Node similarity within subgraphs of protein interaction networks |
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Authors: | Orion Penner Gabriel Musso Peter Grassberger Maya Paczuski |
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Institution: | a Complexity Science Group, University of Calgary, Calgary, Alberta T2N 1N4, Canada b Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta T2N 1N4, Canada c Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario M5S 3E1, Canada d Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada |
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Abstract: | We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. The twinness of a pair of nodes is the number of connected, labeled subgraphs of size n in which the two nodes possess identical neighbours. The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). These include an Escherichia coli PIN and three Saccharomyces cerevisiae PINs — each obtained using state-of-the-art high-throughput methods. In almost all cases, the average twinness of node pairs is vastly higher than that expected from a null model obtained by switching links. For all n, we observe a difference in the ratio of type twins (which are unlinked pairs) to type twins (which are linked pairs) distinguishing the prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is expected due to gene duplication, and whole genome duplication paralogues in S. cerevisiae have been reported to co-cluster into the same complexes. Indeed, we find that these paralogous proteins are over-represented as twins compared to pairs chosen at random. These results indicate that twinness can detect ancestral relationships from currently available PIN data. |
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Keywords: | 87 14 Ee 02 70 Uu 87 10 +e 89 75 Fb 89 75 Hc |
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