GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome
Author(s) -
Fuyi Li,
Chen Li,
Mingjun Wang,
Geoffrey I. Webb,
Yang Zhang,
James C. Whisstock,
Jiangning Song
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btu852
Subject(s) - glycosylation , proteome , in silico , human proteome project , computational biology , computer science , glycan , identification (biology) , biology , cheminformatics , proteomics , bioinformatics , glycoprotein , biochemistry , gene , botany
Glycosylation is a ubiquitous type of protein post-translational modification (PTM) in eukaryotic cells, which plays vital roles in various biological processes (BPs) such as cellular communication, ligand recognition and subcellular recognition. It is estimated that >50% of the entire human proteome is glycosylated. However, it is still a significant challenge to identify glycosylation sites, which requires expensive/laborious experimental research. Thus, bioinformatics approaches that can predict the glycan occupancy at specific sequons in protein sequences would be useful for understanding and utilizing this important PTM.
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