Abstract
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.