Community detection within clusters helps large scale protein annotation: Preliminary results of modularity maximization for the bar+ database

Given the exponentially increasing amount of available data, electronic annotation procedures for protein sequences are a core topic in bioinformatics. In this paper we present the refinement of an already published procedure that allows a fine grained level of detail in the annotation results. This enhancement is based on a graph representation of the similarity relationship between sequences within a cluster, followed by the application of community detection algorithms. These algorithms identify groups of highly connected nodes inside a bigger graph. The core idea is that sequences belonging to the same community share more features in respect to all the other sequences in the same graph.