykActive+YesActive\r\n #Btkactive+\r\n@@ -1884,23 +1961,24 @@\n \r\n #create the MCC table\r\n \r\n-threshold<-c(1:100)\r\n-threshold<-order(threshold,decreasing = TRUE)\r\n+threshold<-c(1:100,(1:9)/10,(1:9)/100,0,-.1)\r\n+threshold<-threshold[order(threshold,decreasing = TRUE)]\r\n+threshold\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+Truepositives<-c(1:120)\r\n+Falsenegatives<-c(1:120)\r\n+Sensitivity<-c(1:120)\r\n+TrueNegatives<-c(1:120)\r\n+FalsePositives<-c(1:120)\r\n+Specificity<-c(1:120)\r\n+Accuracy<-c(1:120)\r\n+MCC<-c(1:120)\r\n+EER<-c(1:120)\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+for (z in 1:120) {\r\n+ thres<-threshold[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@@ -1909,10 +1987,10 @@\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+ 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+Characterization<-cbind.data.frame(threshold,Truepositives,Falsenegatives,Sensitivity,TrueNegatives,FalsePositives,Specificity,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@@ -1931,4 +2009,5 @@\n \r\n # header<-colnames(RanksPeptides)\r\n # RanksPeptides<-rbind.data.frame(header,RanksPeptides)\r\n+write.table(x="Off Target Kinase activity (your kinase of interest should have zeros here because it is ON-target)",file = FILENAME3,append = FALSE,row.names = FALSE,col.names = TRUE,sep = ",")\r\n write.table(RanksPeptides,file = FILENAME3,append = FALSE,row.names = FALSE,col.names = TRUE,sep = ",")\r\n' |
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| diff -r 15b5d4ae4480 -r 26ef4add9f7b kinatestid_r/kinatestid_r.xml --- a/kinatestid_r/kinatestid_r.xml Thu Feb 08 15:49:05 2018 -0500 +++ b/kinatestid_r/kinatestid_r.xml Wed Feb 28 14:09:19 2018 -0500 |
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| @@ -8,7 +8,7 @@ ln -s '$substrates' input1 && ln -s '$negatives' input2 && ln -s '$SBF' input3 && - Rscript '$__tool_directory__/Kinatest-R.R' && + Rscript '$__tool_directory__/Kinatest-R.R' outGroup && mv output1 output1.csv && mv output2 output2.csv && mv output3 output3.csv @@ -17,11 +17,12 @@ <param format="csv" name="substrates" type="data" label="Positive/Phosphorylated Substrate List"/> <param format="csv" name="negatives" type="data" label="Negative/unPhosphorylated Substrate List"/> <param format="csv" name="SBF" type="data" label="Substrate Background Frequency List"/> + <param name="outGroup" type="text" value="kinase" label="Kinase Name"/> </inputs> <outputs> - <data format="csv" name="SDtable" from_work_dir="output1.csv" label="Standard Deviation Table"/> - <data format="csv" name="EPM" from_work_dir="output2.csv" label="Endogenous Probability Matrix"/> - <data format="csv" name="Characterization" from_work_dir="output3.csv" label="Characterization Table"/> + <data format="csv" name="SDtable" from_work_dir="output1.csv" label="${outGroup}_Standard Deviation Table"/> + <data format="csv" name="EPM" from_work_dir="output2.csv" label="${outGroup}_Characterization Table"/> + <data format="csv" name="Characterization" from_work_dir="output3.csv" label="${outGroup}_Generated Sequences"/> </outputs> <tests> <test> @@ -36,7 +37,24 @@ <help><![CDATA[ +Important! +**Please write the name of your kinase in ALL CAPS so the R script can recognize it! Thank you!