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dc.contributor.authorAoki-Kinoshita, Kiyoko F.
dc.contributor.authorBolleman, Jerven
dc.contributor.authorCampbell, Matthew P.
dc.contributor.authorKawano, Shin
dc.contributor.authorKim, Jin-Dong
dc.contributor.authorLutteke, Thomas
dc.contributor.authorMatsubara, Masaaki
dc.contributor.authorOkuda, Shijiro
dc.contributor.authorRanzinger, Rene
dc.contributor.authorSawaki, Hiromichi
dc.contributor.authorShikanai, Toshihide
dc.contributor.authorShinmachi, Daisuke
dc.contributor.authorSuzuki, Yoshinori
dc.contributor.authorToukach, Philip
dc.contributor.authorYamada, Iaaku
dc.contributor.authorPacker, Nicolle H.
dc.contributor.authorNarimatsu, Hisashi
dc.date.accessioned2017-05-12T02:39:10Z
dc.date.available2017-05-12T02:39:10Z
dc.date.issued2013
dc.identifier.issn2041-1480
dc.identifier.doi10.1186/2041-1480-4-39
dc.identifier.urihttp://hdl.handle.net/10072/336676
dc.description.abstractBackground: Glycoscience is a research field focusing on complex carbohydrates (otherwise known as glycans)a, which can, for example, serve as “switches” that toggle between different functions of a glycoprotein or glycolipid. Due to the advancement of glycomics technologies that are used to characterize glycan structures, many glycomics databases are now publicly available and provide useful information for glycoscience research. However, these databases have almost no link to other life science databases. Results: In order to implement support for the Semantic Web most efficiently for glycomics research, the developers of major glycomics databases agreed on a minimal standard for representing glycan structure and annotation information using RDF (Resource Description Framework). Moreover, all of the participants implemented this standard prototype and generated preliminary RDF versions of their data. To test the utility of the converted data, all of the data sets were uploaded into a Virtuoso triple store, and several SPARQL queries were tested as “proofs-of-concept” to illustrate the utility of the Semantic Web in querying across databases which were originally difficult to implement. Conclusions: We were able to successfully retrieve information by linking UniCarbKB, GlycomeDB and JCGGDB in a single SPARQL query to obtain our target information. We also tested queries linking UniProt with GlycoEpitope as well as lectin data with GlycomeDB through PDB. As a result, we have been able to link proteomics data with glycomics data through the implementation of Semantic Web technologies, allowing for more flexible queries across these domains.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central Ltd
dc.relation.ispartofpagefrom39-1
dc.relation.ispartofpageto39-7
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Biomedical Semantics
dc.relation.ispartofvolume4
dc.subject.fieldofresearchBiological Sciences not elsewhere classified
dc.subject.fieldofresearchOther Biological Sciences
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode069999
dc.subject.fieldofresearchcode0699
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.titleIntroducing glycomics data into the Semantic Web
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 39.
gro.rights.copyright© Aoki-Kinoshita et al.; licensee BioMed Central Ltd. 2013. 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.
gro.hasfulltextFull Text
gro.griffith.authorCampbell, Matthew
gro.griffith.authorPacker, Nicki


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