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Abstract：
Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.
摘要：
RNA测序（RNA-seq）数据的差异表达分析可以识别细胞RNA水平的变化，但提供了有关此类变化背后的动力学机制的有限信息。核苷酸重新编码RNA-seq方法(NR-seq；例如，TimeLapse-seq,SLAM-seq,等。）解决了这一缺点，是广泛使用的方法来识别RNA合成和降解动力学的变化。虽然先进的统计模型在用户友好的软件中实现(例如，DESeq2）确保了差异表达分析的统计严谨性，不存在这样的工具，便于用NR-seq进行微分动力学分析。在这里，我们报告了RNA（bakR）动力学的贝叶斯分析的发展，一个R包来满足这一需求。BakR依靠NR-seq数据的贝叶斯分层建模，通过跨转录本共享信息来提高统计能力。对模拟数据的分析证实，分层模型的bakR实现优于使用现有模型分析微分动力学的尝试。BakR还发现真实NR-seq数据集中的生物信号，并提供对现有数据集的改进分析。这项工作将bakR确立为鉴定差异RNA合成和降解动力学的重要工具。
