Crowgey Erin L, Matlock Andrea, Venkatraman Vidya, Fert-Bober Justyna, Van Eyk Jennifer E
Nemours Alfred I. DuPont Hospital for Children, 1701 Rockland Road, Wilmington, DE, 19803, USA.
Advanced Clinical BioSystems Research Institute, Cedars Sinai Medical Center, Heart Institute, Los Angeles, CA, 90048, USA.
Methods Mol Biol. 2017;1558:395-413. doi: 10.1007/978-1-4939-6783-4_19.
Data-independent acquisition mass spectrometry (DIA-MS) strategies and applications provide unique advantages for qualitative and quantitative proteome probing of a biological sample allowing constant sensitivity and reproducibility across large sample sets. These advantages in LC-MS/MS are being realized in fundamental research laboratories and for clinical research applications. However, the ability to translate high-throughput raw LC-MS/MS proteomic data into biological knowledge is a complex and difficult task requiring the use of many algorithms and tools for which there is no widely accepted standard and best practices are slowly being implemented. Today a single tool or approach inherently fails to capture the full interpretation that proteomics uniquely supplies, including the dynamics of quickly reversible chemically modified states of proteins, irreversible amino acid modifications, signaling truncation events, and, finally, determining the presence of protein from allele-specific transcripts. This chapter highlights key steps and publicly available algorithms required to translate DIA-MS data into knowledge.
数据非依赖型采集质谱法（DIA-MS）策略及应用为生物样品的定性和定量蛋白质组探测提供了独特优势，可在大型样本集上实现恒定的灵敏度和重现性。液相色谱-串联质谱（LC-MS/MS）的这些优势正在基础研究实验室和临床研究应用中得以体现。然而，将高通量原始LC-MS/MS蛋白质组学数据转化为生物学知识是一项复杂且困难的任务，需要使用许多算法和工具，目前尚无广泛接受的标准，最佳实践也在逐步实施。如今，单一工具或方法本质上无法全面解读蛋白质组学所特有的信息，包括蛋白质快速可逆化学修饰状态的动态变化、不可逆氨基酸修饰、信号截断事件，以及最终确定等位基因特异性转录本中蛋白质的存在情况。本章重点介绍将DIA-MS数据转化为知识所需的关键步骤和公开可用的算法。
