RVC PhD: Characterisation of the Mycobacterium tuberculosis mutations underlying drug resistance in tuberculosis disease
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis, is an important global public health issue. TB drug resistance, caused by genetic mutations in the M. tuberculosis genome, poses serious challenges for effective control. Current molecular diagnostic tests are imperfect as they do not target all resistance mechanisms and drugs, nor do they inform on transmission clusters, and are therefore unable to guide completely effective individualized therapy. Advances in whole genome sequencing (WGS) technology are allowing increasingly rapid and accurate characterization of entire bacterial genomes, providing an unprecedented depth of information. WGS studies have characterized M. tuberculosis genomic variation, including single nucleotide polymorphisms (SNPs) and other variations such as insertions and deletions (indels), across many thousands of samples. The full repertoire of genetic loci and mutations underpinning drug resistance is unknown, but the application of statistical and machine learning methods to TB “big data” has the potential to uncover known and novel markers, and lead to the development of informatics tools for M. tuberculosis profiling and clinical decision making.
The overarching aim of this PhD studentship is to identify and extract insightful and actionable information from the high-quality pathogen WGS data and laboratory susceptibility test drug phenotypes across 14 drugs (sourced and curated either from the global TB drug resistance study across >20 countries or the public domain from years 2000 - 2019). Specifically, the PhD project objectives are to:
(1) develop effective predictive analytical algorithms of multi-drug resistance;
(2) identify novel markers that underlie drug resistance to be included in the development of informatics platforms for rapid antimicrobial resistance profiling;
(3) refine or develop new machine learning methods to produce outputs that are interpretable to users, and therefore assist decision making.
This studentship provides an ideal interdisciplinary environment for the student to benefit from experts’ guidance in statistics (Chang), genomic epidemiology (Clark), artificial intelligence and machine learning (Peng) and bioinformatics (Xia). Student will be trained to develop bespoke research skills in analyzing whole genome sequencing analysis and utilizing machine learning techniques to improve clinical decision making.
- Essential Requirements -
-Applicants must hold, or be expected to achieve, a first or high upper second-class undergraduate honours degree or equivalent (for example BA, BSc, MSc) or a Masters degree in a relevant subject.
-Excellent written English.
-Excellent organisational skills.
-Excellent oral and written communication skills.
-Excellent analytical and computing skills.
-Excellent interpersonal skills, and able to work as part of team and also independently.
- Desirable Requirements -
- Prior research experience or a Master degree
- Knowledge of genomics, infectious disease, drug resistance, or machine learning techniques.
- Familiarity with a computing language (e.g. R, Python)
This is a 3 year fully-funded studentship, and is open to Home/EU applicants. International students are welcome to apply but must be able to fund the difference between UK/EU and international tuition fees.
The studentship will commence October 2020.
If you are interested in applying for this position, please follow the link below. Please use your personal statement to demonstrate any previous skills or research experience you have in drug resistance, big data analysis or machine learning methods.
1- Chen et al. (2019) Beyond multidrug resistance: leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.
2- Coll et al. (2018) Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nature Genetics 50 (2): 307-316.
3- Kouchaki et al. (2019) Application of machine learning techniques to tuberculosis drug resistance analysis. Bioinformatics, 35 (13): 2276-2282.