Date of Graduation

2014

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Chemical and Biomedical Engineering

Committee Chair

Jeremy M Dawson

Committee Co-Chair

Donald A Adjeroh

Committee Member

Lawrence A Hornak

Committee Member

Letha J Sooter

Abstract

Molecular and bio-molecular biometrics are an advancing field that involves the analysis of a person's unique biological markers at a molecular level to ascertain identity. Bacteria communities found on the skin of the human hand have shown to be highly diverse and to have a low percentage of similarity between individuals. The goal of this research effort is to see if a person's demographics, primarily ethnicity, share a relationship with the bacteria communities that exist on their hand. A sample collection was carried out in which the left and right inner palms of 250 individuals were swabbed to obtain a total of 500 bacteria samples. Of these, 104 samples covering a range of age, gender, and ethnicity of the participants, were sequenced using 150 paired-end multiplex reads on an Illumina MiSeq. The reads contained the third hypervariable region DNA of the microbial 16S rRNA gene commonly used for microbial identification. Sequences were analyzed using a combination of commercial and custom bioinformatics tools. Results indicated that women that participated in the sample collection had a 15.7% higher diversity of bacteria at the genus level than men. Using a support vector machine with a 60% train and 40% test approach, ethnicities of individuals who provided samples could be classified with a range of 64-93% accuracy depending on the method used. Principal coordinate plots generated by using the unique fraction (UniFrac) algorithm devised by Lozupone et al at University of Colorado at Boulder showed that similar clustering appeared with people of Turkish, Asian Indian, and Middle Eastern descent and less clustering with people of Caucasian and African American descent. Although focused on a small subset of the human population with no temporal variance in bacterial diversity explored, these results provide a basis for performing identification based on human bacteria that can be expanded upon using time varying sampling and other regions of the 16S rRNA gene.

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