{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"Sequence modeling and design from molecular to genome scale with Evo","authors":"Nguyen, E.; Poli, M.; Durrant, M. G.; Thomas, A. W.; Kang, B.; Sullivan, J.; Ng, M. Y.; Lewis, A.; Patel, A.; Lou, A.; Ermon, S.; Baccus, S. A.; Hernandez-Boussard, T.; Re, C.; Hsu, P. D.; Hie, B. L.","author_corresponding":"Brian L Hie","author_corresponding_institution":"Stanford University","doi":"10.1101\/2024.02.27.582234","date":"2024-02-27","version":"1","type":"new results","license":"cc_by","category":"synthetic biology","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2024\/02\/27\/2024.02.27.582234.source.xml","abstract":"The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.","published":"10.1126\/science.ado9336","server":"bioRxiv"},{"title":"Sequence modeling and design from molecular to genome scale with Evo","authors":"Nguyen, E.; Poli, M.; Durrant, M. G.; Thomas, A. W.; Kang, B.; Sullivan, J.; Ng, M. Y.; Lewis, A.; Patel, A.; Lou, A.; Ermon, S.; Baccus, S. A.; Hernandez-Boussard, T.; Re, C.; Hsu, P. D.; Hie, B. L.","author_corresponding":"Brian L Hie","author_corresponding_institution":"Stanford University","doi":"10.1101\/2024.02.27.582234","date":"2024-03-06","version":"2","type":"new results","license":"cc_by","category":"synthetic biology","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2024\/03\/06\/2024.02.27.582234.source.xml","abstract":"The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.","published":"10.1126\/science.ado9336","server":"bioRxiv"}]}