Ritter Alexander J, Wallace Andrew, Ronaghi Neda, Sanford Jeremy R
Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA.
Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA.
NAR Genom Bioinform. 2024 Aug 9;6(3):lqae093. doi: 10.1093/nargab/lqae093. eCollection 2024 Sep.
Alternative splicing (AS) is emerging as an important regulatory process for complex biological processes. Transcriptomic studies therefore commonly involve the identification and quantification of alternative processing events, but the need for predicting the functional consequences of changes to the relative inclusion of alternative events remains largely unaddressed. Many tools exist for the former task, albeit each constrained to its own event type definitions. Few tools exist for the latter task; each with significant limitations. To address these issues we developed junctionCounts, which captures both simple and complex pairwise AS events and quantifies them with straightforward exon-exon and exon-intron junction reads in RNA-seq data, performing competitively among similar tools in terms of sensitivity, false discovery rate and quantification accuracy. Its partner utility, cdsInsertion, identifies transcript coding sequence (CDS) information via translation from annotated start codons, including the presence of premature termination codons. Finally, findSwitchEvents connects AS events with CDS information to predict the impact of individual events to the isoform-level CDS. We used junctionCounts to characterize splicing dynamics and NMD regulation during neuronal differentiation across four primates, demonstrating junctionCounts' capacity to robustly characterize AS in a variety of organisms and to predict its effect on mRNA isoform fate.
可变剪接（Alternative splicing，AS）正逐渐成为复杂生物过程中的一个重要调控过程。因此，转录组学研究通常涉及可变剪接事件的识别和定量分析，但对于预测可变剪接事件相对包含情况变化的功能后果的需求在很大程度上仍未得到满足。有许多工具可用于前一项任务，尽管每种工具都局限于其自身的事件类型定义。用于后一项任务的工具很少；每种工具都有很大的局限性。为了解决这些问题，我们开发了junctionCounts，它可以捕获简单和复杂的成对可变剪接事件，并通过RNA-seq数据中直接的外显子-外显子和外显子-内含子连接读数对其进行定量分析，在灵敏度、错误发现率和定量准确性方面在类似工具中表现出色。它的配套工具cdsInsertion通过从注释的起始密码子进行翻译来识别转录本编码序列（CDS）信息，包括提前终止密码子的存在情况。最后，findSwitchEvents将可变剪接事件与CDS信息联系起来，以预测单个事件对异构体水平CDS的影响。我们使用junctionCounts来表征四种灵长类动物神经元分化过程中的剪接动态和无义介导的mRNA衰变（NMD）调控，证明了junctionCounts能够在多种生物体中稳健地表征可变剪接，并预测其对mRNA异构体命运的影响。
