MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins
MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins
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DOI：
10.1074/mcp.ra119.001509
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发表时间：
2019-09-01
影响因子：
7
通讯作者：
Zimmer, Ralf
中科院分区：
文献类型：
--
作者：
Ammar, Constantin;Gruber, Markus;Zimmer, Ralf
基于质谱的蛋白质组学是在广泛的生物学和生物医学应用中定量蛋白质表达的全基因组差异变化的首选方法。蛋白质表达的变化需要从许多测量的肽强度及其相应的肽折叠变化中可靠地得出。对于给定的蛋白质，这些肽折叠变化有很大的不同。许多仪器设置旨在减少这种可变性，而目前的计算方法只能隐式地解释这个问题。我们引入了一种新的方法，MS-EmpiRe，它明确地解释了肽折叠变化背后的噪声。我们推导了数据集特异性，强度依赖的经验误差折叠变化分布，用于肽折叠变化的个体称重以检测差异表达蛋白（DEPs）。在最近发表的蛋白质组范围的基准数据集中，MS-EmpiRe在估计的FDR截止点上正确识别dep的数量比最先进的工具增加了一倍。我们还在模拟数据上证实了MS-EmpiRe的优越性能。MS-EmpiRe只需要肽强度映射到蛋白质，因此，可以应用于任何常见的定量蛋白质组学设置。我们将我们的方法应用于不同的MS数据集，并在深度数据集中观察到超过1000个额外的重要蛋白质的敏感性一致增加，包括对多个患者的临床研究。同时，我们观察到，即使是被其他方法归类为最不显著但被MS-EmpiRe分类为显著的蛋白，在肽强度水平上也表现出非常明显的调控。MS- empire提供快速处理（6 LC-MS/MS运行< 2分钟（3小时梯度）），并在github.com/zimmerlab/MS-EmpiRe下公开提供手册，包括示例。
Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs).In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.