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KEGG Database

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem.

Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of molecular networks called KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology (KO) system.

As the result, KEGG is widely used as a reference knowledge base for integration and interpretation of large-scale datasets generated by genome sequencing and other high-throughput experimental technologies. In addition to maintaining the aspects to support basic research, KEGG is being expanded towards more practical applications integrating human diseases, drugs and other health-related substances.

KEGG is developed by Kanehisa Laboratories.

Please cite the following article(s) when using KEGG.
  • 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] [pdf]
  • Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000). [pubmed] [pdf]
Please see the following for background and basic concepts of KEGG.

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]
  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] [pdf]
  5. Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000). [pubmed] [pdf]
  6. Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A.; The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42-46 (2002). [pubmed] [pdf]
  7. Kanehisa, M.; The KEGG database. Novartis Found. Symp. 247, 91-103 (2002). [pubmed] [pdf]
  8. 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] [pdf]
  9. 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] [pdf] [Thomson]
  10. 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] [pdf]
  11. 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] [pdf]
  12. 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] [pdf]
  13. 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] [pdf]

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] [pdf]
  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] [pdf]
  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] [pdf]
  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] [pdf]
(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]
    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] [pdf]
  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]
    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] [pdf]
  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]
    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] [pdf]
  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] [pdf]
(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] [pdf]
  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] [pdf]
(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]
  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

The KEGG project is partially supported by:
The computational resources are provided by:
Past supports include:
  • Grand-in-aid for scientific research on the priority area "Genome Informatics" (1995) from the Ministry of Education
  • Grand-in-aid for scientific research on the priority area "Genome Science" (1996-2000) from the Ministry of Education
  • Research for the Future Program (2000-2004) from the Japan Society for the Promotion of Science
  • Bioinformatics Research and Development (2001-2005) of the Japan Science and Technology Agency
  • 21st Century COE Program "Genome Science" (2003-2007) from the Ministry of Education
  • Grant-in-aid for scientific research on the priority area "Comprehensive Genomics" (2005-2009) from the Ministry of Education
  • Bioinformatics Research and Development (2006-2010) of the Japan Science and Technology Agency
More details of each project can be found in the Kanehisa Laboratories Archive.

Last updated: December 1, 2013
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