Electronic Theses and Dissertations

Identifier

449

Date

2011

Document Type

Thesis

Degree Name

Master of Science

Major

Biology

Committee Chair

Tit- Yee Wong

Committee Member

King Thom Chung

Committee Member

Lih Yuan Deng

Abstract

Metagenomics is the study of microbes in their natural environments without the need for isolation and lab cultivation. The DNA fragments obtained from sequencing of a sample of mixed species requires taxonomic characterization called binning. My research concerns binning of metagenomic data using a novel approach. Each genomic sequence was codified based on their Cistronic Stop Signal Ratio (CSSR) values. Since the genic CSSR values of phylogenetically related organisms often share a definable pattern, a neural network was trained to recognize the genic CSSR patterns of known species.The trained neural network was then used to cluster the CSSR values from the metagenomic data. To show the validity of this method, a total of 15,000 genic CSSR values were calculated from five different bacterial species. The data was randomly mixed and a neural network was used to recognize the originality of these genes, based on their unique CSSR values. Results showed that better than 95% of the genes were correctly binned to the rightful species. The metagenomic sequences from the fecal samples of 124 individuals were reanalyzed based on the CSSR - neural network method by training the genic values of a set of known enteric bacteria. The resulting clusters were discussed.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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