HELIX: a scalable model for predicting context-dependent regulation of RNA splicing and isoform usage

Context-dependent alternative splicing plays a critical role in disease pathogenesis and organ development, but its complex regulation remains challenging to predict. Here, to address this, we developed HELIX, a hierarchical deep learning framework that integrates pre-mRNA sequence and RNA-binding protein expression profiles to predict tissue- and condition-specific splicing patterns and transcript isoform usage simultaneously. By leveraging both short-read and long-read RNA sequencing data during training, HELIX achieves greater accuracy than existing splicing prediction models and conventional short-read-based methods in predicting differential splicing events, splicing strength at highly regulated splice sites, and isoform usage. The model enables systematic identification of tissue-specific splicing quantitative trait loci and their functional impacts. Furthermore, HELIX predicts patient-specific splicing dysregulation with quantitative attribution to genetic variants and abnormal RNA-binding protein expression in colon cancer cohorts. Through transfer learning, the HELIX model can be adapted to single-cell RNA sequencing data, thereby enabling the prediction of cell-type-specific isoforms.