Skip to main content

KEGG Bioinformatics Resource for Plant Genomics and Metabolomics

  • Protocol

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1374))

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

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. 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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. UniProt Consortium (2014) Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191–D198

    Article  Google Scholar 

  4. 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

    Article  PubMed Central  PubMed  Google Scholar 

  5. 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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Kanehisa M (2013) Chemical and genomic evolution of enzyme-catalyzed reaction networks. FEBS Lett 587:2731–2737

    Article  CAS  PubMed  Google Scholar 

  7. McDonald AG, Boyce S, Tipton KF (2009) ExplorEnz: the primary source of the IUBMB enzyme list. Nucleic Acids Res 37:D593–D597

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Hattori M, Tanaka N, Kanehisa M, Goto S (2010) SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res 38:W652–W656

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. 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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minoru Kanehisa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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

Publish with us

Policies and ethics