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Nucleic Acids Research
Article . 2020 . Peer-reviewed
License: CC BY NC
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Nucleic Acids Research
Article
License: CC BY NC
Data sources: UnpayWall
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KEGG: integrating viruses and cellular organisms

Authors: Minoru Kanehisa; Miho Furumichi; Yoko Sato; Mari Ishiguro-Watanabe; Mao Tanabe;

KEGG: integrating viruses and cellular organisms

Abstract

Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

Keywords

Genome, Cells, Viruses, Database Issue, Humans, Apoptosis, Gene Regulatory Networks, Molecular Sequence Annotation, Metabolic Networks and Pathways

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3K
Top 0.01%
Top 0.1%
Top 0.01%
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gold