据介绍，基因组是一种能编码DNA、RNA和蛋白质的序列，这些序列协调了生物体的功能。
研究人员推出了Evo，一种具有前沿架构的长上下文基因组基础模型，训练数据覆盖了数百万个原核生物和噬菌体基因组，并报告了DNA的扩展规律，以补充语言和视觉领域的观察结果。Evo能够在DNA、RNA和蛋白质之间实现泛化，具备零样本功能预测能力，其表现可与领域专用语言模型媲美。
此外，它能够生成功能性CRISPR-Cas和转座子系统，代表了语言模型在蛋白质-RNA和蛋白质-DNA协同设计中的首个示例。Evo还能够学习小突变如何影响整个生物体的适应性，并生成具有合理基因组结构的百万碱基规模序列。
总之，Evo的预测和生成能力跨越了分子到基因组的复杂性尺度，推动了人们对生物学的理解和控制能力的进步。
附：英文原文
Title: Sequence modeling and design from molecular to genome scale with Evo
Author: 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
Issue&Volume: 2024-11-15
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.
DOI: ado9336
Source: https://www.science.org/doi/10.1126/science.ado9336