SpliceMamba: Tissue-Conditioned State-Space Modeling of Cassette-Exon Inclusion

Cassette exon inclusion is the predominant form of alternative splicing in mammals and a central mechanism of tissue-specific gene regulation. Predicting cassette exon inclusion remains challenging because cassette exons vary substantially in length, while their splicing outcomes are determined by surrounding cis-regulatory sequences, and existing sequence models are largely optimized for classifying individual splice sites rather than quantifying inclusion at the level of the complete splicing event. We introduce SpliceMamba, a selective state-space architecture for tissue-specific prediction of cassette exon Percent Spliced In (PSI). SpliceMamba constructs a unified token representation integrating nucleotide sequence, genomic annotation, and tissue identity; encodes four splice-site-centered windows using a shared selective state-space block; and integrates information across these windows to generate a single tissue-conditioned PSI prediction. On a 56-tissue benchmark, SpliceMamba achieves a Pearson correlation of 0.94 across event–tissue pairs using only 0.181M parameters, compared with 0.82–0.85 for splice-site models adapted to the same task. Because pooled correlation can be dominated by between-event variation, we additionally evaluate pairwise tissue-differential inclusion, for which SpliceMamba achieves a correlation of 0.50, compared with 0.23–0.37 for the baselines. In silico mutagenesis recovers canonical donor and acceptor motifs and identifies positions whose substitution shifts predicted inclusion between tissues. Ablations within a fixed model scaffold isolate the contributions of genomic features, cross-window integration, and the sequence encoder. Together, these results show that event-level, tissue-conditioned modeling of splice-site-proximal sequence provides an effective and parameter-efficient framework for quantitative PSI prediction.