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

21 December 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using CWL description on WorkflowHub with the DOI https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.510.2. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use the instructions presented in this snapshot as a base template to adopt FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.

Keywords

metabolomics
cheminformatics
workflow
CWL
CommonWL
Workflow RO-Crate
Docker
WorkflowHub
BioSchemas
FAIR

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.