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dc.contributor.author
Clough, Timothy
dc.contributor.author
Thaminy, Safia
dc.contributor.author
Ragg, Susanne
dc.contributor.author
Aebersold, Ruedi
dc.contributor.author
Vitek, Olga
dc.date.accessioned
2018-09-04T14:30:35Z
dc.date.available
2017-06-10T13:24:22Z
dc.date.available
2018-09-04T14:30:35Z
dc.date.issued
2012-11
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-13-S16-S6
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/62751
dc.identifier.doi
10.3929/ethz-b-000062751
dc.description.abstract
Background Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs. Results We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background. Conclusions We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html.
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
Label-free LC-MS/MS
en_US
dc.subject
linear mixed effects models
en_US
dc.subject
protein quantification
en_US
dc.subject
quantitative proteomics
en_US
dc.subject
statistical design of experiments
en_US
dc.title
Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
13
en_US
ethz.journal.issue
Supplement 16
en_US
ethz.pages.start
S6
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
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 / Aebersold, Rudolf
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 / Aebersold, Rudolf
ethz.date.deposited
2017-06-10T13:26:26Z
ethz.source
ECIT
ethz.identifier.importid
imp5936504a8a45688936
ethz.ecitpid
pub:99628
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T19:34:25Z
ethz.rosetta.lastUpdated
2020-02-15T14:44:47Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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