In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences
Kenji Gerhardt
Defends his thesis:
Tools for interactive analysis of metagenomic read recruitment, sensitive detection and quantification of genes in metagenomes, and rapid, large scale genome relatedness estimation
August 12th (Monday), 2024
2pm Eastern
Room: ES&T L1118 building
Zoom link: https://gatech.zoom.us/j/94881105498
Thesis Advisor:
Dr. Konstantinos T. Konstantinidis, School of Civil and Environmental Engineering and School of Biological Sciences (by courtesy), Georgia Institute of Technology, USA
Committee Members:
Dr. I. King Jordan, School of Biological Sciences, Georgia Institute of Technology, USA
Dr. Srinivas Aluru, School of School of Computational Science and Engineering (CSE), Georgia Institute of Technology, USA
Dr. Luis M. Rodriguez-R, Department of Microbiology & Digital Science Center (DiSC), University of Innsbruck, Austria
Dr. Andrew Huang, The Centers for Disease Control (CDC), Atlanta, USA
Abstract:
Microbial genome research has become a field of big data. Modern bioinformatic tools must be capable of scaling efficiently to very large datasets without a loss in accuracy and with a transparency that connects researchers to their data instead of serving as a black box. This thesis introduces three software tools that achieve these goals. The first tool, RecruitPlotEasy allows for the interactive visualization of microbial populations in metagenomes, enabling biologists to rapidly characterize the distinctive features of their target species within an environment such as population structure (or extent of clonality) and relative abundance. The second tool, ROCkI/O, enables the rapid and highly accurate retrieval of short reads carrying a specific (target) gene function such as a specific antibiotic resistance gene family from complex metagenomic or amplicon datasets. Finally, FastAAI v2 enables the comparison of novel genome sequences against massive public collections of genomes using an alignment-free approach that is orders of magnitude faster than comparable, alignment-based methods, and with improved robustness compared to the original FastAAI algorithm. FastAAI v2 additionally improves upon its predecessor by extending the capacity for rapid database searches from prokaryotic to fungal genomes, and provides a template for doing the same with other small eukaryotes.
