University of New South Wales (UNSW), Kensington, NSW 2052, Australia; Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia; Data 61, CSIRO, Eveleigh, NSW 2015, Australia.
J Mol Biol. 2019 Apr 5;431(8):1519-1539. doi: 10.1016/j.jmb.2019.01.044. Epub 2019 Feb 13.
The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.
epiproteome 描述了构成细胞或生物体的蛋白质所发生的所有翻译后修饰(PTMs)的集合。epiproteome 的范围在很大程度上仍然未知;然而,实验技术的进步开始产生大量的数据,这些数据跟踪了细胞刺激下 epiproteome 的动态变化。这些数据有可能彻底改变我们对生物学和疾病的理解。这篇综述涵盖了一系列最近开发的专门用于动态 epiproteome 数据集的可视化方法和工具。这些方法主要是为磷酸化数据集设计的,因为这是研究最多的 PTM;然而,这些方法中的大多数也适用于其他类型的 PTMs。不幸的是,目前可用的方法通常不能满足现有数据集的要求;因此,要挖掘 epiproteome 数据集中的潜力,需要新的、定制的生物信息学方法,以帮助研究人员分析、可视化和交互式探索这些复杂的数据集。