Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
Focus on the spectra that matter by clustering of quantification data in shotgun proteomics
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DOI：
10.1038/s41467-020-17037-3
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发表时间：
2020-06-26
影响因子：
16.6
通讯作者：
Kall, Lukas
中科院分区：
文献类型：
--
作者：
The, Matthew;Kall, Lukas
在鸟枪法蛋白质组学中，无标记定量实验的分析通常受到定量数据中的识别率和噪声水平的限制。这通常导致差异表达分析中的低灵敏度。在这里，我们提出了一个量化的肽，逆转了经典的识别第一的工作流程，从而防止有价值的信息被丢弃在识别阶段的方法。具体来说，我们介绍了一种方法，量子密度，适用于MS1和MS2水平的无监督聚类总结所有感兴趣的分析物，而不分配身份。由于数据减少，这减少了搜索时间。我们现在可以采用开放修饰和从头搜索来识别在传统管道中未被注意到的感兴趣的分析物。Quandenser+Triqler优于最先进的方法MaxQuant+Perseus，始终报告所有测试数据集的差异丰富蛋白质。在Apache 2.0许可证下，可在https：//github.com/aparticalbiotechnology/quandenser上获得适用于所有主要操作系统的软件。将质谱与肽序列匹配是蛋白质组学数据分析的第一步，通常随后是肽定量。在这里，作者表明，在肽鉴定之前聚类和量化质谱特征可以增加无标记定量蛋白质组学的灵敏度。
In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license. Matching mass spectra to peptide sequences is the usual first step in proteomics data analysis, often followed by peptide quantification. Here, the authors show that clustering and quantifying mass spectral features prior to peptide identification can increase the sensitivity of label-free quantitative proteomics.