Browsing by Subject "DNase-seq"
- Results Per Page
- Sort Options
Item Open Access A Bayesian Model for Nucleosome Positioning Using DNase-seq Data(2015) Zhong, JianlingAs fundamental structural units of the chromatin, nucleosomes are involved in virtually all aspects of genome function. Different methods have been developed to map genome-wide nucleosome positions, including MNase-seq and a recent chemical method requiring genetically engineered cells. However, these methods are either low resolution and prone to enzymatic sequence bias or require genetically modified cells. The DNase I enzyme has been used to probe nucleosome structure since the 1960s, but in the current high throughput sequencing era, DNase-seq has mainly been used to study regulatory sequences known as DNase hypersensitive sites. This thesis shows that DNase-seq data is also very informative about nucleosome positioning. The distinctive oscillatory DNase I cutting patterns on nucleosomal DNA are shown and discussed. Based on these patterns, a Bayes factor is proposed to be used for distinguishing nucleosomal and non-nucleosomal genome positions. The results show that this approach is highly sensitive and specific. A Bayesian method that simulates the data generation process and can provide more interpretable results is further developed based on the Bayes factor investigations. Preliminary results on a test genomic region show that the Bayesian model works well in identifying nucleosome positioning. Estimated posterior distributions also agree with some known biological observations from external data. Taken together, methods developed in this thesis show that DNase-seq can be used to identify nucleosome positioning, adding great value to this widely utilized protocol.
Item Open Access Genome-wide Analysis of Chromatin Structure across Diverse Human Cell Types(2013) Winter, Deborah R.Chromatin structure plays an important role in gene regulation, especially in differentiating the diverse cell types in humans. In this dissertation, we analyze the nucleosome positioning and open chromatin profiles genome-wide and investigate the relationship with transcription initiation, the activity of regulatory elements, and expression levels. We mainly focus on the results of DNase-seq experiments, but also employ annotations from MNase-seq, FAIRE-seq, ChIP-seq, CAGE, and RNA microarrays. Our methods are based on computational approaches including managing large data sets, statistical analysis, and machine learning. We find that different transcription initiation patterns lead to distinct chromatin structures, suggesting diverse regulatory strategies. Moreover, we present a tool for comparing genome-wide annotation tracks and evaluate DNase-seq against a unique assay for detecting open chromatin. We also demonstrate how DNase-seq can be used to successfully predict rotationally stable nucleosomes that are conserved across cell types. We conclude that DNase-seq can be used to study genome-wide chromatin structure in an effort to better understand how it regulates gene expression.
Item Open Access Genome-wide Cross-species Analysis Linking Open Chromatin, Differential Expression and Positive Selection(2012) Shibata, YoichiroDeciphering the molecular mechanisms driving the phenotypic differences between humans and primates remains a daunting challenge. Mutations found in protein coding DNA alone has not been able to explain these phenotypic differences. The hypothesis that mutations in non-coding regulatory DNA are responsible for altered gene expression leading to these phenotypic changes has now been widely supported by differential gene expression experiments. Yet, comprehensive identification of all regulatory DNA elements across different species has not been performed. To identify the genetic source of regulatory change, genome-wide DNaseI hypersensitivity assays, marking all types of active gene regulatory element sites, were performed in human, chimpanzee, macaque, orangutan, and mouse. Many DNaseI hypersensitive (DHS) sites were conserved among all 5 species, but we also identified hundreds of novel human- and chimpanzee-specific DHS gains and losses that showed signatures of positive selection. Species-specific DHS gains were enriched in distal non-coding regions, associated with active histone modifications, and positively correlated with increased expression - indicating that these are likely to be functioning as enhancers. Comparison to mouse DHS data indicate that human or chimpanzee DHS gains are likely to have been a result of single events that occurred primarily on the human- or chimpanzee-specific branch, respectively. In contrast, DHS losses are associated with events that occurred on multiple branches. At least one mechanism contributing to DHS gains and losses are species-specific variants that lead to sequence changes at transcription factor binding motifs, affecting the binding of TFs such as AP1. These variants were functionally verified by DNase footprinting and ChIP-qPCR analyses.
Item Open Access Modeling Nuclease Digestion Data to Predict the Dynamics of Genome-wide Transcription Factor Occupancy(2016) Luo, KaixuanIdentifying and deciphering the complex regulatory information embedded in the genome is critical to our understanding of biology and the etiology of complex diseases. The regulation of gene expression is governed largely by the occupancy of transcription factors (TFs) at various cognate binding sites. Characterizing TF binding is particularly challenging since TF occupancy is not just complex but also dynamic. Current genome-wide surveys of TF binding sites typically use chromatin immunoprecipitation (ChIP), which is limited to measuring one TF at a time, thus less scalable in profiling the dynamics of TF occupancy across cell types or conditions. This dissertation develops novel computational frameworks to model sequencing data from DNase and/or MNase nuclease digestion assays that allows multiple TFs to be surveyed in a single experiment, in both human and yeast. We predicted occupancy landscapes and constructed a cell-type specificity map for many TFs across human cell types, revealed novel relationships between TF occupancy and TF expression, and monitored the occupancy dynamics of various TFs in response to androgen and estrogen hormone simulations. The TF/cell type occupancy matrix generated from our model expands the total output of the ENCODE ChIP-seq efforts by a factor of nearly 200 times. These computational frameworks serve as an innovative and cost effective strategy which enables efficient profiling of TF occupancy landscapes across different cell types or dynamic conditions in a high-throughput manner.