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dc.contributor.author
Brusniak, Mi-Youn
dc.contributor.author
Bodenmiller, Bernd
dc.contributor.author
Campbell, David
dc.contributor.author
Cooke, Kelly
dc.contributor.author
Eddes, James
dc.contributor.author
Garbutt, Andrew
dc.contributor.author
Lau, Hollis
dc.contributor.author
Letarte, Simon
dc.contributor.author
Müller, Lukas N.
dc.contributor.author
Sharma, Vagisha
dc.contributor.author
Vitek, Olga
dc.contributor.author
Zhang, Ning
dc.contributor.author
Aebersold, Ruedi
dc.contributor.author
Watts, Julian D.
dc.date.accessioned
2018-09-03T08:46:08Z
dc.date.available
2017-06-08T20:51:22Z
dc.date.available
2018-09-03T08:46:08Z
dc.date.issued
2008-12
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-9-542
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/13099
dc.identifier.doi
10.3929/ethz-b-000013099
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Normal Glucose Tolerance
en_US
dc.subject
Liquid Chromatography Mass Spectrometry
en_US
dc.subject
Quantitative Proteomics
en_US
dc.subject
High Mass Accuracy
en_US
dc.subject
Differential Abundance
en_US
dc.title
Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
9
en_US
ethz.pages.start
542
en_US
ethz.size
22 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03663 - Aebersold, Rudolf (emeritus) / Aebersold, Rudolf (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03663 - Aebersold, Rudolf (emeritus) / Aebersold, Rudolf (emeritus)
ethz.date.deposited
2017-06-08T20:51:32Z
ethz.source
ECIT
ethz.identifier.importid
imp59364c238313712534
ethz.ecitpid
pub:24485
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T19:24:03Z
ethz.rosetta.lastUpdated
2022-03-28T21:10:33Z
ethz.rosetta.versionExported
true
ethz.COinS
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