Past Research
Genomic Islands
My PhD research was focused on identifying and characterizing genomic islands (GIs). GIs are large regions (8-250 kb) that are thought to have been horizontally transferred in bacterial genomes. GIs are often linked to pathogenicity, antibiotic resistance, and adaptation to particular environments. During this research project I developed a novel GI prediction program, called IslandPick, that uses comparative genomics for GI identification. After evaluating the accurary of several other GI prediction programs, I incorporated the most accurate methods into a single comprehensive website for GI prediction called IslandViewer. I also was involved in a collaborative project for the annotation and identification of GIs in a particaular virulent strain of Pseudomonas aeruginosa. I also describe some interesting relationships between GIs, CRISPRs, and phage. All of these are projects are outlined in my PhD thesis.
Standards development for flow cytometry data
My PhD program included research rotations before choosing a PhD supervisor for the remainder of my thesis research. One of these rotations was spent on developing standards for flow cytometry. I learned quite a bit about onlotology development (OWL) and file speficiations using XML and XSLT. More information is in the publication.
Medical image segmentation algorithms
I dabbled in the field of medical image analysis, while taking an elective course with Dr. Hamarneh during the start of my PhD. This research was focused on developing new segmentation algorithms (think magic wand tool in Photoshop) in 2D and 3D MRI images. This gave me a crash course in learning Matlab and C. The specifics on the research can be found in two publications: here and here.
Gene duplication in Drosophila
While completing my concurrent undergraduate Bachelor's of Science and Bachelor's of Computer Science degrees at UNB, I worked for two summers with Dr. Clark on identifying retrotransposed (DNA->RNA->DNA) gene duplications in Drospophila melanogaster and looking at gene expression differences between the parent and child copies using microarray data. More information about this project is explained in the publication.