Statistical Methods for Functional Metagenomic Analysis Based on Next-Generation Sequencing Data

Persistent Link:
http://hdl.handle.net/10150/320986
Title:
Statistical Methods for Functional Metagenomic Analysis Based on Next-Generation Sequencing Data
Author:
Pookhao, Naruekamol
Issue Date:
2014
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Release 30-Apr-2015
Abstract:
Metagenomics is the study of a collective microbial genetic content recovered directly from natural (e.g., soil, ocean, and freshwater) or host-associated (e.g., human gut, skin, and oral) environmental communities that contain microorganisms, i.e., microbiomes. The rapid technological developments in next generation sequencing (NGS) technologies, enabling to sequence tens or hundreds of millions of short DNA fragments (or reads) in a single run, facilitates the studies of multiple microorganisms lived in environmental communities. Metagenomics, a relatively new but fast growing field, allows us to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Also, it assists us to identify significantly different metabolic potentials in different environments. Particularly, metagenomic analysis on the basis of functional features (e.g., pathways, subsystems, functional roles) enables to contribute the genomic contents of microbes to human health and leads us to understand how the microbes affect human health by analyzing a metagenomic data corresponding to two or multiple populations with different clinical phenotypes (e.g., diseased and healthy, or different treatments). Currently, metagenomic analysis has substantial impact not only on genetic and environmental areas, but also on clinical applications. In our study, we focus on the development of computational and statistical methods for functional metagnomic analysis of sequencing data that is obtained from various environmental microbial samples/communities.
Type:
text; Electronic Dissertation
Keywords:
Feature comparative; Functional metagenomics; Negative binomial model; Next generation sequencing; Agricultural & Biosystems Engineering; Elastic-net
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Agricultural & Biosystems Engineering
Degree Grantor:
University of Arizona
Advisor:
An, Lingling

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleStatistical Methods for Functional Metagenomic Analysis Based on Next-Generation Sequencing Dataen_US
dc.creatorPookhao, Naruekamolen_US
dc.contributor.authorPookhao, Naruekamolen_US
dc.date.issued2014-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.releaseRelease 30-Apr-2015en_US
dc.description.abstractMetagenomics is the study of a collective microbial genetic content recovered directly from natural (e.g., soil, ocean, and freshwater) or host-associated (e.g., human gut, skin, and oral) environmental communities that contain microorganisms, i.e., microbiomes. The rapid technological developments in next generation sequencing (NGS) technologies, enabling to sequence tens or hundreds of millions of short DNA fragments (or reads) in a single run, facilitates the studies of multiple microorganisms lived in environmental communities. Metagenomics, a relatively new but fast growing field, allows us to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Also, it assists us to identify significantly different metabolic potentials in different environments. Particularly, metagenomic analysis on the basis of functional features (e.g., pathways, subsystems, functional roles) enables to contribute the genomic contents of microbes to human health and leads us to understand how the microbes affect human health by analyzing a metagenomic data corresponding to two or multiple populations with different clinical phenotypes (e.g., diseased and healthy, or different treatments). Currently, metagenomic analysis has substantial impact not only on genetic and environmental areas, but also on clinical applications. In our study, we focus on the development of computational and statistical methods for functional metagnomic analysis of sequencing data that is obtained from various environmental microbial samples/communities.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectFeature comparativeen_US
dc.subjectFunctional metagenomicsen_US
dc.subjectNegative binomial modelen_US
dc.subjectNext generation sequencingen_US
dc.subjectAgricultural & Biosystems Engineeringen_US
dc.subjectElastic-neten_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAgricultural & Biosystems Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorAn, Linglingen_US
dc.contributor.committeememberAn, Linglingen_US
dc.contributor.committeememberSlack, Donalden_US
dc.contributor.committeememberBillheimer, Deanen_US
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