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结合高分辨率和精确校准以提高统计功效:用于高分辨率 MS2 数据的校准良好的评分函数。

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data.

机构信息

Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States.

Department of Computer Science and Engineering , University of Washington , Seattle , Washington 98195 , United States.

出版信息

J Proteome Res. 2018 Nov 2;17(11):3644-3656. doi: 10.1021/acs.jproteome.8b00206. Epub 2018 Oct 18.

Abstract

To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine's scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the "residue evidence" (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a "combined p value" score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit ( http://crux.ms ).

摘要

为了实现肽序列与观察到的碎片谱的准确匹配,鸟枪法蛋白质组学数据库搜索工具必须充分利用最先进的质谱仪所产生的非常高的分辨率信息。然而,在利用这些信息的同时,还必须确保搜索引擎的得分得到良好的校准,也就是说,一个谱的得分可以与不同谱的得分进行有意义的比较,这已经被证明是具有挑战性的。在这里,我们描述了一个数据库搜索评分函数,即“残基证据”(res-ev)评分,它同时实现了这两个目标。我们还演示了如何将校准后的 res-ev 分数与校准后的 XCorr 分数相结合,生成一个“组合 p 值”评分函数。我们提供了一个由四个质谱数据集组成的基准,用于将组合 p 值与几个现有搜索引擎使用的评分函数进行比较。我们的结果表明,组合 p 值的性能达到了最新水平,通常优于 MS Amanda 和 Morpheus,与 MS-GF+ 的性能相当。res-ev 和组合 p 值评分函数可作为 Crux 质谱工具包中 Tide 搜索引擎的一部分免费获得(http://crux.ms)。

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