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KEGG Database

The KEGG database project was initiated in 1995 under the Japanese Human Genome Project, foreseeing the need for a reference resource that would enable understanding of the biological systems, such as the cell and the organism, from genome sequence data.

Major efforts have been undertaken to represent the biological systems in terms of molecular networks (molecular wiring diagrams), especially in the form of KEGG pathway maps that are manually created by capturing knowledge from published literature. Continuous efforts have also been made to develop and improve the KO (KEGG Orthology) system for representation of gene/protein functional orthologs in molecular networks. Consequently, KEGG has been used in biological big data analysis, for example, for uncovering systemic functions of an organism hidden in its genome sequence through the KEGG mapping procedure.

In addition to such basic research aspects, KEGG has been expanded into practical areas promoting health science applications for use in society. Specifically, the drug labels of all marketed drugs in Japan and the USA are integrated with the research-oriented KEGG original drug and disease databases. KEGG has become one of the most utilized biological databases accessed by millions of visitors per month.

KEGG is developed by Kanehisa Laboratories.

Please cite the following article(s) when using KEGG.
Please see the following for background and basic concepts of KEGG.
Copyright permission of KEGG pathway maps, etc. in academic publications may be obtained by using the copyright permission request form.

References on KEGG

  1. Kanehisa, M.; Toward pathway engineering: a new database of genetic and molecular pathways. Science & Technology Japan, No. 59, pp. 34-38 (1996). [pdf]
  2. Goto, S., Bono, H., Ogata, H., Fujibuchi, W., Nishioka, T., Sato, K., and Kanehisa, M.; Organizing and computing metabolic pathway data in terms of binary relations. Pacific Symp. Biocomputing 1997, 175-186 (1996). [pubmed] [pdf]
  3. Kanehisa, M.; A database for post-genome analysis. Trends Genet. 13, 375-376 (1997). [pubmed] [doi]
  4. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., and Kanehisa, M.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29-34 (1999). [pubmed] [doi]
  5. Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000). [pubmed] [doi]
  6. Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A.; The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42-46 (2002). [pubmed] [doi]
  7. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., and Hattori, M.; The KEGG resources for deciphering the genome. Nucleic Acids Res. 32, D277-D280 (2004). [pubmed] [doi]
  8. Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., and Hirakawa, M.; From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354-357 (2006). [pubmed] [doi] [Thomson]
  9. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., and Yamanishi, Y.; KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480-D484 (2008). [pubmed] [doi]
  10. Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., and Hirakawa, M.; KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38, D355-D360 (2010). [pubmed] [doi]
  11. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M.; KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res. 40, D109-D114 (2012). [pubmed] [doi]
  12. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014). [pubmed] [doi]
  13. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457-D462 (2016). [pubmed] [doi]
  14. Kanehisa, Furumichi, M., Tanabe, M., Sato, Y., and Morishima, K.; KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017). [pubmed] [doi]
  15. Kanehisa, M., Sato, Y., Furumichi, M., Morishima, K., and Tanabe, M.; New approach for understanding genome variations in KEGG. Nucleic Acids Res. 47, D590-D595 (2019). [pubmed] [doi]
  16. Kanehisa, M; Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947-1951 (2019) [pubmed] [doi]
  17. Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., and Tanabe, M.; KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545-D551 (2021). [pubmed] [doi]

