Augmenting transcriptome annotations through the lens of splicing evolution

Transcriptome annotations remain incomplete despite enormous efforts. Annotations are largely driven by experimental data, whereas little is understood from an evolutionary perspective. Here we present TENNIS, a model for isoform representation and inference. TENNIS models isoforms in a transcript group as nodes of a connected graph, in which the edges represent basic alternative splicing events, and predicts missing isoforms using a novel algorithm. Our analysis indicates that approximately 80% of the analyzed isoform groups satisfy our model, whereas the identified missing transcripts show high accuracy. TENNIS achieves these results without using additional sequencing data, offering insights into alternative splicing and a powerful tool for constructing annotations.