A comparison of community identication algorithms for regulatory network motifs

In the recent years high throughput data about biological processes has become available and thus opened a wide range of possibilities of research in multi-disciplinary areas, like network science. An idea that has been widely accepted is the fact that no life can exist without complex systems formed by interacting macromolecules. Rather than a single gene being responsible for a single phenotype (central dogma), it has been shown that the interaction between several genes is responsible for a given phenotype, a concept called System Biology. Identifying patterns of interactions (motifs) in these complex networks has attracted the attention in the scientific community, given that these networks are often very dense and dynamic. In this work we focus on a particular kind of biological network, a regulatory network where each node is a transcription factor and two nodes are connected if one of them encodes a transcription factor to another one that is regulated by this transcription factor. We focus on a specific kind of motif, a dense overlapping region (DOR) that claims that a set of genes regulated by different transcription factors are more overlapping than expected at a random network. We use different community identification algorithms in order to identify which algorithm best suits to the task of identification of this particular motif.