CLADES — Contrastive Learning Augmented DifferEntial Splicing with Orthologous Positive Pairs

Alternative splicing (AS) reshapes transcript and protein repertoires across biological, e.g. cellular, contexts. However, learning sequence → content-specific splicing mappings is challenging due to limited labels across tissues and cell types and variability introduced by experimental protocols. We propose a contrastive representation learning pre-training approach grounded in evolutionary conservation. Orthologous exon-intron junction sequences are treated as semantically consistent views of the same regulatory program: evolutionary orthologs are positive pairs, non-homologous junctions are negatives. This discriminative objective aligns embeddings of regulatory equivalents while separating functionally unrelated sequences, inducing invariances to unconstrained sequence and emphasizing conserved motif/RBP and positional signals. We show that this pre-training strategy provides representations that help predict <i>Δψ</i> , the change in exon inclusion between conditions, which encodes both direction and magnitude of splicing shifts. Specifically, we finetune a lightweight supervised head on available labels to predict <i>Δψ</i> . To make these predictions biologically meaningful, we further introduce an interpretable, splice-motif-aware classification framework grounded in known regulatory signals. On benchmarks spanning tissue- and cell-type differential splicing, the learned representations yield strong <i>Δψ classification</i> performance (AUPRC/AUROC for increased/decreased inclusion) and competitive results for <i>regression</i> (RMSE, Spearman). These findings indicate that evolution-as-augmentation, instantiated via contrastive learning, is an effective and biologically principled route to context-resolved splicing prediction.