Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search
Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search
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DOI：
10.1021/ac025747h
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发表时间：
2002-10-15
影响因子：
7.4
通讯作者：
Aebersold, R
中科院分区：
文献类型：
--
作者：
Keller, A;Nesvizhskii, AI;Aebersold, R
我们提出了一个统计模型来估计肽分配的准确性串联质谱（MS/MS）谱数据库搜索应用程序，如SEQUEST。采用期望最大化算法，分析学习区分正确和不正确的数据库搜索结果，计算概率，基于数据库搜索分数和肽的胰蛋白酶末端的数量，肽分配到光谱是正确的。使用SEQUEST搜索结果从已知蛋白质组分的样品中产生的光谱，我们证明了计算的概率是准确的，并具有很高的功率来区分正确和不正确分配的肽。Ibis分析可以过滤大量MS/MS数据库搜索结果，并具有可预测的错误识别错误率，可以作为比较不同研究组结果的通用标准。
We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities, that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. Ibis analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.