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Bioinformatics Advance Access published online on October 24, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn551
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MS-specific noise model reveals the potential of iTRAQTM in quantitative proteomics.

C. Hundertmark a,*, R. Fischer a,*, T. Reinl a, S. May b, F. Klawonn b,{dagger} and L. Jänsch a,{dagger}

aHelmholtz Centre for Infection Research, Department for Cell Biology, Inhoffenstr. 7, D-38124 Braunschweig, Germany, bUniversity of Applied Sciences BS/WF, Department of Computer Science, Salzdahlumer Str. 46/48, D-38302 Wolfenbuettel, Germany

{dagger}To whom correspondence should be addressed. Frank Klawonn, E-mail: f.klawonn{at}fh-wolfenbuettel.de

{dagger}To whom correspondence should be addressed. Dr. Lothar Jänsch, E-mail: lothar.jaensch{at}helmholtz-hzi.de


  Abstract

Motivation: Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS.

Results: We suggest a procedure to model instrument and workflowspecific noise behaviour of iTRAQTM reporter ions that can provide regulatory information during automated peptide sequencing by LC-MS/MS. The established mathematical model representatively predicts possible variations of iTRAQTM reporter ions in an MS data-dependent manner. The model can be utilised to calculate the robustness of regulatory information systematically at the peptide level in so-called bottom-up proteome approaches. It allows to determine the best fitting regulation factor and in addition to calculate the probability of alternative regulations. The result can be visualised as likelihood curves summarising both the quantity and quality of regulatory information. Likelihood curves basically can be calculated from all peptides belonging to different regions of proteins if they are detected in LC-MS/MS experiments. Therefore, this approach renders excellent opportunities to detect and statistically validate dynamic post-translational modifications usually affecting only particular regions of the whole protein. The detection of known phosphorylation events at protein kinases served as a first proof of concept in this study and underscores the potential for noise models in quantitative proteomics.

Contact: lothar.jaensch{at}helmholtz-hzi.de, f.klawonn{at}fh-wolfenbuettel.de

Associate Editor: Prof. David Rocke

*contributed equally

Received on May 15, 2008; revised on October 8, 2008; accepted on October 18, 2008

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