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Fundamentals of FAIR biomedical data analyses in the cloud using custom pipelines
Seth R Berke1, Kanika Kanchan2,3, Mary L Marazita4
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Plos Computational Biology
|July 2, 2025
Summary
Learn fundamental concepts for creating custom cloud-based analytic pipelines in biomedical research. This guide helps scientists overcome challenges with cloud infrastructure and software, enabling advanced data analysis.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- The biomedical ecosystem is adopting FAIR data principles, promoting cloud-based multimodal datasets.
- Cloud computing offers accelerated discovery but researcher adoption is hindered by a lack of training, particularly for custom analytic pipelines.
Purpose of the Study:
- To present three fundamental, cloud-agnostic concepts for creating custom analytic pipelines in the cloud.
- To provide foundational education for biomedical scientists to build custom workflows on any cloud platform.
Main Methods:
- Developed three core concepts applicable to any cloud provider and workflow.
- Illustrated these concepts using a custom analysis pipeline for orofacial cleft (OFC) risk detection.
Main Results:
- The presented concepts are workflow and cloud provider agnostic, enhancing their broad applicability.
- A custom analysis for sex-specific genetic effects on OFC risk was successfully implemented on the CAVATICA cloud platform.
Conclusions:
- Addressing the educational gap in cloud infrastructure is crucial for wider researcher adoption.
- The presented fundamental concepts empower scientists to build custom pipelines for diverse biomedical cloud analyses.
