Kubota Naoto, Chen Liang, Zheng Sika
Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA 92521, United States.
Center for RNA Biology and Medicine, University of California, Riverside, CA 92521, United States.
Nucleic Acids Res. 2025 Feb 8;53(4). doi: 10.1093/nar/gkaf098.
Alternative pre-mRNA splicing (AS) is a fundamental regulatory process that generates transcript diversity and cell type variation. We developed Shiba, a comprehensive method that integrates transcript assembly, splicing event identification, read counting, and differential splicing analysis across RNA-seq platforms. Shiba excels in capturing annotated and unannotated AS events with superior accuracy, sensitivity, and reproducibility. It addresses the often-overlooked issue of junction read imbalance, significantly reducing false positives to aid target prioritization and downstream analyses. Unlike other tools that require large numbers of biological replicates or resulting in low sensitivity and high false positives, Shiba's statistics framework is agnostic to sample size, as demonstrated by simulated data and its effective application to real n= 1 RNA-seq datasets. To extend its utility to single-cell RNA-seq, we developed scShiba, which applies Shiba's pseudobulk approach to analyze splicing at the cluster level. scShiba successfully revealed AS regulation in developmental dopaminergic neurons and differences between excitatory and inhibitory neurons. Both Shiba and scShiba are available in Docker/Singularity containers and Snakemake pipelines, ensuring reproducibility. With their comprehensive capabilities, Shiba and scShiba enable systematic quantification of alternative splicing events across various platforms, laying a solid foundation for mechanistic exploration of the functional complexity in RNA splicing.
可变前体mRNA剪接（AS）是一种基本的调控过程，可产生转录本多样性和细胞类型变异。我们开发了Shiba，这是一种综合方法，整合了转录本组装、剪接事件识别、读数计数以及跨RNA测序平台的差异剪接分析。Shiba在捕获注释和未注释的AS事件方面表现出色，具有卓越的准确性、灵敏度和可重复性。它解决了常常被忽视的接头读数不平衡问题，显著减少假阳性，有助于目标优先级排序和下游分析。与其他需要大量生物学重复或导致低灵敏度和高假阳性的工具不同，Shiba的统计框架与样本量无关，模拟数据及其在实际n = 1 RNA测序数据集上的有效应用证明了这一点。为了将其应用扩展到单细胞RNA测序，我们开发了scShiba，它应用Shiba的伪批量方法在簇水平分析剪接。scShiba成功揭示了发育中的多巴胺能神经元中的AS调控以及兴奋性和抑制性神经元之间的差异。Shiba和scShiba都可在Docker/Singularity容器和Snakemake管道中使用，确保可重复性。凭借其全面的功能，Shiba和scShiba能够对各种平台上的可变剪接事件进行系统定量，为RNA剪接功能复杂性的机制探索奠定了坚实基础。
