Nucleic Acids Research, 2000, Vol. 28, No. 20 4021-4028
© 2000 Oxford University Press
A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters
Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
The availability of computerized knowledge on biochemical pathways in the KEGG database opens new opportunities for developing computational methods to characterize and understand higher level functions of complete genomes. Our approach is based on the concept of graphs; for example, the genome is a graph with genes as nodes and the pathway is another graph with gene products as nodes. We have developed a simple method for graph comparison to identify local similarities, termed correlated clusters, between two graphs, which allows gaps and mismatches of nodes and edges and is especially suitable for detecting biological features. The method was applied to a comparison of the complete genomes of 10 microorganisms and the KEGG metabolic pathways, which revealed, not surprisingly, a tendency for formation of correlated clusters called FRECs (functionally related enzyme clusters). However, this tendency varied considerably depending on the organism. The relative number of enzymes in FRECs was close to 50% for Bacillus subtilis and Escherichia coli, but was <10% for Synechocystis and Saccharomyces cerevisiae. The FRECs collection is reorganized into a collection of ortholog group tables in KEGG, which represents conserved pathway motifs with the information about gene clusters in all the completely sequenced genomes.
* To whom correspondence should be addressed. Tel: +81 774 38 3270; Fax: +81 774 38 3269; Email: kanehisa@kuicr.kyoto-u.ac.jp Present addresses: Hiroyuki Ogata, Information Génétique et Structurale, CNRS-UMR 1889, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France Wataru Fujibuchi, National Center for Biotechnology Information, National Institutes of Health, Building 38A, Room B2N14, Bethesda, MD 20894, USA
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