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Evolution And Natural Selection Of Olfactory Receptor Genes In Hawaiian Drosophila, Ngoc H. Ly
Evolution And Natural Selection Of Olfactory Receptor Genes In Hawaiian Drosophila, Ngoc H. Ly
UNLV Theses, Dissertations, Professional Papers, and Capstones
The olfactory system is a powerful tool for sensing countless odorants. In Drosophila, the olfactory system is critical for detecting food, finding mates, laying eggs, avoiding predators, and adapting to new environments. Understanding the olfactory system in Drosophila will advance our knowledge of sensory biology in various insects and vertebrates, including humans. Drosophila has been a valuable model for biology since the early 1900s, and the Drosophila melanogaster olfactory system is well-studied. The Hawaiian Drosophila represent approximately 1/3 of the world’s Drosophila, however, there is limited research on Hawaiian Drosophila olfactory genes. We conducted a comparative analysis of …
Sequestered Sequences: A Bioinformatic Approach To The Forgotten Genome, Dylan Barth
Sequestered Sequences: A Bioinformatic Approach To The Forgotten Genome, Dylan Barth
UNLV Theses, Dissertations, Professional Papers, and Capstones
As high throughput sequencing generates ever increasing amounts of genetic and epigenetic data new lines of inquiry open up in the field of genomic research. In this thesis, we discuss three ways in which we can utilize public databases of next generation genomic data in order to study areas of the genome previously ignored by traditional approaches. These include the study of linker regions between domains of proteins, indirect enhancers that do not strongly contact promoters of genes they regulate, and transposon-derived enhancer elements. The work uncovers many exceptions to known biological principles, and adds nuance to our understanding of …
Applying Unsupervised Multi-Omic Learning To Identify Patterns Of Human Genomic Regulatory Regions With An Emphasis In Characterizing Hervh Loci., Corinne Sexton
UNLV Theses, Dissertations, Professional Papers, and Capstones
With the increase of diverse genomic data types, machine learning provides an opportunity to integrate several omics datasets into one cohesive annotation. In this dissertation, I apply an unsupervised clustering approach to a novel representation of 3D chromosome conformation data and chromatin mark data. Specifically I use this new method to annotate the regulatory function of human endogenous retrovirus H (HERVH). In chapter 1, I propose a synthesized model of HERVH function as an activating lncRNA based on previously published work. As HERVH and transposable elements in general are repetitive due to their methods of retrotransposition, in chapter 2 I …