Taykhoom Dalal

Abstract In the face of rapidly accumulating genomic data, our ability to predict key mature RNA properties that underlie transcript function and regulation remains limited. Pretrained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks; however, existing models are trained using strategies borrowed from textual domains that do not leverage biological domain knowledge.

Abstract RNA-binding proteins (RBPs) are key regulators of gene expression; however, their RNA-binding specificities, that is, motifs, have not been comprehensively determined. Here we introduce Eukaryotic Protein–RNA Interactions (EuPRI), a freely available resource of RNA motifs for 34,746 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of predicted motifs.

Abstract Messenger RNA (mRNA) is central in gene expression, and its half-life, localization, and translation efficiency drive phenotypic diversity in eukaryotic cells. While supervised learning has widely been used to study the mRNA regulatory code, self-supervised foundation models support a wider range of transfer learning tasks. However, the dearth and homogeneity of standardized benchmarks limit efforts to pinpoint the strengths of various models.