{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"Genome modeling and design across all domains of life with Evo 2","authors":"Brixi, G.; Durrant, M. G.; Ku, J.; Poli, M.; Brockman, G.; Chang, D.; Gonzalez, G. A.; King, S. H.; Li, D. B.; Merchant, A. T.; Naghipourfar, M.; Nguyen, E.; Ricci-Tam, C.; Romero, D. W.; Sun, G.; Taghibakshi, A.; Vorontsov, A.; Yang, B.; Deng, M.; Gorton, L.; Nguyen, N.; Wang, N. K.; Adams, E.; Baccus, S. A.; Dillmann, S.; Ermon, S.; Guo, D.; Ilango, R.; Janik, K.; Lu, A. X.; Mehta, R.; Mofrad, M. R. K.; Ng, M. Y.; Pannu, J.; Re, C.; Schmok, J. C.; St. John, J.; Sullivan, J.; Zhu, K.; Zynda, G.; Balsam, D.; Collison, P.; Costa, A. B.; Hernandez-Boussard, T.; Ho, E.; Liu, M.-Y.; McGrath, T.; P","author_corresponding":"Brian Hie","author_corresponding_institution":"Arc Institute; Stanford University","doi":"10.1101\/2025.02.18.638918","date":"2025-02-21","version":"1","type":"new results","license":"cc_by_nd","category":"genomics","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2025\/02\/21\/2025.02.18.638918.source.xml","abstract":"All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.","published":"NA","server":"bioRxiv"}]}