From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.

Automated summary

The biomedical research community is heavily investing in biomedical cloud platforms, which hold great promise for addressing big data challenges and ensuring reproducibility in biology. However, cloud platforms themselves do not automatically support FAIRness, and various challenges, such as platform lock-in and difficulty integrating across platforms, have emerged. To alleviate these difficulties, the prioritization of microservices and modular data access in smaller chunks or summarized form is proposed to improve interoperability.