SpliceDecoder: A High-Throughput Tool for Guiding the Functional Interpretation of Differential Splicing Events

Abstract

Alternative splicing generates different mRNA isoforms from single genes, generating protein diversity essential for normal development and tissue function. Dysregulation of splicing is implicated in numerous diseases, including cancer, neurodegeneration, diabetes, and rare genetic disorders. Although thousands of spliced isoforms have been identified in disease-relevant contexts, the functional significance of most remains unknown, hindering our understanding of splicing-driven disease mechanisms and limiting therapeutic discovery. A major challenge lies in prioritizing biologically meaningful splicing events identified through short-read RNA sequencing. Existing splicing analysis tools typically rank target events based on splicing change magnitude or gene-level annotation, often without evaluating how resulting isoforms impact protein structure or function. This creates a critical bottleneck in translating splicing data into biological and therapeutic insights. To address this, we developed SpliceDecoder, a computational workflow that predicts how each splicing event or isoform impacts transcript productivity, protein sequence, and functional domains. Each event is assigned a functional effect score to guide evidence-based prioritization. SpliceDecoder facilitates a more informed interpretation of splicing data, reduces reliance on prior knowledge, and enables identification of events with potential biological and clinical relevance. We demonstrate its utility by validating known splicing alterations and identifying novel disease-associated isoform switches across public datasets.