Evo 2Translation site

What's Evo 2?

Evo 2 is the world's largest biology AI model developed by leading organizations including Arc Institute, NVIDIA, Stanford University, UC Berkeley and UCSF. Trained on 9.3 trillion nucleotides of data from more than 128,000 genomes, the model utilizes the innovative StripedHyena 2 architecture, which is capable of processing gene sequences up to 1 million base pairs long.

Evo 2 aims to provide a deeper understanding of the complexity of genomics, enable accurate prediction of genetic variation and genome sequence generation, and revolutionize breakthroughs in the life sciences. It is open for use by researchers worldwide and provides a wealth of resources and tools to accelerate biological research and innovation.

Evo 2 Core Features

• Powerful modeling capabilities: Evo 2 provides a deep understanding of the complexity of genomics through the power of AI, enabling breakthroughs in accurate prediction of genetic variants and genome sequence generation.
• cross-species comparison: Evo 2 provides cross-species comparisons of genetic variants, bringing unprecedented research potential and applications to the life sciences.
• Openness and Interpretability: Evo 2 is open for use by researchers worldwide, with rich examples and detailed documentation. Its internal representation is capable of capturing a wide range of biological features, and researchers can extract features relevant to biological functions through sparse autoencoders (SAE).

Evo 2 Technical Architecture and Data

• Training data: Evo 2 was trained using a highly curated genome map containing 9.3 trillion DNA base pairs from bacteria, archaea, eukaryotes and phages.
• model size: Evo 2 is available in two versions, with 7 and 40 billion parameters, and is capable of handling context windows up to 1 million base pairs long.
• Architecture Innovation: Evo 2 utilizes the StripedHyena 2 architecture, a new convolutional hybrid architecture that combines input-dependent convolutional and attentional mechanisms to improve training efficiency and performance.

Evo 2 Key Features and Applications

• Genetic variation prediction: Evo 2 is able to accurately predict the impact of genetic variants on protein function, RNA function, and organismal adaptations without the need for task-specific fine-tuning. It can also predict the pathogenicity of human clinical variants, both in coding and non-coding regions.
• Genome Sequence Generation: Evo 2 is capable of generating genome-scale sequences of mitochondria, prokaryotes and eukaryotes with greater naturalness and coherence than previous methods.
• Bioinformatics analysis: Evo 2 can be used for DNA sequence analysis and comparison, providing a powerful tool for bioinformatics research.
• Genetic Disease Research: Evo 2 helps researchers study the mechanisms of genetic diseases and provides new ideas for disease prevention and treatment.

Evo 2 Performance

• zero-sample prediction: Evo 2 demonstrates its strong generalization ability through zero-sample prediction. Its zero-sample prediction performance significantly outperforms other models in several benchmark tests, especially on non-coding variants and complex variant types.
• Supervised Learning Classification: Embeddings from Evo 2 can also be used to train supervised learning classifiers to further improve prediction performance.

Evo 2 Recommended Reasons

• Revolutionary innovations: Evo 2 is the largest AI model in biology to date, and its release marks a new era in biological research.
• Prospects for a wide range of applications: Evo 2 offers an unprecedented breadth of functionality, enabling prediction and design tasks from the molecular to the genomic scale and across all three domains of life.
• Openness and accessibility: Evo 2 is open for use by researchers worldwide and provides a wealth of resources and tools that lower the research barrier.

Project Address

Project website::https://arcinstitute.org/news/blog/evo2

GitHub repository::https://github.com/ArcInstitute/evo2

HuggingFace Model Library::https://huggingface.co/arcinstitute

Technical Papers::https://arcinstitute.org/manuscripts/Evo2