IDENTIFYING DIFFERENCES IN PROTEIN EXPRESSION LEVELS BY SPECTRAL COUNTING AND FE

通过光谱计数和 FE 识别蛋白质表达水平的差异

• 批准号: 7957728
• 负责人: JOHN L YATES
• 金额: $ 2.09万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957728
• 关键词: Biology; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Funding; Fungal Genome; Grant; Hybrids; Institution; Liquid Chromatography; Machine Learning; Methods; Modeling; Proteins; Relative (related person); Research; Research Personnel; Resources; Signal Transduction; Simulate; Source; Students; Testing; United States National Institutes of Health; Weight; Yeasts; base; indexing; protein complex; protein expression; research study; tandem mass spectrometry

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. Spectral counting is a strategy to quantify relative protein concentrations in pre-digested protein mixtures analyzed by liquid chromatography online with tandem mass spectrometry. In the present study, we used combinations of normalization and statistical (feature selection) methods on spectral counting data to verify whether we could pinpoint which and how many proteins were differentially expressed when comparing complex protein mixtures. These combinations were evaluated on real, but controlled, experiments (yeast lysates were spiked with protein markers at different concentrations to simulate differences), which were therefore verifiable. The following normalization methods were applied: total signal, Z-normalization, hybrid normalization, and log preprocessing. The feature selection methods were: the Golub index, the Student t-test, a strategy based on the weighting used in a forward-support vector machine (SVM-F) model, and SVM recursive feature elimination. The results showed that Z-normalization combined with SVM-F correctly identified which and how many protein markers were added to the yeast lysates for all different concentrations. The software we used is available at http://pcarvalho.com/patternlab.

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。 光谱计数是一种定量预消化蛋白质混合物中相对蛋白质浓度的策略，该混合物通过液相色谱与串联质谱在线分析。在本研究中，我们使用光谱计数数据的归一化和统计（特征选择）方法的组合，以验证我们是否可以确定哪些以及有多少蛋白质在比较复杂的蛋白质混合物时差异表达。在真实的但受控的实验中评价了这些组合（向酵母裂解物中掺入不同浓度的蛋白质标记物以模拟差异），因此这些实验是可验证的。应用以下归一化方法：总信号、Z-归一化、混合归一化和对数预处理。特征选择方法有：Golub指数、学生t检验、基于前向支持向量机（SVM-F）模型中使用的加权策略和SVM递归特征消除。结果表明，Z-归一化结合SVM-F正确地鉴定了对于所有不同浓度的酵母裂解物添加了哪些和多少蛋白质标记物。我们使用的软件可在http://pcarvalho.com/patternlab上获得。