为精准发酵建立统一数据标准，打通实验室间数据壁垒
PREFER: An Ontology for the PREcision FERmentation Community
- 构建基于BFO的开放开源本体PREFER，统一生物过程数据描述
- 支持高通量流程中结构化元数据，实现跨平台自动执行
- 助力机器学习模型训练，适合合成生物学与工业生物技术研究者
精准发酵依赖微生物细胞工厂生产可持续食品、药品、化学品和生物燃料。专门实验室如生物制造中心正利用高通量生物反应器平台推进该技术，生成海量数据。然而，缺乏社区标准限制了数据可访问性与互操作性，阻碍了跨平台整合。为此，我们提出PREFER——一个开源本体，旨在建立生物过程数据的统一标准。PREFER遵循广泛采用的基本形式本体（BFO），并与多个社区本体互联，确保一致性与跨领域兼容性，覆盖整个精准发酵流程。将PREFER集成至高通量生物过程开发工作流中，可实现结构化元数据，支持自动化跨平台执行与高保真数据捕获。此外，其标准化潜力可弥合分散的数据孤岛，生成机器可操作的数据集，对训练可预测、鲁棒的合成生物学机器学习模型至关重要。本工作为可扩展、互操作的生物过程系统奠定基础，推动生物制造向数据驱动转型。
原文摘要 · Abstract (English)
Precision fermentation relies on microbial cell factories to produce sustainable food, pharmaceuticals, chemicals, and biofuels. Specialized laboratories such as biofoundries are advancing these processes using high-throughput bioreactor platforms, which generate vast datasets. However, the lack of community standards limits data accessibility and interoperability, preventing integration across platforms. In order to address this, we introduce PREFER, an open-source ontology designed to establish a unified standard for bioprocess data. Built in alignment with the widely adopted Basic Formal Ontology (BFO) and connecting with several other community ontologies, PREFER ensures consistency and cross-domain compatibility and covers the whole precision fermentation process. Integrating PREFER into high-throughput bioprocess development workflows enables structured metadata that supports automated cross-platform execution and high-fidelity data capture. Furthermore, PREFER's standardization has the potential to bridge disparate data silos, generating machine-actionable datasets critical for training predictive, robust machine learning models in synthetic biology. This work provides the foundation for scalable, interoperable bioprocess systems and supports the transition toward more data-driven bioproduction.
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