Relating mutational signature exposures to clinical data in cancers via signeR 2.0.

Background: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures.

Results: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access.

Conclusion: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).