Date of Award

2023

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Bioinformatics & Computational Biology

Abstract

Advancements in sequencing technologies have enabled scientists to gain insight into the microbes that inhabit the human body, including the urinary tract. Cataloging the bacteria that inhabit the urinary tract has primarily relied on amplification and sequencing of specific variable regions of the 16S rRNA gene enabling genus-level taxonomic identification. Recently, shotgun metagenomic sequencing has been employed such that bacterial taxonomy as well as the functionality that they encode can be inferred. In this study, we compare taxonomies assigned by 16S sequencing and shotgun metagenomic sequencing of the urinary microbiota (urobiome) of females with and without a clinical diagnosis of a urinary tract infection (UTI). Rather than target specific variable regions of the 16S rRNA gene, we employed long-read sequencing technology which captures all nine variable regions such that species-level taxonomic assignments can be made. First, we characterize the bacterial constituents of the urobiomes of the two cohorts from the full-length 16S sequence. To assess the power of full-length rather than single variable regions, we computationally derived short-reads and compared these predictions to our full-length analyses. Next, we compared the results of the taxonomic predictions from full- length 16S to those of shotgun metagenomic sequencing of a subset of our samples. We found that long-read sequencing created more accurate taxonomic classifications than shotgun sequencing and single variable regions. Both sequencing approaches suggest that multiple strains of species colonize the urobiome.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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