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Title page for ETD etd-11142019-155857


Type of Document Dissertation
Author Chen, Ling
Author's Email Address chenlingnk@gmail.com
URN etd-11142019-155857
Title UNDERSTANDING THE EVOLUTION AND SEQUENCE ARCHITECTURE OF GENE REGULATORY ELEMENTS THROUGH MACHINE LEARNING
Degree PhD
Department Biological Sciences
Advisory Committee
Advisor Name Title
Antonis Rokas Committee Chair
Emily Hodges Committee Member
John A. Capra Committee Member
Maulik Patel Committee Member
Thomas A. Lasko Committee Member
Keywords
  • regulatory genome
  • deep learning
  • enhancer
  • machine learning
Date of Defense 2019-10-30
Availability restrictsix
Abstract
Enhancers are genomic regions distal to promoters that bind transcription factors (TFs) to regulate the dynamic spatiotemporal patterns of gene expression required for proper differentiation and development of multi-cellular organisms. It is critical to understand the mechanisms underlying enhancer evolution and function, as alterations in their activity influence both speciation and disease. Recent genome-wide profiling of histone modifications associated with enhancer activity revealed that the regulatory landscape changes dramatically between species—active regions with enhancer activity are extremely variable across closely related mammals. In this dissertation, I present the work I have done investigating evolution of enhancers and dissecting their sequence architectures. In Chapter I, I outline the background and the motivation for studying these questions. In Chapter II, I show that conserved enhancers in mammalian species are more pleiotropic than species-specific enhancers, suggesting evolutionary constraint underpinning the loss and gain of enhancers. In Chapter III, I demonstrate the conservation of enhancer sequence properties despite the rapid turnover of the location of active enhancers in mammalian species through a machine learning based, cross-species prediction framework. In Chapter IV, I investigate the power of a state-of-art enhancer prediction algorithm, deep neural networks, at modeling enhancer architecture. Finally, in Chapter V, I summarize the conclusions of the proceeding chapters and discuss future work that could be done to answer questions raised by the findings in this dissertation.
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