PREFER: An Ontology for the PREcision FERmentation Community

• URL: http://arxiv.org/abs/2602.16755v1
• Date: Wed, 18 Feb 2026 09:29:30 GMT
• Title: PREFER: An Ontology for the PREcision FERmentation Community
• Authors: Txell Amigó, Shawn Zheng Kai Tan, Angel Luu Phanthanourak, Sebastian Schulz, Pasquale D. Colaianni, Dominik M. Maszczyk, Ester Milesi, Ivan Schlembach, Mykhaylo Semenov Petrov, Marta Reventós Montané, Lars K. Nielsen, Jochen Förster, Bernhard Ø. Palsson, Suresh Sudarsan, Alberto Santos,
• Abstract summary: PREFER is an open-source ontology designed to establish a unified standard for bioprocess data.<n> PREFER is built in alignment with the widely adopted Basic Formal Ontology (BFO)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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