Back to Search Start Over

Workflow sharing with automated metadata validation and test execution to improve the reusability of published workflows

Authors :
Hirotaka Suetake
Tsukasa Fukusato
Takeo Igarashi
Tazro Ohta
Source :
GigaScience. 12
Publication Year :
2022
Publisher :
Oxford University Press (OUP), 2022.

Abstract

BackgroundMany open-source workflow systems have made bioinformatics data analysis procedures portable. Sharing these workflows provides researchers easy access to high-quality analysis methods without the requirement of computational expertise. However, published workflows are not always guaranteed to be reliably reusable. Therefore, a system is needed to lower the cost of sharing workflows in a reusable form.ResultsWe introduce Yevis, a system to build a workflow registry that automatically validates and tests workflows to be published. The validation and test are based on the requirements we defined for a workflow being reusable with confidence. Yevis runs on GitHub and Zenodo and allows workflow hosting without the need of dedicated computing resources. A Yevis registry accepts workflow registration via a GitHub pull request, followed by an automatic validation and test process for the submitted workflow. As a proof of concept, we built a registry using Yevis to host workflows from a community to demonstrate how a workflow can be shared while fulfilling the defined requirements.ConclusionsYevis helps in the building of a workflow registry to share reusable workflows without requiring extensive human resources. By following Yevis’s workflow-sharing procedure, one can operate a registry while satisfying the reusable workflow criteria. This system is particularly useful to individuals or communities that want to share workflows but lacks the specific technical expertise to build and maintain a workflow registry from scratch.

Details

ISSN :
2047217X
Volume :
12
Database :
OpenAIRE
Journal :
GigaScience
Accession number :
edsair.doi.dedup.....08f3bbd369b52d1caf69fcafb698d886
Full Text :
https://doi.org/10.1093/gigascience/giad006