Authors
Jilong Liu, Liguang Yang, Qingyu Xiao, Yanqing Wang, Zhen Wang, Rui Zhang, Zhaoqiang Li, Jianwen Zhou, Hengshen Liu, Lu Yao, Kaixin Yang, Ziru Chen, Xiang Wan, Tsunghui Chang, Ye Li, Tao Huang, Wenming Zhao, Xin Zhang, Yixue Li
Published in
Genomics, proteomics & bioinformatics. Aug 20, 2026. Epub Aug 20, 2026.
Abstract
The increasing volume and complexity of biomedical data, especially driven by high-throughput biotechnologies, have highlighted the need for scalable, interoperable, and reproducible data analysis frameworks. While cloud computing has revolutionized data processing and collaboration in life sciences, challenges remain in building cloud platforms tailored to biomedical research and ensuring platform interoperability. To address these limitations, we propose Bio-OS, an open-source, cloud-native framework designed to support the rapid development of interoperable biomedical cloud platforms. Bio-OS aligns with the Global Alliance for Genomics and Health (GA4GH) cloud workstream standards-including Workflow Execution Service (WES), Task Execution Service (TES), Data Repository Service (DRS), and Tool Registry Service (TRS) - while introducing innovations such as interactive execution models, workspace-centric data management, and AI-driven enhancements. These features facilitate adherence to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, promoting reproducibility and collaboration in biomedical research. Deployments across multiple research institutions demonstrated Bio-OS's ability to build biomedical cloud platforms and validated its efficacy in supporting data-intensive applications in life sciences.
PMID:
42625390
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.
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