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The effects of incomplete protein interaction data on structural and evolutionary inferences

Eric de Silva1, Thomas Thorne1, Piers Ingram12, Ino Agrafioti1, Jonathan Swire1, Carsten Wiuf34 and Michael PH Stumpf15*

Author Affiliations

1 Theoretical Genomics Group, Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London, UK

2 Department of Mathematics, Imperial College London, London, UK

3 Bioinformatics Research Center, University of Aarhus, Aarhus, Denmark

4 Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark

5 Institute of Mathematical Sciences, Imperial College London, London, UK

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BMC Biology 2006, 4:39  doi:10.1186/1741-7007-4-39

Published: 3 November 2006



Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis.


Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences.


Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.