Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

被引:51
作者
Brusniak, Mi-Youn [1 ]
Bodenmiller, Bernd [2 ,3 ]
Campbell, David [1 ]
Cooke, Kelly [1 ]
Eddes, James [1 ]
Garbutt, Andrew [1 ]
Lau, Hollis [1 ]
Letarte, Simon [1 ]
Mueller, Lukas N. [2 ,3 ]
Sharma, Vagisha [1 ]
Vitek, Olga [4 ,5 ]
Zhang, Ning [1 ]
Aebersold, Ruedi [1 ,2 ,3 ,6 ]
Watts, Julian D. [1 ]
机构
[1] Inst Syst Biol, Seattle, WA 98103 USA
[2] ETH, Inst Mol Syst Biol, Zurich, Switzerland
[3] ETH, Competence Ctr Syst Physiol & Metab Dis, Zurich, Switzerland
[4] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA
[5] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA
[6] Univ Zurich, Fac Sci, Zurich, Switzerland
基金
瑞士国家科学基金会;
关键词
D O I
10.1186/1471-2105-9-542
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
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页数:22
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