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

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and limitations. Indeed, tools for differential transcript usage or rare disease diagnosis through splicing and expression outliers, either lack performance, discard information, or do not scale to large datasets. We show that replacing normalization offsets unlocks bulk RNA-seq tools for differential usage and aberrant splicing, providing a single framework for various short- and long-read applications. We then introduce saseR, a tool for prioritizing expression and usage outliers that is much faster than state-of-the-art methods, and significantly outperforms these for aberrant splicing detection.