arcinstitute/evo2

Genome modeling and design across all domains of life

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Summary Information

Updated 44 minutes ago
Added to GitGenius on September 16th, 2026
Created on February 13th, 2025
Open Issues & Pull Requests: 55 (+0)
GitHub issues: Enabled
Number of forks: 559
Total Stargazers: 4,223 (+1)
Total Subscribers: 48 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 40.8 hours
Mean response time: 10.1 days
90th percentile: 32.7 days
Tracked items: 142

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How this project is maintained

100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 77% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 54
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 357 days
Stale 30+ days: 51
Stale 90+ days: 49

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Evo2 is a genome modeling and design tool that enables computational work across all domains of life.

The tool addresses the challenge of building predictive models and performing design tasks on genomic sequences. It provides a framework for genome-scale analysis and manipulation, allowing researchers and practitioners to work with genetic information programmatically. The approach centers on making genome modeling accessible as a computational task that can span diverse organisms and biological contexts.

Evo2 suits projects where genomic prediction or design is central to the research question or application. It is relevant for synthetic biology workflows, evolutionary studies, and any work requiring systematic manipulation or analysis of genetic sequences across different life forms. The tool is particularly valuable for teams that need to integrate genome modeling into larger computational pipelines rather than treating it as a standalone analysis step.

The project shows active development with ongoing refinement of its core capabilities. Work continues on expanding the scope and robustness of genome modeling functions. The codebase receives regular updates that suggest sustained attention to both new features and existing functionality.