OmniSplice: detection of non-canonical splicing events from RNA-seq

Abstract

Splicing generates mature mRNA by removing introns from nascent transcripts and is widely studied using RNA sequencing. However, most RNA-seq analysis pipelines classify RNA-seq reads according to predefined splice-junction structures and discard those that do not conform to such predefined models, potentially obscuring biologically meaningful splicing events. In this study, we developed OmniSplice, a computational framework that captures and analyzes RNA-seq reads that overlap annotated exon ends without assuming predefined splicing architectures. This approach enables systematic detection of non-canonical splicing events that are often overlooked by conventional analyses. Applying OmniSplice to Drosophila splicing factor mutants and mouse TDP-43 mutant datasets, we found widespread splicing defects with non-canonical junctions that were not previously recognized, including back-splicing and trans-splicing. Together, these results demonstrate that RNA-seq datasets may contain a substantial reservoir of overlooked splicing information, warranting more comprehensive approaches for analyzing RNA-seq data for splicing events.