Abstract
In the era of high-throughput biology it is necessary to develop not only elaborate computational methods but also well-curated databases that can be used as reference for data interpretation. KEGG (http://www.kegg.jp/) is such a reference knowledge base with two specific aims. One is to compile knowledge on high-level functions of the cell and the organism in terms of the molecular interaction and reaction networks, which is implemented in KEGG pathway maps, BRITE functional hierarchies, and KEGG modules. The other is to expand knowledge on genes and proteins involved in the molecular networks from experimentally observed organisms to other organisms using the concept of orthologs, which is implemented in the KEGG Orthology (KO) system. Thus, KEGG is a generic resource applicable to all organisms and enables interpretation of high-level functions from genomic and molecular data. Here we first present a brief overview of the entire KEGG resource, and then give an introduction of how to use KEGG in plant genomics and metabolomics research.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:D199–D205
Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30
UniProt Consortium (2014) Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191–D198
Moriya Y, Itoh M, Okuda S, Yoshizawa A, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:W182–W185
Muto A, Kotera M, Tokimatsu T, Nakagawa Z, Goto S, Kanehisa M (2013) Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J Chem Inf Model 53:613–622
Kanehisa M (2013) Chemical and genomic evolution of enzyme-catalyzed reaction networks. FEBS Lett 587:2731–2737
McDonald AG, Boyce S, Tipton KF (2009) ExplorEnz: the primary source of the IUBMB enzyme list. Nucleic Acids Res 37:D593–D597
Hattori M, Tanaka N, Kanehisa M, Goto S (2010) SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res 38:W652–W656
Moriya Y, Shigemizu D, Hattori M, Tokimatsu T, Kotera M, Goto S, Kanehisa M (2010) PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res 38:W138–W143
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Kanehisa, M. (2016). KEGG Bioinformatics Resource for Plant Genomics and Metabolomics. In: Edwards, D. (eds) Plant Bioinformatics. Methods in Molecular Biology, vol 1374. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3167-5_3
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3167-5_3
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3166-8
Online ISBN: 978-1-4939-3167-5
eBook Packages: Springer Protocols