Comparative evaluation of label-free quantification methods for shotgun proteomics
Comparative evaluation of label-free quantification methods for shotgun proteomics
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DOI：
10.1002/rcm.7829
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发表时间：
2017-04-01
影响因子：
2
通讯作者：
Gorshkov, Mikhail V.
中科院分区：
文献类型：
--
作者：
Bubis, Julia A.;Levitsky, Lev I.;Gorshkov, Mikhail V.
RationaleLabel-Free Quantitation(LFQ)是一种流行的鸟枪式蛋白质组学方法。近年来，各种LFQ算法相继问世。方法比较了5种LFQ方法：基于光谱计数的算法SIN、emPAI和NSAF，以及依赖于提取离子色谱图(XIC)强度的方法、MaxLFQ和QUANT。我们使用了三个标准来评估性能：重复之间蛋白质丰度的变异系数(CV)；方差分析(ANOVA)；以及对数计算的浓缩比的均方根误差，称为标准量化误差(SQE)。结果MaxLFQ和NSAF的重复间可重复性最好，但它们的标准量化误差较大。使用NSAF，所有定量注释的蛋白质在ANOVA检验的Bonferronni校正结果中都被正确识别。在SQE方面，SIN被发现是最准确的。最后，基于XIC的LFQ方法的当前实现并不优于基于光谱计数的方法。结论令人惊讶的是，使用三个独立的度量来测量基于XIC的方法的性能可以与更直接、更简单的基于MS/MS的光谱计数方法相媲美。这项研究显示，在后者中没有明显的领导者。版权所有(C)2017 John Wiley&Sons，Ltd.
RationaleLabel-free quantification (LFQ) is a popular strategy for shotgun proteomics. A variety of LFQ algorithms have been developed recently. However, a comprehensive comparison of the most commonly used LFQ methods is still rare, in part due to a lack of clear metrics for their evaluation and an annotated and quantitatively well-characterized data set.MethodsFive LFQ methods were compared: spectral counting based algorithms SIN, emPAI, and NSAF, and approaches relying on the extracted ion chromatogram (XIC) intensities, MaxLFQ and Quanti. We used three criteria for performance evaluation: coefficient of variation (CV) of protein abundances between replicates; analysis of variance (ANOVA); and the root-mean-square error of logarithmized calculated concentration ratios, referred to as standard quantification error (SQE). Comparison was performed using a quantitatively annotated publicly available data set.ResultsThe best results in terms of inter-replicate reproducibility were observed for MaxLFQ and NSAF, although they exhibited larger standard quantification errors. Using NSAF, all quantitatively annotated proteins were correctly identified in the Bonferronni-corrected results of the ANOVA test. SIN was found to be the most accurate in terms of SQE. Finally, the current implementations of XIC-based LFQ methods did not outperform the methods based on spectral counting for the data set used in this study.ConclusionsSurprisingly, the performances of XIC-based approaches measured using three independent metrics were found to be comparable with more straightforward and simple MS/MS-based spectral counting approaches. The study revealed no clear leader among the latter. Copyright (c) 2017 John Wiley & Sons, Ltd.