Electronic Thesis and Dissertation Repository


Master of Science




Dr. Gregory B. Gloor


With the advent of next generation DNA and RNA sequencing, scientists can obtain a more comprehensive snapshot of the bacterial communities on the human body (known as the `human microbiome'), leading to information about the bacterial composition, what genes are present, and what proteins are produced. The scientific community is in a phase of developing the experiments and accompanying statistical techniques to investigate the mechanisms by which the human microbiome affects health and disease. In this thesis, I explore alternatives to the standard weighted and unweighted UniFrac distance metric that measure the difference between microbiome samples. These alternative weightings allow for the extraction of subtle differences between samples and identification of outliers not visible with traditional methods. I also apply next generation DNA sequencing and computational analysis techniques to gut microbiome data from a nonalcoholic fatty liver disease cohort to examine the potential role of the microbiota in this condition.