Applied bioinformatics for exploring diversity patterns in meta-omic data
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 s...
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Georgia Institute of Technology
2018
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ftgeorgiatech:oai:smartech.gatech.edu:1853/59947 2023-05-15T17:11:58+02:00 Applied bioinformatics for exploring diversity patterns in meta-omic data Ranjan, Piyush Stewart, Frank J. Biology Glass, Jennifer B. Choi, Jung H. 2018-05-31T18:17:23Z application/pdf http://hdl.handle.net/1853/59947 en_US eng Georgia Institute of Technology http://hdl.handle.net/1853/59947 Bioinformatics CRISPR Oxygen minimum zone Atribacteria Text Thesis 2018 ftgeorgiatech 2023-01-30T18:41:24Z 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. M.S. Thesis Methane hydrate Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech |
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Open Polar |
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Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech |
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ftgeorgiatech |
language |
English |
topic |
Bioinformatics CRISPR Oxygen minimum zone Atribacteria |
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Bioinformatics CRISPR Oxygen minimum zone Atribacteria Ranjan, Piyush Applied bioinformatics for exploring diversity patterns in meta-omic data |
topic_facet |
Bioinformatics CRISPR Oxygen minimum zone Atribacteria |
description |
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. M.S. |
author2 |
Stewart, Frank J. Biology Glass, Jennifer B. Choi, Jung H. |
format |
Thesis |
author |
Ranjan, Piyush |
author_facet |
Ranjan, Piyush |
author_sort |
Ranjan, Piyush |
title |
Applied bioinformatics for exploring diversity patterns in meta-omic data |
title_short |
Applied bioinformatics for exploring diversity patterns in meta-omic data |
title_full |
Applied bioinformatics for exploring diversity patterns in meta-omic data |
title_fullStr |
Applied bioinformatics for exploring diversity patterns in meta-omic data |
title_full_unstemmed |
Applied bioinformatics for exploring diversity patterns in meta-omic data |
title_sort |
applied bioinformatics for exploring diversity patterns in meta-omic data |
publisher |
Georgia Institute of Technology |
publishDate |
2018 |
url |
http://hdl.handle.net/1853/59947 |
genre |
Methane hydrate |
genre_facet |
Methane hydrate |
op_relation |
http://hdl.handle.net/1853/59947 |
_version_ |
1766068721760075776 |