In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation proposal
Novel Alignment Based Clustering Algorithms for Pan-Genome Analysis of Bacteria Species
Understanding the basic rules of bacterial evolution and adaptation is critical in developing new anti-bacterial drugs, using bacteria in biotechnology applications as well as combating undesired consequences of bacterial presence in industrial and environmental settings such as corrosion, product spoilage and degradation.
Accumulation of single nucle otide mutations beneficial (or neutral) for bacterial survival is a well-studied mechanism of bacterial adaptation which also reflects the time of species/strain separation from a common ancestor (molecular clock hypothesis). The gene loss or gain due to horizontal gene transfer is another much more dynamic mechanism of bacterial adaptation. Using this mechanism bacteria can acquire new features such as virulence factors, locomotion ability (flagella), heat or drug resistance.
A major functional characteristic of bacterial species is the presence of particular gene sets common to species/strains (core genome) together with genes that are specific to individual or groups of species/strains (pan genome). The technical difficulties however, lie in the way how one can identify the same genes or gene families in evolutionarily distant organisms.
The goal of the presented r esearch is to resolve these difficulties by developing and implementing algorithms to identify justifiable similarity threshold in order to distinguish between similar and dissimilar genes in nucleotides space using appropriate alignment scoring system and biologically meaningful clustering method of genes.
Resulting gene profiles of individual bacteria in a set allow to build the “functional similarity” tree of the species reflecting gene composition similarity (using Mutual Information and Jaccard Distance) among individuals. Performed analysis also allows to identify co-appearance and co-avoidance patterns of genes in bacterial species. We have validated our approach using ~34,000 genes from 22 Bartonella species.
Date: Wednesday, December 2, 2015
Time: 1:00 PM
Place: HBS 302
Advisor: Dr. Ioannis Pavlidis, Dr. Yuriy Fofanov
Faculty, students, and the general public are invited.