Network modeling of topological domains using Hi-C data

54 mins 4 secs, 206.79 MB, iPod Video 480x270, 29.97 fps, 44100 Hz, 522.19 kbits/sec

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Description:	Bickel, P (University of California, Berkeley) Wednesday 24th August 2016 - 10:00 to 11:00


Created:	2016-08-31 16:44
Collection:	Theoretical Foundations for Statistical Network Analysis
Publisher:	Isaac Newton Institute
Copyright:	Bickel, P
Language:	eng (English)


Abstract:	Genome-wide chromosome conformation capture techniques such as Hi-C enable the generation of 3D genome contact maps and offer new pathways toward understanding the spatial organization of genome. It is widely recognized that chromosomes form domains of enriched interactions playing significant roles in gene regulation and development. In particular, it is of interest to identify densely interacting, contiguous regions at the sub-megabase scale known as topologically associating domains (TADs), which are believed to be conserved across cell types and even species. Although a few algorithms have been proposed to detect TADs, developing statistical frameworks capable of incorporating the hierarchical nature of TADs and known biological covariates remains a nascent field. We develop a network model that explicitly makes use of cell-type specific CTCF binding sites to detect multiscale domains. Our model leads to a likelihood objective that can be efficiently optimized via relaxation. We demonstrate the domains identified have desirable epigenetic features and compare them across different cell types.

Abstract:

Genome-wide chromosome conformation capture techniques such as Hi-C enable the generation of 3D genome contact maps and offer new pathways toward understanding the spatial organization of genome. It is widely recognized that chromosomes form domains of enriched interactions playing significant roles in gene regulation and development. In particular, it is of interest to identify densely interacting, contiguous regions at the sub-megabase scale known as topologically associating domains (TADs), which are believed to be conserved across cell types and even species. Although a few algorithms have been proposed to detect TADs, developing statistical frameworks capable of incorporating the hierarchical nature of TADs and known biological covariates remains a nascent field. We develop a network model that explicitly makes use of cell-type specific CTCF binding sites to detect multiscale domains. Our model leads to a likelihood objective that can be efficiently optimized via relaxation. We demonstrate the domains identified have desirable epigenetic features and compare them across different cell types.

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