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

该子项目是利用资源的许多研究子项目之一 由NIH/NCRR资助的中心赠款提供。次级项目的主要支助 子项目的主要研究者可能是由其他来源提供的， 包括其他NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， NCRR赠款不直接向子项目或子项目工作人员提供资金。 在鸟枪法蛋白质组学中，通过评分函数来量化观察到的光谱和肽序列之间的假设匹配的质量。因为评分函数位于任何肽鉴定管道的核心，所以该函数极大地影响蛋白质组学测定的最终结果。因此，有效的统计方法来评估一个给定的分数函数的质量是非常重要的。以前，几个研究小组已经使用已知蛋白质组成的样本来评估给定评分函数的质量。我们证明了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因素。然后，我们提出了一种替代使用相同类型的数据来验证得分函数。我们的方法的中心思想是，在纯化的样品中没有任何蛋白质解释的数据库匹配包括不正确匹配的鲁棒表示。我们将我们的替代评估方案应用于几个常用的评分函数，我们表明，我们的方法生成了一个可重复的测量给定的肽鉴定方法的校准。此外，我们展示了我们的质量测试如何在开发新的评分函数中发挥作用。

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.