Clust&See: A Cytoscape plugin for the identification, visualization and manipulation of network clusters

International audienceBackground and scope Large networks, such as protein interaction networks, are extremely difficult to analyze as a whole. We developed Clust&See, a Cytoscape plugin dedicated to the identification, visualization and analysis of clusters extracted from such networks. Implementation and performance Clust&See provides the ability to apply three different, recently developed graph clustering algorithms to networks and to visualize: (i) the obtained partition as a quotient graph in which nodes correspond to clusters and (ii) the obtained clusters as their corresponding subnetworks. Importantly, tools for investigating the relationships between clusters and vertices as well as their organization within the whole graph are supplied

Multifunctional proteins revealed by overlapping clustering in protein interaction network

Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters

OCG - Overlapping Class Generator

This program builds an overlapping class system from an unweighted simple graph G=(V,E). Let |V|=n and |E|=m. It is essentially a hierarchical ascending algorithm joining two classes at each step. The optimized criterion is the modularity. It can be either the average gain or the total gain. The initial overlapping class system can be : - the set of all maximal cliques (it can take a long time to establish) - the set of edges (many initial classes (m) implying many steps (O(m)) - the set of "centered cliques" (at most n), giving a fast solution for large graphs. Two class systems can be calculated, the one maximazing the modularity, or the final one. In that case, the expected minimum number of clusters and the maximum caldinality of the final clusters are required. Fusion of classes are realized until one of these conditions is fullfiled. When no more class fusion can be realized the algorithm stops. Let p be the number of initial classes. The complexity is this algorithm is O(p^3)

Extreme multifunctional proteins identified from a human protein interaction network.

International audienceMoonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of 'extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct subgroup of proteins, characterized by specific features, which form a signature of extreme multi-functionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins