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dc.creatorDragićević, Milan
dc.creatorPaunović, Danijela
dc.creatorBogdanović, Milica
dc.creatorTodorović, Slađana
dc.creatorSimonović, Ana
dc.date.accessioned2020-07-08T19:52:32Z
dc.date.available2020-09-11
dc.date.issued2020
dc.identifier.issn0959-6658
dc.identifier.urihttps://radar.ibiss.bg.ac.rs/handle/123456789/3756
dc.description.abstractHydroxyproline-rich glycoproteins (HRGPs) are one of the most complex families of macromolecules found in plants, due to the diversity of glycans decorating the protein backbone, as well as the heterogeneity of the protein backbones. While this diversity is responsible for a wide array of physiological functions associated with HRGPs, it hinders attempts for homology-based identification. Current approaches, based on identifying sequences with characteristic motifs and biased amino acid composition, are limited to prototypical sequences. Ragp is an R package for mining and analysis of HRGPs, with emphasis on arabinogalactan proteins. The ragp filtering pipeline exploits one of the HRGPs key features, the presence of hydroxyprolines which represent glycosylation sites. Main package features include prediction of proline hydroxylation sites, amino acid motif and bias analyses, efficient communication with web servers for prediction of N-terminal signal peptides, glycosylphosphatidylinositol modification sites and disordered regions and the ability to annotate sequences through hmmscan and subsequent GO enrichment, based on predicted Pfam domains. As such, ragp extends R’s rich ecosystem for high-throughput sequence data analyses. The ragp R package is available under the MIT Open Source license and is freely available to download from GitHub at: https://github.com/missuse/ragp.
dc.language.isoen
dc.publisherOxford University Press
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/31019/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/173024/RS//
dc.relation.isreferencedbyhttps://radar.ibiss.bg.ac.rs/handle/123456789/4966
dc.rightsembargoedAccess
dc.sourceGlycobiology
dc.subjectArabinogalactan
dc.subjectGlycoprotein annotation
dc.subjectHRGP
dc.subjectHydroxyproline-prediction
dc.subjectMachine learning
dc.titleragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in Ren
dc.typearticle
dc.rights.licenseARR
dcterms.abstractДрагићевић, Милан; Пауновић, Данијела; Богдановић, Милица; Тодоровић, Слађана; Симоновић, Aна;
dc.rights.holder© 2020 Oxford University Press
dc.citation.issue1
dc.citation.issue1
dc.citation.volume30
dc.citation.volume30
dc.description.noteThis is a pre-copyedited, author-produced version of an article accepted for publication in Glycobiology following peer review. The version of record Dragićević MB, Paunović DM, Bogdanović MD, Todorović SI, Simonović AD. ragp: Pipeline for mining of plant hydroxyprolinerich glycoproteins with implementation in R. Glycobiology. 2019. is available online at: [http://doi.org/10.1093/glycob/cwz072].
dc.identifier.doi10.1093/glycob/cwz072
dc.identifier.pmid31508799
dc.identifier.scopus2-s2.0-85096222453
dc.identifier.wos000537391100002
dc.citation.apaDragićević, M. B., Paunović, D. M., Bogdanović, M. D., Todorović, S. I., & Simonović, A. D. (2019). ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R. Glycobiology, 30(1), 19-35.
dc.citation.vancouverDragićević MB, Paunović DM, Bogdanović MD, Todorović SI, Simonović AD. ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R. Glycobiology. 2020;30(1):19-35.
dc.citation.spage19
dc.citation.spage19
dc.citation.epage35
dc.citation.epage35
dc.type.versionacceptedVersion
dc.identifier.fulltexthttps://radar.ibiss.bg.ac.rs/bitstream/id/6453/cwz072.pdf
dc.citation.rankM21


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