ScanGEO: parallel mining of high-throughput gene expression data

Bioinformatics. 2017 Nov 1;33(21):3500-3501. doi: 10.1093/bioinformatics/btx452.

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.

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.

Contact: katja.koeppen@dartmouth.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Data Mining
  • Gene Expression Profiling / methods*
  • Humans
  • Software*