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

Katja Koeppen1, Bruce A Stanton1, Thomas H Hampton1

  • 1Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.

Bioinformatics (Oxford, England)
|October 17, 2017
PubMed
Summary
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ScanGEO simplifies analyzing gene expression data from NCBI

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Publicly available gene expression data in NCBI's Gene Expression Omnibus (GEO) is vast.
  • Current tools often require manual intervention for multi-study or multi-gene analyses.
  • Efficiently mining this data for specific gene sets across numerous studies is challenging.

Purpose of the Study:

  • To develop a user-friendly application for comprehensive analysis of GEO data.
  • To enable identification of differentially expressed genes across multiple studies for flexible gene sets.
  • To streamline the process of data mining and visualization for researchers.

Main Methods:

  • Developed ScanGEO as a Shiny web application using R.
  • The application is accessible online and its source code is available.

Related Experiment Videos

  • Implemented functionality to identify differentially expressed genes across user-specified criteria and gene sets.

    • Main Results:

    • ScanGEO allows for single-command analysis of multiple GEO studies.
    • It identifies differentially expressed genes for flexible gene sets.
    • Provides visualization, summary statistics, and reports for analyzed data.

    Conclusions:

    • ScanGEO offers a user-friendly solution for reanalyzing multiple GEO studies.
    • It automates the identification of differentially expressed genes across studies for specific gene sets.
    • Facilitates efficient data mining and interpretation of gene expression data.