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Bioinformatics Advance Access originally published online on November 19, 2007
Bioinformatics 2008 24(2):218-224; doi:10.1093/bioinformatics/btm565
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Estimating node degree in bait-prey graphs

Denise Scholtens 1,*, Tony Chiang 2, Wolfgang Huber 2 and Robert Gentleman 3

1Department of Preventive Medicine, Northwestern University Medical School, 680 N. Lake Shore Drive Suite 1102, Chicago, IL 60611-4402, USA, 2EMBL Outstation – Hinxton, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK and 3Fred Hutchinson Cancer Research Center, Computational Biology Group, 1100 Fairview Avenue North – M2-B876, P.O. Box 19024, Seattle, Washington 98109-1024, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Proteins work together to drive biological processes in cellular machines. Summarizing global and local properties of the set of protein interactions, the interactome, is necessary for describing cellular systems. We consider a relatively simple per-protein feature of the interactome: the number of interaction partners for a protein, which in graph terminology is the degree of the protein.

Results: Using data subject to both stochastic and systematic sources of false positive and false negative observations, we develop an explicit probability model and resultant likelihood method to estimate node degree on portions of the interactome assayed by bait-prey technologies. This approach yields substantial improvement in degree estimation over the current practice that naïvely sums observed edges. Accurate modeling of observed data in relation to true but unknown parameters of interest gives a formal point of reference from which to draw conclusions about the system under study.

Availability: All analyses discussed in this text can be performed using the ppiStats and ppiData packages available through the Bioconductor project (http://www.bioconductor.org).

Contact: dscholtens{at}northwestern.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Chris Stoeckert

Received on September 7, 2007; revised on November 7, 2007; accepted on November 7, 2007

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