ON USING SAMPLES OF KNOWN PROTEIN CONTENT TO ASSESS THE STATISTICAL CALIBRATION

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 在鸟枪式蛋白质组学中，观察到的光谱和肽序列之间的假设匹配的质量是通过得分函数来量化的。由于分数函数位于任何多肽鉴定管道的核心，因此该函数极大地影响蛋白质组学分析的最终结果。因此，评估给定得分函数的质量的有效统计方法是极其重要的。此前，几个研究小组曾使用已知蛋白质组成的样本来评估给定得分函数的质量。我们证明这种方法是有问题的，因为结果可能取决于得分函数本身以外的其他因素。然后，我们建议使用相同类型的数据来验证得分函数。我们方法的中心思想是，纯化样本中任何蛋白质都不能解释的数据库匹配包括不正确匹配的稳健表示。我们将我们的替代评估方案应用于几个常用的得分函数，并且我们表明，我们的方法产生了对给定肽识别方法的校准的可重复性测量。此外，我们还展示了我们的质量测试如何在开发新的分数函数时有用。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. In shotgun proteomics, the quality of a hypothesized match between an observed spectrum and a peptide sequence is quantified by a score function. Because the score function lies at the heart of any peptide identification pipeline, this function greatly affects the final results of a proteomics assay. Consequently, valid statistical methods for assessing the quality of a given score function are extremely important. Previously, several research groups have used samples of known protein composition to assess the quality of a given score function. We demonstrate that this approach is problematic, because the outcome can depend on factors other than the score function itself. We then propose an alternative use of the same type of data to validate a score function. The central idea of our approach is that database matches that are not explained by any protein in the purified sample comprise a robust representation of incorrect matches. We apply our alternative assessment scheme to several commonly used score functions, and we show that our approach generates a reproducible measure of the calibration of a given peptide identification method. Furthermore, we show how our quality test can be useful in the development of novel score functions.