Shiba: A unified computational method for robust identification of differential RNA splicing across platforms.

Abstract

Alternative pre-mRNA splicing (AS) is a fundamental regulatory process that generates transcript diversity and cell type variation. We developed Shiba, a robust method integrating transcript assembly, splicing event identification, read counting, and statistical analysis, to efficiently quantify exon splicing levels across various types of RNA-seq datasets. Compared to existing pipelines, Shiba excels in capturing both annotated and unannotated or cryptic differential splicing events with superior accuracy, sensitivity, and reproducibility. Furthermore, Shiba's unique consideration of junction read imbalance and exon-body read coverage reduces false positives, essential for downstream functional analyses. We have further developed scShiba for single-cell/nucleus (sc/sn) RNA-seq data, enabling the exploration of splicing variations in heterogeneous cell populations. Both simulated and real data demonstrate Shiba's robustness across multiple sample sizes, including n=1 datasets and individual cell clusters from scRNA-seq. Application of Shiba on single replicates of RNA-seq identified new AS-NMD targets, and scShiba on snRNA-seq revealed intricate temporal AS regulation in dopaminergic neurons. Both Shiba and scShiba are provided in Docker/Singularity containers and Snakemake pipeline, enhancing accessibility and reproducibility. The comprehensive capabilities of Shiba and scShiba allow systematic and robust quantification of alternative splicing events, laying a solid foundation for mechanistic exploration of functional complexity in RNA splicing.