| Next changeset 1:ae988a95b761 (2019-01-14) |
|
Commit message:
Uploaded |
|
added:
KT-ID fisher test/7-7-fisher-galaxy_working.R KT-ID fisher test/OnlyTheRequiredSubBackFreqData.RData KT-ID fisher test/kinatestid_r_fisher.xml KT-ID fisher test/screener7-7.csv |
| b |
| diff -r 000000000000 -r 6f7fd13c1a05 KT-ID fisher test/7-7-fisher-galaxy_working.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/KT-ID fisher test/7-7-fisher-galaxy_working.R Mon Jan 14 11:12:59 2019 -0500 |
| [ |
| b'@@ -0,0 +1,924 @@\n+oldw <- getOption("warn")\r\n+options(warn = -1)\r\n+\r\n+PositiveSubstrateList<- read.csv("substrates.csv", stringsAsFactors=FALSE)\r\n+NegativeSubstrateList<- read.csv("negatives.csv", stringsAsFactors=FALSE)\r\n+SubstrateBackgroundFrequency<- read.csv("SBF.csv", stringsAsFactors=FALSE)\r\n+\r\n+ScreenerFilename<-"screener"\r\n+screaner<-read.csv(ScreenerFilename, header = FALSE, stringsAsFactors = FALSE)\r\n+\r\n+DataFilename<-"thedata.RData"\r\n+load(DataFilename)\r\n+\r\n+\r\n+SDtableAndPercentTable<-"output1.csv"\r\n+NormalizationScore_CharacterizationTable<-"output2.csv"\r\n+SequenceScoringAndScreening<-"output3.csv"\r\n+\r\n+\r\n+\r\n+\r\n+\r\n+SiteSelectivityTable_EndogenousProbabilityMatrix_NormalizationScore_CharacterizationTable<-NormalizationScore_CharacterizationTable\r\n+FILENAME2<-NormalizationScore_CharacterizationTable\r\n+FILENAME3<-SequenceScoringAndScreening\r\n+substrates<-matrix(rep("A",times=((nrow(PositiveSubstrateList)-1)*15)),ncol = 15)\r\n+\r\n+for (i in 2:nrow(PositiveSubstrateList))\r\n+{\r\n+ substratemotif<-PositiveSubstrateList[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+substrates2<-substrates\r\n+substrates2[substrates2==""]<-"O"\r\n+\r\n+#I will make it so that all blank values in substrates get a O after I\'m done with it\r\n+\r\n+# SpacesToOs<-c(""="O",)\r\n+# substrates<-SpacesToOs[substrates]\r\n+\r\n+\r\n+\r\n+#create the percent table\r\n+if (1==1){\r\n+Column1<-substrates[,1]\r\n+Column2<-substrates[,2]\r\n+Column3<-substrates[,3]\r\n+Column4<-substrates[,4]\r\n+Column5<-substrates[,5]\r\n+Column6<-substrates[,6]\r\n+Column7<-substrates[,7]\r\n+Column8<-substrates[,8]\r\n+Column9<-substrates[,9]\r\n+Column10<-substrates[,10]\r\n+Column11<-substrates[,11]\r\n+Column12<-substrates[,12]\r\n+Column13<-substrates[,13]\r\n+Column14<-substrates[,14]\r\n+Column15<-substrates[,15]\r\n+\r\n+spaces1<-sum(Column1%in% "")\r\n+spaces2<-sum(Column2%in% "")\r\n+spaces3<-sum(Column3%in% "")\r\n+spaces4<-sum(Column4%in% "")\r\n+spaces5<-sum(Column5%in% "")\r\n+spaces6<-sum(Column6%in% "")\r\n+spaces7<-sum(Column7%in% "")\r\n+spaces8<-sum(Column8%in% "")\r\n+spaces9<-sum(Column9%in% "")\r\n+spaces10<-sum(Column10%in% "")\r\n+spaces11<-sum(Column11%in% "")\r\n+spaces12<-sum(Column12%in% "")\r\n+spaces13<-sum(Column13%in% "")\r\n+spaces14<-sum(Column14%in% "")\r\n+spaces15<-sum(Column15%in% "")\r\n+OllOs<-cbind(spaces1,spaces2,spaces3,spaces4,spaces5,spaces6,spaces7,spaces8,spaces9,spaces10,spaces11,\r\n+ spaces12,spaces13,spaces14,spaces15)\r\n+\r\n+A1<-sum(Column1 %in% "A")\r\n+A2<-sum(Column2 %in% "A")\r\n+A3<-sum(Column3 %in% "A")\r\n+A4<-sum(Column4 %in% "A")\r\n+A5<-sum(Column5 %in% "A")\r\n+A6<-sum(Column6 %in% "A")\r\n+A7<-sum(Column7 %in% "A")\r\n+A8<-sum(Column8 %in% "A")\r\n+A9<-sum(Column9 %in% "A")\r\n+A10<-sum(Column10 %in% "A")\r\n+A11<-sum(Column11 %in% "A")\r\n+A12<-sum(Column12 %in% "A")\r\n+A13<-sum(Column13 %in% "A")\r\n+A14<-sum(Column14 %in% "A")\r\n+A15<-sum(Column15 %in% "A")\r\n+AllAs<-cbind(A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12,A13,A14,A15)\r\n+\r\n+C1<-sum(Column1 %in% "C")\r\n+C2<-sum(Column2 %in% "C")\r\n+C3<-sum(Column3 %in% "C")\r\n+C4<-sum(Column4 %in% "C")\r\n+C5<-sum(Column5 %in% "C")\r\n+C6<-sum(Column6 %in% "C")\r\n+C7<-sum(Column7 %in% "C")\r\n+C8<-sum(Column8 %in% "C")\r\n+C9<-sum(Column9 %in% "C")\r\n+C10<-sum(Column10 %in% "C")\r\n+C11<-sum(Column11 %in% "C")\r\n+C12<-sum(Column12 %in% "C")\r\n+C13<-sum(Column13 %in% "C")\r\n+C14<-sum(Column14 %in% "C")\r\n+C15<-sum(Column15 %in% "C")\r\n+CllCs<-cbind(C1,C2,C3,C4,C5,C6,C7,C8,C9,C10,C11,C12,C13,C14,C15)\r\n+\r\n+D1<-sum(Column1 %in% "D")\r\n+D2<-sum(Column2 %in% "D")\r\n+D3<-sum(Column3 %in% "D")\r\n+D4<-sum(Column4 %in% "D")\r\n+D5<-sum(Column5 %in% "D")\r\n+D6<-sum(Column6 %in% "D")\r\n+D7<-sum(Column7 %in% "D")\r\n+D8<-sum(Column8 %in% "D")\r\n+D9<-sum(Column9 %in% "D")\r\n+D10<-sum(Column10 %in% "D")\r\n+D11<-sum(Column11 %in% "D")\r\n+D12<-sum(Column12 %in% "D")\r\n+D13<-sum(Column13 %in% "D")\r\n+D14<-sum(Column14 %in% "D")\r\n+D15<-sum(Column15 %in% "D")\r\n+DllDs<-'..b'SigmaAAs[i]<-ExpectedValue*(length(substrates[,i])-sum(substrates[,i]%in% ""))/100\r\n+# }\r\n+# \r\n+# SelectivityRow<-SumOfSigmaAAs/SumOfExpectedSigmaAAs\r\n+# SuperRow<-SelectivityRow\r\n+\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,(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(1:120)\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+One_Minus_Specificity<-c(1:120)\r\n+Accuracy<-c(1:120)\r\n+MCC<-c(1:120)\r\n+EER<-c(1:120)\r\n+Precision<-c(1:120)\r\n+F_One_Half<-c(1:120)\r\n+F_One<-c(1:120)\r\n+F_Two<-c(1:120)\r\n+FalsePositiveRate<-c(1:120)\r\n+#MAKE DAMN SURE THAT THE ACCESSION NUMBERS FOLLOW THE MOTIFS\r\n+\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+ 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+ One_Minus_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+ EER[z]<-(FalsePositives[z]+Falsenegatives[z])/(Truepositives[z]+TrueNegatives[z]+FalsePositives[z]+Falsenegatives[z])\r\n+ Precision[z]<-Truepositives[z]/(Truepositives[z]+FalsePositives[z])\r\n+ F_One_Half[z]<-(1.5*Precision[z]*Sensitivity[z])/(.25*Precision[z]+Sensitivity[z])\r\n+ F_One[z]<-(2*Precision[z]*Sensitivity[z])/(Precision[z]+Sensitivity[z])\r\n+ F_Two[z]<-(5*Precision[z]*Sensitivity[z])/(4*Precision[z]+Sensitivity[z])\r\n+ FalsePositiveRate[z]<-FalsePositives[z]/(TrueNegatives[z]+FalsePositives[z])\r\n+}\r\n+Characterization<-cbind.data.frame(threshold,Truepositives,Falsenegatives,Sensitivity,TrueNegatives,FalsePositives,One_Minus_Specificity,Accuracy,MCC,EER,Precision,FalsePositiveRate,F_One_Half,F_One,F_Two)\r\n+\r\n+positiveheader<-c(1,2,3,4,5,6,7,8,9,10,11,12,13,"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+options(warn = oldw)\r\n' |
