PATTERNLAB FOR PROTEOMICS: A TOOL FOR DIFFERENTIAL SHOTGUN PROTEOMICS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 背景：蛋白质组学的目标是通过识别蛋白质表达差异来区分生物系统的状态。Liu等人展示了一种通过将每种蛋白质获得的串联质谱的数量或“光谱计数”与其在混合物中的丰度相关联来在鸟枪蛋白质组学数据中进行半相对蛋白质定量的方法;然而，两个问题仍然存在：如何标准化光谱计数数据以及如何有效地查明谱之间的差异。此外，Chen等人最近展示了如何通过在进行蛋白水解消化的同时用不同的MS相容性洗涤剂分析样品来增加鸟枪蛋白质组学中鉴定的蛋白质的数量。从数据分析的角度来看，后者带来了新的挑战，因为没有获得重复读数。结果：为了解决上述问题，我们提出了一个名为PatternLab的蛋白质组学程序。该计划实施了现有的策略，并增加了两种新的方法来确定蛋白质谱的差异。第一种方法，ACFold，解决了每个状态少于三次重复的实验，或者具有通过不同方案获得的测定，如Chen等人所述。ACFold使用基于表达倍数变化、AC测试和错误发现率的组合标准，并且可以提供差异表达蛋白质的“鸟瞰图”。另一种方法解决了具有来自每个状态的多个读数的实验设计，并且被称为nSVM（自然支持向量机），因为它起源于进化计算和统计学习理论。我们的观察结果表明，nSVM的利基包括项目，选择一个最小的蛋白质组进行分类的目的，例如，开发一个早期检测试剂盒为给定的病理。我们证明了每种方法对实验数据的有效性，并与现有的策略。结论：PatternLab提供了一个简单而统一的访问各种特征选择和标准化策略，每个都有自己的利基。此外，绘图工具可用于帮助分析高通量实验数据。PatternLab可在http://pcarvalho.com/patternlab上获得

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. BACKGROUND: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired. RESULTS: To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen et al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies. CONCLUSION: PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at http://pcarvalho.com/patternlab