Integrating diverse databases into an unified analysis framework: a Galaxy approach

• Type of Material: Article
• Authors: Daniel Blankenberg, Penn State University; Nathan Coraor, Penn State University; Gregory Von Kuster, Penn State University; James Taylor, Emory University; Anton Nekrutenko, Penn State University
• Language: English
• Date: 2011-04-29
• Publisher: Oxford University Press (OUP): Policy C - Option A
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2011. Published by Oxford University Press.
• License: Creative Commons Attribution-NonCommercial 2.5 Generic
• Final Published Version (URL): http://database.oxfordjournals.org/content/2011/bar011
• Title of Journal or Parent Work: Database
• ISSN: 1758-0463
• Volume: 2011
• Start Page: 1
• End Page: 9
• Grant/Funding Information: The Beckman Foundation Young Investigator Award (to A.N.); National Science Foundation (DBI 0543285) and National Institutes of Health (HG004909 to A.N. and J.T.); National Institutes of Health (HG005133 and HG005542 to J.T. and A.N.); the Penn State University and the Huck Institutes for the Life Sciences (to A.N.); the Emory University (to J.T.). Additional funding is provided, in part, under a grant with the Pennsylvania Department of Health using Tobacco Settlement Funds. The Department specifically disclaims responsibility for any analyses, interpretations or conclusions. Funding for open access charge: Penn State University.
• Research Categories: Computer Science; Chemistry, Biochemistry

Abstract

Recent technological advances have lead to the ability to generate large amounts of data for model and non-model organisms. Whereas, in the past, there have been a relatively small number of central repositories that serve genomic data, an increasing number of distinct specialized data repositories and resources have been established. Here, we describe a generic approach that provides for the integration of a diverse spectrum of data resources into a unified analysis framework, Galaxy (http://usegalaxy.org). This approach allows the simplified coupling of external data resources with the data analysis tools available to Galaxy users, while leveraging the native data mining facilities of the external data resources.