The dNdScv R package is a group of maximum-likelihood dN/dS methods designed to quantify selection in cancer and somatic evolution (Martincorena et al., 2017). The package contains functions to quantify dN/dS ratios for missense, nonsense and essential splice mutations, at the level of individual genes, groups of genes or at whole-exome level. The dndscv function within the package was designed to detect cancer driver genes (i.e. genes under positive selection in cancer) on datasets ranging from a few samples to thousands of samples, in whole-exome/genome or targeted sequencing studies.
Although initially designed for cancer genomic studies, this package can also be used to quantify selection in other resequencing studies, such as SNP analyses, mutation accumulation studies in bacteria or for the discovery of mutations causing developmental disorders using data from human trios.
The background mutation rate of each gene is estimated by combining local information (synonymous mutations in the gene) and global information (variation of the mutation rate across genes, exploiting epigenomic covariates), and controlling for the sequence composition of the gene and mutational signatures. Unlike traditional implementations of dN/dS, dNdScv uses trinucleotide context-dependent substitution matrices to avoid common mutation biases affecting dN/dS (Greenman et al., 2006).
You can use devtools::install_github() to install dndscv from this repository:
> library(devtools); install_github("im3sanger/dndscv")For a tutorial on dNdScv see the vignette included with the package. This includes examples for whole-exome/genome data and for targeted data.
Tutorial: getting started with dNdScv
By default, dNdScv assumes that mutation data is mapped to the GRCh37/hg19 assembly of the human genome. Users interested in trying dNdScv on a different set of transcripts, a different assembly or a different species can follow this tutorial.
Precomputed reference files (RefCDS objects) to run dNdScv on other popular assemblies (e.g. GRCh38/hg38) or species (e.g. mouse, rat, cow, dog, yeast or SARS-CoV-2) are available for download from this link.
Martincorena I, et al. (2017) Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. http://www.cell.com/cell/fulltext/S0092-8674(17)31136-4
Moritz Gerstung and Peter Campbell.
Federico Abascal for extensive testing, feedback and ideas.