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Related Experiment Videos

KEGG as a glycome informatics resource.

Kosuke Hashimoto1, Susumu Goto, Shin Kawano

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.

Glycobiology
|July 15, 2005
PubMed
Summary

Glycome informatics requires comprehensive data resources. KEGG now offers GLYCAN, pathways, and Composite Structure Map (CSM) for genome-scale glycan analysis.

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Area of Science:

  • Bioinformatics
  • Glycobiology
  • Computational Biology

Background:

  • Glycobiology research increasingly relies on large-scale protein and carbohydrate data.
  • Existing experimental data is insufficient for comprehensive genome-scale glycan bioinformatics analysis.
  • Glycome informatics necessitates integrated resources for analyzing complex carbohydrate data.

Purpose of the Study:

  • To develop a comprehensive glycome informatics resource within the KEGG knowledge base.
  • To provide tools and databases for genome-scale bioinformatics analysis of glycan data.
  • To integrate carbohydrate structures, proteins, and reactions for pathway analysis.

Main Methods:

  • Development of the GLYCAN database for carbohydrate structures.
  • Integration of glycan biosynthesis and metabolism pathways within KEGG.

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  • Creation of the Composite Structure Map (CSM) for visualizing carbohydrate structure variations.
  • Inclusion of tools like KegDraw and KEGG Carbohydrate Matcher (KCaM).

    • Main Results:

    • The KEGG glycome informatics resource includes the GLYCAN database, glycan-related pathways, and CSM.
    • GLYCAN offers KegDraw for structure drawing and KCaM for search and alignment.
    • KEGG pathways integrate carbohydrate structures, proteins, and reactions.
    • CSM bridges carbohydrate functions and structures, enabling visualization of expression data.

    Conclusions:

    • The developed KEGG glycome informatics resource provides essential tools for genome-scale glycan analysis.
    • The integration of structural, pathway, and functional information facilitates deeper understanding of glycans.
    • Representing objects as graphs enhances computational analysis and data integration in glycobiology.