To rigorously determine whether a gene or a population of genes have alterations that are involved in carcinogenesis requires comparison of the prevalence of identified changes to the background mutation frequency present in tumor DNA. To facilitate this task, we develop a testing approach and the associated R library, called TRAB, that evaluates whether the frequency of somatic mutation is higher than an unknown, but estimable, background. We test the null hypothesis that the frequency belongs to background population of frequencies against the alternative hypothesis that the frequency is higher. Background mutation frequencies are themselves allowed to be variable. TRAB computes the a posteriori probability and the Bayes factor for the hypothesis using a hierarchical Bayesian approach. Software Availability: http://astor.som.jhmi.edu/


Bioinformatics | Computational Biology