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Open source workflow management system

  • Guru Ananda
  • Dan Blankenberg
  • Wen-Yu Chung
  • Nate Coraor
  • Sergei Pond
  • Greg von Kuster
  • Ross Lazarus
  • Anton Nekrutenko
  • James Taylor


Short Description

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Home Page

Galaxy is an open source workflow management system that can be used to access and analyze genomic data sets without any prior knowledge of programming.[1][2]

Galaxy allows users to operate on tabular data, using a variety of preinstalled tools.


The Galaxy project and public instance are located at Penn State University.


Galaxy is a web application. For most users, all you need is a browser.

If you want to set up your own Galaxy instance, it is supported on Linux and Mac OS/X. Windows is not currently supported. Python is required. See Get Galaxy at the Galaxy Wiki.

User notes

Getting started with Galaxy

Selecting E. coli data at the Penn State Galaxy

These notes are for the main Galaxy instance, and are written from an E. coli-centric point of view.

Preloaded data for E. coli is in a counterintuitive location. Use Get Data->UCSC Archaea Table Browser. Select gamma proteobacteria

The first thing you will usually want to do is get some data set into your Galaxy workflow. Select Get Data from the tool menu. Galaxy gives you a variety of choices. You can upload your own file or load preloaded data. Galaxy's upload system can recognize Axt, Fasta, Fastqsolexa, Gff, Gff3, Html, Lav, Maf, Tabular, Wiggle, Bed and Interval (Bed with headers) formats. As of October 2010, Galaxy has preloaded data for several E. coli genomes. However, it may be better to upload gff3 files so you can use the latest RefSeq record.

Once you have tab-delimited data in Galaxy, you can perform calculations, sorting, filtering and so on. For example...

See Also


See Help:References for how to manage references in EcoliWiki.

  1. Taylor, J et al. (2007) Using galaxy to perform large-scale interactive data analyses. Curr Protoc Bioinformatics Chapter 10 Unit 10.5 PubMed
  2. Blankenberg, D et al. (2007) A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly. Genome Res. 17 960-4 PubMed