The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics
- 1Berlin Institute of Health at Charite - Universitaetsmedizin Berlin
Abstract
Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.
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BibTeX
@misc{schneiderlunitz2026ga4gh,
title = {{The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics}},
author = {V. Schneider-Lunitz and L. Kraatz and P. Magee and V. S. Malladi and B. D. O'Connor and S. Varma and J. Eddy and H. Suetake and J. Gentry and M. R. Crusoe and A. Pizarro and J. Zhang and D. Yuen and K. Ellrott and P. R. Kensche and M. Lukowski and R. Thomas and S. V. Siegel and A. Kanitz and T. Ohta and S. O. Twardziok},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.760340},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.760340v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics
AU - V. Schneider-Lunitz
AU - L. Kraatz
AU - P. Magee
AU - V. S. Malladi
AU - B. D. O'Connor
AU - S. Varma
AU - J. Eddy
AU - H. Suetake
AU - J. Gentry
AU - M. R. Crusoe
AU - A. Pizarro
AU - J. Zhang
AU - D. Yuen
AU - K. Ellrott
AU - P. R. Kensche
AU - M. Lukowski
AU - R. Thomas
AU - S. V. Siegel
AU - A. Kanitz
AU - T. Ohta
AU - S. O. Twardziok
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.760340
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.760340v1
AB - Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.
ER -