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Sharpening the Edge of Tools for Microbial Diversity Analysis
by
Xiaolin Hao
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR AND COMPUTATIONAL BIOLOGY)
December 2012
Copyright 2012
Xiaolin Hao
Object Description
| Title | Sharpening the edge of tools for microbial diversity analysis |
| Author | Hao, Xiaolin |
| Author email | xiaolinh@usc.edu;haoxiaolin86@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computational Biology and Bioinformatics |
| School | College of Letters, Arts And Sciences |
| Date defended/completed | 2012-04-16 |
| Date submitted | 2012-11-27 |
| Date approved | 2012-11-27 |
| Restricted until | 2012-11-27 |
| Date published | 2012-11-27 |
| Advisor (committee chair) | Chen, Ting |
| Advisor (committee member) |
Sun, Fengzhu Wang, Kai Waterman, Michael S. Finkel, Steve Liu, Yan |
| Abstract | Metagenomics studies have prospered from the rapid development of next-generation sequencing. However, microbial diversity analysis as an essential component of metagenomics is still facing three major challenges: handling errors in data, performing analysis efficiently for large data and avoiding primer bias issue. Since 16S rRNA gene sequences have been frequently used to profile microbial diversity, we focus on this data and successfully provide solutions to all three challenges: our proposed unsupervised Bayesian clustering method termed Clustering 16S rRNA for OTU Prediction (CROP) can find clusters based on the natural organization of data without setting a hard cutoff threshold (3%/5%) as required by hierarchical clustering methods. By applying our method to several datasets, we demonstrate that CROP is robust against sequencing errors and that it efficiently produces more accurate results than conventional hierarchical clustering methods. We also built a generic model for comparing 16S rRNA gene fragment data extracted from metagenomic shotgun sequencing data with targeted 16S rRNA sequencing data. This model, when combined with future benchmarking studies, could help validating 16S rRNA gene fragment data’s ability to avoid primer bias and provide unbiased microbial diversity estimates. Our proposed analysis pipeline could also be implemented for future 16S rRNA gene fragment-based studies. |
| Keyword | bioinformatics; computational biology; metagenomics; clustering; bayesian |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Hao, Xiaolin |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume6/etd-HaoXiaolin-1351.pdf |
Description
| Title | Page 1 |
| Full text | Sharpening the Edge of Tools for Microbial Diversity Analysis by Xiaolin Hao A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (MOLECULAR AND COMPUTATIONAL BIOLOGY) December 2012 Copyright 2012 Xiaolin Hao |
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