Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

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dc.contributor.authorGrüning, Björn
dc.contributor.authorRasche, Eric
dc.contributor.authorRebolledo-Jaramillo, Boris
dc.contributor.authorEberhard, Carl
dc.contributor.authorHouwaart, Torsten
dc.contributor.authorChilton, John
dc.contributor.authorCoraor, Nate
dc.contributor.authorBackofen, Rolf
dc.contributor.authorTaylor, James
dc.contributor.authorNekrutenko, Anton
dc.date.accessioned2017-09-04T15:11:23Z
dc.date.available2017-09-04T15:11:23Z
dc.date.issued2017
dc.description.abstractWhat 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., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.
dc.format.extent10
dc.identifier.citationPLoS Comput Biol. 2017 May; 13(5): e1005425
dc.identifier.urihttp://hdl.handle.net/11447/1637
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pcbi.1005425
dc.language.isoen_US
dc.publisherPLoS
dc.subjectBiomedical Research/methods
dc.subjectBiomedical Research/organization & administration
dc.subjectComputational Biology
dc.subjectHigh-Throughput Nucleotide Sequencing
dc.subjectHumans
dc.subjectResearch Personnel
dc.subjectSoftware
dc.titleJupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers
dc.typeArtículo

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