| b |
| diff -r 000000000000 -r 6f7fd13c1a05 KT-ID fisher test/OnlyTheRequiredSubBackFreqData.RData |
| b |
| Binary file KT-ID fisher test/OnlyTheRequiredSubBackFreqData.RData has changed |
| b |
| diff -r 000000000000 -r 6f7fd13c1a05 KT-ID fisher test/kinatestid_r_fisher.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/KT-ID fisher test/kinatestid_r_fisher.xml Mon Jan 14 11:12:59 2019 -0500 |
| [ |
| @@ -0,0 +1,47 @@ +<tool id="kinatestid_fisher_r" name="Kinatest-ID using Fisher's Exact Test" version="0.5.0"> + <description>determine kinase's preferred sequence motif</description> + <requirements> + <requirement type="package">R</requirement> + </requirements> + <command><![CDATA[ + ln -s '$substrates' substrates.csv && + ln -s '$negatives' negatives.csv && + ln -s '$SBF' SBF.csv && + ln -s '$__tool_directory__/screener7-7.csv' screener && + ln -s '$__tool_directory__/OnlyTheRequiredSubBackFreqData.RData' thedata.RData && + Rscript '$__tool_directory__/7-7-fisher-galaxy_working.R' + ]]></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"/> + <param name="outGroup" type="text" value="kinase" label="Kinase Name"/> + </inputs> + <outputs> + <data format="csv" name="odds_table" from_work_dir="output1.csv" label="${outGroup}_Fisher Odds Table"/> + <data format="csv" name="char_table" from_work_dir="output2.csv" label="${outGroup}_Characterization Table"/> + </outputs> + <tests> + <test> + <param name="substrates" ftype="csv" value="substrates.csv"/> + <param name="negatives" ftype="csv" value="negatives.csv"/> + <param name="SBF" ftype="csv" value="SBF.csv"/> + <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 a Kinamine tool and a Negative Motif Finder tool. Using the outputs from those two functions (The Positive and Negative substrates as well as the Substrate Background Frequency) this tool calculates the kinases preferred substrate motif. + + + ]]></help> + <citations> + <citation type="doi">10.1074/mcp.RA118.001111</citation> + </citations> +</tool> + |
| b |
| diff -r 000000000000 -r 6f7fd13c1a05 KT-ID fisher test/screener7-7.csv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/KT-ID fisher test/screener7-7.csv Mon Jan 14 11:12:59 2019 -0500 |
| b |
| b'@@ -0,0 +1,375 @@\n+Amino Acid,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,Abl\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-06,0.696189059,1.106253027,1.204123634,0.85951787,0.640589342,1.018652536,0.370120265,\r\n+K,0.669287351,0.556720292,0.527019051,0.449763522,0.300628958,0.207284601,0.064185784,4.69E-06,0.671039696,0.326624025,0.533951228,1.003908723,1.510736428,1.414902568,2.170178704,\r\n+L,0.839940831,0.922246591,0.880085062,0.981550259,0.634044531,0.634085382,1.349870963,4.04E-06,0.606216945,0.693101076,1.538241708,0.857760324,0.627591522,0.870254818,0.530765953,\r\n+M,0.760904105