The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
KEGG mapping tools for uncovering hidden features in biological data
Open accessAbstract
In contrast to artificial intelligence and machine learning approaches, KEGG (https://www.kegg.jp) has relied on human intelligence to develop “models” of biological systems, especially in the form of KEGG pathway maps that are manually created by capturing knowledge from published literature. The KEGG models can then be used in biological big data analysis, for example, for uncovering systemic functions of an organism hidden in its genome sequence through the simple procedure of KEGG mapping. Here we present an updated version of KEGG Mapper, a suite of KEGG mapping tools reported previously (Kanehisa and Sato, Protein Sci 2020; 29:28–35), together with the new versions of the KEGG pathway map viewer and the BRITE hierarchy viewer. Significant enhancements have been made for BRITE mapping, where the mapping result can be examined by manipulation of hierarchical trees, such as pruning and zooming. The tree manipulation feature has also been implemented in the taxonomy mapping tool for linking KO (KEGG Orthology) groups and modules to phenotypes.
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Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Self-citationKEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Self-citationToward understanding the origin and evolution of cellular organisms
Kanehisa, M. (2019) · Protein Science · doi:10.1002/pro.3715 · Cited by 5,509 · cited 1×
“The KEGG database resource has been developed as a reference knowledge base for uncovering cellular and organism-level functions from genome sequences and other molecular datasets.(1) This is accomplished by the procedure of KEGG mapping, especially with the concept of functional orthologs.”
MethodSelf-citationKAAS: an automatic genome annotation and pathway reconstruction server
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C., & Kanehisa, M. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm321 · Cited by 4,153
Self-citationKanehisa, M., Sato, Y., & Morishima, K. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.11.006 · Cited by 4,055
Self-citationKEGG: integrating viruses and cellular organisms
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., & Tanabe, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa970 · Cited by 3,436
Self-citationKofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
Aramaki, T., Blanc-Mathieu, R., Endo, H., Ohkubo, K., Kanehisa, M., Goto, S., & Ogata, H. (2019) · doi:10.1101/602110 · Cited by 2,054
Self-citationFederhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436 · cited 1×
“conversion through functional orthologs is also available, including metabolites mapped to metabolic pathways, drugs and diseases mapped to BRITE hierarchical classifications, and cellular organisms and viruses mapped to NCBI taxonomy.(4) This article reports KEGG Mapper Version 5, an updated collection of KEGG mappin…”
MethodVirus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV)
Lefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., & Smith, D. B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx932 · Cited by 1,227
KEGG Mapper for inferring cellular functions from protein sequences
Kanehisa, M., & Sato, Y. (2020) · Protein Science · doi:10.1002/pro.3711 · Cited by 1,180
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Cited by
KEGG for taxonomy-based analysis of pathways and genomes
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac963 · Cited by 5,833
Self-citationA common vesicle proteome drives fungal biofilm development
Zarnowski, R., Sanchez, H., Jaromin, A., Zarnowska, U. J., Nett, J. E., Mitchell, A. P., & Andes, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2211424119 · Cited by 63
Viruses of sulfur oxidizing phototrophs encode genes for pigment, carbon, and sulfur metabolisms
Hesketh-Best, P. J., Bosco-Santos, A., Garcia, S. L., O’Beirne, M. D., Werne, J. P., Gilhooly, W. P., & Silveira, C. B. (2023) · Communications Earth & Environment · doi:10.1038/s43247-023-00796-4 · Cited by 16
(2025) · doi:10.1128/jvi.01061-25 · Cited by 5
Deciphering enzymatic potential in metagenomic reads through DNA language models
Prabakaran, R., & Bromberg, Y. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf836 · Cited by 4
Metadata
- Authors
- Kanehisa, M., Sato, Y., & Kawashima, M.
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2021-08-26
- Volume
- 31
- Issue
- 1
- Pages
- 47-53
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- OpenAlex
- W3193861087
- Cited by
- 734
- References
- 12
- FWCI
- 34.48
- Type
- journal article
- Language
- en
- Open access
- bronze
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