Sequence modeling and design from molecular to genome scale with Evo
Tuesday April 16th, 4-5pm EST | Eric Nguyen and Brian Hie, PhD
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 multi- scale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.
Preprint: https://arcinstitute.org/manuscripts/Evo
Eric is a PhD student at Stanford in the BioEngineering department. He is co-advised by Steve Baccus in neurobiology, Chris Ré in computer science, and Brian Hie in chemical engineering. He is a part of Hazy Research and Evo Design lab.
Eric is an Assistant Professor of Chemical Engineering and Data Science at Stanford University, a David T. Morgenthaler II Faculty Fellow in the Stanford School of Engineering, and an Innovation Investigator at Arc Institute. He supervises the Laboratory of Evolutionary Design, which conducts research at the intersection of biology and machine learning.