ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R
2020
Тип документа:
Чланак у часопису (Рецензирана верзија)
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© 2020 Oxford University Press
Метаподаци
Приказ свих података о документуАпстракт:
Hydroxyproline-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.
Напомена:
This 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.
Кључне речи:
Arabinogalactan; Glycoprotein annotation; HRGP; Hydroxyproline-prediction; Machine learningИзвор:
Glycobiology, 2020, 30, 1, 19-35Финансирање / пројекти:
- Развој и примена биотехнолошких поступака у добијању здравог садног материјала украсних биљака (RS-MESTD-Technological Development (TD or TR)-31019)
- Физиолошка, хемијска и молекуларна анализа диверзитета одабраних ретких и угрожених биљних врста у циљу еx ситу заштите и продукције биолошки активних једињења (RS-MESTD-Basic Research (BR or ON)-173024)
Повезане информације:
- Повезани садржај
https://radar.ibiss.bg.ac.rs/handle/123456789/4966
DOI: 10.1093/glycob/cwz072
ISSN: 0959-6658
PubMed: 31508799