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糖组学信息学:基于 MS 的糖蛋白质组学中数据挖掘的当前策略和工具。

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

机构信息

Research Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA.

出版信息

Proteomics. 2013 Jan;13(2):341-54. doi: 10.1002/pmic.201200149. Epub 2012 Dec 19.

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.

摘要

糖组学信息学是一个快速发展的领域,为基于 MS 的糖蛋白质组学研究提供了重要支持。MS 的最新进展极大地提高了高通量糖肽分析的技术能力。然而,根据识别聚糖结构、连接点和糖基化键的角度来解释 MS 的输出仍然存在诸多挑战。在这里,我们讨论了基于 MS 的糖蛋白质组学中当前使用的策略以及用于基于 MS 的糖肽和聚糖分析的生物信息学工具。我们还简要概述了糖组学信息学领域的最新进展,例如新的 UniCarbKB 倡议,旨在开发更全面和统一的糖组学信息学平台。关于糖组学信息学工具和应用,我们并不表达个人偏好或偏见,而是侧重于简洁地描述每个应用程序的主要功能和特点,旨在帮助读者做出自己的选择,并找到最适合实现其实验目标的糖组学信息学工具。

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