Cargando…
Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers
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 t...
Autores principales: | Grüning, Björn A., Rasche, Eric, Rebolledo-Jaramillo, Boris, Eberhard, Carl, Houwaart, Torsten, Chilton, John, Coraor, Nate, Backofen, Rolf, Taylor, James, Nekrutenko, Anton |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444614/ https://www.ncbi.nlm.nih.gov/pubmed/28542180 http://dx.doi.org/10.1371/journal.pcbi.1005425 |
Ejemplares similares
-
An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy
por: Kumar, Anup, et al.
Publicado: (2023) -
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update
por: Afgan, Enis, et al.
Publicado: (2016) -
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update
por: Afgan, Enis, et al.
Publicado: (2018) -
Tool recommender system in Galaxy using deep learning
por: Kumar, Anup, et al.
Publicado: (2021) -
Harnessing cloud-computing for biomedical research with Galaxy Cloud
por: Afgan, Enis, et al.
Publicado: (2011)