Lazar Iulia M
Department of Biological Sciences, Virginia Tech, Integrated Life Sciences Building (ILSB), Room 2011, 1981 Kraft Drive, Blacksburg, VA, 24061, USA.
Methods Mol Biol. 2017;1647:267-295. doi: 10.1007/978-1-4939-7201-2_19.
Developments in mass spectrometry (MS) instrumentation have supported the advance of a variety of proteomic technologies that have enabled scientists to assess differences between healthy and diseased states. In particular, the ability to identify altered biological processes in a cell has led to the identification of novel drug targets, the development of more effective therapeutic drugs, and the growth of new diagnostic approaches and tools for personalized medicine applications. Nevertheless, large-scale proteomic data generated by modern mass spectrometers are extremely complex and necessitate equally complex bioinformatics tools and computational algorithms for their interpretation. A vast number of commercial and public resources have been developed for this purpose, often leaving the researcher perplexed at the overwhelming list of choices that exist. To address this challenge, the aim of this chapter is to provide a roadmap to the basic steps that are involved in mass spectrometry data acquisition and processing, and to describe the most common tools that are available for placing the results in biological context.
质谱(MS)仪器的发展推动了多种蛋白质组学技术的进步,这些技术使科学家能够评估健康状态与疾病状态之间的差异。特别是,识别细胞中改变的生物过程的能力已导致新型药物靶点的识别、更有效治疗药物的开发以及用于个性化医疗应用的新诊断方法和工具的发展。然而,现代质谱仪产生的大规模蛋白质组学数据极其复杂,需要同样复杂的生物信息学工具和计算算法来进行解读。为此已经开发了大量商业和公共资源,这常常使研究人员对存在的众多选择感到困惑。为应对这一挑战,本章的目的是提供一份质谱数据采集和处理所涉及基本步骤的路线图,并描述用于将结果置于生物学背景下的最常用工具。