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Bioinformatics Advance Access published online on October 27, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp607
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments

Marc Kirchner 1,2,{ddagger}, Bernhard Y. Renard 1,3,{ddagger}, Ullrich Köthe 3, Darryl J. Pappin 4, Fred A. Hamprecht 1,3, Hanno Steen 1,2,{dagger},* and Judith A. J. Steen 5,{dagger}

1Proteomics Center, Dept. of Pathology, Children's Hospital Boston, Boston, MA, USA
2Dept. of Pathology, Harvard Medical School, Boston, MA, USA
3Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany
4Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
5Dept. of Neurobiology, Harvard Medical School and T. M. Kirby Neurobiology Center, Children's Hospital, Boston, MA, USA

*To whom correspondence should be addressed. Hanno Steen, E-mail: hanno.steen{at}childrens.harvard.edu


  Abstract

Motivation: The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis worflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account.

Results: We use protein profile similarity screening to identify candidate proteins whose abundances are post-transcriptionally controlled by the Anaphase Promoting Complex (APC/C), a specific E3 ubiquitin ligase that is a master regulator of the cell cycle. Results are compared with an established protein correlation profiling method. The proposed procedure yields a 50.9-fold enrichment of co-regulated protein candidates and a 2.5-fold improvement over the previous method.

Availability: A MATLAB toolbox is available from http://hci.iwr.uni-heidelberg.de/mip/proteomics.

Contact: hanno.steen{at}childrens.harvard.edu

Associate Editor: Prof. Burkhard Rost

{ddagger}, {dagger} Authors contributed equally.

Received on May 22, 2009; revised on September 14, 2009; accepted on October 7, 2009

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