Methodology article

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

Mi-Youn Brusniak¹ , Bernd Bodenmiller^2,3 , David Campbell¹ , Kelly Cooke¹ , James Eddes¹ , Andrew Garbutt¹ , Hollis Lau¹ , Simon Letarte¹ , Lukas N Mueller^2,3 , Vagisha Sharma¹ , Olga Vitek⁴ , Ning Zhang¹ , Ruedi Aebersold^1,2,3,5 and Julian D Watts¹

¹  Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA

²  Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

³  Competence Center for Systems Physiology and Metabolic Disease, ETH Zurich, Zurich, Switzerland

⁴  Department of Statistics and Department of Computer Science, Purdue University, West Lafayette, IN, USA

⁵  Faculty of Science, University of Zurich, Zurich, Switzerland

author email corresponding author email

BMC Bioinformatics 2008, 9:542doi:10.1186/1471-2105-9-542

Published:	16 December 2008

Abstract

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.