Literature DB >> 18436199

GlycoCT-a unifying sequence format for carbohydrates.

S Herget1, R Ranzinger, K Maass, C-W V D Lieth.   

Abstract

As part of the EUROCarbDB project (www.eurocarbdb.org) we have carefully analyzed the encoding capabilities of all existing carbohydrate sequence formats and the content of publically available structure databases. We have found that none of the existing structural encoding schemata are capable of coping with the full complexity to be expected for experimentally derived structural carbohydrate sequence data across all taxonomic sources. This gap motivated us to define an encoding scheme for complex carbohydrates, named GlycoCT, to overcome the current limitations. This new format is based on a connection table approach, instead of a linear encoding scheme, to describe the carbohydrate sequences, with a controlled vocabulary to name monosaccharides, adopting IUPAC rules to generate a consistent, machine-readable nomenclature. The format uses a block concept to describe frequently occurring special features of carbohydrate sequences like repeating units. It exists in two variants, a condensed form and a more verbose XML syntax. Sorting rules assure the uniqueness of the condensed form, thus making it suitable as a direct primary key for database applications, which rely on unique identifiers. GlycoCT encompasses the capabilities of the heterogeneous landscape of digital encoding schemata in glycomics and is thus a step forward on the way to a unified and broadly accepted sequence format in glycobioinformatics.

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Year:  2008        PMID: 18436199     DOI: 10.1016/j.carres.2008.03.011

Source DB:  PubMed          Journal:  Carbohydr Res        ISSN: 0008-6215            Impact factor:   2.104


  61 in total

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Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2015-04-13

Review 2.  Systems glycobiology: biochemical reaction networks regulating glycan structure and function.

Authors:  Sriram Neelamegham; Gang Liu
Journal:  Glycobiology       Date:  2011-03-24       Impact factor: 4.313

3.  GlycoRDF: an ontology to standardize glycomics data in RDF.

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Journal:  Bioinformatics       Date:  2014-11-11       Impact factor: 6.937

Review 4.  Glycosaminoglycanomics: where we are.

Authors:  Sylvie Ricard-Blum; Frédérique Lisacek
Journal:  Glycoconj J       Date:  2016-11-30       Impact factor: 2.916

5.  Glycan fingerprints: calculating diversity in glycan libraries.

Authors:  Christoph Rademacher; James C Paulson
Journal:  ACS Chem Biol       Date:  2012-03-09       Impact factor: 5.100

6.  GlycoPattern: a web platform for glycan array mining.

Authors:  Sanjay B Agravat; Joel H Saltz; Richard D Cummings; David F Smith
Journal:  Bioinformatics       Date:  2014-08-20       Impact factor: 6.937

7.  X-ray structure of the mouse serotonin 5-HT3 receptor.

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Journal:  Nature       Date:  2014-08-03       Impact factor: 49.962

8.  Exploring site-specific N-glycosylation microheterogeneity of haptoglobin using glycopeptide CID tandem mass spectra and glycan database search.

Authors:  Kevin Brown Chandler; Petr Pompach; Radoslav Goldman; Nathan Edwards
Journal:  J Proteome Res       Date:  2013-07-22       Impact factor: 4.466

Review 9.  The Glycoscience of Immunity.

Authors:  Julie Y Zhou; Douglas M Oswald; Kelsey D Oliva; Lori S C Kreisman; Brian A Cobb
Journal:  Trends Immunol       Date:  2018-05-11       Impact factor: 16.687

Review 10.  Bioinformatics and molecular modeling in glycobiology.

Authors:  Martin Frank; Siegfried Schloissnig
Journal:  Cell Mol Life Sci       Date:  2010-04-04       Impact factor: 9.261

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