%0 Journal Article
%T Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences
%A Jeremy Goecks
%A Anton Nekrutenko
%A James Taylor
%A The Galaxy Team
%J Genome Biology
%D 2010
%I BioMed Central
%R 10.1186/gb-2010-11-8-r86
%X Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays, such as ChIP-seq [1] and RNA-seq [2] (and many others), that require increasingly complex analysis tools [3]. However, sudden reliance on computation has created an 'informatics crisis' for life science researchers: computational resources can be difficult to use, and ensuring that computational experiments are communicated well and hence reproducible is challenging. Galaxy helps to address this crisis by providing an open, web-based platform for performing accessible, reproducible, and transparent genomic science.The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Tutorials [4,5], software libraries such as Bioconductor [6] and Bioperl [7], and web-based interfaces for tools [8,9] all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience.However, making tools accessible does not necessarily address the crucial problem of reproducibility. Reproducing experimental results is an essential facet of scientific inquiry, providing the foundation for understanding, integrating, and extending results toward new discoveries. Learning a programming language might enable a scientist to perform a given analysis, but ensuring that analysis is documented in a form another scientist can reproduce
%U http://genomebiology.com/2010/11/8/R86