BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing

Abstract

AbstractMotivationWhile many pipelines have been developed for calling genotypes using RNA-sequencing data, they all have adapted DNA genotype callers that do not model biases specific to RNA-sequencing such as reference panel bias or allele specific expression.ResultsHere, we present BBmix, a Bayesian Beta-Binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than one hour is sufficient to call genotypes in a large number of samples.AvailabilityWe implemented BBmix as an R package that is available for free under a GPL-2 licence athttps://gitlab.com/evigorito/bbmixand accompanying pipeline athttps://gitlab.com/evigorito/bbmix_pipeline.