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      KEGG for linking genomes to life and the environment

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          Abstract

          KEGG ( http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.

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          Whole-genome random sequencing and assembly of Haemophilus influenzae Rd.

          An approach for genome analysis based on sequencing and assembly of unselected pieces of DNA from the whole chromosome has been applied to obtain the complete nucleotide sequence (1,830,137 base pairs) of the genome from the bacterium Haemophilus influenzae Rd. This approach eliminates the need for initial mapping efforts and is therefore applicable to the vast array of microbial species for which genome maps are unavailable. The H. influenzae Rd genome sequence (Genome Sequence DataBase accession number L42023) represents the only complete genome sequence from a free-living organism.
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            Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions.

            The EC (Enzyme Commission) numbers represent a hierarchical classification of enzymatic reactions, but they are also commonly utilized as identifiers of enzymes or enzyme genes in the analysis of complete genomes. This duality of the EC numbers makes it possible to link the genomic repertoire of enzyme genes to the chemical repertoire of metabolic pathways, the process called metabolic reconstruction. Unfortunately, there are numerous reactions known to be present in various pathways, but they will never get EC numbers because the EC number assignment requires published articles on full characterization of enzymes. Here we report a computerized method to automatically assign the EC numbers up to the sub-subclasses, i.e., without the fourth serial number for substrate specificity, given pairs of substrates and products. The method is based on a new classification scheme of enzymatic reactions, named the RC (reaction classification) number. Each reaction in the current dataset of the EC numbers is first decomposed into reactant pairs. Each pair is then structurally aligned to identify the reaction center, the matched region, and the difference region. The RC number represents the conversion patterns of atom types in these three regions. We examined the correspondence between computationally assigned RC numbers and manually assigned EC numbers by the jackknife cross-validation test and found that the EC sub-subclasses could be assigned with the accuracy of about 90%. Furthermore, we examined the correlation with genomic information as represented by the KEGG ortholog clusters (OC) and confirmed that the RC numbers are correlated not only with elementary reaction mechanisms but also with protein families.
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              Comprehensive analysis of distinctive polyketide and nonribosomal peptide structural motifs encoded in microbial genomes.

              We developed a highly accurate method to predict polyketide (PK) and nonribosomal peptide (NRP) structures encoded in microbial genomes. PKs/NRPs are polymers of carbonyl/peptidyl chains synthesized by polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS). We analyzed domain sequences corresponding to specific substrates and physical interactions between PKSs/NRPSs in order to predict which substrates (carbonyl/peptidyl units) are selected and assembled into highly ordered chemical structures. The predicted PKs/NRPs were represented as the sequences of carbonyl/peptidyl units to extract the structural motifs efficiently. We applied our method to 4529 PKSs/NRPSs and found 619 PKs/NRPs. We also collected 1449 PKs/NRPs whose chemical structures have been determined experimentally. The structural sequences were compared using the Smith-Waterman algorithm, and clustered into 271 clusters. From the compound clusters, we extracted 33 structural motifs that are significantly related with their bioactivities. We used the structural motifs to infer functions of 13 novel PKs/NRPs clusters produced by Pseudomonas spp. and Burkholderia spp. and found a putative virulence factor. The integrative analysis of genomic and chemical information given here will provide a strategy to predict the chemical structures, the biosynthetic pathways, and the biological activities of PKs/NRPs, which is useful for the rational design of novel PKs/NRPs.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2008
                12 December 2007
                12 December 2007
                : 36
                : Database issue , Database issue
                : D480-D484
                Affiliations
                1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, 2Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo 108-8639 and 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.+81 774 38 3270+81 774 38 3269 kanehisa@ 123456kuicr.kyoto-u.ac.jp
                Article
                10.1093/nar/gkm882
                2238879
                18077471
                ac779377-b075-4207-bcd4-3169e9cc753e
                © 2007 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 September 2007
                : 30 September 2007
                : 1 October 2007
                Categories
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                Genetics
                Genetics

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