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Toward FAIR Representations of Microbial Interactions.

Alan R Pacheco1, Charlie Pauvert2, Dileep Kishore3

  • 1Institute of Microbiology, ETH Zurichgrid.5801.c, Zurich, Switzerland.

Msystems
|August 25, 2022
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Summary

Making microbial interaction data findable, accessible, interoperable, and reusable (FAIR) is crucial for advancing microbiome research. Implementing FAIR principles will unlock new insights and applications by enabling data integration across studies.

Keywords:
FAIRaccessibilityco-occurrencedata sharingmetadatamicrobial ecologymicrobial interactionsmicrobial networksmicrobiomereproducibility

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Data Science

Background:

  • Microbiome research generates vast interaction data, but accessibility and compatibility issues hinder progress.
  • Current data limitations impede cross-study inferences and the full potential of microbiome analysis.

Purpose of the Study:

  • To propose guiding principles for making microbial interaction data FAIR (Findable, Accessible, Interoperable, Reusable).
  • To highlight use cases and the potential for novel insights through integrated microbial interaction data.

Main Methods:

  • Development of guiding principles for data FAIRness.
  • Identification of specific use cases across environmental, industrial, and medical microbiome research.
  • Discussion of integration strategies and community momentum.

Main Results:

  • A framework for enhancing the FAIRness of microbial interaction data.
  • Demonstration of potential applications, including synthetic community design and novel interaction discovery.
  • Vision for deploying FAIR data through existing resources and standards.

Conclusions:

  • Adopting FAIR principles is essential for overcoming data challenges in microbiome research.
  • Integrated and FAIR microbial interaction data will drive innovation in synthetic biology and ecological studies.
  • Community-driven efforts are key to realizing the potential of FAIR microbial interaction data.