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Glycobioinformatics: Current strategies and tools for data mining in MS-based glycoproteomics

Authors

  • Feng Li,

    1. Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA
    2. Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
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  • Olga V. Glinskii,

    1. Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA
    2. Department of Medical Pharmacology and Physiology, University of Missouri, Columbia, MO, USA
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  • Vladislav V. Glinsky

    Corresponding author
    1. Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
    • Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA
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  • Colour Online: See the article online to view Figs. 1–3 in colour.

Correspondence: Dr. Vladislav V. Glinsky, M263 Medical Sciences Bldg., Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO 65212, USA

E-mail: glinskiivl@missouri.edu

Fax: +1-573-814-6551

Abstract

Glycobioinformatics is a rapidly developing field providing a vital support for MS-based glycoproteomics research. Recent advances in MS greatly increased technological capabilities for high throughput glycopeptide analysis. However, interpreting MS output, in terms of identifying glycan structures, attachment sites and glycosylation linkages still presents multiple challenges. Here, we discuss current strategies used in MS-based glycoproteomics and bioinformatics tools available for MS-based glycopeptide and glycan analysis. We also provide a brief overview of recent efforts in glycobioinformatics such as the new initiative UniCarbKB directed toward developing more comprehensive and unified glycobioinformatics platforms. With regards to glycobioinformatics tools and applications, we do not express our personal preferences or biases, but rather focus on providing a concise description of main features and functionalities of each application with the goal of assisting readers in making their own choices and identifying and locating glycobioinformatics tools most suitable for achieving their experimental objectives.

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