We systematically investigate the community structure of the yeast protein interaction network . We employ methods that allow us to identify communities in the network at multiple resolutions as, a priori, there is no single scale of interest. We use novel, stringent tests to find strong evidence for a link between topology and function. Crucially, our tests control for the fact that interacting partners are more similar than a randomly-chosen pair, which is essential for a fair test of functional similarity. We find that many biologically homogeneous communities , that are robust over many resolutions, are surprisingly large. We thus not only identify complexes from the interaction data but also larger collections of strongly-interacting proteins . Communities that contain interactions from tandem affinity purification and co-immunoprecipitation data are far more likely to be biologically homogeneous than those from yeast-two-hybrid and split-ubiquitin data. We find that high clustering coefficient is a very good indicator of biological homogeneity in small communities. For larger communities, we find that link density is also important. Our results significantly improve the understanding of the modular structure of the protein interaction network- and how that modularity is reflected in biological homogeneity and experimental type. Our results suggest a method to select groups of functionally similar proteins even when no annotation is yet known, thereby yielding a valuable diagnostic tool to predict groups that act concertedly within the cell .
Motivation: If biology is modular then clusters, or communities, of proteins derived using only protein-protein interaction network structure might define protein modules with similar biological roles. We investigate the connection between biological modules and network communities in yeast and ask how the functional similarity of the communities that we find depends on the scales at which we probe the network. Results: We find many proteins lie in functionally homogeneous communities (a maximum of 2777 out of 4028 proteins) which suggests that network structure does indeed help identify sets of proteins with similar functions. The homogeneity of the communities depends on the scale selected. We use a novel test and two independent characterizations of protein function to determine the functional homogeneity of communities. We exploit the connection between network structure and biological function to select groups of proteins which are likely to participate in similar biological functions. We show that high mean clustering coefficient and low mean node betweenness centrality can be used to predict functionally homogeneous communities. Availability: All the data sets and the community detection algorithm are available online .
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We systematically investigate the community structure of the yeast protein interaction network . We employ methods that allow us to identify communities in
Motivation: If biology is modular then clusters, or communities, of proteins derived using only protein-protein interaction network structure might define protein modules with similar biological roles. We investigate the connection between biological modules and network communities in yeast and ask how the functional similarity of
R
network at multiple resolutions as, a priori, there is no single scale of interest. We use novel, stringent tests to find strong evidence for a link between topology and function. Crucially, our tests control for the fact that interacting partners are more similar than a randomly-chosen pair, which is essential for a fair test of functional similarity. We find that many biologically homogeneous communities , that are robust over many resolutions, are surprisingly large. We thus not only identify complexes from the interaction data but also larger collections of strongly-interacting proteins . Communities that contain interactions from tandem affinity purification and co-immunoprecipitation data are far more likely to be biologically homogeneous than those from yeast-two-hybrid
communities that we find depends on the scales at which we probe the network. Results: We find many proteins lie in functionally homogeneous communities (a maximum of 2777 out of 4028 proteins) which suggests that network structure does indeed help identify sets of proteins with similar functions. The homogeneity of the communities depends on the scale selected. We use a novel test and two independent characterizations of protein function to determine the functional homogeneity of communities. We exploit the connection between network structure
R
split-ubiquitin data. We find that high clustering coefficient is a very good indicator of biological homogeneity in small communities. For larger communities, we find that link density is also important. Our results significantly improve the understanding of the modular structure of the protein interaction network- and how that modularity is reflected in biological homogeneity and experimental type. Our results suggest a method
biological function
R
functionally similar proteins even when no annotation is yet known, thereby yielding a valuable diagnostic tool to predict groups that act concertedly within the cell
proteins which are likely to participate in similar biological functions. We show that high mean clustering coefficient and low mean node betweenness centrality can be used to predict functionally homogeneous communities. Availability: All the data sets and the community detection algorithm are available online