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bioRxiv · 10.1101/2022.12.02.518817

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

Vigorito, E.·Barton, A.·Pitzalis, C.·Lewis, M. J.·Wallace, C.
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Abstract

MotivationWhile 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 at https://gitlab.com/evigorito/bbmix and accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

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Vigorito, E., Barton, A., Pitzalis, C., Lewis, M. J., Wallace, C.. 2022-12-03. BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing. https://doi.org/10.1101/2022.12.02.518817

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