Galaxy

Overview Galaxy is an open-source, browser-accessible platform designed to make computational research accessible, reproducible, and collaborative. Rather than requiring command-line expertise, Galaxy exposes thousands of community-maintained analysis tools through a graphical interface, captures full analysis histories for reproducibility, and enables easy sharing and publication of tools, workflows and visualizations. The project is supported by a global community (over 500,000 registered users across many public instances) and by a network of funded infrastructures and compute providers. Core capabilities At its core Galaxy lets users import data, run complex tool chains, and author reusable workflows that record every parameter and intermediate result. Tool installation is managed through a Tool Shed or added manually by admins; the Galaxy SDK automates creation, testing and deployment of tools, workflows and interactive tutorials. The platform supports interactive environments like Jupyter and RStudio alongside standard command-line style tools, so users can combine notebook-driven exploration with production workflows. Galaxy captures histories and metadata to help teams repeat and understand analyses, and it provides community-vetted, hands-on tutorials via the Galaxy Training Network (GTN) to bring new users up to speed. Deployments, scaling and integrations Galaxy is designed to run on everything from institutional servers to national clouds and HPC systems. Public community instances (for example UseGalaxy.eu, UseGalaxy.it and others) give immediate access to thousands of ready-to-use tools and reference datasets, while deployment frameworks such as Laniakea enable automated provisioning of private, production-grade Galaxy instances with cluster-backed or stand-alone configurations and encrypted storage. Galaxy distributes computational jobs across local clusters, cloud resources and national infrastructures (compute support is provided by providers like ACCESS-CI, TACC and JetStream2 in some deployments). Integrations with WorkflowHub register GTN workflows automatically; Galaxy can also access external data repositories and services (for example TAP servers exposing mission data), and supports interactive tool integration (Jupyter, RStudio) for hybrid workflows. Use cases and community content Galaxy is used across many domains including genomics (RNA‑seq, variant analysis, de novo transcriptome assembly), metagenomics, epigenetics, qPCR analysis and pathogen genome annotation. The community maintains hundreds of tutorials and example workflows on topics such as RNA‑seq visualization (volcano plots), insect microbiome FAIR workflows (FAIRyMAGs), SARS‑CoV‑2 genome annotation (CorGAT), ITS metabarcoding (ITSoneWB) and RT‑qPCR pipelines (PIPE‑T). Trainers can request temporary compute increases through Training Infrastructure as a Service (TIaaS) and make use of curated datasets and step-by-step GTN materials. Galaxy’s design supports FAIR research practices by enabling publication and registration of workflows, and by integrating with registries like WorkflowHub. Administration and operational notes Administrators install and manage Galaxy using documented practices (Galaxy requires Python 3.9+ for server installations) and can configure tool dependencies via the admin interface. Large-file uploads are supported (for example FTP import is available on many servers), and quota and retention policies are managed per instance—some public servers offer a baseline storage quota (example: 50 GB on certain national instances) with extensions on request or temporary increases for training. The project is stewarded by regional teams (European, US, Australian, etc.) and sustained through a mixture of grants, institutional support and contributions from an active global community. Why teams choose Galaxy Teams adopt Galaxy when they need a user-friendly, auditable, and extensible analysis platform that lowers the barrier to bioinformatics while preserving reproducibility and scale. Its combination of a rich tool ecosystem, workflow sharing, integrated training, and flexible deployment options makes Galaxy well suited for groups that want to move from exploratory analysis to reproducible, production-grade pipelines without forcing every user into command-line tooling.