Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics
Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics
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DOI：
10.1186/1471-2105-9-542
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发表时间：
2008-12-16
影响因子：
3
通讯作者：
Watts, Julian D.
中科院分区：
文献类型：
--
作者：
Brusniak, Mi-Youn;Bodenmiller, Bernd;Watts, Julian D.
背景：定量蛋白质组学对于识别代表不同生理或疾病状态的群体之间丰度差异的蛋白质具有巨大的前景。现在有一系列计算工具可用于基于同位素标记和无标记液相色谱质谱 (LC-MS) 的定量蛋白质组学。然而，它们在功能、用户界面、信息输入/输出方面通常彼此不具有可比性，并且不容易促进适当的统计数据分析。这些限制以及一系列的选择，为生物学家和其他未受过生物信息学培训、希望使用基于 LC-MS 的定量蛋白质组学的研究人员带来了令人畏惧的前景。 结果：我们开发了 Corra，一种用于基于发现的 LC-MS 蛋白质组学的计算框架和工具。 Corra 扩展并调整了用于基于 LC-MS 的蛋白质组学的现有算法，以及最初为微阵列数据分析开发的统计算法，适用于 LC-MS 数据分析。 Corra 还采用软件工程技术（例如 Google Web Toolkit、分布式处理），以便计算密集型数据处理和统计分析可以在远程服务器上运行，而用户通过简单的 Web 界面从自己的计算机控制和管理该过程。 Corra 还允许用户以与随后通过串联质谱 (MS/MS) 进行序列鉴定兼容的形式输出显着差异丰富的 LC-MS 检测到的肽特征。我们提出了两个案例研究来说明 Corra 在常用的基于 LC-MS 的生物工作流程中的应用：对从与 2 型糖尿病相关的人类血浆样本中分离出的糖蛋白进行的试点生物标志物发现研究，以及在酵母中进行的研究，以通过磷酸肽分析识别蛋白激酶 Ark1 的体内靶点。 结论：Corra 计算框架利用计算创新，使生物学家或其他研究人员能够处理、分析和可视化 LC-MS 数据，否则这些数据将变得复杂且不方便用户使用工具套件。 Corra 能够进行适当的统计分析，并控制错误发现率，最终为后续通过 MS/MS 对差异丰度肽进行靶向鉴定提供信息。对于未接受过生物信息学培训的用户来说，Corra 代表了一个完整的、可定制的、免费的开源计算平台，支持基于 LC-MS 的蛋白质组工作流程，因此解决了 LC-MS 蛋白质组领域未满足的需求。
Background: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.Results: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e. g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.Conclusion: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.