Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

中文翻译：

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用于基于质谱的高通量定量蛋白质组学平台的生物信息学工具

确定全局蛋白质组变化对于推进细胞过程的系统生物学观点和发现生物标志物非常重要。液相色谱与质谱联用已被广泛用作发现生物样品中差异表达蛋白质的蛋白质组学技术。然而，尽管大量高通量研究已经鉴定出差异调节的蛋白质，但这些结果中只有一小部分得到了复制和独立验证。使用不同的数据处理和分析方法是导致结论不一致的因素之一。这个观点为常用的基于质谱的定量蛋白质组学的生物信息学方法提供了一个全面和关键的概述，采用稳定同位素标记和无标记方法。我们评估与当前定量蛋白质组学技术相关的挑战，特别强调数据分析。随着质谱仪的灵敏度、质量分辨率、质量准确度和通量的提高，处理和解释蛋白质组学数据集的复杂性已成为一个核心问题。许多计算机程序可用于解决这些挑战，并在此处进行了审查。我们专注于信号处理、降噪和蛋白质丰度估计方法。质谱仪的质量准确度和通量得到了提高。许多计算机程序可用于解决这些挑战，并在此处进行了审查。我们专注于信号处理、降噪和蛋白质丰度估计方法。质谱仪的质量准确度和通量得到了提高。许多计算机程序可用于解决这些挑战，并在此处进行了审查。我们专注于信号处理、降噪和蛋白质丰度估计方法。