Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.

中文翻译：

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用于糖组学和糖蛋白组学的糖信息平台的最新进展。

就修饰的蛋白质数量和产生的多样性而言，蛋白质糖基化是最复杂，最普遍的翻译后修饰。要了解糖蛋白的功能作用，重要的是深入了解目前的寡糖库。糖型的比较和相对定量结合位点特异性的鉴定和占有是在这个方向上的必要步骤。计算平台在帮助研究人员解释此类糖组学和糖蛋白组学数据集方面一直在不断成熟，但经常支持专用的工作流程，并且用户依靠数据的人工解释来获得对糖蛋白组学的深入了解。特定于站点的知识的增长也导致了机器学习算法的实施，以预测糖基化，现在已经将其集成到糖蛋白组学流水线中。这篇简短的评论介绍了商业和开放访问的数据库和软件，重点是那些为维护当前分析工作流而积极维护和设计的数据库和软件。