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      From genomics to chemical genomics: new developments in KEGG

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          Abstract

          The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource ( http://www.genome.jp/kegg/) provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

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          The KEGG databases at GenomeNet.

          The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the primary database resource of the Japanese GenomeNet service (http://www.genome.ad.jp/) for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. KEGG consists of the PATHWAY database for the computerized knowledge on molecular interaction networks such as pathways and complexes, the GENES database for the information about genes and proteins generated by genome sequencing projects, and the LIGAND database for the information about chemical compounds and chemical reactions that are relevant to cellular processes. In addition to these three main databases, limited amounts of experimental data for microarray gene expression profiles and yeast two-hybrid systems are stored in the EXPRESSION and BRITE databases, respectively. Furthermore, a new database, named SSDB, is available for exploring the universe of all protein coding genes in the complete genomes and for identifying functional links and ortholog groups. The data objects in the KEGG databases are all represented as graphs and various computational methods are developed to detect graph features that can be related to biological functions. For example, the correlated clusters are graph similarities which can be used to predict a set of genes coding for a pathway or a complex, as summarized in the ortholog group tables, and the cliques in the SSDB graph are used to annotate genes. The KEGG databases are updated daily and made freely available (http://www.genome.ad.jp/kegg/).
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            Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways.

            Cellular functions result from intricate networks of molecular interactions, which involve not only proteins and nucleic acids but also small chemical compounds. Here we present an efficient algorithm for comparing two chemical structures of compounds, where the chemical structure is treated as a graph consisting of atoms as nodes and covalent bonds as edges. On the basis of the concept of functional groups, 68 atom types (node types) are defined for carbon, nitrogen, oxygen, and other atomic species with different environments, which has enabled detection of biochemically meaningful features. Maximal common subgraphs of two graphs can be found by searching for maximal cliques in the association graph, and we have introduced heuristics to accelerate the clique finding and to detect optimal local matches (simply connected common subgraphs). Our procedure was applied to the comparison and clustering of 9383 compounds, mostly metabolic compounds, in the KEGG/LIGAND database. The largest clusters of similar compounds were related to carbohydrates, and the clusters corresponded well to the categorization of pathways as represented by the KEGG pathway map numbers. When each pathway map was examined in more detail, finer clusters could be identified corresponding to subpathways or pathway modules containing continuous sets of reaction steps. Furthermore, it was found that the pathway modules identified by similar compound structures sometimes overlap with the pathway modules identified by genomic contexts, namely, by operon structures of enzyme genes.
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              A database for post-genome analysis.

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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Research
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 January 2006
                01 January 2006
                28 December 2005
                : 34
                : Database issue
                : D354-D357
                Affiliations
                1Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto 611-0011, Japan
                2Human Genome Center, Institute of Medical Science, University of Tokyo Minato-ku, Tokyo 108-8639, Japan
                3Institute for Bioinformatics Research and Development, Japan Science and Technology Agency Chiyoda-ku, Tokyo 102-8666, Japan
                Author notes
                *To whom correspondence should be addressed. Tel: +81 774 38 3270; Fax: +81 774 38 3269; Email: kanehisa@ 123456kuicr.kyoto-u.ac.jp
                Article
                10.1093/nar/gkj102
                1347464
                16381885
                20714491-a4a4-4c42-90fb-e17a4f4818fd
                © The Author 2006. Published by Oxford University Press. All rights reserved

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@ 123456oxfordjournals.org

                History
                : 14 September 2005
                : 17 October 2005
                : 17 October 2005
                Categories
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                Genetics
                Genetics

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