Browsing by Subject "Epigenomics"

The accumulation of high-throughput data from vast sources has drawn a lot attentions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combine the disparate information, which goes beyond modeling sparsity and dimension reduction. This dissertation focuses on the innovations in the area of heterogeneous data integration.

Chapter 1 contextualizes this dissertation by introducing different aspects of meta-analysis and model frameworks for high-dimensional genomic data.

Chapter 2 introduces a novel technique, joint Bayesian sparse factor analysis model, to vertically integrate multi-dimensional genomic data from different platforms.

Chapter 3 extends the above model to a nonparametric Bayes formula. It directly infers number of factors from a model-based approach.

On the other hand, chapter 4 deals with horizontal integration of diverse gene expression data; the model infers pathway activities across various experimental conditions.

All the methods mentioned above are demonstrated in both simulation studies and real data applications in chapters 2-4.

Finally, chapter 5 summarizes the dissertation and discusses future directions.

Enhancers possess both structural elements mediating promoter looping and functional elements mediating gene expression. Traditional models of enhancer-mediated gene regulation imply genomic overlap or immediate adjacency of these elements. We test this model by combining densely-tiled CRISPRa screening with nucleosome-resolution Region Capture Micro-C topology analysis. Using this integrated approach, we comprehensively define the cis-regulatory landscape for the tumor suppressor PTEN, identifying and validating 10 distinct enhancers and defining their 3D spatial organization. Unexpectedly, we identify several long-range functional enhancers whose promoter proximity is facilitated by chromatin loop anchors several kilobases away, and demonstrate that accounting for this spatial separation improves the computational prediction of validated enhancers. Thus, we propose a new model of enhancer organization incorporating spatial separation of essential functional and structural components.

The epigenome underlies cell type and state and in post-mitotic neurons, and it regulates the ability for rapid response to activity. Since neurons exit the cell cycle early in development and are long lived, remodeling of brain function requires that neurons show transcriptional plasticity to let then change in function in response to stimuli including psychostimulants and developmental cues. This response is driven by the epigenomic regulation in a cell-type-specific manner. Many studies assessing experience driven genomic responses have been carried out in bulk tissues so cell-type-specific genomic responses to stimuli that drive neuronal plasticity remains poorly understood. To understand the epigenomic and transcriptomic mechanisms driving neuronal plasticity, here we study multi-omic genomic data from two contexts in the mouse brain: 1) psychostimulant responses in the nucleus accumbens and 2)the postnatal and postmitotic maturation of developing cerebellar granule neurons. In both systems, I implemented integrative bioinformatic approaches to predict transcription factor (TF) activity in regulating the transcriptome. I elucidated cell-type-specific amphetamine induced transcriptomic responses, identified canonical activity regulated transcription factors regulating those responses, and determined collaborators and developmental targets of the Zic family TFs, revealing novel roles of Zics regulating migration and synaptic maturation in CGN development. The studies reveal novel mechanistic insights into neuronal plasticity in different neuronal cell types by using integrative computational approaches to model chromatin topology, chromatin accessibility, gene expression, and TF binding.