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Brusniak MY, Bodenmiller B, Campbell D, et al. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008;9:542doi: 10.1186/1471-2105-9-542.
Brusniak, M. Y., Bodenmiller, B., Campbell, D., Cooke, K., Eddes, J., Garbutt, A., Lau, H., Letarte, S., Mueller, L. N., Sharma, V., Vitek, O., Zhang, N., Aebersold, R., & Watts, J. D. (2008). Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC bioinformatics, 9542. https://doi.org/10.1186/1471-2105-9-542
Brusniak, Mi-Youn, et al. "Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics." BMC bioinformatics vol. 9 (2008): 542. doi: https://doi.org/10.1186/1471-2105-9-542
Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008 Dec 16;9:542. doi: 10.1186/1471-2105-9-542. PMID: 19087345; PMCID: PMC2651178.
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BMC Bioinformatics. 2008 Dec 16;9:542. doi: 10.1186/1471-2105-9-542.

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

BMC bioinformatics

Mi-Youn Brusniak, Bernd Bodenmiller, David Campbell, Kelly Cooke, James Eddes, Andrew Garbutt, Hollis Lau, Simon Letarte, Lukas N Mueller, Vagisha Sharma, Olga Vitek, Ning Zhang, Ruedi Aebersold, Julian D Watts

Affiliations

  1. Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. [email protected]

PMID: 19087345 PMCID: PMC2651178 DOI: 10.1186/1471-2105-9-542

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.

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