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You searched for +publisher:"University of North Carolina" +contributor:("Purvis, Jeremy"). Showing records 1 – 3 of 3 total matches.

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University of North Carolina

1. Baran-Gale, Jeanette. Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells.

Degree: 2016, University of North Carolina

Cellular signaling leads to broad changes in gene expression that reprogram the cell and alter cell state. Signaling often begins with cellular receptors binding a ligand and initiating a transcriptional response. One example of this is the estrogen receptor, which binds the ligand estrogen and translocates to the nucleus where it binds to estrogen response elements and regulates the expression numerous target RNAs. The regulatory network of both messenger RNAs (mRNAs) and microRNAs (miRNAs) responding to estrogen stimulation is a complex, dynamic and multilayered program that is critical to the etiology of breast cancer. Estrogen receptor α (ERα) is an important biomarker of breast cancer severity and a common therapeutic target. Recent studies have demonstrated that in addition to its role in promoting proliferation, ERα also protects tumors against metastatic transformation. Current therapeutic strategies inhibit estrogen stimulated signaling and interfere with both beneficial and detrimental signaling pathways regulated by ERα. Additionally, ERα cyclically binds estrogen response elements and induces bursts of transcriptional activity. Together these observations suggest that ERα regulated genes and miRNAs may exhibit temporal variation in expression. Furthermore, it remains unclear if estrogen stimulated pathways exhibit the same temporal expression patterns, or if different pathways exhibit different temporal expression patterns. By combining both RNA-sequencing and small RNA-sequencing of cells responding to estrogen, we uncover the dynamics of both mRNA and miRNA expression in response to estrogen stimulation. Furthermore, we identify a regulatory circuit with potential therapeutic relevance to breast cancer that more specifically inhibits ERα-stimulated growth and survival pathways without interfering with its protective features. In response to estrogen stimulation, MCF7 cells (an estrogen receptor positive model cell line) exhibit induction of miR-503, and repression of the oncogene ZNF217. miR-503 inhibits proliferation in MCF7 cells, in part through its inhibition of the oncogene ZNF217 and the cell-cycle gene CCND1. While numerous regulatory interactions can be mined from this temporal profile of estrogen responsive mRNAs and miRNAs, the induction of the anti-proliferative microRNA, miR-503, both highlights the protective aspects of estrogen signaling and indicates that miR-503 holds promise as a therapeutic for breast cancer. Advisors/Committee Members: Baran-Gale, Jeanette, Sethupathy, Praveen, Purvis, Jeremy, Furey, Terrence, Prins, Jan, Laederach, Alain, Gomez, Shawn.

Subjects/Keywords: School of Medicine; Curriculum in Bioinformatics and Computational Biology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Baran-Gale, J. (2016). Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:4adf8ac6-d9fb-4c71-b553-20f271377458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Baran-Gale, Jeanette. “Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:4adf8ac6-d9fb-4c71-b553-20f271377458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Baran-Gale, Jeanette. “Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells.” 2016. Web. 04 Dec 2020.

Vancouver:

Baran-Gale J. Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:4adf8ac6-d9fb-4c71-b553-20f271377458.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Baran-Gale J. Dynamics of mRNA and microRNA Expression in the Estrogen Response of Breast Cancer Cells. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:4adf8ac6-d9fb-4c71-b553-20f271377458

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Carolina

2. Welch, Joshua. Computational Methods for Inferring Transcriptome Dynamics.

Degree: Computer Science, 2017, University of North Carolina

The sequencing of the human genome paved the way for a new type of medicine, in which a molecular-level, cell-by-cell understanding of the genomic control system informs diagnosis and treatment. A key experimental approach for achieving such understanding is measuring gene expression dynamics across a range of cell types and biological conditions. The raw outputs of these experiments are millions of short DNA sequences, and computational methods are required to draw scientific conclusions from such experimental data. In this dissertation, I present computational methods to address some of the challenges involved in inferring dynamic transcriptome changes. My work focuses two types of challenges: (1) discovering important biological variation within a population of single cells and (2) robustly extracting information from sequencing reads. Three of the methods are designed to identify biologically relevant differences among a heterogenous mixture of cells. SingleSplice uses a statistical model to detect true biological variation in alternative splicing within a population of single cells. SLICER elucidates transcriptome changes during a sequential biological process by positing the process as a nonlinear manifold embedded in high-dimensional gene expression space. MATCHER uses manifold alignment to infer what multiple types of single cell measurements obtained from different individual cells would look like if they were performed simultaneously on the same cell. These methods gave insight into several important biological systems, including embryonic stem cells and cardiac fibroblasts undergoing reprogramming. To enable study of the pseudogene ceRNA effect, I developed a computational method for robustly computing pseudogene expression levels in the presence of high sequence similarity that confounds sequencing read alignment. AppEnD, an algorithm for detecting untemplated additions, allowed the study of transcript modifications during RNA degradation. Advisors/Committee Members: Welch, Joshua, Prins, Jan, Hartemink, Alexander, Jones, Corbin, Marzluff, William, McMillan, Leonard, Purvis, Jeremy, University of North Carolina at Chapel Hill.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Welch, J. (2017). Computational Methods for Inferring Transcriptome Dynamics. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Welch, Joshua. “Computational Methods for Inferring Transcriptome Dynamics.” 2017. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Welch, Joshua. “Computational Methods for Inferring Transcriptome Dynamics.” 2017. Web. 04 Dec 2020.

Vancouver:

Welch J. Computational Methods for Inferring Transcriptome Dynamics. [Internet] [Thesis]. University of North Carolina; 2017. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Welch J. Computational Methods for Inferring Transcriptome Dynamics. [Thesis]. University of North Carolina; 2017. Available from: https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Carolina

3. Stanley, Natalie. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.

Degree: 2018, University of North Carolina

Networks have become a common data mining tool to encode relational definitions between a set of entities. Whether studying biological correlations, or communication between individuals in a social network, network analysis tools enable interpretation, prediction, and visualization of patterns in the data. Community detection is a well-developed subfield of network analysis, where the objective is to cluster nodes into 'communities' based on their connectivity patterns. There are many useful and robust approaches for identifying communities in a single, moderately-sized network, but the ability to work with more complicated types of networks containing extra or a large amount of information poses challenges. In this thesis, we address three types of challenging network data and how to adapt standard community detection approaches to handle these situations. In particular, we focus on networks that are large, attributed, and multilayer. First, we present a method for identifying communities in multilayer networks, where there exist multiple relational definitions between a set of nodes. Next, we provide a pre-processing technique for reducing the size of large networks, where standard community detection approaches might have inconsistent results or be prohibitively slow. We then introduce an extension to a probabilistic model for community structure to take into account node attribute information and develop a test to quantify the extent to which connectivity and attribute information align. Finally, we demonstrate example applications of these methods in biological and social networks. This work helps to advance the understand of network clustering, network compression, and the joint modeling of node attributes and network connectivity. Advisors/Committee Members: Stanley, Natalie, Mucha, Peter, Purvis, Jeremy, Niethammer, Marc, Berg, Tamara, Gotz, David, Miller, Laura, University of North Carolina at Chapel Hill.

Subjects/Keywords: School of Medicine; Curriculum in Bioinformatics and Computational Biology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Stanley, N. (2018). Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Stanley, Natalie. “Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.” 2018. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Stanley, Natalie. “Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.” 2018. Web. 04 Dec 2020.

Vancouver:

Stanley N. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stanley N. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.