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一种改进的适用于准确估计假发现率的诱饵肽 MS/MS 谱构建方法。

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates.

机构信息

Proteome Informatics Group, Swiss Institute of Bioinformatics, Geneva, Switzerland.

出版信息

Proteomics. 2011 Oct;11(20):4085-95. doi: 10.1002/pmic.201000665. Epub 2011 Sep 7.

Abstract

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.

摘要

随着高通量实验中产生的蛋白质组学数据的增加,注释 MS/MS 谱库的相关性也在不断增加。这些参考库为快速准确地识别新获得的 MS/MS 谱提供了一种方法。在多重假设检验的背景下,通过计算假发现率(FDR)来控制最终结果列表中预期的假阳性识别数量。在经典的序列搜索中,将实验 MS/MS 谱与从序列数据库计算得到的理论肽谱进行比较,通过搜索随机化或诱饵序列数据库来估计 FDR。尽管关于如何准确计算 FDR 的讨论仍在继续,但这种方法在蛋白质组学领域被广泛接受。最近,也有类似的方法被讨论用于控制谱库搜索的 FDR。我们在本文中详细分析了不同肽段谱之间的相似性,为我们自己的诱饵库创建(DeLiberator)解决方案奠定了基础。它与之前发表的结果在一些关键点上有所不同,主要是在实施新的方法时,防止诱饵谱与原始库谱过于相似,同时保留真实 MS/MS 谱的重要特征。我们使用不同的蛋白质组学数据集和库创建方法来评估我们的方法,并将其与替代方法进行比较。

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