A new study presents 'Evo' – a machine learning model capable of decoding and designing DNA, RNA, and protein sequences, from molecular to genome scale, with unparalleled accuracy. Evo's ability to predict, generate, and engineer entire genomic sequences could change the way synthetic biology is done.
American Association for the Advancement of Science Nov 15 2024 A new study presents "Evo" – a machine learning model capable of decoding and designing DNA , RNA , and protein sequences, from molecular to genome scale, with unparalleled accuracy. Evo's ability to predict, generate, and engineer entire genomic sequences could change the way synthetic biology is done.
With a vocabulary of just four nucleotides, DNA encodes all the genetic information essential for life. Variations in the genomic sequence reflect adaptations selected for specific biological functions. These variations drive evolution by enabling organisms to adapt to new or changing environments. Advances in DNA sequencing technologies have allowed for genomic variations to be mapped at the whole-genome scale.
Here, Eric Nguyen and colleagues present Evo – a large-scale genomic foundation model, equipped with 7 billion parameters and designed to generate DNA sequences up to whole-genome scale. Built on the StripedHyena architecture, Evo was trained on a dataset of 2.7 million evolutionary diverse microbial genomes. According to Nguyen et al.
At the genomic scale, Evo can generate sequences over 1 megabase in length, a capability vastly surpassing prior models. "Future models may learn from diverse human and other eukaryotic genomes, using larger context lengths to capture distant genomic interactions over larger genomic scales," writes Theodoris in the Perspective.Journal reference:Nguyen, E., et al. . Sequence modeling and design from molecular to genome scale with Evo. Science. doi.org/10.1126/science.ado9336.
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