,0.905087148,0.637816676,0.66172158,0.661409839,0.736434917,0.627102998,1.47E-05,0.789822075,0.63478147,0.679064181,0.670393334,0.489816005,0.61317325,0.641095939,\r\n+N,1.344461327,1.207804111,1.09282418,1.19578616,1.453838282,1.589861684,1.208775991,9.42E-06,0.697778622,1.418639623,0.945341271,1.096429538,1.213904132,1.013079107,0.9954638,\r\n+P,1.177253197,0.989406853,1.047729698,0.777329852,0.654285177,1.053307412,0.379471342,6.72E-06,0.022629526,0.936255322,2.633066293,0.866093682,1.090636489,1.325150713,1.175571888,\r\n+Q,1.055028624,1.428706685,1.53289453,0.986320541,1.409399838,1.486723008,1.154705589,8.13E-06,1.587930849,1.546009242,0.580624841,1.453448891,1.183665742,1.190270175,1.451884828,\r\n+R,0.23114842,0.197398944,0.279870881,0.167515522,0.042299774,0.035794422,0.030480344,5.94E-06,1.029713138,0.301828266,0.607400563,1.098491631,1.72887863,1.596604736,1.839333348,\r\n+S,1.238337917,1.175941697,0.991260956,1.54261904,1.249438053,1.451175393,0.247146467,5.16E-06,1.415677733,1.699542231,1.135015884,1.29744777,1.144947022,1.186706706,1.27834536,\r\n+T,1.497448153,1.395064382,1.191819252,1.045752952,1.013082468,1.264837658,0.945418017,6.99E-06,0.57699664,1.527398321,1.282392889,1.192116705,1.068274409,0.856716114,0.917576316,\r\n+V,1.174873487,1.381211351,0.915186655,0.774038465,0.873029816,1.245977013,1.999934145,5.49E-06,0.748344433,1.384326163,1.270919521,0.866877682,0.628613056,0.672165541,0.719915467,\r\n+W,0.243928566,0.178554076,0.68156511,0.106066451,0.267831113,0.151093952,0.514649526,3.76E-05,0.379798515,0.495469776,1.233591091,0.781501343,0.358911728,0.336976132,0.959097021,\r\n+Y,0.58250686,0.692885567,0.895175983,1.203122999,0.772833902,0.586325562,0.979355038,31.81943721,1.114814012,1.204321329,1.472921532,0.909793293,0.937441332,0.771177087,0.736182352,\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,1.009231339,1.394228537,0.986803975,1.085283395,1.236762726,1.222676115,0.767569945,1.71E-05,2.031174242,1.219024597,0.742440294,0.930534476,0.757461474'..b',1,1,1.504238373,1.182522651,0.421517346,2.445623654,29.22520267,3.487781388,0.88946269,0.921515399,1.432607974,1,1,1,\r\n+Normalize Factor,,,,,,,,,,,,,,,,\r\n+0.316928299,,,,,,,,,,,,,,,,\r\n+Threshold,,,,,,,,,,,,,,,,\r\n+45,,,,,,,,,,,,,,,,\r\n+Amino Acid,,,,-4,-3,-2,-1,0,1,2,3,4,,,,\r\n+A,0.99097455,1.211191117,1.268866884,1.174950753,1.124456203,1.14978229,0.99118732,1.21E-05,1.789102307,0.920598239,0.566100196,0.810188653,0.618446393,1.115520413,0.634433442,Alk\r\n+C,0.276662332,0.589023029,0.869510185,0.316310004,0.256850195,1.083370296,1.164387553,6.07E-05,1.402809766,1.670594425,1.058534139,0.980157315,0.266388399,0.846158411,1.147753445,\r\n+D,1.141004408,1.673473132,1.633628534,1.478454216,2.165671112,1.608483884,1.956427527,1.67E-05,1.122023891,0.565319139,0.254658955,0.725548599,0.927734442,0.901507643,0.876581164,\r\n+E,0.660275186,1.444795703,1.210504508,1.300100904,1.753838373,1.407684854,0.952029632,1.21E-05,1.686636281,0.779509141,0.17104955,0.809729367,0.618095803,1.051781168,0.63407379,\r