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Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study.

Mahnoor Zulfiqar1,2, Michael R Crusoe3, Birgitta König-Ries2,4,5

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|February 23, 2024
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Summary

Implementing FAIR principles in scientific workflows enhances transparency and reusability. This study details applying these principles to metabolomics workflows using the Metabolome Annotation Workflow (MAW) for better data analysis.

Keywords:
BioschemasCWLCommonWLDockerFAIRWorkflow RO-CrateWorkflowHubcheminformaticsmetabolomicsworkflow

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Area of Science:

  • Bioinformatics and Cheminformatics
  • Computational Biology
  • Data Science

Background:

  • Scientific workflows automate complex data analysis pipelines.
  • Implementing FAIR (Findable, Accessible, Interoperable, Reusable) principles is crucial for workflow transparency and reusability.
  • Metabolomics research generates large datasets requiring robust and reproducible analysis methods.

Purpose of the Study:

  • To demonstrate the practical implementation of FAIR principles for scientific workflows.
  • To use the Metabolome Annotation Workflow (MAW) as a case study for FAIRification.
  • To provide a guideline for researchers applying FAIR practices to their bioinformatics and cheminformatics workflows.

Main Methods:

  • Specifying the Metabolome Annotation Workflow (MAW) using the Common Workflow Language (CWL).
  • Registering MAW on WorkflowHub with its CWL description.
  • Packaging MAW using the Workflow RO-Crate profile with Bioschemas metadata during submission.

Main Results:

  • Successful registration and packaging of MAW on WorkflowHub, adhering to FAIR principles.
  • Demonstrated reusability of MAW across different workflow engines due to CWL specification.
  • Incorporation of Bioschemas metadata enhances discoverability and interoperability.

Conclusions:

  • The FAIR implementation for MAW serves as a practical model for other scientific workflows.
  • Adopting CWL and WorkflowHub facilitates the FAIRification of computational workflows.
  • This approach promotes transparency, reusability, and discoverability in scientific data analysis.