Large scale analysis of MASCOT results using a Mass Accuracy-based THreshold (MATH) effectively improves data interpretation.
Large scale analysis of MASCOT results using a Mass Accuracy-based THreshold (MATH) effectively improves data interpretation.
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DOI：
10.1021/pr0500509
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发表时间：
2005-06
影响因子：
4.4
通讯作者：
P. Rudnick;Yueju Wang;Erin L Evans;Cheng S. Lee;B. Balgley
中科院分区：
文献类型：
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作者：
P. Rudnick;Yueju Wang;Erin L Evans;Cheng S. Lee;B. Balgley
在本报告中，我们采用启发式方法来研究质量容差设置和数据库大小对 MASCOT 灵敏度和特异性的影响。我们还检查了 MASCOT Identity Threshold 作为鉴别器应用于平均质量精度为 10 ppm 或更好的 QqTOF 数据时的功效。正如预测的那样，任意大的质量容差设置会对 MASCOT 的特异性产生负面影响，并在较小程度上影响灵敏度。增加的质量容差也使得显着性阈值的生成效率降低。为了研究这些影响，我们使用贝叶斯定律来计算 MASCOT 的预测值。由于搜索数据库 (Human IPI) 相对较小，MASCOT 与 MASCOT 的身份阈值相结合时的平均阳性预测值为 0.993。然而，相应的平均阴性预测值，或在没有得分或得分低于阈值的情况下离子不存在的概率，随着质量公差的收紧而降低，平均值为 0.717。通过使用反向数据库搜索和新算法快速识别误报识别来推断经验阈值，对该值进行了改进。使用经验阈值可平均减少 17% 的假阴性识别，同时将假阳性率限制在 5% 以下；使用接近实验数据实际误差两倍的质量公差可以获得更大的减少。报告了该策略在通过 IEF/LC-MS/MS 分析的显微解剖多形性胶质母细胞瘤样品的简单应用，同时还描述了使用这种替代方法实施大规模分析所需的工具。
In this report, we take a heuristic approach to studying the effects of mass tolerance settings and database size on the sensitivity and specificity of MASCOT. We also examine the efficacy of the MASCOT Identity Threshold as a discriminator when applied to QqTOF data with an average mass accuracy of 10 ppm or better. As predicted, arbitrarily large mass tolerance settings negatively affect MASCOT's specificity, and to a lesser degree, sensitivity. Increased mass tolerances also render the generation of a significance threshold less effective. To study these effects, we used Bayes' Law to calculate MASCOT's predictive values. With a relatively small search database (Human IPI), MASCOT had a mean positive predictive value of 0.993 when combined with MASCOT's Identity Threshold. However, the corresponding average negative predictive value, or the probability that an ion was not present given no score or a score below threshold, was reduced as mass tolerances were tightened, and had an average value of 0.717. This value was improved upon by extrapolating an empirical threshold using a reversed database search and a new algorithm to rapidly identify false positive identifications. Using the empirical threshold reduced false negative identifications on the average 17% while limiting the false positive rate to below 5%; even larger reductions were obtained using mass tolerances approaching two times the actual error of the experimental data. A simple application of this strategy to the analysis of a microdissected glioblastoma multiforme sample analyzed by IEF/LC-MS/MS is reported, as is a description of the tools required to implement a large scale analysis using this alternative approach.