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. 2024 Aug 2;40(8):btae486.
doi: 10.1093/bioinformatics/btae486.

Galaxy Helm chart: a standardized method for deploying production Galaxy servers

Nuwan Goonasekera  1 , Alexandru Mahmoud  2 , Keith Suderman  3 , Enis Afgan  3
Affiliations

Galaxy Helm chart: a standardized method for deploying production Galaxy servers

Nuwan Goonasekera et al. Bioinformatics. .

Abstract

Motivation: The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance.

Results: We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one's own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud.

Availability and implementation: The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
The components of the Galaxy Helm chart, capturing the installation architecture for the Galaxy application. The bottom two layers represent cluster storage resources, visualized as storage classes and corresponding persistent volumes. The next layer-up are dependent applications that are deployed and configured for use by Galaxy. The top layer are the Galaxy application processes, each independently scalable and hot-swappable without downtime. To deliver a comprehensive installation, the chart relies on several community resources. Visualized in the right-most column, these include tool containers from Biocontainers, the Galaxy container image used by Galaxy processes, default resource configuration for tools as captured in the Total Perspective Vortex (TPV) shared database (Goonasekera et al., 2023), and a centrally managed repository of Galaxy’s reference data on CVMFS.
See this image and copyright information in PMC

References

    1. Afgan E, Lonie A, Taylor J. et al. CloudLaunch: discover and deploy cloud applications. Future Gener Comput Syst 2019;94:802–10. - PMC - PubMed
    1. Afgan E, Baker D, Coraor N. et al. Harnessing cloud computing with galaxy cloud. Nat Biotechnol 2011;29:972–4. - PMC - PubMed
    1. Aguado Sanchez C, Blomer J, Buncic P. et al. 2008. CVMFS—a file system for the CernVM virtual appliance. ui.adsabs.harvard.edu.
    1. Galaxy Community The galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res 2024;52:W83–W94. - PMC - PubMed
    1. Goonasekera N, Bromhead C, Gladman S. et al. Right-sizing compute resource allocations for bioinformatics tools with Total Perspective Vortex, 2023, arXiv [cs.DC].

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