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

Yifan Gao1, Zakariyya Mughal2, Jose A Jaramillo-Villegas3,4

  • 1Center for Alternative to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.

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Summary
This summary is machine-generated.

BioBricks.ai is a new open repository providing modular biological and chemical datasets. It significantly reduces the time needed to integrate diverse data for research, accelerating scientific discovery.

Keywords:
BioBricks.aibioinformaticscheminformaticsdata integrationmachine learningpublic health data

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Area of Science:

  • Biomedicine
  • Public Health
  • Bioinformatics
  • Data Science

Background:

  • Biomedical and public health researchers face significant delays due to manual data acquisition, cleaning, and integration from various sources.
  • This manual process leads to inefficiencies, slow discovery, and inconsistent analytical pipelines.

Purpose of the Study:

  • To introduce BioBricks.ai, an open, centralized repository designed to streamline access to and integration of public biological and chemical datasets.
  • To provide modular, version-controlled data packages (bricks) with integrated extract-transform-load (ETL) pipelines.

Main Methods:

  • Developed BioBricks.ai as a DVC Git repository system for public datasets.
  • Implemented a package-manager-like interface for data installation, dependency management, and updates.
  • Established a unified backend for data delivery.

Main Results:

  • The current release offers over 90 curated datasets across genomics, proteomics, cheminformatics, and epidemiology.
  • Combining bricks programmatically allows for rapid assembly of multi-dataset analytic cohorts, reducing time from days to minutes.
  • Demonstrated significant acceleration in data access and integration compared to traditional methods.

Conclusions:

  • BioBricks.ai accelerates data access, enhances workflow reproducibility, and lowers barriers to integrating heterogeneous public datasets.
  • The platform fosters community contributions and reduces redundant engineering efforts by treating data as version-controlled software.
  • Future work will expand data coverage and automated provenance tracking to improve FAIR data practices.