KEGG: Kyoto Encyclopedia of Genes and Genomes

By Minoru Kanehisa, Susumu Goto. Nucleic Acids Res., 2000

26,580 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cc9344f7ef22f27ec864fdc492e781cdfc81b407

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In Molecular Biology

Citations in the corpus

Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (2012, background): “Example of enriched KEGG pathway gene cluster (Huang et al., 2009; Benjamini p < 0.05) shown below Figure 5C.”
Molecular signatures database (MSigDB) 3.0 (2011, background): “publications, and entire collections of sets derived from specialized resources such as GO, KEGG (Kanehisa and Goto, 2000), TRANSFAC and L2L.”
High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED (2015, methodology): “Reads were drawn from twenty bacterial genomes obtained from the KEGG database [43, 44].”
Similarity Search in Graph Databases: A Multi-Layered Indexing Approach (2017, methodology): “In bioinformatics, graph similarity tools are devised for biological pathway enumeration and protein interaction detection [20].”
starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data (2010, methodology): “To provide valuable insights as to the function of each miRNA, we carried out a comprehensive gene set analysis of the miRNA target sets by combining the KEGG pathways (37), the BioCarta pathways and the Gene Ontologies (GO) categories (36).”
Identification of functional modules using network topology and high-throughput data (2007, background): “…used four annotation databases: (a) GO "biological process" ontology (level 7; 474 categories) [29]; (b) GO complexes annotation (subterms of "protein complex" term, 213 com-BMC plexes); (c) MIPS deletion phenotype annotations [30] (181 phenotypes); (d) KEGG molecular pathways (310 pathways) [14].”
pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks (2019, methodology): “…via active subnetwork identification, and (iii) pathway/gene set annotations from sources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000; Kanehisa et al., 2017), Reactome (Fabregat et al., 2018), BioCarta (Nishimura, 2001), and Gene Ontology (GO) (Ashburner et…”
Selected reaction monitoring for quantitative proteomics: a tutorial (2008, background): “…and Ponten (2005) Gene ontology groups GO http://www.geneontology.org/ Ashburner et al (2000) Functional groups KEGG http://www.genome.jp/kegg/ Kanehisa and Goto (2000) Protein–protein interactions IntAct http://www.ebi.ac.uk/intact/ Kerrien et al (2007) Protein–protein interactions MINT…”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, neutral): “Shotgun Metagenomics Taxonomic classification MetaPhlAn4 [158], Kaiju [159], Kraken [160] Assembly of metagenomic reads metaSPAdes [86], metaIDBA [87] Protein databases for taxonomic classification NCBI non-redundant protein database [83] Gene annotation Prokka [88], MetaGeneMark [89] Databases for ”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “These knowledge bases include KEGG [32], MetaCyc [33], Reactome [34], RegulonDB [35], STKE (http://stke.sciencemag.org), BioCarta (http://www. biocarta.com), and PantherDB [36].”
The human microbiome (2017, methodology): “The functional capacity of the metagenome can be determined by comparing predicted protein-coding genes, identified by a BLASTX search, to databases such as the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database [9] and/or COG (Clusters of Orthologous Groups of proteins) functional cate”
NetPath: a public resource of curated signal transduction pathways (2010, methodology): “Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes.”