MicroGlycoDB: A database of microbial glycans using Semantic Web technologies
•A novel database for microbial glycosylation in model microbial organisms has been developed.•This MicroGlycoDB database provides the foundation for a system where microbial glycan-related information can be stored in a standardized and comprehensive manner.•The database allows automatic visualizat...
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Veröffentlicht in: | BBA advances 2024-01, Vol.6, p.100126, Article 100126 |
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Sprache: | eng |
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Zusammenfassung: | •A novel database for microbial glycosylation in model microbial organisms has been developed.•This MicroGlycoDB database provides the foundation for a system where microbial glycan-related information can be stored in a standardized and comprehensive manner.•The database allows automatic visualization of glycosylation pathways is possible, including microbial structures.•The database is developed based on Semantic Web techniques.•The data is described in machine-readable and standard format for data integration with a wide range of data.
Glycoconjugates are present on microbial surfaces and play critical roles in modulating interactions with the environment and the host. Extensive research on microbial glycans, including elucidating the structural diversity of the glycan moieties of glycoconjugates and polysaccharides, has been carried out to investigate the function of glycans in modulating the interactions between the host and microbes, to explore their potential applications in the therapeutic targeting of pathogenic species, and in the use as probiotics in gut microbiomes. However, glycan-related information is dispersed across numerous databases and a vast amount of literature, which makes it laborious and time-consuming to identify and gather the relevant information about microbial glycosylation. This challenge can be addressed by a comprehensive database, which could offer insight into the fundamental processes underlying glycosylation. We have developed a MicroGlycoDB database to provide integrated glycan information on important model microorganisms. The data is described using Semantic Web Technologies, which allow microbial glycan data to be represented in a structured format accessible by machines, thus facilitating data sharing and integration with other resources that catalog features such as pathways, diseases, or interactions. This semantic data based on ontologies will contribute to the discovery of new knowledge in the field of microbiology, along with the expansion of information on the glycosylation of other microorganisms. |
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ISSN: | 2667-1603 2667-1603 |
DOI: | 10.1016/j.bbadva.2024.100126 |