Selected references on analysis tools

See also the publication list of Minoru Kanehisa.
(Genome analysis tools)
  1. Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A., and Kanehisa, M.; KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182-W185 (2007). [pubmed] [doi]
  2. Okuda, S., Yamada, T., Hamajima, M., Itoh, M., Katayama, T., Bork, P., Goto, S., and Kanehisa, M.; KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36, W423-W426 (2008). [pubmed] [doi]
  3. Kotera, M., Yamanishi, Y., Moriya, Y., Kanehisa, M., and Goto, S.; GENIES: gene network inference engine based on supervised analysis. Nucleic Acids Res. 40, W162-W167 (2012). [pubmed] [doi]
  4. Nakaya, A., Katayama, T., Itoh, M., Hiranuka, K., Kawashima, S., Moriya, Y., Okuda, S., Tanaka, M., Tokimatsu, T., Yamanishi, Y., Yoshizawa, A.C., Kanehisa, M., and Goto, S.; KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res. 41, D353-D357 (2013). [pubmed] [doi]
  5. Kanehisa, M., Sato, Y., and Morishima, K.; BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726-731 (2016). [pubmed] [doi]
  6. Kanehisa, M.; KEGG bioinformatics resource for plant genomics and metabolomics. Methods Mol. Biol. 1374, 55-70 (2016). [pubmed] [doi]
  7. Kanehisa, M.; Enzyme annotation and metabolic reconstruction using KEGG. Methods Mol. Biol. 1611, 135-145 (2017). [pubmed] [doi]
  8. Kanehisa, M.; Inferring antimicrobial resistance from pathogen genomes in KEGG. Methods Mol. Biol. 1807, 225-239 (2018). [pubmed] [doi]
  9. Kanehisa, M. and Sato, Y.; KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 29, 28-35 (2019) [pubmed] [doi]
  10. Kanehisa, M., Sato, Y., and Kawashima, M.; KEGG mapping tools for uncovering hidden features in biological data. Protein Sci. 31, 47-53 (2022). [pubmed] [doi]
(Chemical analysis tools)
  1. Hattori, M., Okuno, Y., Goto, S., and Kanehisa, M.; Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125, 11853-11865 (2003). [pubmed] [doi]
    Hattori, M., Tanaka, N., Kanehisa, M., and Goto, S.; SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res. 38, W652-W656 (2010). [pubmed] [doi]
  2. Kotera, M., Okuno, Y., Hattori, M., Goto, S., and Kanehisa, M.; Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions. J. Am. Chem. Soc. 126, 16487-16498 (2004). [pubmed] [doi]
    Yamanishi, Y., Hattori, M., Kotera, M., Goto, S., and Kanehisa, M.; E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics 25, i79-i86 (2009). [pubmed] [doi]
  3. Oh, M., Yamada, T., Hattori, M., Goto, S., and Kanehisa, M.; Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J. Chem. Inf. Model. 47, 1702-1712 (2007). [pubmed] [doi]
    Moriya, Y., Shigemizu, D., Hattori, M., Tokimatsu, T., Kotera, M., Goto, S., and Kanehisa, M.; PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res. 38, W138-W143 (2010). [pubmed] [doi]
  4. Muto, A., Kotera, M., Tokimatsu, T., Nakagawa, Z., Goto, S., and Kanehisa, M.; Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J. Chem. Inf. Model. 53, 613-622 (2013). [pubmed] [doi]
(Glycan analysis tools)
  1. Hashimoto, K., Goto, S., Kawano, S., Aoki-Kinoshita, K.F., Ueda, N., Hamajima, M., Kawasaki, T., and Kanehisa, M.; KEGG as a glycome informatics resource. Glycobiology 16, 63R-70R (2006). [pubmed] [doi]
  2. Aoki, K.F., Yamaguchi, A., Ueda, N., Akutsu, T., Mamitsuka, H., Goto, S., and Kanehisa, M.; KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains. Nucleic Acids Res. 32, W267-W272 (2004). [pubmed] [doi]
(DBGET/LinkDB)
  1. Akiyama, Y., Goto, S., Uchiyama, I., and Kanehisa, M.; WebDBGET: an integrated database retrieval system which provides hyper-links among related entries. MIMBD'95: Second Meeting on the Interconnection of Molecular Biology Databases (1995). [pdf]
  2. Goto, S., Akiyama, Y., and Kanehisa, M.; LinkDB: a database of cross links between molecular biology databases. MIMBD'95: Second Meeting on the Interconnection of Molecular Biology Databases (1995). [pdf]
  3. Kanehisa, M.; Linking databases and organisms: GenomeNet resources in Japan. Trends Biochem Sci. 22, 442-444 (1997). [pubmed] [doi]
  4. Fujibuchi, W., Goto, S., Migimatsu, H., Uchiyama, I., Ogiwara, A., Akiyama, Y., and Kanehisa, M.; DBGET/LinkDB: an integrated database retrieval system. Pacific Symp. Biocomputing 1998, 683-694 (1997). [pubmed] [pdf]

Acknowledgments

KEGG is developed in collaboration with:
The health information category of KEGG (KEGG MEDICUS) is partially supported by:
Past supports include:
More details of each project can be found in the Kanehisa Laboratories Archive.

Last updated: April 1, 2022

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