Document Type

Dissertation

Date of Degree

2015

Degree Name

PhD (Doctor of Philosophy)

Degree In

Genetics

First Advisor

Kai Tan

Abstract

Transcriptional enhancers represent the primary basis for differential gene expression. These elements regulate cell type specificity, development, and evolution, with many human diseases resulting from altered enhancer activity. To date, a key gap in our knowledge is how enhancers select specific promoters for activation.

To fill this gap, in this thesis, I first developed an Integrated Method for Predicting Enhancer Targets (IM-PET). Leveraging abundant “omics” data, I devised and characterized multiple genomic features for distinguishing true enhancer-promoter (EP) pairs from non-interacting pairs. I integrated these features into a probabilistic predictor for EP interactions. Multiple validation experiments demonstrated a significant improvement over extent state-of-the-art approaches. Systematic analyses of EP interactions across twelve human cell types reveals global features of EP interactions.

Second, we used a well-established viral infection model to map the dynamic changes of enhancers and super-enhancers during the CD8+ T cell responses. Our analysis illustrated the complexity and dynamics of the underlying EP interactome during cell differentiation. Taking advantage of the predicted EP interactions, we constructed stage-specific transcriptional regulatory networks, which is critical for understanding the regulatory mechanism during CD8+ T cell differentiation.

Third, recent progress in mapping technologies for chromatin interactions has led to a rapid increase in this type of interaction data. However, there is a lack of a comprehensive depository for chromatin interactions identified by all major technologies. To address this problem, we have developed the 4DGenome database through comprehensive literature curation of experimentally derived interactions. We envision a wide range of investigations will benefit from this carefully curated database.

Public Abstract

Transcriptional enhancers are arguably the most important class of non-coding regulatory elements in our genome. These elements regulate cell type specificity, development, and evolution, with many human diseases resulting from altered enhancer activity. To date, a key gap in our knowledge is how enhancers select specific promoters for activation.

To fill this gap, I first developed an Integrated Method for Predicting Enhancer Targets (IM-PET), a data integration tool to identify EP pairs. Capitalizing on the wealth of available ENCODE data, I devised multiple genomic features and integrated them probabilistically to make robust predictions of EP pairs. I applied IM-PET algorithm to generate a comprehensive catalog of the EP interactome across multiple human cell types, and revealed global features of EP interactions.

Second, I applied our tools to explore the EP interactomes of three stages during CD8+ T cell differentiation. The analysis illustrated the complexity and dynamics of the underlying EP interactome during cell differentiation. Taking advantage of the predicted EP interactions, we constructed the transcriptional regulatory networks, which is critical for understanding the regulatory mechanism during CD8+ T cell differentiation.

Finally, I developed the 4DGenome database, a general repository for chromatin interactions. A comprehensive depository for chromatin interactions will help the annotation of EP pairs, and facilitate the investigation of genome structure/function relationships.

Keywords

publicabstract, CD8 T cell, chromatin interaction, computational biology, database, enhancer, transcriptional regulation

Pages

xviii, 179

Bibliography

163-179

Comments

This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: http://www.lib.uiowa.edu/sc/contact/

Copyright

Copyright 2015 Bing He

Included in

Genetics Commons

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