KEGG for integration and interpretation of large-scale molecular data sets

By Minoru Kanehisa, Susumu Goto, Yoko Sato, Miho Furumichi, Mao Tanabe. Nucleic Acids Res., 2011

4,886 citations recorded by the source corpus.

Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8552adffd9783b1a63e82757f3886e93d5ab015d

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Citations in the corpus

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data (2013, methodology): “The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (34) were downloaded from the KEGG database.”
Probiotics, gut microbiota, and their influence on host health and disease (2017, methodology): “Bowtie2 tool [111] enables read alignment to the human genome (reference) and to the KEGG database [112].”
Transcriptional responses of three model diatoms to nitrate limitation of growth (2014, methodology): “2.27+, e-value 1 × 10 −5 ) to proteins within the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2012-05-09 database (Altschul et al., 1990; Kanehisa and Goto, 2000; Kanehisa et al., 2012).”
MicroScope—an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data (2012, methodology): “These metabolic data sets, together with the mapping of the predicted enzymatic functions onto the KEGG metabolic maps (12), are made available through the ‘Metabolic tools’ menu of the web interface.”
BugBase predicts organism-level microbiome phenotypes (2017, methodology): “BugBase uses input from various databases, such as Integrated Microbial Genomes (IMG) [34], the Kyoto Encyclopedia of Genes and Genomes (KEGG) [15] and the Pathosystems Resource Integration Center (PATRIC) [35] to categorize six main phenotype categories: Gram staining, oxygen tolerance, ability to…”
Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “For the metabolites that were not fully connected, a universal database of metabolic reactions 65 was used to predict the fewest reactions that were required to fully connect them.”
The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease (2018, background): “From each map entity, users can access the corresponding entry in the VMH database and obtain further information from external resources, such as the HMDB24, KEGG25, and CHEBI.”
The Placenta Harbors a Unique Microbiome (2014, methodology): “The remaining reads were searched against a protein database of all the ORF from prokaryotic organisms in KEGG v54 (55) using BLASTX.”
Pathway-based personalized analysis of cancer (2013, methodology): “The PDS for 548 pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG) (12, 13), BioCarta (14) and the National Cancer Institute – Nature Pathway Interaction Database (15) are presented in a 548 × 455 table (Fig.”
Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization (2012, methodology): “We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug–target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins.”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “Some of these knowledge bases are biologically focused, such as KEGG [8], STRING [9], InterPro [10], and InterDom [11].”
Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction (2017, positive): “such as STRING [34], KEGG [35], InterPro [36], Aceview [37], and Pfam [38].”