Simopoulos Caitlin M A, Figeys Daniel, Lavallée-Adam Mathieu
Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, Canada.
School of Pharmaceutical Sciences, University of Ottawa, Ottawa, ON, Canada.
Methods Mol Biol. 2022;2456:319-338. doi: 10.1007/978-1-0716-2124-0_22.
Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.
质谱技术和实验室工作流程的不断改进，使得对日益复杂的生物样品进行蛋白质组学研究成为可能。微生物群落代表了这类复杂样品，元蛋白质组学分析在这类样品中越来越受欢迎。元蛋白质组学实验程序会产生大量数据，必须从中有效提取生物学相关信号才能得出有意义的结论。这种数据处理需要专门为元蛋白质组学数据开发或能够处理元蛋白质组学数据的合适生物信息学工具。在本章中，我们概述了当前和新颖的工具，这些工具可以执行前沿元蛋白质组学数据分析中最常用的步骤，如肽段和蛋白质的鉴定与定量，以及数据归一化、插补、挖掘和可视化。我们还提供了应使用这些工具的实验设置的详细信息。
