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PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways.

Bernhard Mlecnik1, Marcel Scheideler, Hubert Hackl

  • 1Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz 8010, Austria.

Nucleic Acids Research
|June 28, 2005
PubMed
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PathwayExplorer facilitates the analysis of high-throughput gene expression data by mapping it onto biological pathways. This tool aids researchers in interpreting complex biological data for better understanding of gene and protein functions.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • High-throughput gene expression data generation is common, but its systematic analysis within a biological context remains challenging.
  • Interpreting large-scale expression profiles requires tools that can integrate data with existing biological pathway knowledge.
  • Existing methods may lack the ease of use and systematic approach needed for rapid biological interpretation.

Purpose of the Study:

  • To develop a user-friendly tool for the presentation and analysis of high-throughput expression data.
  • To enable the simultaneous mapping of gene or protein expression profiles onto multiple biological pathways.
  • To provide a systematic method for identifying relevant biological pathways based on expression data.

Main Methods:

Related Experiment Videos

  • Development of PathwayExplorer, a platform-independent web server and standalone Java application.
  • Integration of data from major pathway databases including KEGG, BioCarta, and GenMAPP.
  • Implementation of a SOAP (simple object access protocol) interface for data exchange.
  • Development of a ranking system for pathways based on expression data relevance.
  • Incorporation of an intuitive color-coding system to visualize expression profiles within pathways.

    • Main Results:

    • PathwayExplorer successfully maps expression profiles onto regulatory, metabolic, and cellular pathways.
    • The tool provides ranked pathways, allowing for easy identification of biologically relevant pathways.
    • Expression profiles are displayed intuitively using a selectable color code.
    • Pathway maps are downloadable in various formats (PNG, JPG, SVG).

    Conclusions:

    • PathwayExplorer addresses the obstacle of systematic presentation and analysis of high-throughput expression data.
    • The tool enhances biological interpretation by integrating expression data with pathway information.
    • PathwayExplorer offers a valuable resource for researchers in genomics and systems biology.