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

Scientific workflows facilitate the automation of different data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency, accessibility, and reusability in workflows, it is essential to implement the 17 FAIR principles as much as possible. To do so, the research data management community has suggested specific guidelines and practices. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using our Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), a standardised and interoperable workflow specification language, allowing for the subsequent execution of the workflow on different workflow engines. The CWL description for MAW and the source code for the Python and R scripts are available on GitHub (https://github.com/zmahnoor14/MAW), and the environments are stored as docker images (https://hub.docker.com/repository/docker/zmahnoor/maw-r/general, https://hub.docker.com/repository/docker/zmahnoor/maw-metfrag_2.5.0/general, and https://hub.docker.com/repository/docker/zmahnoor/maw-py/general). These workflow elements set the baseline for the FAIRification process and enable interoperability. MAW is registered using the 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 to facilitate Findability, Accessibility, Interoperability, and Reusability. 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.