Speaker
Description
Computational analysis of high-resolution CLIP-seq data has enabled precise mapping of RBP binding sites and cis-regulatory elements underlying post-transcriptional RNA regulation. Recently, the field has shifted with the emergence of RNA foundation models—self-supervised models trained on vast unlabeled RNA sequences—that enable holistic modeling of RNA function and in silico hypothesis generation. Leveraging such models represents the next frontier, allowing prediction of multiple RNA regulatory processes, as well as in silico design of RNA-based therapeutics. Building on the premise that RNA function is encoded in its interaction partners, we developed Parnet, a multi-task model that densely represents the RNA interactome. Extending our earlier single-task model RBPNet, Parnet is trained end-to-end on raw CLIP-seq profiles from hundreds of RBPs to predict genome-wide binding profiles directly from sequence. In contrast to unsupervised RNA language models, Parnet learns embeddings that capture the combinatorial “RBP code” underlying post-transcriptional regulation. As a result, Parnet generalizes across downstream tasks—including splicing, intron retention, mRNA translation and degradation, and therapeutic design—often with minimal or no fine-tuning.