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Publication Detail

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

Authors: Koeppen, Katja; Stanton, Bruce A; Hampton, Thomas H

Published In Bioinformatics, (2017 Nov 01)

Abstract: 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.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.katja.koeppen@dartmouth.edu.Supplementary data are available at Bioinformatics online.

PubMed ID: 29036513 Exiting the NIEHS site

MeSH Terms: Data Mining; Gene Expression Profiling/methods*; Humans; Software*

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