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<p>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.</p>

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Orthrus: toward evolutionary and functional RNA foundation models

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Philip Fradkin,
  • Ruian “Ian” Shi,
  • Taykhoom Dalal,
  • Keren Isaev,
  • Brendan J. Frey,
  • Leo J. Lee,
  • Quaid Morris,
  • Bo Wang

摘要

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.