diff --git a/R/VariantReport/VariantReport.R b/R/VariantReport/VariantReport.R
new file mode 100644
index 000000000..36290ce3b
--- /dev/null
+++ b/R/VariantReport/VariantReport.R
@@ -0,0 +1,61 @@
+source(paste(Sys.getenv("STING_DIR"), "/R/gsacommons.R", sep=""));
+
+args = commandArgs(TRUE);
+
+evalRoot = args[1];
+#if (is.na(evalRoot)) { evalRoot = "/home/radon01/kiran/scr1/projects/ESPGabrielDownsampling/results/v1/merged.vcf.eval120/eval"; }
+if (is.na(evalRoot)) { evalRoot = "/home/radon01/kiran/scr1/projects/ESPGabrielDownsampling/results/v1/merged.vcf.eval.v2/eval"; }
+
+plotRoot = args[2];
+if (is.na(plotRoot)) { plotRoot = "plot"; }
+
+eval = read.eval(evalRoot);
+
+# Venn diagram
+plot.begin(plotRoot, "venn");
+plot.callsetConcordance(eval);
+plot.end(plotRoot);
+
+# Venn by AC
+plot.begin(plotRoot, "venn_by_ac.all", width=12, height=8);
+plot.callsetConcordanceByAC(eval, novelty_name="all");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "venn_by_ac.known", width=12, height=8);
+plot.callsetConcordanceByAC(eval, novelty_name="known");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "venn_by_ac.novel", width=12, height=8);
+plot.callsetConcordanceByAC(eval, novelty_name="novel");
+plot.end(plotRoot);
+
+# Allele count spectrum
+plot.begin(plotRoot, "acs.all", width=12, height=8);
+plot.alleleCountSpectrum(eval, novelty_name="all");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "acs.known", width=12, height=8);
+plot.alleleCountSpectrum(eval, novelty_name="known");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "acs.novel", width=12, height=8);
+plot.alleleCountSpectrum(eval, novelty_name="novel");
+plot.end(plotRoot);
+
+# Ti/Tv spectrum
+plot.begin(plotRoot, "titv.all", width=12, height=8);
+plot.titvSpectrum(eval, novelty_name="all");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "titv.known", width=12, height=8);
+plot.titvSpectrum(eval, novelty_name="known");
+plot.end(plotRoot);
+
+plot.begin(plotRoot, "titv.novel", width=12, height=8);
+plot.titvSpectrum(eval, novelty_name="novel");
+plot.end(plotRoot);
+
+# Per-sample
+plot.begin(plotRoot, "variants_per_sample", width=12, height=8);
+plot.variantsPerSample(eval);
+plot.end(plotRoot);
diff --git a/R/gsacommons.R b/R/gsacommons.R
new file mode 100644
index 000000000..30fd2fc46
--- /dev/null
+++ b/R/gsacommons.R
@@ -0,0 +1,256 @@
+plot.begin <- function(plotRoot, name, width=10, height=10) {
+    if (!is.na(plotRoot)) {
+        filename = paste(plotRoot, ".", name, ".pdf", sep="");
+        print(sprintf("Plotting '%s' to '%s'", name, filename));
+
+        pdf(filename, width=width, height=height);
+    } else {
+        print(sprintf("Plotting '%s' to X11", name));
+
+        x11(width=width, height=height);
+    }
+}
+
+plot.end <- function(plotRoot) {
+    if (!is.na(plotRoot)) {
+        dev.off();
+    }
+}
+
+plot.venn <- function(a, b, c=0, a_and_b, a_and_c=0, b_and_c=0,
+                     col=c("#FF6342", "#63C6DE", "#ADDE63"),
+                     pos=c(0.20, 0.20, 0.80, 0.82),
+                     debug=0
+                    ) {
+    library(png);
+    library(graphics);
+
+    # Set up properties
+    for (i in 1:length(col)) {
+        rgbcol = col2rgb(col[i]);
+        col[i] = sprintf("%02X%02X%02X", rgbcol[1], rgbcol[2], rgbcol[3]);
+    }
+
+    chco = paste(col[1], col[2], col[3], sep=",");
+    chd = paste(a, b, c, a_and_b, a_and_c, b_and_c, sep=",");
+
+    props = c(
+        'cht=v',
+        'chs=525x525',
+        'chds=0,10000000000',
+        paste('chco=', chco, sep=""),
+        paste('chd=t:', chd, sep="")
+    );
+    proplist = paste(props[1], props[2], props[3], props[4], props[5], sep='&');
+
+    # Get the venn diagram (as a temporary file)
+    filename = tempfile("venn");
+    cmd = paste("wget -O ", filename, " 'http://chart.apis.google.com/chart?", proplist, "' > /dev/null 2>&1", sep="");
+
+    if (debug == 1) {
+        print(cmd);
+    }
+    system(cmd);
+
+    # Render the temp png file into a plotting frame
+    a = readPNG(filename);
+    
+    plot(0, 0, type="n", xaxt="n", yaxt="n", bty="n", xlim=c(0, 1), ylim=c(0, 1), xlab="", ylab="");
+    rasterImage(a, pos[1], pos[2], pos[3], pos[4]);
+
+    # Clean up!
