Life Sciences Commons^™

Towards More Complete Metagenomic Analyses Through Circularized Genomes And Conjugative Elements, Benjamin R. Joris

Electronic Thesis and Dissertation Repository

Advancements in sequencing technologies have revolutionized biological sciences and led to the emergence of a number of fields of research. One such field of research is metagenomics, which is the study of the genomic content of complex communities of bacteria. The goal of this thesis was to contribute computational methodology that can maximize the data generated in these studies and to apply these protocols human and environmental metagenomic samples.

Standard metagenomic analyses include a step for binning of assembled contigs, which has previously been shown to exclude mobile genetic elements, and I demonstrated that this phenomenon extends to all conjugative …

Measuring The Human Gut Microbiome: New Tools And Non Alcoholic Fatty Liver Disease, Ruth G. Wong

Electronic Thesis and Dissertation Repository

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 …

Optimal Clustering Techniques For Metagenomic Sequencing Data, Erik T. Cameron

Electronic Thesis and Dissertation Repository

Metagenomic sequencing techniques have made it possible to determine the composition of bacterial microbiota of the human body. Clustering algorithms have been used to search for core microbiota types in the vagina, but results have been inconsistent, possibly due to methodological differences. We performed an extensive comparison of six commonly-used clustering algorithms and four distance metrics, using clinical data from 777 vaginal samples across 5 studies, and 36,000 synthetic datasets based on these clinical data. We found that centroid-based clustering algorithms (K-means and Partitioning around Medoids), with Euclidean or Manhattan distance metrics, performed well. They were best at correctly clustering …