From genomics to chemical genomics: new developments in KEGG

By Minoru Kanehisa, Susumu Goto, Masahiro Hattori, Kiyoko F. Aoki-Kinoshita, Masumi Itoh, Shuichi Kawashima, Toshiaki Katayama, M. Araki, Mika Hirakawa. Nucleic Acids Res., 2005

3,219 citations recorded by the source corpus.

The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource () provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/e5ba538944747a6b2ba2a59213268ca2f8df5456

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Citations in the corpus

SIRIUS: decomposing isotope patterns for metabolite identification (2008, background): “The majority of known metabolites have mass 1000 Da: 96.5% of sum formulas in the KEGG COMPOUND database (Kanehisa et al., 2006) fall into this mass range.”
Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. (2017, methodology): “0) (Powell et al., 2014) and the Kyoto Encyclopedia of Genes and Genomes 140 (KEGG, V59) (Tatusov et al., 2000; Kanehisa et al., 2006) databases using BLASTP (V2.”
PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways (2006, background): “KEGG and MetaCyc have collected mostly metabolic pathways, while STKE and Reactome contain well curated, publicly available data on signaling pathways.”
Linking Biochemical Pathways and Networks to Adverse Drug Reactions (2014, methodology): “The relationship between drugs and pathways was constructed by mapping protein targets extracted from DrugBank [17] to the corresponding KEGG biological pathways [18], [19] through their protein-coding gene symbols.”
eggNOG: automated construction and annotation of orthologous groups of genes (2007, positive): “These annotations are produced by a pipeline, which summarizes the available functional information on the proteins in each cluster: (1) the textual annotation for these proteins, (2) their annotated Gene Ontology (GO) terms (14), (3) their membership to KEGG pathways (15) and (4) the presence of pr”
Environments that Induce Synthetic Microbial Ecosystems (2010, background): “The randomly added reactions were taken from the subset of KEGG reactions [70] involving metabolites present in the joint model.”
ChEBI: a database and ontology for chemical entities of biological interest (2007, background): “KEGG COMPOUND—”
The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013 (2012, background): “However, other cross-references, including MetaCyc, HMDB and KEGG, are manually annotated and checked in ChEBI as part of the curation process, and therefore growth in coverage in these database links is slower.”
Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals (2017, methodology): “…ions were putatively annotated by as searching matching metabolites ATF4 regulates mitochondrial stress response • Quirós et al. 2041 in the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa et al., 2006) using an m/z tolerance of 0.001 and including all common electrospray derivatives.”
Predicting Network Activity from High Throughput Metabolomics (2013, methodology): “The reference metabolic network model The genome-scale human metabolic network in mummichog is based on KEGG [38], UCSD Recon1 [15] and Edinburgh human metabolic network [16].”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “The target similarities, on the other hand, were calculated based on the amino acid sequences of target proteins, which were retrieved from the KEGG GENES database [32].”
STITCH: interaction networks of chemicals and proteins (2007, methodology): “Finally, there are many pathway databases most prominently KEGG (12), MetaCyc (13) and Reactome (14).”
Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production (2009, background): “Often this involves visualizing expression data on metabolic maps [49].”
DrugBank: a knowledgebase for drugs, drug actions and drug targets (2007, background): “DrugBank is a database that contains extensive links to almost all major bioinformatics and biomedical databases (GenBank, SwissProt/UniProt, PDB, ChEBI, KEGG, PubChem and PubMed).”