Recent studies involving the 3-dimensional conformation of chromatin have revealed the important role it has to play in different processes within the cell. These studies have also led to the discovery of densely interacting segments of the chromosome, called topologically associating domains. The accurate identification of these domains from Hi-C interaction data is an interesting and important computational problem for which numerous methods have been proposed. Unfortunately, most existing algorithms designed to identify these domains assume that they are non-overlapping whereas there is substantial evidence to believe a nested structure exists. We present an efficient methodology to predict hierarchical chromatin domains using chromatin conformation capture data. Our method predicts domains at different resolutions and uses these to construct a hierarchy that is based on intrinsic properties of the chromatin data. The hierarchy consists of a set of non-overlapping domains, that maximize intra-domain interaction frequencies, at each level. We show that our predicted structure is highly enriched for CTCF and various other chromatin markers. We also show that large-scale domains, at multiple resolutions within our hierarchy, are conserved across cell types and species. Our software, Matryoshka, is written in C++11 and licensed under GPL v3; it is available at https://github.com/COMBINE-lab/matryoshka.
['Laraib Malik', 'Robert Patro']
Rich chromatin structure prediction from Hi-C data