Galaxy is a web application developed at Pennsylvania State University, designed for analysis of genomics data <ref>Template:Cite pmid</ref><ref>http://main.g2.bx.psu.edu/ Galaxy website at PennState University</ref><ref>http://www.openhelix.com/galaxy Galaxy tutorial sponsored by Galaxy Group</ref>. The Galaxy web application is designed to help two communities that rarely talk to each other, experimental biologists who have little time or knowledge to invest in developing algorithms and computer programs, and computational biologists who develop algorithms, but do not provide interfaces. Galaxy is written mostly in Python.
Galaxy is "an open, web-based platform for performing accessible, reproducible, and transparent genomic science."<ref>Template:Cite pmid</ref> Galaxy is a scientific workflow system that aims to make computational biology accessible to research scientists that do not have computer programming experience. Although it was initially developed for genomics research, it is largely domain agnostic and is now used a general bioinformatics workflow management system.<ref>http://galaxyproject.org/wiki/Public%20Galaxy%20Servers</ref>
Galaxy is available:
- As a free public web server,<ref>http://usegalaxy.org/</ref> supported by the Galaxy Project.<ref>http://galaxyproject.org/</ref>. This server includes many bioinformatics tools that are widely useful in many areas of genomics research. Users can create logins, and save histories, workflows, and datasets on the server. These saved items can also be shared with others.
- As open-source software that can be downloaded, installed and customized to address specific needs.<ref>http://getgalaxy.org/</ref>. Galaxy can be installed locally or using a computing cloud.Template:Cite pmid</ref>
- Public web servers hosted by other organizations.<ref>http://galaxyproject.org/wiki/Public%20Galaxy%20Servers</ref> Several organizations with their own Galaxy installation have also opted to make those servers available to others.
Galaxy is a scientific workflow system. These systems provide a means to build multi-step computational analyses akin to a recipe. They typically provide a graphical user interface for specifying what data to operate on, what steps to take, and what order to do them in.
Computational biology is a specialized domain that often requires knowledge of computer programming. Galaxy aims to give biomedical researchers access to computational biology without also requiring them to understand computer programming. Galaxy does this by stressing a simple user interface over the ability to build complex workflows. This design choice makes it relatively easy to build typical analyses, but more difficult to build complex workflows that include, for example, looping constructs. (See Taverna for an example system that supports looping.)
Reproducibility is a key goal of science: When scientific results are published the publications should include enough information that others can repeat the experiment and get the same results. There have been many recent efforts to extend this goal from the bench (the "wet lab") to computational experiments (the "dry lab") as well. This has proved to be a more difficult task than initially expected.<ref>Template:Cite pmid</ref>
Galaxy supports reproducibility by capturing sufficient information about every step in a computational analysis, so that the analysis can be repeated, exactly, at any point in the future. This includes keeping track of all input, intermediate, and final datasets, as well as the parameters provided to, and the order of each step of the analysis.
Galaxy supports transparency in scientific research by enabling researchers to share any of their Galaxy Objects either publicly, or with specific individuals. Shared items can be examined in detail, rerun at will and copied and modified to test hypotheses.
Galaxy Objects: Histories Workflows, Datasets and Pages
Galaxy objects are anything that can be saved, persisted, and shared in Galaxy:
- Histories are computational analyses (recipes) run with specified input datasets, computational steps and parameters. Histories include all intermediate and output datasets as well.
- Workflows are computational analyses that specify all the steps (and parameters) in the analysis, but none of the data. Workflows are used to run the same analysis against multiple sets of input data.
- Datasets includes any input, intermediate, or output dataset, used or produced in an analysis.
- Histories, workflows and datasets can include user-provided annotation. Galaxy Pages enables the creation of a virtual paper that describes the how and why of the overall experiment. Tight integration of Pages with Histories, Workflows, and Datasets supports this goal.
Galaxy is open-source software implemented using the Python programming language. It is developed by the Galaxy team<ref>http://galaxyproject.org/wiki/Galaxy%20Team</ref> at Penn State and Emory Univeristy, and the Galaxy Community.
Galaxy is an open source project and the community includes users, organizations that install their own instance, Galaxy developers, and bioinformatics tool developers. The Galaxy project has mailing lists<ref>http://galaxyproject.org/wiki/Mailin%20Lists</ref>, a community wiki<ref>http://galaxyproject.org/wiki</ref>, and annual meetings.<ref>http://galaxyproject.org/wiki/Events</ref>.