diff --git a/R/plot_OptimizationCurve.R b/R/plot_OptimizationCurve.R
index d2f72c078..70f068277 100755
--- a/R/plot_OptimizationCurve.R
+++ b/R/plot_OptimizationCurve.R
@@ -6,7 +6,36 @@ verbose = TRUE
 input = args[1]
 targetTITV = as.numeric(args[2])
 
+# -----------------------------------------------------------------------------------------------
+# Useful general routines
+# -----------------------------------------------------------------------------------------------
 
+MIN_FP_RATE = 0.01
+
+titvFPEst <- function(titvExpected, titvObserved) { 
+    max(min(1 - (titvObserved - 0.5) / (titvExpected - 0.5), 1), MIN_FP_RATE) 
+}
+
+titvFPEstV <- function(titvExpected, titvs) {
+    sapply(titvs, function(x) titvFPEst(titvExpected, x))
+}
+
+nTPFP <- function(nVariants, FDR) {
+    return(list(TP = nVariants * (1 - FDR/100), FP = nVariants * (FDR / 100)))
+}
+
+leftShift <- function(x, leftValue = 0) {
+    r = rep(leftValue, length(x))
+    for ( i in 1:(length(x)-1) ) {
+        print(list(i=i))
+        r[i] = x[i+1]
+    }
+    r
+}
+
+# -----------------------------------------------------------------------------------------------
+# optimization curve
+# -----------------------------------------------------------------------------------------------
 data = read.table(input,sep=",",head=T)
 maxVars = max(data$numKnown, data$numNovel)
 maxTITV = max(data$knownTITV[is.finite(data$knownTITV) & data$numKnown>2000], data$novelTITV[is.finite(data$novelTITV) & data$numNovel > 2000], targetTITV)
@@ -14,7 +43,6 @@ maxTITV = min(maxTITV, targetTITV + 1)
 minTITV = min(data$knownTITV[length(data$knownTITV)], data$novelTITV[length(data$novelTITV)], targetTITV)
 maxPCut = max(data$pCut[data$numKnown>0 | data$numNovel>0])
 
-
 outfile = paste(input, ".optimizationCurve.pdf", sep="")
 pdf(outfile, height=7, width=8)
 
@@ -34,16 +62,49 @@ mtext("Number of Variants", side=4, line=2, col="DarkGreen",cex=1.4)
 legend("topright", c("Known","Novel"), col=c("Green","DarkGreen"), pch=c(20,20),cex=0.7)
 dev.off()
 
-
+# -----------------------------------------------------------------------------------------------
+# Tranches plot
+# -----------------------------------------------------------------------------------------------
 data2 = read.table(paste(input,".tranches",sep=""),sep=",",head=T)
+cols = c("cornflowerblue", "cornflowerblue", "darkorange", "darkorange")
+density=c(20, -1, -1, 20)
 outfile = paste(input, ".FDRtranches.pdf", sep="")
 pdf(outfile, height=7, width=8)
-alpha = (data2$novelTITV - 0.5) / (targetTITV - 0.5);
+alpha = 1 - titvFPEstV(targetTITV, data2$novelTITV)
+print(alpha)
+
 numGood = round(alpha * data2$numNovel);
+
+#numGood = round(data2$numNovel * (1-data2$FDRtranche/100))
 numBad = data2$numNovel - numGood;
-d=matrix(c(numGood,numBad),2,byrow=TRUE)
-barplot(d,horiz=TRUE,col=c(1,2),space=0.2,xlab="Number of Novel Variants",ylab="Novel Ti/Tv   -->   FDR (%)")
-legend('topright',c('implied TP','implied FP'),col=c(1,2),lty=1,lwd=16)
+
+numPrevGood = leftShift(numGood, 0)
+numNewGood = numGood - numPrevGood
+numPrevBad = leftShift(numBad, 0)
+numNewBad = numBad - numPrevBad
+
+d=matrix(c(numPrevGood,numNewGood, numNewBad, numPrevBad),4,byrow=TRUE)
+print(d)
+barplot(d/1000,horiz=TRUE,col=cols,space=0.2,xlab="Number of Novel Variants (1000s)",ylab="Novel Ti/Tv   -->   FDR (%)", density=density) # , xlim=c(250000,350000))
+#abline(v= d[2,dim(d)[2]], lty=2)
+#abline(v= d[1,3], lty=2)
+legend(10000/1000, 2.25, c('Cumulative TPs','Tranch-specific TPs', 'Tranch-specific FPs', 'Cumulative FPs' ), fill=cols, density=density, bg='white', cex=1.25)
 axis(2,line=-1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$FDRtranche)
 axis(2,line=0.4,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$novelTITV)
 dev.off()
+
+
+#
+#data2 = read.table(paste(input,".tranches",sep=""),sep=",",head=T)
+#cols = c("steelblue","orange")
+#outfile = paste(input, ".FDRtranches.pdf", sep="")
+#pdf(outfile, height=7, width=8)
+#alpha = (data2$novelTITV - 0.5) / (targetTITV - 0.5);
+#numGood = round(alpha * data2$numNovel);
+#numBad = data2$numNovel - numGood;
+#d=matrix(c(numGood,numBad),2,byrow=TRUE)
+#barplot(d,horiz=TRUE,col=cols,space=0.2,xlab="Number of Novel Variants",ylab="Novel Ti/Tv   -->   FDR (%)")
+#legend('topright',c('implied TP','implied FP'),col=cols,lty=1,lwd=16)
+#axis(2,line=-1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$FDRtranche)
+#axis(2,line=0.4,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$novelTITV)
+#dev.off()