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ScanGEO: parallel mining of high-throughput gene expression data.

Authors :
Koeppen K
Stanton BA
Hampton TH
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2017 Nov 01; Vol. 33 (21), pp. 3500-3501.
Publication Year :
2017

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)

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