\n+F,0.460549542,0.32684161,0.482480473,0.965340851,1.033291779,0.300574404,0.646104285,2.53E-05,1.194139169,1.631149793,1.541839771,1.192346168,2.365050168,0.704284292,0.636874224,\r\n+G,1.096135101,1.105440152,1.299428327,1.165273833,0.892666221,1.40064819,0.485610424,1.27E-05,1.17008988,0.602935208,0.469055524,1.053386351,0.759168817,1.146899818,1.303054639,\r\n+H,3.060672048,1.764822995,1.603209168,1.239331182,1.361548962,0.649195945,1.073453095,4.20E-05,0.969941547,1.288375506,0.960618537,1.720335513,1.227922141,1.389511263,0.881765042,\r\n+I,1.033407755,0.611155124,0.992399511,0.787668273,0.746203213,0.517075473,2.718311705,1.89E-05,1.270271374,1.360025434,2.12110686,0.704067221,1.243789838,0.75723375,0.317567844,\r\n+K,0.547996504,0.544461043,0.746319049,0.501222629,0.457878409,0.343340205,0.038439175,1.20E-05,0.138930161,0.267266625,0.786255854,1.075258006,1.108057653,1.215115155,1.818722112,\r\n+L,0.776845403,0.861421314,0.762973164,0.925180057,0.646088017,0.633753329,1.373646564,1.07E-05,0.760938569,1.578666163,1.567412261,0.873294959,1.246661722,0.723920509,0.514752561,\r\n+M,1.890782248,0.975887612,0.45018625,0.851596414,0.691513712,0.673094004,0.281333784,3.77E-05,1.267724479,1.077854756,1.274222819,0.374749635,0.827530692,0.657143909,0.3961639,\r\n+N,1.526310257,1.470040754,1.370564312,1.661944127,1.821869057,1.593816967,2.243223135,2.39E-05,0.603048297,1.645795198,0.57355137,1.21815077,1.119718324,0.958637203,1.25635062,\r\n+P,1.291845852,0.91679383,0.930436596,0.953880667,0.399778384,1.096048287,0.849529066,1.77E-05,0.074434928,0.937580063,0.637146799,0.770124885,0.803334514,1.759451193,1.414263625,\r\n+Q,1.210214661,1.123127253,1.121554246,0.815996948,1.49806623,1.798400477,0.674770003,2.04E-05,1.566274886,1.189130159,0.645582737,1.598313645,0.926242643,0.996526254,1.80228891,\r\n+R,0.135418605,0.288310578,0.248267442,0.206433401,0.104767591,0.070703997,0.015831547,1.49E-05,0.046816146,0.267328282,1.11720349,1.236304763,1.325629606,1.579027998,1.716592029,\r\n+S,1.5184984,1.25006707,1.46354322,1.57407305,1.447350342,1.47466343,0.440261836,1.33E-05,1.105929616,0.945141384,0.53741639,1.026286036,0.857770343,1.044957193,1.34391447,\r\n+T,1.055646882,1.09493895,0.978310114,1.29976922,1.10569821,1.377920957,0.968323584,1.78E-05,0.917059394,0.848663409,1.029167445,1.349918509,0.964319749,0.682982622,0.973205887,\r\n+V,1.159309001,1.09698056,0.742203117,0.662723098,0.65773243,0.992006108,1.746866467,1.41E-05,1.20236905,1.495802828,3.126493961,0.965365541,0.723500386,0.615571306,0.653138743,\r\n+W,0.434872974,0.000905043,0.455581211,0.165731118,0.269154117,0.113526709,0.305041332,9.55E-05,0.701595064,1.615957922,1.039913633,0.829588758,0.418723845,0.166254755,0.00038182,\r\n+Y,0.778909209,0.73703237,0.680000103,0.989480465,0.883826221,0.711689429,1.214145112,31.41600477,1.196800182,1.35673725,0.931304489,0.955216361,1.708297342,0.595564072,0.359040055,\r\n+Normalize Factor,,,,,,,,,,,,,,,,\r\n+0.103305785,,,,,,,,,,,,,,,,\r\n+Threshold,,,,,,,,,,,,,,,,\r\n+23,,,,,,,,,,,,,,,,\r\n' |