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DEVELOPING STATISTICAL AND ALGORITHMIC METHODS FOR
SHOTGUN METAGENOMICS AND TIME SERIES ANALYSIS
by
Li Charlie Xia
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY AND BIOINFORMATICS)
May 2013
Copyright 2013 Li Charlie Xia
Object Description
| Title | Developing statistical and algorithmic methods for shotgun metagenomics and time series analysis |
| Author | Xia, Li Charlie |
| Author email | li.xia@usc.edu;lxia.usc@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 | 2013-03-05 |
| Date submitted | 2013-03-05 |
| Date approved | 2013-03-05 |
| Restricted until | 2013-03-05 |
| Date published | 3/5/2013 |
| Advisor (committee chair) | Sun, Fengzhu Z. |
| Advisor (committee member) |
Fuhrman, Jed A. Kuo, Jay C.C. |
| Abstract | Recent developments in experimental molecular techniques, such as microarray, next generation sequencing technologies, have led molecular biology into a high-throughput era with emergent omics research areas, including metagenomics and transcriptomics. Massive-size omics datasets generated and being generated from the experimental laboratories put new challenges to computational biologists to develop fast and accurate quantitative analysis tools. We have developed two statistical and algorithmic methods, GRAMMy and eLSA, for metagenomics and microbial community time series analysis. GRAMMy provides a unified probabilistic framework for shotgun metagenomics, in which maximum likelihood method is employed to accurately compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). We extended the Local Similarity Analysis technique (eLSA) to time series data with replicates, capturing statistically significant local and potentially time-delayed associations. Both methods are validated through simulation studies and their capability to reveal new biology is also demonstrated through applications to real datasets. We implemented GRAMMy and eLSA as C++ extensions to Python, with both superior computational efficiency and easy-to-integrate programming interfaces. GRAMMy and eLSA methods will be increasingly useful tools as new omics researches accelerating their pace. |
| Keyword | metagenomics; time series; local association; EM algorithm; microbial ecology |
| 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 |
| Contributing entity | University of Southern California |
| Rights | Xia, Li Charlie |
| Physical access | 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@dots.usc.edu |
| Archival file | uscthesesreloadpub_Volume7/etd-XiaLiCharl-1461.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | DEVELOPING STATISTICAL AND ALGORITHMIC METHODS FOR SHOTGUN METAGENOMICS AND TIME SERIES ANALYSIS by Li Charlie Xia A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTATIONAL BIOLOGY AND BIOINFORMATICS) May 2013 Copyright 2013 Li Charlie Xia |
