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.

High-throughput data production has revolutionized molecular biology. However, massive increases in data generation capacity require analysis approaches that are more sophisticated, and often very computationally intensive. Thus making sense of high-throughput data requires informatics support. Galaxy (http://galaxyproject.org) is a software system that provides this support through a framework that gives experimentalists simple interfaces to powerful tools, while automatically managing the computational details. Galaxy is available both as a publicly available web service, which provides tools for the analysis of genomic, comparative genomic, and functional genomic data, or a downloadable package that can be deployed in individual labs. Either way, it allows experimentalists without informatics or programming expertise to perform complex large-scale analysis with just a web browser.

Summary: Here, we describe a tool suite that functions on all of the commonly known FASTQ format variants and provides a pipeline for manipulating next generation sequencing data taken from a sequencing machine all the way through the quality filtering steps.Availability and Implementation: This open-source toolset was implemented in Python and has been integrated into the online data analysis platform Galaxy (public web access: http://usegalaxy.org; download: http://getgalaxy.org). Two short movies that highlight the functionality of tools described in this manuscript as well as results from testing components of this tool suite against a set of previously published files are available at http://usegalaxy.org/u/dan/p/fastqContact: james.taylor@emory.edu; anton@bx.psu.eduSupplementary information: Supplementary data are available at Bioinformatics online.