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  5. Accurate Simulation of Reads and Improved Strategies for Abundance Estimation Supporting Reduced Representation Sequencing for Metagenomics
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Accurate Simulation of Reads and Improved Strategies for Abundance Estimation Supporting Reduced Representation Sequencing for Metagenomics

Date Issued
December 1, 2022
Author(s)
Kuster, Ryan  
Advisor(s)
Margaret E. Staton
Additional Advisor(s)
Margaret E. Staton
Bonnie H. Ownley
Jennifer M. DeBruyn
Phillip A. Wadl
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/28814
Abstract

Next generation sequencing has impacted all areas of biology by providing affordable investigations into some of the most complex processes underpinning life. With its ubiquitous application, there is still benefit in considering the nuances of the technology and its downstream analysis. Sequencing libraries produced by fragmenting DNA with restriction enzyme digests limit the scope of sequencing to a reduced set of genomic loci, allowing for deeper sequencing of those regions at a reduced cost per sample. These sequencing libraries have been used to determine genetic markers within populations of closely related individuals due to their sensitivity and preservation within populations. A lesser-studied application of enzyme digested DNA samples is for profiling metagenomes as an alternative to marker gene or random whole genome sequencing. Unlike classic approaches to microbial profiling, reduced metagenome sequencing introduces novel challenges to taxonomic assignment as well as relative abundance estimates. Through the development and application of novel simulation software, I investigate the possible strengths and weaknesses of applying this technique to complex microbial communities and find alternative strategies for handling the data produced by this promising application.

Subjects

simulation

bioinformatics

metagenomics

microbiome

reduced representatio...

Disciplines
Bioinformatics
Degree
Doctor of Philosophy
Major
Entomology, Plant Pathology and Nematology
Embargo Date
December 15, 2023
File(s)
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auto_convert.pdf

Size

5.57 MB

Format

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Checksum (MD5)

9902ef65cc39e24e966f9854f0c57fba

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ryan_kuster_dissertation_2.docx

Size

12.84 MB

Format

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Checksum (MD5)

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