Orthrus: toward evolutionary and functional RNA foundation models
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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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Deep learning for regulatory genomics: a survey of models, challenges, and applications
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The DNA dialect: a comprehensive guide to pretrained genomic language models
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Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read CoveragePreprint
Holmes, I., Linder, J., & Kelley, D. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.13.638190 · Cited by 7 · see citing works
AI foundation models for RNA biology
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mRNABench: A curated benchmark for mature mRNA property and function predictionPreprint
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Self-citationTowards foundation models that learn across biological scales
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Decoding the language of messenger RNA
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CanBART improves cross-platform prediction with a foundation model of targeted genomic panelsPreprint
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Self-citationCLADES — Contrastive Learning Augmented DifferEntial Splicing with Orthologous Positive PairsPreprint
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RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inferencePreprint
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- Authors
- Fradkin, P., Shi, R. “., Dalal, T., Isaev, K., Frey, B. J., Lee, L. J., Morris, Q., & Wang, B.
- Affiliations
- University of Toronto (CA)
- New York Genome Center (US)
- Memorial Sloan Kettering Cancer Center (US)
- University Health Network (CA)
- Corresponding author
- Lee, Leo J.
- Morris, Quaid
- Wang, Bo
- Venue
- Nature Methods
- Published
- 2026-04-17
- Volume
- 23
- Issue
- 5
- Pages
- 935-945
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11482885
- OpenAlex
- W7154722552
- Cited by
- 22
- References
- 78
- Citations by year
- 2026: 13 · 2025: 9
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- National Human Genome Research Institute (R01HG013328)
- National Cancer Institute (P30CA008748)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- contrastive learning, Mamba, splice variants, transcript regulation, self-supervised pre-training, bioaugmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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