BioBricks.ai: a versioned data registry for life sciences data assets

BioBricks.ai: a versioned data registry for life sciences data assets

IntroductionResearchers in biomedicine and public health often spend weeks locating, cleansing, and integrating data from disparate sources before analysis can begin. This redundancy slows discovery and leads to inconsistent pipelines.MethodsWe created BioBricks.ai, an open, centralized repository t...

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Main Authors: Yifan Gao, Zakariyya Mughal, Jose A. Jaramillo-Villegas, Marie Corradi, Alexandre Borrel, Ben Lieberman, Suliman Sharif, John Shaffer, Karamarie Fecho, Ajay Chatrath, Alexandra Maertens, Marc A. T. Teunis, Nicole Kleinstreuer, Thomas Hartung, Thomas Luechtefeld
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Artificial Intelligence
Subjects:
public health data
BioBricks.ai
data integration
machine learning
cheminformatics
bioinformatics
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1599412/full
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Summary:IntroductionResearchers in biomedicine and public health often spend weeks locating, cleansing, and integrating data from disparate sources before analysis can begin. This redundancy slows discovery and leads to inconsistent pipelines.MethodsWe created BioBricks.ai, an open, centralized repository that packages public biological and chemical datasets as modular “bricks.” Each brick is a Data Version Control (DVC) Git repository containing an extract‑transform‑load (ETL) pipeline. A package‑manager–like interface handles installation, dependency resolution, and updates, while data are delivered through a unified backend (https://biobricks.ai).ResultsThe current release provides >90 curated datasets spanning genomics, proteomics, cheminformatics, and epidemiology. Bricks can be combined programmatically to build composite resources; benchmark use‑cases show that assembling multi‑dataset analytic cohorts is reduced from days to minutes compared with bespoke scripts.DiscussionBioBricks.ai accelerates data access, promotes reproducible workflows, and lowers the barrier for integrating heterogeneous public datasets. By treating data as version‑controlled software, the platform encourages community contributions and reduces redundant engineering effort. Continued expansion of brick coverage and automated provenance tracking will further enhance FAIR (Findable, Accessible, Interoperable, Reusable) data practices across the life‑science community.
ISSN:2624-8212

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