dc.creator | Grüning, Björn | |
dc.creator | Rasche, Eric | |
dc.creator | Rebolledo-Jaramillo, Boris | |
dc.creator | Eberhard, Carl | |
dc.creator | Houwaart, Torsten | |
dc.creator | Chilton, John | |
dc.creator | Coraor, Nate | |
dc.creator | Backofen, Rolf | |
dc.creator | Taylor, James | |
dc.creator | Nekrutenko, Anton | |
dc.date.accessioned | 2017-09-04T15:11:23Z | |
dc.date.accessioned | 2022-10-17T17:53:47Z | |
dc.date.available | 2017-09-04T15:11:23Z | |
dc.date.available | 2022-10-17T17:53:47Z | |
dc.date.created | 2017-09-04T15:11:23Z | |
dc.date.issued | 2017 | |
dc.identifier | PLoS Comput Biol. 2017 May; 13(5): e1005425 | |
dc.identifier | http://dx.doi.org/10.1371/journal.pcbi.1005425 | |
dc.identifier | http://hdl.handle.net/11447/1637 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4423752 | |
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., 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.language | en_US | |
dc.publisher | PLoS | |
dc.subject | Biomedical Research/methods | |
dc.subject | Biomedical Research/organization & administration | |
dc.subject | Computational Biology | |
dc.subject | High-Throughput Nucleotide Sequencing | |
dc.subject | Humans | |
dc.subject | Research Personnel | |
dc.subject | Software | |
dc.title | Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers | |
dc.type | Artículo | |