mRNABench: A curated benchmark for mature mRNA property and function prediction

Messenger RNA (mRNA) is central to gene expression, and its half-life, localization, and translation efficiency drive phenotypic diversity in eukaryotic cells. While supervised learning has been used to study the mRNA regulatory code, self-supervised foundation models support a wider range of transfer learning tasks. However, the dearth of standardized benchmarks limits efforts to pinpoint the strengths of various models. Here, we present mRNABench, a benchmarking suite for mature mRNA biology, focused on human transcripts, that evaluates the representational quality of mature mRNA embeddings from self-supervised nucleotide foundation models. We curate 11 datasets and 79 prediction tasks that broadly capture salient properties of mature mRNA, and assess the performance of 29 families of nucleotide foundation models for a total of 460k experiments. Using these experiments, we study parameter scaling, correlations between sequence compressibility and performance, data-splitting strategies, and the effects of self-supervised training objective on mRNA property prediction.