Variational inference for detecting differential translation in ribosome profiling studies - Walker, David C.; Lozier, Zachary R.; Bi, Ran; Kanodia, Pulkit; Miller, W. Allen; Liu, Peng (Frontiers in Genetics, 2023)
Translational efficiency change is an important mechanism for regulating protein synthesis. Experiments with paired ribosome profiling (Ribo-Seq) and mRNA-sequencing (RNA-seq) allow the study of translational efficiency by simultaneously quantifying the abundances of total transcripts and those that are being actively translated. Existing methods for Ribo-seq data analysis either ignore the pairing structure in the experimental design or treat the paired samples as fixed effects instead of random effects. To address these issues, we propose a hierarchical Bayesian generalized linear mixed effects model which incorporates a random effect for the paired samples according to the experimental design. We provide an analytical software tool, `riboVI', that uses a novel variational Bayesian algorithm to fit our model in an efficient way. Simulation studies demonstrate that `riboVI' outperforms existing methods in terms of both ranking differentially translated genes and controlling false discovery rate. We also analyzed data from a real ribosome profiling experiment, which provided new biological insight into virus-host interactions by revealing changes in hormone signaling and regulation of signal transduction not detected by other Ribo-seq data analysis tools.