Shiba: A versatile computational method for systematic identification of differential RNA splicing across platforms

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Alternative pre-mRNA splicing (AS) is crucial for generating transcript diversity and cell-specific variations.
• Accurate quantification of AS events is essential for understanding gene regulation and cellular function.

Purpose of the Study:

• To develop a comprehensive and accurate computational method for analyzing alternative splicing events across various RNA-seq platforms.
• To address limitations of existing tools, including junction read imbalance, low sensitivity, and high false positives.
• To extend AS analysis capabilities to single-cell RNA sequencing (scRNA-seq) data.

Main Methods:

• Development of Shiba, a method integrating transcript assembly, splicing event identification, read counting, and differential splicing analysis.
• Implementation of scShiba for cluster-level AS analysis in scRNA-seq using a pseudobulk approach.
• Validation using simulated data and real RNA-seq datasets, including n=1 experiments.

Main Results:

• Shiba accurately captures annotated and unannotated AS events with high sensitivity and reproducibility.
• Shiba effectively reduces false positives by addressing junction read imbalance, improving target prioritization.
• Shiba's statistical framework is robust across different sample sizes, including single-sample datasets.
• scShiba successfully identified AS regulation in specific neuronal cell types.

Conclusions:

• Shiba and scShiba provide robust and reproducible methods for systematic quantification of alternative splicing events.
• These tools enhance the analysis of transcript diversity and cell type variation across diverse RNA-seq data.
• Shiba and scShiba lay a foundation for mechanistic exploration of RNA splicing complexity.