一站式工具库，简化基因组模型训练、部署与解释流程。
Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
- 整合数据收集、模型微调、推理与可解释性分析全流程
- 支持多种模型与微调方法，在两个公开基准上验证性能
- 适合基因组研究者快速构建和分析基础模型
我们提出Genome-Factory，首个用于基因组基础模型调优、部署与解释的集成Python库。其核心在于统一基因组模型开发工作流：数据收集、模型微调、推理、基准测试与可解释性分析。数据收集方面，提供自动化管道下载并预处理基因组序列；模型微调支持全量与参数高效微调，适配多种基因组模型；推理支持嵌入提取与DNA序列生成；基准测试包含两个现有基准，并提供灵活接口扩展；可解释性方面，引入基于稀疏自编码器的开源生物学解释器。我们在三个维度验证其有效性：(i) 兼容多种模型与微调方法；(ii) 使用两个开源基准评估下游性能；(iii) 对DNABERT-2学习表征进行生物解释。结果表明其在真实基因组分析中的实用价值。GitHub: https://github.com/WeiminWu2000/Genome_Factory。
原文摘要 · Abstract (English)
We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: https://github.com/WeiminWu2000/Genome_Factory.
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