Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, USA.
J Proteome Res. 2011 Jul 1;10(7):2949-58. doi: 10.1021/pr2002116. Epub 2011 Apr 29.
Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.
基于质谱的鸟枪法蛋白质组学是一种强大的蛋白质鉴定方法，但在复杂样本中灵敏度有限。整合来自多个数据库搜索引擎的肽鉴定是一种很有前途的策略，可以增加肽鉴定的数量，并减少未分配串联质谱的数量。现有的方法在高得分肽被分配给谱后，对来自多个搜索引擎的统计显著性得分（如 p 值或肽谱匹配（PSM）的后验概率）进行汇总，但这些方法缺乏对鉴定错误率的可靠控制，因为数据是从不同的搜索引擎集成的。我们开发了一种用于综合分析的统计一致方法，称为 MSblender。MSblender 将来自搜索引擎的原始搜索得分转换为每个可能的 PSM 的概率得分，并正确考虑了搜索得分之间的相关性。该方法能够可靠地估计假发现率，并在相同的假发现率下比任何单个搜索引擎识别出更多的 PSM。增加的鉴定结果增加了大多数蛋白质的谱计数，并允许对单个搜索引擎无法定量的蛋白质进行定量。我们还证明，增强的定量分析有助于提高差异表达分析的灵敏度。
