ScanGEO: parallel mining of high-throughput gene expression data

• Published in: Bioinformatics (Oxford, England) Vol. 33; no. 21; pp. 3500 - 3501
• Main Authors: Koeppen, Katja, Stanton, Bruce A, Hampton, Thomas H
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.11.2017
• Subjects: Applications Notes; Data Mining; Gene Expression Profiling - methods; Humans; Software
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btx452

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.