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      KEGG: integrating viruses and cellular organisms

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          Abstract

          KEGG ( https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

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          The Hallmarks of Cancer

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            Toward understanding the origin and evolution of cellular organisms

            In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.
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              New approach for understanding genome variations in KEGG

              Abstract KEGG (Kyoto Encyclopedia of Genes and Genomes; https://www.kegg.jp/ or https://www.genome.jp/kegg/) is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It is an integrated database consisting of three generic categories of systems information, genomic information and chemical information, and an additional human-specific category of health information. KEGG pathway maps, BRITE hierarchies and KEGG modules have been developed as generic molecular networks with KEGG Orthology nodes of functional orthologs so that KEGG pathway mapping and other procedures can be applied to any cellular organism. Unfortunately, however, this generic approach was inadequate for knowledge representation in the health information category, where variations of human genomes, especially disease-related variations, had to be considered. Thus, we have introduced a new approach where human gene variants are explicitly incorporated into what we call ‘network variants’ in the recently released KEGG NETWORK database. This allows accumulation of knowledge about disease-related perturbed molecular networks caused not only by gene variants, but also by viruses and other pathogens, environmental factors and drugs. We expect that KEGG NETWORK will become another reference knowledge base for the basic understanding of disease mechanisms and practical use in clinical sequencing and drug development.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2021
                30 October 2020
                30 October 2020
                : 49
                : D1
                : D545-D551
                Affiliations
                Institute for Chemical Research, Kyoto University, Uji , Kyoto 611-0011, Japan
                Institute for Chemical Research, Kyoto University, Uji , Kyoto 611-0011, Japan
                Social ICT Solutions Department, Fujitsu Kyushu Systems Ltd., Hakata-ku , Fukuoka 812-0007, Japan
                Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku , Tokyo 108-8639, Japan
                Institute for Chemical Research, Kyoto University, Uji , Kyoto 611-0011, 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
                Author information
                http://orcid.org/0000-0001-6123-540X
                Article
                gkaa970
                10.1093/nar/gkaa970
                7779016
                33125081
                69a77414-2ee4-47f8-b98d-baaf25143593
                © The Author(s) 2020. 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 Non-Commercial 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
                : 09 October 2020
                : 08 October 2020
                : 13 September 2020
                Page count
                Pages: 7
                Funding
                Funded by: National Bioscience Database Center, DOI 10.13039/501100004696;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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