KEGG: new perspectives on genomes, pathways, diseases and drugs

By Minoru Kanehisa, Miho Furumichi, Mao Tanabe, Yoko Sato, Kanae Morishima. 2016

7,790 citations recorded by the source corpus.

KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.

Subfields: Molecular Biology

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Citations in the corpus

Mordred: a molecular descriptor calculator (2018, methodology): “We used entries from the KEGG-drug database [32] as the benchmark target.”
eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses (2018, methodology): “Orthologous Groups were functionally annotated using updated versions of Gene Ontology (36), KEGG pathways (37), SMART/PFAM domains (38) and expanded to CAZy (39) and KEGG modules.”
miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions (2017, positive): “Likewise, we integrated PubMed, to provide article information, TargetScan (22) and miRanda (23) for target prediction, Gene Expression Omnibus (GEO) (24) and The Cancer Genome Atlas (TCGA) (25,26) for gene and miRNA expression profiling, and KEGG (27) and DAVID (28) for functional annotations of mi”
COG database update: focus on microbial diversity, model organisms, and widespread pathogens (2020, background): “Although there are several other databases of orthologous proteins in bacteria and archaea, such as eggNOG (49), KEGG Orthology (50,51), OMA (52,53), OrthoDB (54) and MBGD (55), which cover a wider selection of organisms than the COGs and provide comprehensive, automated functional annotation of the”
The Comparative Toxicogenomics Database: update 2019 (2018, background): “External integration of CTD content with annotations from Gene Ontology (29), KEGG (30), Reactome (31) and BioGRID (32) produces an additional 9.4 million inferences (Table 1).”
The Reactome Pathway Knowledgebase (2017, background): “A variety of online resources capture aspects of this information at the level of individual reactions such as Rhea (1) or at the level of reaction sequences spanning various domains of biology such as KEGG (2), MetaCyc (3) or PANTHER (4).”
A pathology atlas of the human cancer transcriptome (2017, positive): “The Hallmarks of Cancer (11) has laid an important foundation for understanding cancer pathogenesis, and from the corresponding cellular processes, 2172 genes have recently been defined as hallmark-related genes (16, 20).”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Biological process labels are stored in databases such as MSigDB (Liberzon et al , 2011), the Gene Ontology (Ashburner et al , 2000; The Gene Ontology Consortium, 2017), or the pathway databases KEGG (Kanehisa et al , 2017) and Reactome (Fabregat et al , 2018).”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, methodology): “The database channel is based on manually curated interaction records assembled by expert curators, at KEGG (41), Reactome (50), BioCyc (51) and Gene Ontology (52), as well as legacy datasets from PID and BioCarta.”
ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis (2020, neutral): “Resources like the Gene Ontology (GO) (1), the Kyoto Encyclopedia of Genes and Genomes (KEGG) (2), the Molecular Signatures Database (MSigDB) (3), or Reactome (4) organize existing biological knowledge into gene sets or pathways.”
Pathview Web: user friendly pathway visualization and data integration (2017, background): “KEGG WebLinks (22) and KEGG Mapper (23) (details in Supplementary Note 1).”
PDBsum: Structural summaries of PDB entries (2017, methodology): “3(D)] as obtained from the Kyoto Encyclopedia of Genes and Genomes 35 (KEGG).”
clusterProfiler 4.0: A universal enrichment tool for interpreting omics data (2021, background): “19 Molecular functions are represented by networks of interactions and reactions mainly in the form of KEGG pathways and modules.”