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A Novel Statistical Framework to Detect Complex 3D Genome Organisation Patterns into Topologically Associated Domains

Mikheeva, Liudmila (2022) A Novel Statistical Framework to Detect Complex 3D Genome Organisation Patterns into Topologically Associated Domains. PhD thesis, University of Essex.

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Abstract

Topologically associated domains (TADs) are highly compacted regions of DNA that are suggested to be involved in proper gene regulation and cellular functioning. TADs maintain long-range interactions between distal enhancers and target genes, as well as restrict enhancers contacting genes that are not their target and, consequently, block their inappropriate regulation by these enhancers. The widely used TAD calling tools either restrict TAD borders to be allocated in a “head-to-tail” manner or allow hierarchical TAD folding to be detected. We propose a R-based TAD calling tool that detects start and end TAD border positions separately, so the partial overlapping of TADs as well as large gaps between TADs are also allowed. Using the ratio between the average upstream and downstream Hi-C interaction frequencies, our method detects where the difference between inside-TAD and outside TAD area within the Hi-C matrix is most significant. The novel TAD allocation combined with various genomics data reveals the interplay between architectural proteins and active transcription in the establishment of the TAD border insulation strength and insulation imbalances between neighbouring TADs.

Item Type: Thesis (PhD)
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics > QA76 Computer software
Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Liudmila Mikheeva
Date Deposited: 21 Jun 2022 09:49
Last Modified: 21 Jun 2022 09:49
URI: http://repository.essex.ac.uk/id/eprint/33038

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