Cluster-based assessment of protein-protein interaction confidence.
BACKGROUND
Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.


RESULTS
We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network's inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.


CONCLUSIONS
On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de.
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany. kamburov@molgen.mpg.de
Atanas
A
Kamburov
Arndt
A
Grossmann
Ralf
R
Herwig
Ulrich
U
Stelzl
D000465:Algorithms; D016000:Cluster Analysis; D016001:Confidence Intervals; D006801:Humans; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism
D011506:Proteins