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

关于使用已知蛋白质含量的样品来评估统计校准

基本信息

批准号：
8365887
负责人：
William Noble
金额：
$ 2.14万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-01 至 2012-06-30
项目状态：
已结题

项目摘要

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.

该子项目是利用资源的众多研究子项目之一由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持并且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。子项目可能列出的总成本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，观察到的光谱与肽序列之间的假设匹配的质量通过评分函数进行量化。由于评分函数是任何肽鉴定流程的核心，因此该函数极大地影响蛋白质组学测定的最终结果。因此，评估给定评分函数质量的有效统计方法极其重要。此前，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。我们证明这种方法是有问题的，因为结果可能取决于评分函数本身以外的因素。然后，我们提出使用相同类型的数据来验证评分函数的替代方法。我们方法的中心思想是，纯化样品中任何蛋白质都无法解释的数据库匹配包含错误匹配的可靠表示。我们将我们的替代评估方案应用于几种常用的评分函数，并且我们表明我们的方法生成了给定肽识别方法的校准的可重复测量。此外，我们还展示了我们的质量测试如何在开发新颖的评分函数中发挥作用。