Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.706277
Title: Predicting context specific enhancer-promoter interactions from ChIP-Seq time course data
Author: Dzida, Tomasz
ISNI:       0000 0004 6056 780X
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
We develop machine learning approaches to predict context specific enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. Occupancy of estrogen receptor alpha (ER-alpha), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. Two Bayesian classifiers were developed, unsupervised and supervised. The supervised approach uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features and predicts interactions. The method was trained using experimentally determined interactions from the same system and achieves much higher precision than predictions based on the genomic proximity of nearest ER-alpha binding. We use the method to identify a confident set of ER-alpha target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data shows our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ER-alpha binding proximity alone. Accuracy of the predictions from the supervised model was compared against the second more complex unsupervised generative approach which uses proximity-based prior and temporal binding patterns at enhancers and promoters to infer protein-mediated regulatory complexes involving individual genes and their networks of multiple distant regulatory enhancers.
Supervisor: Rattray, Magnus ; Sharrocks, Andrew Sponsor: University of Manchester
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.706277  DOI: Not available
Keywords: Enhancer-promoter interaction ; Bayesian classifier ; Machine learning ; Estrogen receptor ; ChIP-Seq
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