The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update

Enis Afgan(Johns Hopkins University), Dannon Baker(Johns Hopkins University), Marius van den Beek(Sorbonne Université), Daniel Blankenberg(Pennsylvania State University), Dave Bouvier(Pennsylvania State University), Martin Čech(Pennsylvania State University), John Chilton(Pennsylvania State University), Dave Clements(Johns Hopkins University), Nate Coraor(Pennsylvania State University), Carl Eberhard(Johns Hopkins University), Björn Grüning(University of Freiburg), Aysam Guerler(Johns Hopkins University), Jennifer Hillman‐Jackson(Pennsylvania State University), Greg Von Kuster(Pennsylvania State University), Eric Rasche(Texas A&M University), Nicola Soranzo(Norwich Research Park), Nitesh Turaga(Johns Hopkins University), James Taylor(Johns Hopkins University), Anton Nekrutenko(Pennsylvania State University), Jeremy Goecks(George Washington University)
Nucleic Acids Research
May 2, 2016
Cited by 2,328Open Access
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Abstract

High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.


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