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Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter

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dc.contributor.author Grüning, Björn
dc.contributor.author Rasche, Eric
dc.contributor.author Rebolledo, Boris
dc.contributor.author Eberhard, Carl
dc.contributor.author Houwaart, Torsten
dc.contributor.author Chilton, John
dc.contributor.author Coraor, Nathan
dc.contributor.author Backofen, Rolf
dc.contributor.author Taylor, James
dc.contributor.author Nekrutenko, Anton
dc.date.accessioned 2017-01-03T15:24:49Z
dc.date.available 2017-01-03T15:24:49Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/11447/911
dc.description.abstract What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., list of variable sites). The subsequent exploratory stage is much more ad hoc and requires development of custom scripts making it problematic for biomedical researchers. Here we describe a hybrid platform combining common analysis pathways with exploratory environments. It aims at fully encompassing and simplifying the “raw data-to-publication” pathway and making it reproducible. es_CL
dc.language.iso en_US es_CL
dc.title Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter es_CL
dc.type Artículo es_CL


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