STATISTICAL CALIBRATION OF THE SEQUEST XCORR FUNCTION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 从鸟枪蛋白质组学液相色谱串联质谱（LC-MS/MS）实验中获得准确的肽鉴定需要一个评分函数，该函数始终将正确的肽谱匹配（PSM）排在不正确的匹配之上。我们已经观察到，对于SEQUEST评分函数Xcorr，无法区分正确和不正确的PSM部分是由于评分分布的频谱特定属性。换句话说，一些光谱得分很好，而不管它们针对哪些肽进行评分，而其他光谱得分很好，因为它们针对大量肽进行评分。我们描述了一个协议，用于校准PSM评分函数，我们证明了它的应用程序Xcorr和初步SEQUEST评分函数SP。该协议占频谱和肽特异性的影响，通过计算p值为每个频谱单独，只使用频谱的分数分布。我们证明了这些计算的p值在零分布下是均匀的，因此可以准确地测量显著性。这些p值可用于估计错误发现率，从而消除对诱饵数据库的额外搜索的需要。此外，我们表明，p值比其基础分数更好地校准;因此，当从多个光谱中排名得分最高的PSM时，p值更好地区分正确和不正确的PSM。校准协议通常适用于任何PSM评分函数，可以识别适当的参数族。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Obtaining accurate peptide identifications from shotgun proteomics liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments requires a score function that consistently ranks correct peptide-spectrum matches (PSMs) above incorrect matches. We have observed that, for the SEQUEST score function Xcorr, the inability to discriminate between correct and incorrect PSMs is due in part to spectrum-specific properties of the score distribution. In other words, some spectra score well regardless of which peptides they are scored against, and other spectra score well because they are scored against a large number of peptides. We describe a protocol for calibrating PSM score functions, and we demonstrate its application to Xcorr and the preliminary SEQUEST score function Sp. The protocol accounts for spectrum- and peptide-specific effects by calculating p values for each spectrum individually, using only that spectrum's score distribution. We demonstrate that these calculated p values are uniform under a null distribution and therefore accurately measure significance. These p values can be used to estimate the false discovery rate, therefore eliminating the need for an extra search against a decoy database. In addition, we show that the p values are better calibrated than their underlying scores; consequently, when ranking top-scoring PSMs from multiple spectra, p values are better at discriminating between correct and incorrect PSMs. The calibration protocol is generally applicable to any PSM score function for which an appopriate parametric family can be identified.