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Computational approaches for elucidating the gene regulatory landscape of mammalian development

  • Author(s): Zhao, Yuan
  • Advisor(s): Ren, Bing
  • et al.
Abstract

Mammalian development including embryogenesis requires coordinated gene expression in order to control each cells eventual fate, a complex and finely tuned process driven by epigenetic information. Specifically, these developmental cues are interpreted by cells and lead to remodeling of the chromatin landscape, including changes in accessibility, conformation, and modifications to the chromatin. Thus, the state and accessibility of chromatin serve as key aspects of the cells epigenome, modulating and controlling the expression and function of the underlying genomic sequence. Understanding these developmental regulatory networks are of crucial importance as the dysregulation of developmental pathways has been implicated in numerous disease phenotypes. To address this unmet scientific need, we systematically profiled a comprehensive array of mouse tissues spanning twelve tissues, over seven developmental time points (from 10.5 days after conception until birth), using histone ChIP-seq and ATAC- seq. We used these data to produce a catalog of putative cis-regulatory elements defined by chromatin accessibility (developmental regions of Tn5-accessible chromatin or d-TACs) and characterized their function with chromatin state annotations derived from the histone modification profiles. Within these regions of heightened accessibility, we analyzed the tissue- specific enrichments for human disease-associated sequence variation. Additionally, we studied the developmental dynamics of open chromatin regions over mouse embryogenesis, evaluating the tissue-specific temporal dynamic patterns as well as the relationship between chromatin state and accessibility. Finally, we applied novel machine learning techniques to elucidate the biology behind gene regulation, building a framework for comparing datasets using sequence-based models. Taken as a whole, this thesis provides a comprehensive study of and cutting-edge methods for understanding mouse fetal chromatin dynamics.

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