+    unlink(filename);
+}
+
+read.eval <- function(evalRoot) {
+    fileAlleleCountStats = paste(evalRoot, ".AlleleCountStats.csv", sep="");
+    fileCompOverlap = paste(evalRoot, ".Comp_Overlap.csv", sep="");
+    fileCountVariants = paste(evalRoot, ".Count_Variants.csv", sep="");
+    fileGenotypeConcordance = paste(evalRoot, ".Genotype_Concordance.csv", sep="");
+    fileMetricsByAc = paste(evalRoot, ".MetricsByAc.csv", sep="");
+    fileMetricsBySample = paste(evalRoot, ".MetricsBySample.csv", sep="");
+    fileQuality_Metrics_by_allele_count = paste(evalRoot, ".Quality_Metrics_by_allele_count.csv", sep="");
+    fileQualityScoreHistogram = paste(evalRoot, ".QualityScoreHistogram.csv", sep="");
+    fileSampleStatistics = paste(evalRoot, ".Sample_Statistics.csv", sep="");
+    fileSampleSummaryStatistics = paste(evalRoot, ".Sample_Summary_Statistics.csv", sep="");
+    fileTi_slash_Tv_Variant_Evaluator = paste(evalRoot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep="");
+    fileTiTvStats = paste(evalRoot, ".TiTvStats.csv", sep="");
+    fileVariant_Quality_Score = paste(evalRoot, ".Variant_Quality_Score.csv", sep="");
+
+    eval = list(
+        AlleleCountStats = read.csv(fileAlleleCountStats, header=TRUE, comment.char="#"),
+        CompOverlap = read.csv(fileCompOverlap, header=TRUE, comment.char="#"),
+        CountVariants = read.csv(fileCountVariants, header=TRUE, comment.char="#"),
+        GenotypeConcordance = read.csv(fileGenotypeConcordance, header=TRUE, comment.char="#"),
+        MetricsByAc = read.csv(fileMetricsByAc, header=TRUE, comment.char="#"),
+        MetricsBySample = read.csv(fileMetricsBySample, header=TRUE, comment.char="#"),
+        Quality_Metrics_by_allele_count = read.csv(fileQuality_Metrics_by_allele_count, header=TRUE, comment.char="#"),
+        QualityScoreHistogram = read.csv(fileQualityScoreHistogram, header=TRUE, comment.char="#"),
+        SampleStatistics = read.csv(fileSampleStatistics, header=TRUE, comment.char="#"),
+        SampleSummaryStatistics = read.csv(fileSampleSummaryStatistics, header=TRUE, comment.char="#"),
+        TiTv = read.csv(fileTi_slash_Tv_Variant_Evaluator, header=TRUE, comment.char="#"),
+        TiTvStats = read.csv(fileTiTvStats, header=TRUE, comment.char="#"),
+        Variant_Quality_Score = read.csv(fileVariant_Quality_Score, header=TRUE, comment.char="#"),
+
+        CallsetNames = c(),
+        CallsetOnlyNames = c(),
+        CallsetFilteredNames = c()
+    );
+
+    uniqueJexlExpressions = unique(eval$TiTv$jexl_expression);
+    eval$CallsetOnlyNames = as.vector(uniqueJexlExpressions[grep("Filtered|Intersection|none", uniqueJexlExpressions, invert=TRUE, ignore.case=TRUE)]);
+    eval$CallsetNames = as.vector(gsub("-only", "", eval$CallsetOnlyNames));
+    eval$CallsetFilteredNames = as.vector(c(paste(eval$CallsetNames[1], "-filteredIn", eval$CallsetNames[2], sep=""), paste(eval$CallsetNames[2], "-filteredIn", eval$CallsetNames[1], sep="")));
+
+    eval;
+}
+
+eval.getMetrics <- function(eval, jexl_expression) {
+    callset.counts = eval$CountVariants[which(eval$CountVariants$evaluation_name == "eval" & eval$CountVariants$comparison_name == "dbsnp" & eval$CountVariants$jexl_expression == jexl_expression),]; 
+    callset.counts.titv = eval$TiTv[which(eval$TiTv$evaluation_name == "eval" & eval$TiTv$comparison_name == "dbsnp" & eval$TiTv$jexl_expression == jexl_expression),]; 
+
+    callset.calledCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "all"),]$nVariantLoci;
