Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter
| dc.contributor.author | Grüning, Björn | |
| dc.contributor.author | Rasche, Eric | |
| dc.contributor.author | Rebolledo-Jaramillo, 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.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. | |
| dc.identifier.uri | http://hdl.handle.net/11447/911 | |
| dc.language.iso | en_US | |
| dc.title | Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter | |
| dc.type | Artículo |
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