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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |
|---|---|---|
| Category | Genomics & Bioinformatics | — |
| Subcategory | Genomic Foundation Models | — |
| Company | Arc Institute & Stanford University | — |
| Access mode | Open Source | — |
| Licence conditions | Apache License 2.0 | — |
| Compute requirements | 1x A100/H100 GPU (80GB) for inference; multi-node cluster for fine-tuning | — |
| Biological application | Synthetic biology, CRISPR system design, regulatory sequence engineering, and whole-genome generation | — |
| Research workflow | Input DNA sequence (up to 131kb) -> predict fitness effects or generate de novo genomic sequences | — |
| Evidence level | Peer-reviewed publication (Science 2024) | — |
| Commercial eligibility | Open weights and open-source code | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on GitHub, Hugging Face, and Together AI | — |
| Features | 7 billion parameters trained on 300 billion nucleotides across prokaryotic and viral diversity, 131,072 token context window powered by the StripedHyena architecture, Predicts mutation effects across protein coding and non-coding regulatory elements, Generates novel, functional CRISPR-Cas molecular complexes and synthetic transposable elements | — |
| Entry type | Open Source Model | — |
| AI role | Whole-Genome Sequence Generation & Fitness Prediction | — |
| Input data | 未明确记录 | — |
| Output data | 未明确记录 | — |
| Validation | 未明确记录 | — |
| Type | Long-context genomic foundation model | — |
| Intended use | 未明确记录 | — |
| Compatibility | 未明确记录 | — |
| Manufacturer | Arc Institute, Stanford University & Together AI | — |
| Integration evidence | https://github.com/evo-design/evo | — |
| Laboratory handoff | Generated operons and CRISPR constructs synthesized via commercial gene synthesis for functional validation | — |