** +**If your kinase is one of the following kinases, please use this form of its name: ABL, ARG, BTK, CSK, FYN, HCK, JAK2, LCK, LYN, PYK2, SRC, SYK, YES, FLT3** +**So for example if your kinase is ARG please use ARG and not ABL2** + 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 + +=========== +Formatting +=========== +**Positive Substrates** +The Positive Substrates file should be formatted such that accession numbers are in the third column, the indibidual amino acids of the phosphorylated motif takes up columns 4 to 18, with the phosphorylated Tyrosine in column 11 and the phosphorylated Tyrosine marked as xY instead of simply Y. The list of motifs and accession numbers should start on the second row and not the first. + +**Negative Substrates** +This file should be formatted such that accession numbers are in the first column, the entire motifs are in the second column, and the list of accession numbers plus motifs should start on the second row + +**Substrate Background Frequency list** +Accession numbers and percentages starting on row 2. Accession numbers in column 1, A-Y amino acids in columns 2-21, number of Y in column 34, number of pY in column 35. ]]></help> <citations> <citation type="doi">10.1021/ja507164a</citation> |
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| diff -r 15b5d4ae4480 -r 26ef4add9f7b kinatestid_r/screener7-7.csv --- a/kinatestid_r/screener7-7.csv Thu Feb 08 15:49:05 2018 -0500 +++ b/kinatestid_r/screener7-7.csv Wed Feb 28 14:09:19 2018 -0500 |
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| b'@@ -1,74 +1,74 @@\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+A,1.141092292,1.252907868,1.191434599,1.188212023,1.11003241,1.246224821,0.712752276,4.63E-06,1.932538727,1.176325221,0.875432956,0.962064616,0.773214895,0.995600019,0.792751136,\r\n+C,0.297584,0.762403229,0.914633176,0.841081942,0.762403229,0.783398843,0.58861289,2.29E-05,1.042515919,1.208910299,0.531154015,1.334764597,0.985183393,0.82219731,1.002911629,\r\n+D,1.412523157,1.503938163,1.65046155,1.675095304,1.958252816,1.551036155,1.388499336,6.60E-06,0.94414764,0.819751155,0.12733309,0.644535082,0.991944063,0.709576725,0.697238684,\r\n+E,0.713757649,1.181227064,1.248619518,1.173971536,1.976283741,1.393623191,1.227645926,4.78E-06,0.926104788,0.909495968,0.230792977,0.586599944,0.684918956,0.628768593,0.470639684,\r\n+F,0.894539082,0.748338988,0.678421446,0.916854876,0.795110175,0.415570778,0.859409971,9.85E-06,0.779306846,1.075364616,1.539628507,1.146374131,0.799125882,0.735574249,0.681909474,\r\n+G,1.176676384,0.954434822,1.066304513,1.424321117,1.280339395,1.221325491,0.595487419,4.90E-06,1.865104209,0.756703305,0.870367732,1.293975641,1.228314585,1.098327357,1.089649912,\r\n+H,1.997402571,1.000374152,1.58617263,1.097080815,1.192753797,1.009318238,2.578421304,1.62E-05,1.364021946,1.006659065,0.813101932,1.414582759,1.469469568,1.210228112,1.062885173,\r\n+I,0.988398967,0.620144076,0.973171532,0.8390971,0.844084992,1.020388256,3.264582628,7.25E-06,0.696189059,1.106253027,1.204123634,0.85951787,0.640589342,1.018652536,0.370'..b'45759,0.170632689,0.138140512,0.133735449,0,0.239681249,0.167541039,0.066972821,0.178386603,1,1,1,\r\n-Y,1,1,0.062989876,0.056350406,1.379350437,0.055447075,0.058337001,527.638191,1.041051068,2.509611431,0.924436086,0.065058245,1,1,1,\r\n+A,0.393157175,1.226183269,1.104857755,1.094585317,0.872617145,0.86003132,0.543893325,0.000209969,2.203830259,0.832288375,0.852923624,1.003369874,1.043900086,1.298160484,0.819077448,BTK\r\n+C,0.014079146,1.366095326,2.014242744,0.391976216,0.673051844,2.012144574,0.81803732,0.001052671,1.077934593,1.669060016,2.280588892,0.718623062,0.475778