A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.

ABSTRACT: Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statisti

SUBMITTER: Thompson A 

PROVIDER: S-EPMC7431084 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature