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Mass Spectrom Rev. 2017 Jul;36(4):475-498. doi: 10.1002/mas.21487. Epub 2016 Jan 4.

Algorithms and design strategies towards automated glycoproteomics analysis.

Author information

1
Bioinformatics Program, Boston University, Boston, MA 02215.
2
Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA 02118.

Abstract

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community.

KEYWORDS:

bioinformatics; glycoinformatics; glycoproteins; glycoproteomics; mass spectrometry

PMID:
26728195
PMCID:
PMC4931994
DOI:
10.1002/mas.21487
[Indexed for MEDLINE]
Free PMC Article

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