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Related Experiment Videos

A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters.

H Ogata1, W Fujibuchi, S Goto

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

Nucleic Acids Research
|October 12, 2000
PubMed
Summary
This summary is machine-generated.

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Computational graph comparison reveals functionally related enzyme clusters (FRECs) in microbial genomes. The prevalence of these conserved pathway motifs varies significantly across different organisms, impacting genome function understanding.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • KEGG database provides biochemical pathway information.
  • Genomes and pathways can be represented as graphs for analysis.

Purpose of the Study:

  • Develop computational methods to understand higher-level genome functions.
  • Identify local similarities between genome and pathway graphs.

Main Methods:

  • Graph comparison method to detect local similarities (correlated clusters).
  • Method allows for gaps and mismatches in nodes and edges.
  • Applied to 10 complete microbial genomes and KEGG metabolic pathways.

Main Results:

  • Identified functionally related enzyme clusters (FRECs) in genomes.

Related Experiment Videos

  • FREC prevalence varied significantly by organism (e.g., ~50% in E. coli, <10% in yeast).
  • FRECs represent conserved pathway motifs and are organized into ortholog group tables.

    • Conclusions:

    • Computational graph analysis is effective for identifying biological features.
    • Organism-specific variations in FRECs highlight differences in metabolic pathway organization.
    • KEGG's FREC collection aids in understanding conserved pathway motifs across sequenced genomes.