The study of the genomes of Borrelia burgdorferi, the organism that causes Lyme disease and is spread to animal hosts by ticks such as Ixodes scapularis, has been hindered by two challenges - the difficulty of capturing the whole genome of Borrelia from an infected tick, and the presence of multiple strains in a single tick vector. The first challenge was recently overcome by my colleagues and collaborators on the East Coast. However, the second challenge still remains. This project will aim at addressing this challenge by applying innovative computational biology techniques and data structures. In particular, there will be two phases to this project. In the first phase, we will develop algorithms to accurately identify those variants of a particular gene that are present inside a given sample, by using a library of known variants and a variety of tools for genome analysis, and calibrate those algorithms using simulated data. In the second phase, we will develop novel methods to accurately estimate the fractions of each variant present in a given sample, and calibrate those using simulation data as well. If successful, this project would be the first to manage to detect multiple variants within a single tick using whole-genome capture data from Borrelia. This could have significant implications on our understanding of the ecology and host specificity of Borrelia.
Team Lead: Leonid Chindelevitch | [email protected] | @Leonardini | Professor | Simon Fraser University