Department of Human Genetics, McGill University, Montréal, QC, Canada.
McGill Genome Centre, Montréal, QC, Canada.
Methods Mol Biol. 2022;2515:129-150. doi: 10.1007/978-1-0716-2409-8_9.
The RNA abundance of each gene is determined by its rates of transcription and RNA decay. Biochemical experiments that measure these rates, including transcription inhibition and metabolic labelling, are challenging to perform and are largely limited to in vitro settings. Most transcriptomic studies have focused on analyzing changes in RNA abundances without attributing those changes to transcriptional or posttranscriptional regulation. Estimating differential transcription and decay rates of RNA molecules would enable the identification of regulatory factors, such as transcription factors, RNA binding proteins, and microRNAs, that govern large-scale shifts in RNA expression. Here, we describe a protocol for estimating differential stability of RNA molecules between conditions using standard RNA-sequencing data, without the need for transcription inhibition or metabolic labeling. We apply this protocol to in vivo RNA-seq data from individuals with Alzheimer's disease and demonstrate how estimates of differential stability can be leveraged to infer the regulatory factors underlying them.
每个基因的 RNA 丰度由其转录和 RNA 降解的速率决定。测量这些速率的生化实验,包括转录抑制和代谢标记,具有挑战性,并且在很大程度上仅限于体外环境。大多数转录组学研究都集中在分析 RNA 丰度的变化上,而没有将这些变化归因于转录或转录后调控。估计 RNA 分子的差异转录和降解速率将能够识别调节因子,如转录因子、RNA 结合蛋白和 microRNAs,这些调节因子可以控制 RNA 表达的大规模变化。在这里,我们描述了一种使用标准 RNA-seq 数据估计条件之间 RNA 分子差异稳定性的方案,而无需转录抑制或代谢标记。我们将该方案应用于来自阿尔茨海默病患者的体内 RNA-seq 数据,并展示了如何利用差异稳定性的估计来推断其背后的调节因子。