KEGG as a reference resource for gene and protein annotation

M Kanehisa, Y Sato, M Kawashima… - Nucleic acids …, 2016 - academic.oup.com
M Kanehisa, Y Sato, M Kawashima, M Furumichi, M Tanabe
Nucleic acids research, 2016academic.oup.com
Abstract KEGG (http://www. kegg. jp/or http://www. genome. jp/kegg/) is an integrated
database resource for biological interpretation of genome sequences and other high-
throughput data. Molecular functions of genes and proteins are associated with ortholog
groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE
hierarchies and KEGG modules are developed as networks of KO nodes, representing high-
level functions of the cell and the organism. Currently, more than 4000 complete genomes …
Abstract
KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.
Oxford University Press