Walker David C, Lozier Zachary R, Bi Ran, Kanodia Pulkit, Miller W Allen, Liu Peng
Department of Statistics, Iowa State University, Ames, IA, United States.
Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United States.
Front Genet. 2023 Jun 23;14:1178508. doi: 10.3389/fgene.2023.1178508. eCollection 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.
翻译效率变化是调节蛋白质合成的重要机制。配对核糖体谱分析（Ribo-seq）和mRNA测序（RNA-seq）实验通过同时定量总转录本和正在被积极翻译的转录本的丰度，来研究翻译效率。现有的Ribo-seq数据分析方法要么忽略实验设计中的配对结构，要么将配对样本视为固定效应而非随机效应。为了解决这些问题，我们提出了一种分层贝叶斯广义线性混合效应模型，该模型根据实验设计为配对样本纳入了随机效应。我们提供了一个分析软件工具“riboVI”，它使用一种新颖的变分贝叶斯算法来高效地拟合我们的模型。模拟研究表明，“riboVI”在对差异翻译基因进行排名和控制错误发现率方面均优于现有方法。我们还分析了来自真实核糖体谱分析实验的数据，通过揭示其他Ribo-seq数据分析工具未检测到的激素信号变化和信号转导调控，为病毒-宿主相互作用提供了新的生物学见解。
