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Neighbor Overlap Is Enriched in the Yeast Interaction Network: Analysis and Implications

Overview of attention for article published in PLOS ONE, June 2012
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Title
Neighbor Overlap Is Enriched in the Yeast Interaction Network: Analysis and Implications
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039662
Pubmed ID
Authors

Ariel Feiglin, John Moult, Byungkook Lee, Yanay Ofran, Ron Unger

Abstract

The yeast protein-protein interaction network has been shown to have distinct topological features such as a scale free degree distribution and a high level of clustering. Here we analyze an additional feature which is called Neighbor Overlap. This feature reflects the number of shared neighbors between a pair of proteins. We show that Neighbor Overlap is enriched in the yeast protein-protein interaction network compared with control networks carefully designed to match the characteristics of the yeast network in terms of degree distribution and clustering coefficient. Our analysis also reveals that pairs of proteins with high Neighbor Overlap have higher sequence similarity, more similar GO annotations and stronger genetic interactions than pairs with low ones. Finally, we demonstrate that pairs of proteins with redundant functions tend to have high Neighbor Overlap. We suggest that a combination of three mechanisms is the basis for this feature: The abundance of protein complexes, selection for backup of function, and the need to allow functional variation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 7%
Germany 1 7%
Unknown 13 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Professor 3 20%
Researcher 2 13%
Student > Bachelor 1 7%
Unspecified 1 7%
Other 2 13%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 53%
Biochemistry, Genetics and Molecular Biology 3 20%
Computer Science 1 7%
Unspecified 1 7%
Unknown 2 13%