Glycoproteomic and glycomic databases

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Hizal D., Wolozny D., Colao J., Jacobson E., Tian Y., Krag S. S. , ...More

CLINICAL PROTEOMICS, vol.11, 2014 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 11
  • Publication Date: 2014
  • Doi Number: 10.1186/1559-0275-11-15
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Acibadem Mehmet Ali Aydinlar University Affiliated: No


Protein glycosylation serves critical roles in the cellular and biological processes of many organisms. Aberrant glycosylation has been associated with many illnesses such as hereditary and chronic diseases like cancer, cardiovascular diseases, neurological disorders, and immunological disorders. Emerging mass spectrometry (MS) technologies that enable the high-throughput identification of glycoproteins and glycans have accelerated the analysis and made possible the creation of dynamic and expanding databases. Although glycosylation-related databases have been established by many laboratories and institutions, they are not yet widely known in the community. Our study reviews 15 different publicly available databases and identifies their key elements so that users can identify the most applicable platform for their analytical needs. These databases include biological information on the experimentally identified glycans and glycopeptides from various cells and organisms such as human, rat, mouse, fly and zebrafish. The features of these databases - 7 for glycoproteomic data, 6 for glycomic data, and 2 for glycan binding proteins are summarized including the enrichment techniques that are used for glycoproteome and glycan identification. Furthermore databases such as Unipep, GlycoFly, GlycoFish recently established by our group are introduced. The unique features of each database, such as the analytical methods used and bioinformatical tools available are summarized. This information will be a valuable resource for the glycobiology community as it presents the analytical methods and glycosylation related databases together in one compendium. It will also represent a step towards the desired long term goal of integrating the different databases of glycosylation in order to characterize and categorize glycoproteins and glycans better for biomedical research.