A complex network framework for unbiased statistical analyses of DNA-DNA contact maps

Experimental techniques for the investigation of three-dimensional (3D) genome organization are being developed at a fast pace. Currently, the associated computational methods are mostly specific to the individual experimental approach. Here we present a general statistical framework that is widely applicable to the analysis of genomic contact maps, irrespective of the data acquisition and normalization processes. Within this framework DNA-DNA contact data are represented as a complex network, for which a broad number of directly applicable methods already exist. In such a network representation, DNA segments and contacts between them are denoted as nodes and edges, respectively. Furthermore, we present a robust method for generating randomized contact networks that explicitly take into account the inherent 3D nature of the genome and serve as realistic null-models for unbiased statistical analyses. By integrating a variety of large-scale genome-wide datasets we demonstrate that meiotic crossover sites display enriched genomic contacts and that cohesin-bound genes are significantly colocalized in the yeast nucleus. We anticipate that the complex network framework in conjunction with the randomization of DNA-DNA contact networks will become a widely used tool in the study of nuclear architecture.