+    callset.calledCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "all"),]$ti.tv.ratio;
+
+    callset.knownCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "known"),]$nVariantLoci;
+    callset.knownCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "known"),]$ti.tv.ratio;
+
+    callset.novelCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "novel"),]$nVariantLoci;
+    callset.novelCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "novel"),]$ti.tv.ratio;
+
+    callset.filteredCounts = callset.counts[which(callset.counts$filter_name == "filtered" & callset.counts$novelty_name == "all"),]$nVariantLoci;
+    callset.filteredCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "filtered" & callset.counts.titv$novelty_name == "all"),]$ti.tv.ratio;
+    
+    metrics = list(
+        all = callset.calledCounts,
+        all.titv = callset.calledCounts.titv,
+
+        known = callset.knownCounts,
+        known.titv = callset.knownCounts.titv,
+
+        novel = callset.novelCounts,
+        novel.titv = callset.novelCounts.titv,
+
+        filtered = callset.filteredCounts,
+        filtered.titv = callset.filteredCounts.titv
+    );
+}
+
+.plot.callsetConcordance.getLabelText <- function(name, metrics, union) {
+    text = sprintf("%s (%0.1f%% of union)\nAll: %d, Ti/Tv: %0.1f\nKnown: %d, Ti/Tv: %0.1f\nNovel: %d, Ti/Tv: %0.1f\n",
+                   name,             100.0*metrics$all/union$all,
+                   metrics$all,      metrics$all.titv,
+                   metrics$known,    metrics$known.titv,
+                   metrics$novel,    metrics$novel.titv
+    );
+}
+
+plot.callsetConcordance <- function(eval, col=c("#FF6342", "#63C6DE", "#ADDE63")) {
+    aonly = eval.getMetrics(eval, eval$CallsetOnlyNames[1]);
+    intersection = eval.getMetrics(eval, "Intersection");
+    bonly = eval.getMetrics(eval, eval$CallsetOnlyNames[2]);
+
+    union = list(
+        all = intersection$all + aonly$all + bonly$all,
+        known = intersection$known + aonly$known + bonly$known,
+        novel = intersection$novel + aonly$novel + bonly$novel,
+        filtered = intersection$filtered + aonly$filtered + bonly$filtered
+    );
+
+    par.def = par(no.readonly = TRUE);
+    par(mar=c(0.1, 0.1, 0.1, 0.1));
+
+    plot.venn(aonly$all + intersection$all, bonly$all + intersection$all, 0, intersection$all, 0, 0, pos=c(0.32, 0.32, 0.68, 0.70), col=col);
+
+    text(0, 0.5, cex=1.2, pos=4, .plot.callsetConcordance.getLabelText(eval$CallsetNames[1], aonly, union));
+    text(0.5, 0.75, cex=1.2, adj=c(0.5, 0.33), .plot.callsetConcordance.getLabelText("Intersection", intersection, union));
+    text(1, 0.5, cex=1.2, pos=2, .plot.callsetConcordance.getLabelText(eval$CallsetNames[2], bonly, union));
+
+    par(par.def);
+}
+
+plot.callsetConcordanceByAC <- function(eval, normalize=TRUE, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
+    aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+
+    title = paste("Callset concordance per allele count (", novelty_name, " variants)", sep="");
+
+    if (normalize == TRUE) {
+        norm = aonly$n + intersection$n + bonly$n;
+        matnorm = rbind(aonly$n/norm, intersection$n/norm, bonly$n/norm);
+
+        barplot(matnorm, col=col, xlab="Allele count", ylab="Fraction", main=title, names.arg=intersection$AC, ylim=c(0, 1.3), border=NA);
+    } else {
+        mat = rbind(aonly$n, intersection$n, bonly$n);
+
+        barplot(mat, col=col, xlab="Allele count", ylab="counts", main=title, names.arg=intersection$AC, border="gray");
+    }
+
