DeepSplice: A Transformer-Based Framework for Predicting Alternative Splicing Events from RNA-seq Data
Alternative splicing (AS) is a fundamental post-transcriptional regulatory mechanism that dramatically expands proteome diversity in eukaryotes. Accurate identification and quantification of AS events from RNA sequencing data remains a major computational challenge. Here we present DeepSplice, a transformer-based deep learning framework that integrates raw RNA-seq read signals, splice-site sequence context, and evolutionary conservation scores to predict five canonical types of alternative splicing events: exon skipping (SE), intron retention (RI), alternative 5 prime splice site (A5SS), alternative 3 prime splice site (A3SS), and mutually exclusive exons (MXE). Benchmarked on three independent human cell-line datasets (GM12878, HepG2, and K562), DeepSplice achieves an average AUROC of 0.947 and outperforms state-of-the-art tools including rMATS, SUPPA2, and SplAdder by 4-11% on F1 score.