Max Planck Institute of Biochemistry, Department of Cell Biology, Martinsried, Germany.
Proteomics. 2010 Mar;10(6):1270-83. doi: 10.1002/pmic.200900365.
MS has become a method-of-choice for proteome analysis, generating large data sets, which reflect proteome-scale protein-protein interaction and PTM networks. However, while a rapid growth in large-scale proteomics data can be observed, the sound biological interpretation of these results clearly lags behind. Therefore, combined efforts of bioinformaticians and biologists have been made to develop strategies and applications to help experimentalists perform this crucial task. This review presents an overview of currently available analytical strategies and tools to extract biologically relevant information from large protein lists. Moreover, we also present current research publications making use of these tools as examples of how the presented strategies may be incorporated into proteomic workflows. Emphasis is placed on the analysis of Gene Ontology terms, interaction networks, biological pathways and PTMs. In addition, topics including domain analysis and text mining are reviewed in the context of computational analysis of proteomic results. We expect that these types of analyses will significantly contribute to a deeper understanding of the role of individual proteins, protein networks and pathways in complex systems.
多发性硬化症（MS）已成为蛋白质组分析的首选方法，可生成反映蛋白质组规模的蛋白质-蛋白质相互作用和 PTM 网络的大型数据集。然而，尽管可以观察到大规模蛋白质组学数据的快速增长，但这些结果的合理生物学解释显然明显滞后。因此，生物信息学家和生物学家共同努力，开发了策略和应用程序来帮助实验人员执行这一关键任务。
本文概述了目前可用于从大型蛋白质列表中提取生物学相关信息的分析策略和工具。此外，我们还介绍了当前利用这些工具的研究出版物，作为如何将这些策略纳入蛋白质组学工作流程的示例。重点分析了基因本体术语、相互作用网络、生物途径和 PTM。此外，还在蛋白质组学结果的计算分析背景下审查了域分析和文本挖掘等主题。
我们预计，这些类型的分析将为深入了解单个蛋白质、蛋白质网络和途径在复杂系统中的作用做出重大贡献。
