A NESTED MIXTURE MODEL FOR PROTEIN IDENTIFICATION USING MASS SPECTROMETRY

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。子项目和 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。 质谱法提供了一种高通量的方法来鉴定生物样品中的蛋白质。在一个典型的实验中，样品中的蛋白质首先被分解成它们的组成肽。然后将所得的肽混合物进行质谱分析，质谱分析产生数千个光谱，每个光谱都具有其生成肽的特征。在这里，我们考虑的问题推断，从这些光谱，蛋白质和肽存在于样品中。我们开发了一个统计方法来解决这个问题，基于嵌套混合模型。与常用的两阶段方法相比，该模型提供了一个一阶段的解决方案，可以同时识别哪些蛋白质存在，以及哪些肽被正确识别。通过这种方式，我们的模型结合了蛋白质及其组成肽之间的证据反馈。使用模拟数据和酵母数据集，我们比较和对比我们的方法与现有的广泛使用的方法。对于肽鉴定，我们的单阶段方法始终产生更准确的结果。对于蛋白质鉴定的方法在大多数情况下具有相似的准确性，尽管我们展示了一些现有方法表现不佳的情况。

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. Mass spectrometry provides a high-throughput way to identify proteins in biological samples. In a typical experiment, proteins in a sample are first broken into their constituent peptides. The resulting mixture of peptides is then subjected to mass spectrometry, which generates thousands of spectra, each characteristic of its generating peptide. Here we consider the problem of inferring, from these spectra, which proteins and peptides are present in the sample. We develop a statistical approach to the problem, based on a nested mixture model. In contrast to commonly-used two-stage approaches, this model provides a one-stage solution that simultaneously identifies which proteins are present, and which peptides are correctly identified. In this way our model incorporates the evidence feedback between proteins and their constituent peptides. Using simulated data and a yeast dataset, we compare and contrast our method with existing widely-used approaches. For peptide identification, our single-stage approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some scenarios in which the existing methods perform poorly.