Developing molecular epidemiology frameworks using whole genome sequence data for pathogenic mycobacteria in animals

Full Project Description: Infection of both agricultural and wildlife animals with different species of mycobacteria is a large burden, both in terms of animal health and associated economic costs. Two of the primary mycobacteria infecting such animals are Mycobacterium tuberculosis variants bovis and orygis infecting cattle, sheep, deer, badgers and rhino and Mycobacterium avium subspecies paratuberculosis infecting cattle, sheep, goats, rabbits, deer and bison.
The frameworks used for tracking mycobacterial infections are almost exclusively built around tracking Mycobacterium tuberculosis in humans. They rely on whole genome sequencing data from the pathogens which are then compared to see how many mutations each pair differ by. If this difference is less than five single nucleotide polymorphisms, the two isolates are said to be in a transmission cluster together. However, these approaches have not been tailored for any other mycobacteria, who have different genome sizes and mutation rates.
This project aims to create gold standards for tracking transmission of animal-associated pathogenic mycobacteria similar to those in place for M. tuberculosis in humans. This will be achieved through various work packages including:
Analysis of mutation rates and genome comparison approaches for M tuberculosis animal variants and M. avium subsp paratuberculosis
Create adapted computational frameworks for undertaking track and trace of these pathogens using clinically derived whole genome samples
Extension of these pipelines to look for drug resistance and virulence factors
Use these pipelines to better understand the transmission of pathogenic mycobacteria between animals, with a particular focus of transmission corridors between agricultural and wildlife animals.
The project will be almost wholly computational and develop the student in skills such as:
Bioinformatics pipeline construction
Comparative genomics
Molecular epidemiology, in particular Bayesian phylogenetics
Clinical bacteriology, in particular mycobacteriology
Scripting languages such as python, UNIX and R