evo2

Score, embed, and generate DNA sequences with the Evo 2 model.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/guanxiaol/open-science --skill evo2
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: evo2
Source: https://github.com/guanxiaol/open-science/tree/main/runtime/skills/evo2
Command: npx skills add https://github.com/guanxiaol/open-science --skill evo2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires evo2, and includes scripts (resource) components.

What problem does it solve?

The evo2 Skill addresses the challenge of genomic sequence analysis by providing tools for scoring, embedding, and generating DNA sequences, enabling users to work with long-context genomic foundation models efficiently.

Core Features & Use Cases

  • Sequence Scoring: Compute per-nucleotide or per-sequence likelihoods for variant effect scoring.
  • Sequence Embedding: Embed genomic windows for downstream classification tasks.
  • Sequence Generation: Generate DNA sequences conditioned on a prefix.
  • Regulatory and Coding Region Scoring: Score regulatory or coding regions across species.
  • Use Case: A researcher could use this Skill to generate DNA sequences based on a specific prefix, analyze the likelihood of variant effects, or embed genomic sequences for classification.

Quick Start

Run the evo2 skill to score a DNA sequence using the following command:

python3 score_evo2.py --sequences "ATCG" * 50

Frequently Asked Questions about evo2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I score variant effects on a DNA sequence using a genomic foundation model?

To score variant effects on a DNA sequence, you can compute per-nucleotide or per-sequence likelihoods using the Evo 2 model. This Skill provides scripts to score regulatory or coding regions across species.

Can I generate DNA sequences conditioned on a specific prefix?

Yes, you can generate DNA sequences conditioned on a specific prefix using the Evo 2 model. This allows researchers to synthesize long-context genomic sequences based on provided starting nucleotides.

How do I embed genomic windows for downstream classification tasks?

You can embed genomic windows for downstream classification tasks by applying the sequence embedding functionality of the Evo 2 model. This processes long-context genomic sequences into numerical representations.

Does the evo2 Skill require a GPU to run genomic sequence analysis?

Yes, performing genomic sequence analysis and generation with the evo2 Skill requires a GPU. It also requires specific Python libraries and the Evo 2 dependency to execute long-context genomic foundation model operations.

What is the best way to analyze long-context genomic sequences for likelihood scoring?

The best way to analyze long-context genomic sequences for likelihood scoring is using a dedicated genomic foundation model like Evo 2. It enables per-sequence scoring to evaluate variant effects across regulatory and coding regions.

Are there limitations when using Evo 2 for genomic sequence generation?

Limitations of using Evo 2 for genomic sequence generation include its strict requirement for a GPU and specific Python libraries. Users must also provide a prefix to condition the DNA sequence generation properly.