diff --git a/Makefile b/Makefile
index 8026a56..c14e92a 100644
--- a/Makefile
+++ b/Makefile
@@ -10,7 +10,7 @@ all: ${TARGETS}
 	curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(shell uname)-$(shell uname -m).sh" && bash Mambaforge-$(shell uname)-$(shell uname -m).sh -b -p mamba && rm "Mambaforge-$(shell uname)-$(shell uname -m).sh" && touch .mamba
 
 .tools: .mamba
-	export PATH="${PBASE}/mamba/bin:${PATH}" && mamba install -y -c conda-forge -c bioconda datamash samtools bcftools bedtools htslib delly alfred igv wally minimap2 nanoplot sniffles whatshap longshot && touch .tools
+	export PATH="${PBASE}/mamba/bin:${PATH}" && mamba install -y -c conda-forge -c bioconda datamash samtools bcftools bedtools htslib delly alfred igv wally minimap2 nanoplot sniffles whatshap longshot ont-modkit && touch .tools
 
 .rstats: .mamba .tools
 	export PATH="${PBASE}/mamba/bin:${PATH}" && mamba install -y -c conda-forge -c bioconda bioconductor-genomicfeatures r-ggplot2 r-reshape2 r-gridextra r-cowplot bioconductor-dnacopy && touch .rstats
diff --git a/README.md b/README.md
index bec4bb5..3845f2d 100644
--- a/README.md
+++ b/README.md
@@ -295,8 +295,27 @@ As previously, once IGV has started use 'File' and 'Load from File' to load the
 Let's annotate the small variants of the control genome in the matched tumor.
 
 ```bash
-longshot --no_haps --potential_variants control.longshot.vcf.gz --min_cov 3 --min_alt_count 1 --min_alt_frac 0.01 -s HG008-T --bam tumor.bam --ref genome.fa --out tumor.longshot.vcf
+longshot --no_haps --potential_variants control.longshot.vcf.gz --output-ref --min_cov 3 --min_alt_count 1 --min_alt_frac 0.1 -s HG008-T --bam tumor.bam --ref genome.fa --out tumor.longshot.vcf
 bgzip tumor.longshot.vcf
 tabix tumor.longshot.vcf.gz
 ```
 
+Then we merge the tumor and control VCF files to annotate all heterozygous variants with their allelic depth in the tumor.
+
+```bash
+bcftools merge -O b -o tumor.control.bcf tumor.longshot.vcf.gz control.whatshap.vcf.gz
+bcftools query -f "%CHROM\t%POS[\t%GT\t%AD]\n" tumor.control.bcf  | grep "0|1" | cut -f 1,2,4 | sed 's/,/\t/' | awk '$3+$4>0 {print $1"\t"$2"\t"($3/($3+$4));}' > var.vaf
+bcftools query -f "%CHROM\t%POS[\t%GT\t%AD]\n" tumor.control.bcf  | grep "1|0" | cut -f 1,2,4 | sed 's/,/\t/' | awk '$3+$4>0 {print $1"\t"$2"\t"($4/($3+$4));}' >> var.vaf
+head var.vaf
+```
+
+We can then overlay the variant allele frequencey (B-allele frequency) of small variants with the read-depth information.
+
+```bash
+Rscript cnBafSV.R cnv.cov.gz svs.tsv var.vaf
+```
+
+#### Exercises
+
+* For the somatic duplication, we have an estimated total copy-number of 3. What are the expected B-allele frequencies in that region?
+
diff --git a/scripts/cnBafSV.R b/scripts/cnBafSV.R
index 3426b03..762a313 100644
--- a/scripts/cnBafSV.R
+++ b/scripts/cnBafSV.R
@@ -1,8 +1,8 @@
 library(ggplot2)
 library(scales)
-library(gtable)
-library(grid)
+library(gridExtra)
 library(DNAcopy)
+library(cowplot)
 
 args = commandArgs(trailingOnly=TRUE)
 x = read.table(args[1], header=T)
@@ -44,17 +44,16 @@ if (length(args) == 2) { quit(); }
 
 # Read-depth + SVs + BAF
 baf = read.table(args[3], header=F)
-colnames(baf) = c("pos", "baf")
-q = ggplot(data=baf, aes(x=pos, y=baf))
-q = q + geom_point(fill="black", colour="black", size=0.1, shape=21)
-q = q + ylab("B-Allele Frequency") + xlab("chr2")
+colnames(baf) = c("chr", "pos", "baf")
+baf$chr = factor(baf$chr, levels=c("chr1", "chr5"))
+q = ggplot(data=baf)
+q = q + geom_jitter(aes(x=pos, y=baf), fill="red", colour="red", size=0.1, shape=21, width=0, height=0.05)
+q = q + geom_jitter(aes(x=pos, y=1-baf), fill="blue", colour="blue", size=0.1, shape=21, width=0, height=0.05)
+q = q + ylab("B-Allele Frequency") + xlab("Chromosome")
 q = q + scale_x_continuous(labels=comma)
-q = q + ylim(0,1)
+q = q + scale_y_continuous(labels=comma, breaks=c(0, 0.5, 1), limits=c(0, 1))
 q = q + theme(axis.text.x = element_text(angle=45, hjust=1))
-p3 = p3 + xlim(min(baf$pos), max(baf$pos))
-g1 = ggplotGrob(p3)
-g2 = ggplotGrob(q)
-g = rbind(g1, g2, size="first")
-g$widths = unit.pmax(g1$widths, g2$widths)
-ggsave(g, file="cov.png", width=10, height=7)
+q = q + facet_wrap(~chr, scales="free_x")
+plot_grid(q, p2, align="v", nrow=2, rel_heights=c(1/2, 1/2))
+ggsave(file="cov.png", width=10, height=7)
 print(warnings())