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You searched for +publisher:"University of North Carolina" +contributor:("Gotz, David"). Showing records 1 – 2 of 2 total matches.

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University of North Carolina

1. Tolson, Chanin. Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data.

Degree: 2017, University of North Carolina

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid (RNA) structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2’ Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by “gel gazing.” SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human “gazing” by identifying features of the SHAPE profile that human experts agree “looks” like a riboSNitch. Additionally, when an RNA molecule folds, it does not always adopt a single, well-defined conformation. The folding energy landscape of the RNA is highly dependent on sequence and the molecular environment. Endogenous molecules, especially in the cellular context, will in some cases completely alter the energy landscape and therefore the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for larger RNAs including most messenger RNAs (mRNAs). We propose here a robust approach for visualizing the conformational ensemble of RNAs particularly well suited for in vitro vs. in vivo comparisons. Advisors/Committee Members: Tolson, Chanin, Laederach, Alain, Furey, Terrence, Gomez, Shawn, Gotz, David, Shank, Elizabeth.

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):

Tolson, C. (2017). Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:72d19805-32b0-42ba-85ec-d873d31e9687

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):

Tolson, Chanin. “Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data.” 2017. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:72d19805-32b0-42ba-85ec-d873d31e9687.

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

MLA Handbook (7th Edition):

Tolson, Chanin. “Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data.” 2017. Web. 04 Dec 2020.

Vancouver:

Tolson C. Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data. [Internet] [Thesis]. University of North Carolina; 2017. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:72d19805-32b0-42ba-85ec-d873d31e9687.

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

Council of Science Editors:

Tolson C. Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data. [Thesis]. University of North Carolina; 2017. Available from: https://cdr.lib.unc.edu/record/uuid:72d19805-32b0-42ba-85ec-d873d31e9687

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


University of North Carolina

2. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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