saseR: juggling offsets unlocks RNA-seq tools for fast and scalable differential usage, aberrant splicing and...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA-sequencing (RNA-seq) data analysis involves diverse tools with limitations.
• Current methods for differential transcript usage and rare disease diagnosis via splicing/expression outliers often suffer from poor performance, data loss, or scalability issues.

Purpose of the Study:

• To develop a unified framework for RNA-seq analysis applicable to various read lengths.
• To introduce an efficient and accurate tool for identifying expression and usage outliers.

Main Methods:

• Replaced normalization offsets to adapt bulk RNA-seq tools for differential usage and aberrant splicing.
• Developed saseR, a novel computational tool for outlier prioritization.

Main Results:

• The modified framework successfully integrates bulk RNA-seq tools for differential usage and aberrant splicing detection across short- and long-read applications.
• saseR demonstrates significantly improved speed compared to existing methods.
• saseR achieves superior performance in detecting aberrant splicing events.

Conclusions:

• A unified framework using modified normalization offsets enhances RNA-seq analysis capabilities.
• saseR provides a faster and more accurate solution for identifying expression and usage outliers, particularly for aberrant splicing detection.