027,0.650828434,2.463850499,\r\n+D,2.258525297,1.391390722,2.436203281,2.395405618,2.31361128,2.415367332,0.416592281,0.000268041,1.372367802,1.416637731,0.290352196,1.006403055,1.090322557,0.994319313,0.627368138,\r\n+E,0.738119849,2.046272847,1.508566114,1.54124457,2.142347853,1.453173452,0.510557207,0.000197099,1.866924226,1.614637169,0.266882177,0.807318584,1.33625145,0.487437636,0.615099874,\r\n+F,0.804284114,0.557424634,0.410948088,1.279542909,2.1970688,0.3518743,2.002762815,0.000429534,0.87968575,0.567539194,1.512187075,1.026300042,1.164825269,0.796696528,0.335118381,\r\n+G,1.134048666,0.13099572,1.352028823,1.127605207,0.77447226,1.433311508,0.261473671,0.000201882,1.55045716,1.013632208,0.820076514,0.826910485,1.186188834,0.998533416,1.102547314,\r\n+H,2.115525164,0.004838962,2.161850532,0.280467351,0.722374446,0.205676058,1.755969504,0.000753208,0.192821304,0.199041346,0.407952511,0.771285216,0.002347789,1.397044919,1.175291758,\r\n+I,0.851257178,0.98330037,0.869897846,0.902848522,0.775128487,0.413805572,3.768411221,0.00030308,1.396593807,1.361553819,0.98492566,0.620708359,0.273967827,0.562150966,0.002815004,\r\n+K,0.34028708,0.001167537,0.260803966,0.135341452,0.058097794,0.000206772,0.000186068,0.000181733,0.093047248,0.144073159,1.279591913,1.736881971,1.807041735,1.235948357,1.843221684,\r\n+L,0.743395658,0.644030892,0.759674395,1.034840547,0.571145445,0.433647467,3.188080195,0.000198508,0.558998688,1.101604453,0.967643109,0.677574168,0.62804116,1.350034568,0.154874095,\r\n+M,0.574549326,1.592810011,0.587130698,0.228513936,0.392375149,0.837884433,0.158966217,0.000613685,0.471309994,0.324342361,0.498575861,0.209471108,0.001912887,0.003579428,0.478791105,\r\n+N,2.639862234,1.56823499,0.96345337,2.399874758,1.416511443,2.089890935,0.626054285,0.000402811,0.618717711,1.277352694,0.87268173,0.549971299,0.182059464,0.249043607,1.257077254,\r\n+P,1.487725952,1.443536072,1.520303892,0.710051022,0.812806374,1.041408166,0.987897075,0.000317813,0.000317813,0.755860766,0.774601115,0.759360121,0.287285011,1.375444748,1.735680277,\r\n+Q,0.729848426,0.758753314,0.932288136,0.72570156,2.242948822,2.022288348,0.605803042,0.000389781,1.496759468,1.648044833,0.738896134,1.197407574,1.409362478,0.963950752,2.128724576,\r\n+R,0.219971193,0.304910565,0.11239404,0.000497094,0.000366401,0.000267326,0.00024056,0.000234955,0.360890239,0.248354573,1.590700777,1.523758786,1.805280826,1.743167945,1.833093276,\r\n+S,0.877378109,1.368188635,0.784516922,1.221350208,1.123471968,2.175166561,0.303441045,0.000234285,1.319494421,1.795441089,0.888254697,1.359479101,0.953014153,1.013951116,1.462296848,\r\n+T,1.42713365,1.384743541,1.020869326,1.135219949,0.974627858,1.248741944,1.342520983,0.000304869,0.468278229,1.530715931,1.155860311,1.144680115,1.377922145,1.31942545,0.951422433,\r\n+V,1.751008793,1.66865937,0.894676026,0.783476946,0.672643622,0.57454976,1.513965112,0.000233785,1.196978667,0.988472706,2.27920566,1.117180089,0.422657295,0.722704101,0.547190248,\r\n+W,1.727102004,0.011851499,0.882460878,0.003902929,0.002876791,0.002098909,0.955708619,0.001844744,0.472254454,0.974976938,0.99914992,0.003279545,0.833773381,0.010759802,0.017133948,\r\n+Y,0.441172985,1.22305382,0.450833707,1.228265697,1.054511038,0.257350908,0.976509374,30.88210896,0.603166191,0.124524633,0.765672123,0.804221588,1.490860433,0.874021952,0.367644154,\r\n Normalize Factor,,,,,,,,,,,,,,,,\r\n 0.078014184,,,,,,,,,,,,,,,,\r\n Threshold,,,,,,,,,,,,,,,,\r\n' |