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Sequence modeling and design from molecular to genome scale with Evo

Affiliations
  • 1Arc Institute, Palo Alto, CA, USA.
  • 2Department of Bioengineering, Stanford University, Stanford, CA, USA.
  • 3Department of Computer Science, Stanford University, Stanford, CA, USA.
  • 4TogetherAI, San Francisco, CA, USA.
  • 5Stanford Data Science, Stanford University, Stanford, CA, USA.
  • 6Department of Genetics, Stanford University, Stanford, CA, USA.
  • 7Stanford Center for Biomedical Informatics Research, Stanford, CA, USA.
  • 8Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • 9CZ Biohub, San Francisco, CA, USA.
  • 10Department of Neurobiology, Stanford University, Stanford, CA, USA.
  • 11Department of Bioengineering and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
  • 12Department of Chemical Engineering, Stanford University, Stanford, CA, USA.

Published on:

November 14, 2024

Abstract

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism’s function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

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