Sapporo: A workflow execution service that encourages the reuse of workflows in various languages in bioinformatics.
open science
workflow
workflow execution service
workflow language
Journal
F1000Research
ISSN: 2046-1402
Titre abrégé: F1000Res
Pays: England
ID NLM: 101594320
Informations de publication
Date de publication:
2022
2022
Historique:
accepted:
14
06
2024
medline:
29
7
2024
pubmed:
29
7
2024
entrez:
29
7
2024
Statut:
epublish
Résumé
The increased demand for efficient computation in data analysis encourages researchers in biomedical science to use workflow systems. Workflow systems, or so-called workflow languages, are used for the description and execution of a set of data analysis steps. Workflow systems increase the productivity of researchers, specifically in fields that use high-throughput DNA sequencing applications, where scalable computation is required. As systems have improved the portability of data analysis workflows, research communities are able to share workflows to reduce the cost of building ordinary analysis procedures. However, having multiple workflow systems in a research field has resulted in the distribution of efforts across different workflow system communities. As each workflow system has its unique characteristics, it is not feasible to learn every single system in order to use publicly shared workflows. Thus, we developed Sapporo, an application to provide a unified layer of workflow execution upon the differences of various workflow systems. Sapporo has two components: an application programming interface (API) that receives the request of a workflow run and a browser-based client for the API. The API follows the Workflow Execution Service API standard proposed by the Global Alliance for Genomics and Health. The current implementation supports the execution of workflows in four languages: Common Workflow Language, Workflow Description Language, Snakemake, and Nextflow. With its extensible and scalable design, Sapporo can support the research community in utilizing valuable resources for data analysis.
Identifiants
pubmed: 39070189
doi: 10.12688/f1000research.122924.2
pmc: PMC11282396
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
889Informations de copyright
Copyright: © 2024 Suetake H et al.
Déclaration de conflit d'intérêts
No competing interests were disclosed.