ArticleNature methods2026
Orthrus: toward evolutionary and functional RNA foundation models.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- mArXiv · 2026Article
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- Decoding the language of messenger RNA.Nature methods · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Linking phenotype to genotype using comprehensive genomic comparisons.Current opinion in genetics & development · 2025Review
- mRNABench: A curated benchmark for mature mRNA property and function prediction.bioRxiv : the preprint server for biology · 2025Article
- Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read Coverage.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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. Here we introduce Orthrus, a Mamba-based mature RNA foundation model pretrained using a self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between pairs of RNA transcripts that are formed from splice isoforms of ten model organisms and transcripts from orthologous genes in 400+ mammalian species. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. Orthrus' mature RNA isoform representations outperform genomic foundation models on mRNA property prediction tasks, requiring only a fraction of fine-tuning data. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.
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Registered trials
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