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      KEGG: new perspectives on genomes, pathways, diseases and drugs

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          Abstract

          KEGG ( http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

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          Fifty-five years of enzyme classification: advances and difficulties.

          Since the publication of a list of enzymes classified according to the reactions that they catalysed, by Dixon and Webb in 1958, its content and presentation have undergone a number of significant changes. These have been necessitated by new information, as well as the need to improve clarity. The move from printed versions to the online environment, through the ExplorEnz website, has allowed the process of adding newly reported enzymes to be automated and the information content to be enriched. Search and output facilities have also been enhanced. These and the problems attendant on the use of the Enzyme Commission classification system for some groups of enzymes are the subject of this review. © 2013 FEBS.
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            GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array

            DNA sequences are translated into protein coding sequences and then further assigned to protein families in metagenomic analyses, because of the need for sensitivity. However, huge amounts of sequence data create the problem that even general homology search analyses using BLASTX become difficult in terms of computational cost. We designed a new homology search algorithm that finds seed sequences based on the suffix arrays of a query and a database, and have implemented it as GHOSTX. GHOSTX achieved approximately 131–165 times acceleration over a BLASTX search at similar levels of sensitivity. GHOSTX is distributed under the BSD 2-clause license and is available for download at http://www.bi.cs.titech.ac.jp/ghostx/. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We offer this tool as a potential solution to this problem.
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              Modular Architecture of Metabolic Pathways Revealed by Conserved Sequences of Reactions

              The metabolic network is both a network of chemical reactions and a network of enzymes that catalyze reactions. Toward better understanding of this duality in the evolution of the metabolic network, we developed a method to extract conserved sequences of reactions called reaction modules from the analysis of chemical compound structure transformation patterns in all known metabolic pathways stored in the KEGG PATHWAY database. The extracted reaction modules are repeatedly used as if they are building blocks of the metabolic network and contain chemical logic of organic reactions. Furthermore, the reaction modules often correspond to traditional pathway modules defined as sets of enzymes in the KEGG MODULE database and sometimes to operon-like gene clusters in prokaryotic genomes. We identified well-conserved, possibly ancient, reaction modules involving 2-oxocarboxylic acids. The chain extension module that appears as the tricarboxylic acid (TCA) reaction sequence in the TCA cycle is now shown to be used in other pathways together with different types of modification modules. We also identified reaction modules and their connection patterns for aromatic ring cleavages in microbial biodegradation pathways, which are most characteristic in terms of both distinct reaction sequences and distinct gene clusters. The modular architecture of biodegradation modules will have a potential for predicting degradation pathways of xenobiotic compounds. The collection of these and many other reaction modules is made available as part of the KEGG database.
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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
                04 January 2017
                29 November 2016
                29 November 2016
                : 45
                : Database issue , Database issue
                : D353-D361
                Affiliations
                [1 ]Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
                [2 ]Healthcare Solutions Department, Fujitsu Kyushu Systems Ltd., Hakata-ku, Fukuoka 812-0007, Japan
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +81 774 38 4521; Fax: +81 774 38 3269; Email: kanehisa@ 123456kuicr.kyoto-u.ac.jp
                Article
                10.1093/nar/gkw1092
                5210567
                27899662
                3ae59a84-182f-4017-b492-e4883485ea3b
                © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 26 October 2016
                : 09 October 2016
                Page count
                Pages: 9
                Categories
                Database Issue
                Custom metadata
                04 January 2017

                Genetics
                Genetics

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