Applied bioinformatics for exploring diversity patterns in meta-omic data

Abstract

This thesis explores the utility of applied bioinformatic approaches to better understand sequence space and phylogenetic diversity in meta-omic clinical and environmental datasets. In three chapters, the thesis describes how applied bioinformatic techniques can be used to 1) identify and quantify sequence variation in the form of insertions and deletions generated as an effect of off-target activity by CRISPR-Cas9 nuclease using high throughput targeted gene amplicon sequencing; 2) identify and quantify the abundance of elements of the bacterial defense systems, CRISPRs, to explore viral-microbe interaction dynamics in natural microbial communities living in marine oxygen minimum zones using high-throughput metagenome sequencing; and 3) investigate phylogenetic variation in an underexplored phylum of bacteria, Atribacteria, that are found as dominant members of microbial communities in methane hydrate-bearing marine sediments again using high-throughput metagenome sequencing.