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

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 meth...

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Vydáno v:	Nucleic acids research Ročník 44; číslo W1; s. W3 - W10
Hlavní autoři:	Afgan, Enis, Baker, Dannon, van den Beek, Marius, Blankenberg, Daniel, Bouvier, Dave, Čech, Martin, Chilton, John, Clements, Dave, Coraor, Nate, Eberhard, Carl, Grüning, Björn, Guerler, Aysam, Hillman-Jackson, Jennifer, Von Kuster, Greg, Rasche, Eric, Soranzo, Nicola, Turaga, Nitesh, Taylor, James, Nekrutenko, Anton, Goecks, Jeremy
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 08.07.2016
Témata:	Bioengineering Biomedical Research Computational Biology - methods Computational Biology - statistics & numerical data Databases, Genetic Datasets as Topic - statistics & numerical data Humans Internet Life Sciences Reproducibility of Results User-Computer Interface Web Server issue
ISSN:	0305-1048, 1362-4962
On-line přístup:	Získat plný text
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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.
AbstractList	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.
Author	Afgan, Enis Blankenberg, Daniel Chilton, John Coraor, Nate Turaga, Nitesh Nekrutenko, Anton Taylor, James Goecks, Jeremy Clements, Dave Eberhard, Carl van den Beek, Marius Grüning, Björn Hillman-Jackson, Jennifer Von Kuster, Greg Baker, Dannon Čech, Martin Soranzo, Nicola Guerler, Aysam Rasche, Eric Bouvier, Dave
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27137889$$D View this record in MEDLINE/PubMed https://hal.sorbonne-universite.fr/hal-01360125$$DView record in HAL
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