Date of Award
Master of Science
Tit- Yee Wong
King Thom Chung
Lih Yuan Deng
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.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Bhaskarabhatla, Rekha, "Binning Metagenomic Data by CSSR" (2011). Electronic Theses and Dissertations. 357.