Galaxy

Galaxy is a free, open-source, web-based platform aimed primarily at life-science and biomedical researchers to perform data-intensive computational analyses.
It enables users — even those without deep programming skills — to build, execute, share and reproduce complex workflows (think multi-step pipelines) for tasks such as genomics, transcriptomics, proteomics, imaging, and more.
Originally developed for computational biology (genomics) it has since expanded to other domains of scientific computing. 

Why it matters

• Accessibility: Many researchers face the barrier of needing programming skills and custom compute environments. Galaxy lowers that barrier by offering a graphical interface, web browser access, and public servers. 
• Reproducibility & transparency: Because analyses (tools, parameters, datasets, workflows) are tracked and saved, Galaxy supports reproducible research — you can rerun or inspect every step. 
• Workflow sharing & collaboration: Workflows created in Galaxy can be shared with others, reused, adapted, which speeds up method reuse and standardisation across labs. 
• Massive tool ecosystem & infrastructure flexibility: Galaxy integrates thousands of tools (via projects like BioConda, BioContainers) and supports running on various infrastructure (cloud, HPC). 

Key features

• Web-based interface: Users can upload data, select tools, set parameters and run analyses from their browser. 
• Histories and datasets: Galaxy keeps a history of user actions, tracks datasets (inputs, intermediates, outputs) and tools used. 
• Workflows: Pre-defined or user-built workflows permit linking several tools/steps automatically, reuse of pipelines for multiple datasets. 
• Training & community support: Through the Galaxy Training Network (GTN) there are many tutorials, training materials, community events. 
• Public instances: There are publicly available Galaxy servers (e.g., usegalaxy.org, usegalaxy.eu) which researchers can use without setting up their own infrastructure. 

Limitations & things to watch

• While Galaxy simplifies many analyses, extremely custom or highly complex workflows (especially beyond what the GUI supports) may still require programming or custom infrastructure. 
• Running large-scale analyses (huge datasets, many samples, very heavy compute) may require dedicated infrastructure, public servers may have quotas or usage constraints.
• Users still need to understand bioinformatics/analysis concepts — Galaxy eases the tool use, but the biology and methodology still matter.
• Because workflows are shared and reused, one must check tool versions, parameter choices and provenance carefully for reproducibility and correctness.

Why your lab/institution might use Galaxy

• If your lab generates sequence data (RNA-seq, DNA-seq), imaging data, metabolomics/proteomics, you can use Galaxy to avoid setting up from scratch every computational pipeline, and get up-and-running faster.
• For teaching/training purposes (students, collaborators less familiar with command-line bioinformatics), Galaxy offers a low-barrier platform.
• When you want to share analyses (for example as part of publications or supplementary materials), Galaxy’s workflow sharing feature helps you make your pipelines transparent and reuseable.
• Aligns with open science / FAIR (Findable, Accessible, Interoperable, Reusable) approaches: your data + workflows can be shared in a standardised way.
• If you plan to present a pitch or infrastructure proposal (e.g., for a computational facility), Galaxy could be part of the deck: show how the lab can deploy open-source, web-based analysis capacity without heavy bespoke scripting.

Quick user-journey example

1. Your lab has raw RNA-seq FASTQ files from a small experiment.
2. You log into a public Galaxy instance (or set up a local one).
3. Upload the FASTQ files (or link from e.g., cloud storage).
4. Choose a standard workflow (quality control → trimming → alignment → quantification → differential expression).
5. Run the workflow, the history tracks every step, intermediate dataset, version of tool used, parameters.
6. You download results, visualise via Galaxy or export to R/Python, and share the Galaxy workflow link as part of your supplementary materials.
7. A collaborator re-uses your workflow on a new dataset by importing it into their Galaxy account, modifying inputs.