KEGG for representation and analysis of molecular networks involving diseases and drugs

By Minoru Kanehisa, Susumu Goto, Miho Furumichi, Mao Tanabe, Mika Hirakawa. Nucleic Acids Res., 2009

2,369 citations recorded by the source corpus.

Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ec18289446c8d300d3ac1e8dbce8b81baf2ca00a

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Citations in the corpus

Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource (2010, background): “CAMERA contains microbial metagenome data, selected reference marine microbial reference genomes, phage genomes and diverse data from reference resources such as COG, Pfam, KEGG and Genbank Refseq.”
g:Profiler—a web server for functional interpretation of gene lists (2011 update) (2011, neutral): “For instance, GO functions like ‘defense response to bacterium’ (chr. 6, P=10 8) and ‘viral reproduction’ (chr. 18, P= 10 7) as well as KEGG pathway for ‘systemic lupus erythematosus’ (P=10 12) may indicate accumulated disease resistance.”
Experimental and analytical tools for studying the human microbiome (2011, background): “Community functional potential is almost always analysed by comparing the metagenomic sequences to large databases of metabolic annotation”
ConsensusPathDB: toward a more complete picture of cell biology (2010, positive): “However, KEGG’s signaling pathways are stored in ConsensusPathDB in the form of gene lists for use in the context of gene expression analyses described below.”
KEGG for integration and interpretation of large-scale molecular data sets (2011, background): “In recent years, our efforts have been focused on capturing and representing knowledge on diseases as perturbed states of the molecular network and drugs as perturbants to the molecular network (1).”
DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs (2010, background): “In addition to these external database links, DrugBank has been reciprocally linked to several major resources including Wikipedia, UniProt (16), BioMOBY (25), PubChem (7), KEGG (9), PharmGKB (6), Drugs.com and ChemSpider (11).”
Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks (2010, background): “The first one was directly taken from KEGG (ID: hsa04330) (Figure 3A), which is probably the most complete, well-integrated, and annotated database of biological pathways [33,34].”
ALSoD: A user‐friendly online bioinformatics tool for amyotrophic lateral sclerosis genetics (2012, background): “, ProtScale [Gasteiger et al., 2005], KEGG [Kanehisa et al., 2000], Uniprot [Jain et al., 2009], iHop [Hoffmann and Valencia, 2004], Pathway in KEGG[Kanehisaetal.,2000],GeneTest[Pagonetal.,2002],AmiGO [Carbonetal.,2009],Ensembl[Hubbardetal.,2009],NCBI[Sherry etal.,2001],LifeScienceDB(Japan)[Yoshidae”
Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea (2011, methodology): “In addition, we used an automated assignment against protein domains databases such as CDD [143], KEG [144] and KOG [145].”
New approach for understanding genome variations in KEGG (2018, background): “Diseases have been viewed in KEGG as perturbed states of molecular networks caused by various perturbants of ge-netic and environmental factors, and drugs viewed as different types of perturbants (2).”
PINA v2.0: mining interactome modules (2011, methodology): “Following module identification, we annotated each module by looking for enriched terms from multiple functional databases including Gene Ontology (20), KEGG pathways (21), Pfam domains (22) and the chemical and genetic perturbations collection from MSigDB (23).”
The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored (2010, methodology): “Annotated pathway knowledge is mostly kept in a separate set of resources (22–24) and yet other interactions can be found in various organism-speciﬁc databases (25,26) or text-mining resources (27,28).”
Chipster: user-friendly analysis software for microarray and other high-throughput data (2011, methodology): “The pathway tools include gene enrichment analysis for Gene Ontology (GO) terms [21] and KEGG pathways [22] based on the hypergeometric test implemented in the GOstats package [23].”
Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data (2011, background): “We further investigated the pathways possibly affected by a drug through its target(s), where pathway information was retrieved from the KEGG database [31].”