Abstract
KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY and MODULE databases. Genes in the complete genomes, which are stored in the GENES database, are given KO identifiers by the internal annotation procedure, enabling reconstruction of KEGG pathways and modules for interpretation of higher-level functions. This is possible because all the KEGG pathways and modules are represented as networks of KO nodes. Here we present knowledge-based prediction methods for functional characterization of amino acid sequences using the KEGG resource. Specifically we show how the tools available at the KEGG website including BlastKOALA and KEGG Mapper can be utilized for enzyme annotation and metabolic reconstruction.
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Acknowledgment
This work was partially supported by the National Bioscience Database Center of the Japan Science and Technology Agency. The computational resource for developing and servicing KEGG is provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University.
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Kanehisa, M. (2017). Enzyme Annotation and Metabolic Reconstruction Using KEGG. In: Kihara, D. (eds) Protein Function Prediction. Methods in Molecular Biology, vol 1611. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7015-5_11
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DOI: https://doi.org/10.1007/978-1-4939-7015-5_11
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