+    legend("topright", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
+}
+
+plot.alleleCountSpectrum <- function(eval, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
+    aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+
+    title = paste("Allele count spectrum (", novelty_name, " variants)", sep="");
+
+    suppressWarnings(plot(0, 0, type="n", xlim=c(1, length(intersection$n)), ylim=c(1, max(aonly$n + intersection$n, bonly$n + intersection$n)), xlab="Allele count", ylab="Number of variants", main=title, log="xy", bty="n"));
+    suppressWarnings(points(intersection$AC, aonly$n + intersection$n, type="l", lwd=2, col=col[1]));
+    suppressWarnings(points(intersection$AC, intersection$n, type="l", lwd=2, col=col[2]));
+    suppressWarnings(points(intersection$AC, bonly$n + intersection$n, type="l", lwd=2, col=col[3]));
+
+    legend("topright", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
+}
+
+plot.titvSpectrum <- function(eval, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
+    aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+    bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),]; 
+
+    title = paste("Ti/Tv spectrum (", novelty_name, " variants)", sep="");
+
+    titv.aonly = (aonly$nTi + intersection$nTi)/(aonly$nTv + intersection$nTv);
+    titv.aonly.finite = titv.aonly[which(is.finite(titv.aonly))];
+
+    titv.intersection.finite = intersection$Ti.Tv[which(is.finite(intersection$Ti.Tv))];
+
+    titv.bonly = (bonly$nTi + intersection$nTi)/(bonly$nTv + intersection$nTv);
+    titv.bonly.finite = titv.bonly[which(is.finite(titv.bonly))];
+
+    titv.min = min(titv.aonly.finite, titv.intersection.finite, titv.bonly.finite);
+    titv.max = max(titv.aonly.finite, titv.intersection.finite, titv.bonly.finite);
+
+    plot(0, 0, type="n", xlim=c(1, length(intersection$n)), ylim=c(titv.min, titv.max), xlab="Allele count", ylab="Transition/transversion (Ti/Tv) ratio", main=title, bty="n");
+    points(intersection$AC, (aonly$nTi + intersection$nTi)/(aonly$nTv + intersection$nTv), type="l", lwd=2, col=col[1]);
+    points(intersection$AC, intersection$Ti.Tv, type="l", lwd=2, col=col[2]);
+    points(intersection$AC, (bonly$nTi + intersection$nTi)/(bonly$nTv + intersection$nTv), type="l", lwd=2, col=col[3]);
+
+    abline(h=2.3, lty=2);
+    mtext("2.3", side=4, at=2.3, cex=0.9);
+
+    abline(h=3.3, lty=2);
+    mtext("3.3", side=4, at=3.3, cex=0.9);
+
+    legend("topleft", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
+}
+
+plot.variantsPerSample <- function(eval) {
+    metrics.all = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "all"),]; 
+    metrics.known = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "known"),]; 
+    metrics.novel = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "novel"),]; 
+
+    title = "Calls per sample";
+    indices = order(metrics$nVariants, decreasing=TRUE);
+
+    plot(0, 0, type="n", xaxt="n", xlim=c(1, length(metrics$sample)), ylim=c(0, max(metrics$nVariants)), xlab="", ylab="Number of variants", main=title, bty="n");
+    points(c(1:length(metrics.all$sample)), (metrics.all$nVariants)[indices], pch=21, col="black");
+    points(c(1:length(metrics.known$sample)), (metrics.known$nVariants)[indices], pch=21, col="blue");
+    points(c(1:length(metrics.novel$sample)), (metrics.novel$nVariants)[indices], pch=21, col="red");
+
+    legend("topright", c("All", "Known", "Novel"), pch=21, col=c("black", "blue", "red"));
+
+    axis(1, at=c(1:length(metrics$sample)), labels=(metrics$sample)[indices], las=2, cex.axis=0.4);
+}