BACKGROUND : The field of 3D chromatin interaction mapping is changing our point of view on the genome . Despite the increase in the number of studies, there are surprisingly few network analyses performed on these datasets and the network topology is rarely considered . 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, the Chromatin feature Assortativity Score (ChAS ), to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network. RESULTS: We analyse two chromatin interaction datasets for embryonic stem cells , which were generated with two very recent promoter capture HiC methods. These datasets define networksof interactions amongst promoters and between promoters and other genomic loci . We calculate the presence of a collection of 78 chromatin features in the chromatin fragments that constitute the nodes of the network. Looking at the ChAS of these epigenomic features in the interaction networks, we find Polycomb Group proteins and associated histone marks to be the most assortative factors . Remarkably,  we observe higher ChAS of the actively elongating form of RNA Polymerase 2 compared to the inactive forms in interactions between promoters and other distal elements, suggesting an important role for active elongation in promoter-enhancer contacts. CONCLUSIONS: Our method can be used to compare the association of epigenomic features to different type of genomic contacts. Furthermore, it facilitates the comparison of any number of chromatin interaction datasets in the context of the corresponding epigenomic landscape .
Background : The field of 3D chromatin interaction mapping is changing our point of view on the genome , paving the way for new insights into URLanization. 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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BACKGROUND
Background
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. Despite the increase in the number of studies, there are surprisingly few network analyses performed on these datasets and the network topology is rarely considered
, paving the way for new insights into URLanization. Network analysis is a natural and powerful way of modelling chromatin interactions
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using
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Score (ChAS
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RESULTS: We analyse two chromatin interaction datasets for
Results: We use high-resolution Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from
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, which were generated with two very recent promoter capture HiC methods. These datasets define networksof interactions amongst promoters and between promoters and other genomic loci
to generate promoter-centered interaction networks
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in promoter-centered networks,
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distal elements, suggesting an important role for active elongation in promoter-enhancer contacts. CONCLUSIONS: Our method can be used to compare the association of epigenomic features to different type of genomic contacts. Furthermore, it facilitates
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
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datasets in the context of the corresponding epigenomic landscape
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