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BIOMERO: A scalable and extensible image analysis framework.

Torec T Luik1, Rodrigo Rosas-Bertolini1, Eric A J Reits1

  • 1Amsterdam UMC, Department of Medical Biology, Amsterdam, North-Holland 1105AZ, the Netherlands.

Patterns (New York, N.Y.)
|September 5, 2024
PubMed
Summary
This summary is machine-generated.

BIOMERO connects OMERO, FAIR workflows, and HPC environments for seamless bioimage analysis. This tool democratizes AI and scalable image processing for researchers, promoting workflow reusability.

Keywords:
BIAFLOWSCytomineFAIR workflowsOMEROSlurmbioimaginghigh-content screeninghigh-performance computinghigh-throughput screeningimage analysis

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Area of Science:

  • Bioimaging
  • Computational Biology
  • Data Management

Background:

  • Integrating Findable, Accessible, Interoperable, and Reusable (FAIR) image analysis workflows with bioimaging data management platforms and high-performance computing (HPC) environments presents significant challenges.
  • Existing systems often require specialized technical expertise, limiting accessibility for many researchers.

Purpose of the Study:

  • To introduce BIOMERO (bioimage analysis in OMERO), a novel solution designed to bridge OMERO, FAIR workflows, and HPC environments.
  • To enable seamless execution of FAIR workflows for bioimage analysis, particularly for large datasets.
  • To empower researchers, regardless of technical expertise, to leverage scalable image processing and AI tools.

Main Methods:

  • BIOMERO acts as an intermediary, connecting the OMERO bioimaging data management platform with FAIR workflows and HPC resources.
  • It facilitates the direct execution of complex image analysis pipelines from within OMERO.
  • The system supports the sharing and interoperability of FAIR workflows across different bioimaging communities, including OMERO and Cytomine/BIAFLOWS.

Main Results:

  • BIOMERO enables the seamless integration and execution of FAIR image analysis workflows.
  • It allows for scalable processing of large bioimaging datasets, including those from high-content and high-throughput screening.
  • The platform supports workflow sharing and reuse across diverse bioimaging communities.

Conclusions:

  • BIOMERO significantly simplifies the application of FAIR workflows in bioimaging research.
  • It democratizes access to advanced image processing and AI tools for a broader research community.
  • The widespread adoption of BIOMERO is expected to enhance the reusability and impact of bioimaging research.