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Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods


Grossmann, J; Roschitzki, B; Panse, C; Fortes, C; Barkow-Oesterreicher, S; Rutishauser, D; Schlapbach, R (2010). Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. Journal of Proteomics, 73(9):1740-1746.

Abstract

Tandem mass spectrometry allows for fast protein identification in a complex sample. As mass spectrometers get faster, more sensitive and more accurate, methods were devised by many academic research groups and commercial suppliers that allow protein research also in quantitative respect. Since label-free methods are an attractive alternative to labeling approaches for proteomics researchers seeking for accurate quantitative results we evaluated several open-source analysis tools in terms of performance on two reference data sets, explicitly generated for this purpose. In this paper we present an implementation, T3PQ (Top 3 Protein Quantification), of the method suggested by Silva and colleagues for LC-MS(E) applications and we demonstrate its applicability to data generated on FT-ICR instruments acquiring in data dependent acquisition (DDA) mode. In order to validate this method and to show its usefulness also for absolute protein quantification, we generated a reference data set of a sample containing four different proteins with known concentrations. Furthermore, we compare three other label-free quantification methods using a complex biological sample spiked with a standard protein in defined concentrations. We evaluate the applicability of these methods and the quality of the results in terms of robustness and dynamic range of the spiked-in protein as well as other proteins also detected in the mixture. We discuss drawbacks of each method individually and consider crucial points for experimental designs. The source code of our implementation is available under the terms of the GNU GPLv3 and can be downloaded from sourceforge (http://fqms.svn.sourceforge.net/svnroot/fqms). A tarball containing the data used for the evaluation is available on the FGCZ web server (http://fgcz-data.uzh.ch/public/T3PQ.tgz).

Abstract

Tandem mass spectrometry allows for fast protein identification in a complex sample. As mass spectrometers get faster, more sensitive and more accurate, methods were devised by many academic research groups and commercial suppliers that allow protein research also in quantitative respect. Since label-free methods are an attractive alternative to labeling approaches for proteomics researchers seeking for accurate quantitative results we evaluated several open-source analysis tools in terms of performance on two reference data sets, explicitly generated for this purpose. In this paper we present an implementation, T3PQ (Top 3 Protein Quantification), of the method suggested by Silva and colleagues for LC-MS(E) applications and we demonstrate its applicability to data generated on FT-ICR instruments acquiring in data dependent acquisition (DDA) mode. In order to validate this method and to show its usefulness also for absolute protein quantification, we generated a reference data set of a sample containing four different proteins with known concentrations. Furthermore, we compare three other label-free quantification methods using a complex biological sample spiked with a standard protein in defined concentrations. We evaluate the applicability of these methods and the quality of the results in terms of robustness and dynamic range of the spiked-in protein as well as other proteins also detected in the mixture. We discuss drawbacks of each method individually and consider crucial points for experimental designs. The source code of our implementation is available under the terms of the GNU GPLv3 and can be downloaded from sourceforge (http://fqms.svn.sourceforge.net/svnroot/fqms). A tarball containing the data used for the evaluation is available on the FGCZ web server (http://fgcz-data.uzh.ch/public/T3PQ.tgz).

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2010
Deposited On:27 Jan 2011 18:01
Last Modified:05 Apr 2016 14:40
Publisher:Elsevier
ISSN:1874-3919
Publisher DOI:https://doi.org/10.1016/j.jprot.2010.05.011
PubMed ID:20576481

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