Eric Nguyen, Michael Poli, Matthew G. Durrant, Brian Kang, Dhruva Katrekar, David B. Li, Liam J. Bartie, Armin W. Thomas, Samuel H. King, Garyk Brixi, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu, Brian L. Hie
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引用次数: 0
摘要
基因组是编码 DNA、RNA 和蛋白质的序列,它们协调着生物体的功能。我们介绍的 Evo 是一种长语境基因组基础模型,其前沿架构是在数百万个原核生物和噬菌体基因组上训练出来的,并报告了 DNA 的缩放规律,以补充语言和视觉方面的观察结果。Evo 可在 DNA、RNA 和蛋白质之间进行泛化,实现了与特定领域语言模型竞争的零射频功能预测,并生成了功能性 CRISPR-Cas 和转座子系统,这是利用语言模型进行蛋白质-RNA 和蛋白质-DNA 编码设计的首个实例。Evo 还能了解微小突变如何影响整个生物体的适应性,并生成具有可信基因组结构的巨碱基序列。这些预测和生成能力跨越了从分子到基因组的复杂尺度,促进了我们对生物学的理解和控制。
Sequence modeling and design from molecular to genome scale with Evo
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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