Whole-genome sequencing and targeted amplicon capture

CNVkit is primarily designed for use on hybrid capture sequencing data, where off-target reads are present and can be used improve copy number estimates. However, CNVkit can also be used on whole-genome sequencing (WGS) and targeted amplicon sequencing (TAS) datasets by using alternative command-line options.

The batch command supports these workflows through the -m/--method option.

Whole-Genome Sequencing (WGS)

CNVkit treats WGS data as a capture of all of the genome’s sequencing-accessible regions, with no off-target regions.

The batch --method wgs option uses the given reference genome’s sequencing-accessible regions (“access” BED) as the “targets” – these will be calculated on the fly if not provided. No “antitarget” regions are used. Since the input does not contain useful per-target gene labels, a gene annotation database is required and used to label genes in the outputs:

cnvkit.py batch -m wgs -g data/access-5kb-mappable.hg19.bed --annotate refFlat.txt *.bam


cnvkit.py target data/access-5kb-mappable.hg19.bed --split --short-names --annotate refFlat.txt -o targets.bed
# Create a blank file to substitute for antitargets
touch MT
# For each sample
cnvkit.py coverage Sample.bam targets.bed -p 0 -o Sample.targetcoverage.cnn
cnvkit.py reference *.targetcoverage.cnn --no-edge -o ref-wgs.cnn
cnvkit.py fix Sample.targetcoverage.cnn MT ref-wgs.cnn --no-edge

To speed up and/or improve the accuracy of WGS analyses, try any or all of the following:

  • Instead of analyzing the whole genome, use the “target” BED file to limit the analysis to just the genic regions. You can get such a BED file from the [UCSC Genome Browser](https://genome.ucsc.edu/cgi-bin/hgTables), for example.
  • Increase the “target” average bin size, e.g. to at least 1000 bases for 30x coverage, or proportionally more for lower-coverage sequencing.
  • Specify a smaller p-value threshold (segment -t). For the CBS method, 1e-6 may work well. Or, try the haar segmentation method.
  • Use the -p/--processes option in the batch, coverage and segment commands to ensure all available CPUs are used.
  • Ensure you are using the most recent version of CNVkit. Each release includes some performance improvements.
  • Turn off the “edge” bias correction in the reference and fix commands (–no-edge).

The batch -m wgs option does all of these except the first automatically.

Targeted Amplicon Sequencing (TAS)

When amplicon sequencing is used as a targeted capture method, no off-target reads are sequenced. While this limits the copy number information available in the sequencing data versus hybrid capture, CNVkit can analyze TAS data using only on-target coverages and excluding all off-target regions from the analysis.

The batch -m amplicon option uses the given targets to infer coverage, ignoring off-target regions:

cnvkit.py batch -m amplicon -t targets.bed *.bam


cnvkit.py target targets.bed --split -o targets.split.bed
# Create a blank file to substitute for antitargets
touch MT
# For each sample
cnvkit.py coverage Sample.bam targets.split.bed -p 0 -o Sample.targetcoverage.cnn
cnvkit.py reference *.targetcoverage.cnn --no-edge -o ref-tas.cnn
cnvkit.py fix Sample.targetcoverage.cnn MT ref-tas.cnn --no-edge

This approach does not collect any copy number information between targeted regions, so it should only be used if you have in fact prepared your samples with a targeted amplicon sequencing protocol. It also does not attempt to further normalize each amplicon at the gene level, though this may be addressed in a future version of CNVkit.


Do not mark duplicates in the BAM files for samples sequenced by this method.

Picard MarkDuplicates, samtools rmdup, et al. are designed to flag possible PCR duplicates (originally for WGS datasets, but also useful for hybrid capture). Variant callers like GATK and CNVkit will ignore those reads in their internal calculations, considering these reads to be non-independent measurements. (This SeqAnswers thread has details and background).

In targeted amplicon sequencing, all of the amplified reads are in fact PCR duplicates by design. By marking and thus omitting these reads, the remaining coverage will be low, as if no amplification were performed.