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ScanGEO: parallel mining of high-throughput gene expression data.
- Source :
-
Bioinformatics (Oxford, England) [Bioinformatics] 2017 Nov 01; Vol. 33 (21), pp. 3500-3501. - Publication Year :
- 2017
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Abstract
- Summary: Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command.<br />Availability and Implementation: The ScanGEO source code is written in R and implemented as a Shiny app that can be freely accessed at http://scangeo.dartmouth.edu/ScanGEO/. For users who would like to run a local instantiation of the app, the R source code is available under a GNU GPLv3 license at https://github.com/StantonLabDartmouth/AppScanGEO.<br />Contact: katja.koeppen@dartmouth.edu.<br />Supplementary Information: Supplementary data are available at Bioinformatics online.<br /> (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)
- Subjects :
- Data Mining
Humans
Gene Expression Profiling methods
Software
Subjects
Details
- Language :
- English
- ISSN :
- 1367-4811
- Volume :
- 33
- Issue :
- 21
- Database :
- MEDLINE
- Journal :
- Bioinformatics (Oxford, England)
- Publication Type :
- Academic Journal
- Accession number :
- 29036513
- Full Text :
- https://doi.org/10.1093/bioinformatics/btx452