GlycoBioinformatics

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Aoki-Kinoshita, Kiyoko F
Lisacek, Frédérique
Karlsson, Niclas
Kolarich, Daniel
Packer, Nicolle H
Griffith University Author(s)
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2021
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Abstract

In order to introduce this thematic issue “GlycoBioinformatics” [1] in the Beilstein Journal of Organic Chemistry, it would be appropriate to define what we actually mean by this term. This is important not only for newcomers to the field but also in order for researchers that have used or developed “glycobioinformatics” to place their work into a wider context of this diverse field. The term “bioinformatics” is described by the National Human Genome Research Institute as “a subdiscipline of biology and computer science concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino acid sequences”. Adding the prefix “glyco-“ is about placing genomic and proteomic data into a glycomic context by harvesting information about glyco-related genes and proteins. Glycobioinformatics requires additional information about the expressed glycan, including but not limited to monosaccharide composition, full or partial sequence including linkage and branching structure, type and linkage of glycoconjugate (e.g., N-linked, O-linked glycoprotein, glycolipid, proteoglycan), association with, and regulation of, expression in particular tissues or cell types, and interaction with biological surroundings. With this definition, it is obvious that glycobioinformatics is tightly connected to mainstream bioinformatics. For example, databases and tools from genomics can be used for gaining information about genes encoding for glycosyltransferases, glycosidases, and glycan-binding proteins (lectins), and search engines initially designed for the detection of posttranslational modifications of peptides in proteomics can be adapted to specifically identify glycopeptides. What is also obvious for glycobioinformatics is that it needs an own language that is understood by both computers and researchers to facilitate the exchange of glyco-specific information as well as the development and evolution of dedicated databases that store glyco-related quality information. With glycobioinformatics still being in its infancy, these requirements are continuing to evolve.

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Beilstein Journal of Organic Chemistry

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17

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© 2021 Aoki-Kinoshita et al.; licensee Beilstein-Institut. This is an Open Access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0). Please note that the reuse, redistribution and reproduction in particular requires that the author(s) and source are credited and that individual graphics may be subject to special legal provisions. The license is subject to the Beilstein Journal of Organic Chemistry terms and conditions: (https://www.beilstein-journals.org/bjoc/terms)

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Glycobiology

Glycoconjugates

Organic chemistry

bioinformatics

glycobioinformatics

glycoinformatics

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Aoki-Kinoshita, KF; Lisacek, F; Karlsson, N; Kolarich, D; Packer, NH, GlycoBioinformatics, Beilstein Journal of Organic Chemistry, 17, pp. 2726-2728

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