Tyanova Stefka, Cox Juergen
Computational Systems Biochemistry Group, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152, Martinsried, Germany.
Methods Mol Biol. 2018;1711:133-148. doi: 10.1007/978-1-4939-7493-1_7.
Mass spectrometry-based proteomics is a continuously growing field marked by technological and methodological improvements. Cancer proteomics is aimed at pursuing goals such as accurate diagnosis, patient stratification, and biomarker discovery, relying on the richness of information of quantitative proteome profiles. Translating these high-dimensional data into biological findings of clinical importance necessitates the use of robust and powerful computational tools and methods. In this chapter, we provide a detailed description of standard analysis steps for a clinical proteomics dataset performed in Perseus, a software for functional analysis of large-scale quantitative omics data.
基于质谱的蛋白质组学是一个不断发展的领域,其特点是技术和方法不断改进。癌症蛋白质组学旨在实现准确诊断、患者分层和生物标志物发现等目标,这依赖于定量蛋白质组图谱丰富的信息。将这些高维数据转化为具有临床重要性的生物学发现需要使用强大且稳健的计算工具和方法。在本章中,我们详细描述了在Perseus(一款用于大规模定量组学数据功能分析的软件)中对临床蛋白质组学数据集进行标准分析的步骤。