Title |
Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity
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Published in |
Genome Biology, July 2016
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DOI | 10.1186/s13059-016-1003-3 |
Pubmed ID | |
Authors |
Vera Pancaldi, Enrique Carrillo-de-Santa-Pau, Biola Maria Javierre, David Juan, Peter Fraser, Mikhail Spivakov, Alfonso Valencia, Daniel Rico |
Abstract |
Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity, to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. We use high-resolution promoter capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features with the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centered networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA polymerase 2 (RNAPII) compared with inactive forms only in interactions between promoters and other elements. Contacts among promoters and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers, and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 7 | 13% |
Spain | 6 | 12% |
United Kingdom | 6 | 12% |
France | 2 | 4% |
Germany | 2 | 4% |
Israel | 1 | 2% |
Czechia | 1 | 2% |
Belgium | 1 | 2% |
Unknown | 26 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 26 | 50% |
Scientists | 22 | 42% |
Science communicators (journalists, bloggers, editors) | 3 | 6% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 1% |
Denmark | 2 | <1% |
Spain | 2 | <1% |
Italy | 2 | <1% |
United States | 2 | <1% |
Lithuania | 1 | <1% |
Netherlands | 1 | <1% |
Russia | 1 | <1% |
Germany | 1 | <1% |
Other | 2 | <1% |
Unknown | 195 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 62 | 29% |
Researcher | 51 | 24% |
Student > Bachelor | 21 | 10% |
Student > Master | 19 | 9% |
Professor | 14 | 7% |
Other | 24 | 11% |
Unknown | 21 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 79 | 37% |
Agricultural and Biological Sciences | 74 | 35% |
Computer Science | 23 | 11% |
Physics and Astronomy | 6 | 3% |
Medicine and Dentistry | 3 | 1% |
Other | 7 | 3% |
Unknown | 20 | 9% |