Matthiesen Rune, Carvalho Ana Sofia
Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal.
Methods Mol Biol. 2020;2051:161-197. doi: 10.1007/978-1-4939-9744-2_7.
Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.
通过质谱进行蛋白质定量一直是蛋白质发现中的一项资源丰富的技术，最近它借助了临床蛋白质组学的出现。单次质谱分析实验可提供蛋白质的鉴定和定量以及翻译后修饰图谱的信息。相比之下，蛋白质阵列技术仅限于对靶向蛋白质及其修饰进行定量。目前，有大量用于蛋白质和肽定量的定量质谱方法。本文旨在概述定量蛋白质组学中最常用的质谱方法和算法，并讨论获得蛋白质、肽及其翻译后修饰可靠定量测量的计算方面。使用商业或免费软件开发分析流程是许多实验项目数据分析中的主要挑战之一。R统计编程语言的最新发展使得全面开发定量蛋白质组学的分析流程具有吸引力。我们讨论定量蛋白质组学的概念，这些概念与当前的R包一起可用于构建高度可定制的分析流程。
