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      Chromatin assortativity: integrating epigenomic data and 3D genomic structure

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          Abstract

          Background: The field of 3D chromatin interaction mapping is changing our point of view on the genome, paving the way for new insights into its organization. Network analysis is a natural and powerful way of modelling chromatin interactions. Assortativity is a network property that has been widely used in the social sciences to measure the probability of nodes with similar values of a specific feature to interact preferentially. We propose a new approach, using Chromatin feature Assortativity (ChAs), to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network. Results: We use high-resolution Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from embryonic stem cells to generate promoter-centered interaction networks. We calculate the presence of a collection of 78 chromatin features in the chromatin fragments constituting the nodes of the network. Based on the ChAs of these epigenomic features calculated in 4 different interaction networks, we find Polycomb Group proteins and associated histone marks to play a prominent role. Remarkably, in promoter-centered networks, we observe higher ChAs of the actively elongating form of RNA Polymerase 2 compared to inactive forms in interactions between promoters and other elements. Conclusions: Contacts amongst promoters and between promoters and other elements have different characteristic epigenomic features. Using ChAs we identify a possible role of the elongating form of RNAPII in enhancer activity. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing for the comparison of any number of chromatin interaction networks.

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          Author and article information

          Journal
          2015-12-01
          2015-12-28
          1512.00268

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          41 pages, 6 main figures (new Figure 6)
          q-bio.MN q-bio.GN

          Molecular biology, Genetics

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