Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

By Jeremy Goecks, Anton Nekrutenko, James Taylor. Genome Biology, 2010

3,703 citations recorded by the source corpus.

Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/ebd342793ee80999119cf163f5774d649fc0c928

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Citations in the corpus

Swarm v2: highly-scalable and high-resolution amplicon clustering (2015, neutral): “2, and in Galaxy (Goecks et al., 2010).”
ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines (2014, methodology): “The recently published HTSeq framework (Anders et al., 2014) and the biotoolbox1 suite provide library modules for processing high-throughput data.”
Conducting a Microbiome Study (2014, methodology): “, Lenards, A., et al. (2011). The iPlant Collabora-”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, background): “An alternative to avoid dealing with local software and computational resources is web portals such as Galaxy (Goecks et al., 2010), MG-RAST (Meyer et al., 2008), and IMG-M portal (Markowitz et al., 2012).”
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, methodology): “For these sequences, a phyloP score for each nucleotide (total of 200,000) is calculated using the Galaxy tool [60], [61], [62].”
RSeQC: quality control of RNA-seq experiments (2012, neutral): “…htSeqTools, FASTX-ToolKit (http://hannonlab.cshl.edu/fastx_toolkit/) and SAMStat) only focus on raw sequence-related metrics (Goecks, Nekrutenko, Taylor, & Galaxy Team, 2010; Lassmann, Hayashizaki, & Daub, 2011; Planet, Attolini, Reina, Flores, & Rossell, 2012; Reich et al.,…”
Snakemake - a scalable bioinformatics workflow engine (2012, positive): “Systems such as Biopipe (Hoon et al ., 2003), Taverna (Oinn et al ., 2004), Galaxy (Goecks et al. , 2010), GeneProf (Halbritter et al ., 2011) or PegaSys (Shah et al. , 2004) are easy to learn and use through their graphical user interface.”
Metagenomic biomarker discovery and explanation (2011, methodology): “LEfSe is provided with a graphical interface in the Galaxy framework [46,47], which allows the user to select parameters (the primary three stringency parameters, the multiclass setting, and other computational, statistical, and graphical preferences), to pipeline data between modules in a workflow ”
FireWorks: a dynamic workflow system designed for high‐throughput applications (2015, methodology): “Finally, tools that are geared more towards data-analysis pipelines include Galaxy [38], KNIME [39], and IPython [7,40]/NetworkX [41] and allow beginning users to compose their analysis tasks into a logical sequence.”