Evolutionary Processes Modeling
We have developed a tool to identify genes under positive or negative selection in tumor genomes: Cancer Bayesian Selection Estimation, or CBaSE.
Based on somatic point mutations, CBaSE first estimates the mutation probability at the gene level without the necessity to know external mutation rate covariates. Unlike other methods, the algorithm does not suppose that the mutation probability is gamma-distributed, but instead allows for a wider range of possible model distributions. CBaSE selects the best model and outputs q-values which assess the strength of negative and positive selection for each gene. Extended sequence context can be taken into account up to pentamers.
CBaSE is available as a python script for download and as a user interface. The method is described in Weghorn & Sunyaev (2017).
We co-developed MutPanning, a tool to identify genes under positive selection in tumor genomes.
MutPanning accounts for extended context dependence beyond pentamers, which plays an important role for some cancer types. The algorithm is composed of two tests for selection: The first test assesses whether a gene has a signficant excess of nonsynonymous mutations compared to the expectation under neutral evolution. The second test queries whether nonsynonymous mutations on the gene are distributed according to the estimated neutral pattern. This latter feature boosts power to predict cancer driver genes when only a fraction of nonsynonymous sites are under selection.
MutPanning is available for download as a standalone script and as a module on the GenePattern server. All information can be found at cancer-genes.org. The method is described in Dietlein* & Weghorn* et al. (2020).
Other code
All code generated for the analyses in Rodriguez-Galindo et al. can be found in the bitbucket archive:
https://bitbucket.org/weghornlab/germline_intron_exon_mutrate/src/master/