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dc.contributor.authorCampbell, Matthew P.
dc.contributor.authorRanzinger, Rene
dc.contributor.authorLutteke, Thomas
dc.contributor.authorMariethoz, Julien
dc.contributor.authorHayes, Catherine A.
dc.contributor.authorZhang, Jingyu
dc.contributor.authorAkune, Yukie
dc.contributor.authorAoki-Kinoshita, Kiyoko F.
dc.contributor.authorDamerell, David
dc.contributor.authorCarta, Giorgio
dc.contributor.authorYork, William S.
dc.contributor.authorHaslam, Stuart M.
dc.contributor.authorNarimatsu, Hisashi
dc.contributor.authorRudd, Pauline M.
dc.contributor.authorKarlsson, Niclas G.
dc.contributor.authorPacker, Nicolle H.
dc.contributor.authorLisacek, Frederique
dc.date.accessioned2017-05-29T12:34:22Z
dc.date.available2017-05-29T12:34:22Z
dc.date.issued2014
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/1471-2105-15-S1-S9
dc.identifier.urihttp://hdl.handle.net/10072/336679
dc.description.abstractBackground: Recent progress in method development for characterising the branched structures of complex carbohydrates has now enabled higher throughput technology. Automation of structure analysis then calls for software development since adding meaning to large data collections in reasonable time requires corresponding bioinformatics methods and tools. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to find to for non-glycobiologists. Methods: Following our diagnosis of the causes of the slow development of glycobioinformatics, we review the "objective" difficulties encountered in defining adequate formats for representing complex entities and developing efficient analysis software. Results: Various solutions already implemented and strategies defined to bridge glycobiology with different fields and integrate the heterogeneous glyco-related information are presented. Conclusions: Despite the initial stage of our integrative efforts, this paper highlights the rapid expansion of glycomics, the validity of existing resources and the bright future of glycobioinformatics.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.relation.ispartofpagefrom59-1
dc.relation.ispartofpageto59-11
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume15
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchBiochemistry and cell biology not elsewhere classified
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode310199
dc.subject.fieldofresearchcode46
dc.titleToolboxes for a standardised and systematic study of glycans
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/2.0
dc.description.versionVersion of Record (VoR)
gro.description.notepublicPage numbers are not for citation purposes. Instead, this article has the unique article number of S9.
gro.rights.copyright© Campbell et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
gro.hasfulltextFull Text
gro.griffith.authorPacker, Nicki


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