Evo learns biological complexity from the molecular to genome scale

Access through your institution Buy or subscribe Generative artificial intelligence models of molecular biology are often restricted to individual molecules or DNA segments and are built in

a way that makes them computationally demanding when applied to long sequences. The ability to capture broader genomic interactions will be crucial for both the understanding and engineering

of complex biological processes. Writing in _Science_, Nguyen et al. introduce Evo, a genomic foundation model that can interpret and generate DNA sequences at whole-genome scale while

maintaining single-nucleotide resolution. Evo is built on a StripedHyena architecture and equipped with 7 billion parameters, with a context length of up to 131 kilobases. Trained on 2.7

million microbial genomes, Evo performed well on various tasks that were previously performed with domain-specific models. For example, Evo learned the effects of mutations on protein and

noncoding RNA function, modeled the activity of regulatory elements, and also understood how small mutations affect organismal fitness by predicting gene essentiality. This is a preview of

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Iris Marchal Authors * Iris Marchal View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Iris Marchal. RIGHTS AND

PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Marchal, I. Evo learns biological complexity from the molecular to genome scale. _Nat Biotechnol_ 42, 1793 (2024).

https://doi.org/10.1038/s41587-024-02514-7 Download citation * Published: 11 December 2024 * Issue Date: December 2024 * DOI: https://doi.org/10.1038/s41587-024-02514-7 SHARE THIS ARTICLE

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