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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.

Daphne Wijnbergen1, Núria Queralt-Rosinach2, Valérie Barbié3

  • 1Leiden University Medical Center, Leiden, The Netherlands. d.wijnbergen@lumc.nl.

Journal of Biomedical Semantics
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces minimal metadata to enhance machine actionability for bioinformatics tools and datasets, improving data analytics and FAIR data integration. The proposed schema supports tool sharing and FAIR infrastructures.

Keywords:
BioinformaticsFAIRMachine-actionabilityMetadata

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Area of Science:

  • Bioinformatics
  • Data Science
  • Computational Biology

Background:

  • Machine reusability is key for FAIR data principles, enabling efficient data analytics.
  • Current metadata in repositories often lacks machine actionability due to standardization gaps and missing ontological descriptions.
  • This hinders the full potential of data reuse in fields like bioinformatics.

Purpose of the Study:

  • To identify essential metadata for improving machine actionability of bioinformatics tools.
  • To propose a metadata schema addressing current limitations in tool and dataset interoperability.
  • To enhance machine-driven data analytics through better integration of tools and datasets.

Main Methods:

  • Identification of minimal metadata requirements for machine actionability.
  • Development of a metadata schema encompassing tool identification, selection, validation, and execution.
  • Alignment of tool metadata with dataset metadata for enhanced integration.

Main Results:

  • A minimal set of metadata properties was identified to significantly improve machine actionability.
  • A novel metadata schema was proposed, detailing properties for comprehensive tool management.
  • The proposed schema facilitates seamless integration between bioinformatics tools and datasets for machine-driven analytics.

Conclusions:

  • The identified minimal metadata enhances the machine actionability of both tools and data.
  • The proposed schema can be integrated into platforms for sharing bioinformatics tools and datasets.
  • This work contributes to advancing FAIR data infrastructures by improving machine-readability and reusability.