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GW4 BioMed MRC DTP PhD studentship: Using computational simulation and machine learning to understand bacterial phenotypes of public health importance

  • Full or part time
  • Application Deadline
    Monday, November 25, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

This project is one of a number that are in competition for funding from the ‘GW4 BioMed MRC Doctoral Training Partnership’ which is offering up to 18 studentships for entry in September 2020.

The DTP brings together the Universities of Bath, Bristol, Cardiff and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities.


Lead supervisor: Dr Lauren Cowley, Department of Biology & Biochemistry, University of Bath
Co-supervisors: Prof Sam Sheppard (Bath), Prof Ed Feil (Bath) and Prof Ruth Massey (Bristol)


Genome-wide association studies (GWAS) are a powerful tool in discovering the genetics underlying observed important phenotypes and have been used very successfully in human genetics for several heritable diseases. In bacteria that reproduce asexually, the challenge with GWAS is the high linkage between polymorphisms due to low levels of recombination. A lot of the mutations or genes in bacterial genomes are not acquired independently and it is therefore difficult to differentiate the mutation or gene that is causing the phenotype change from those that are simply hitch hiking with it (linkage disequilibrium).

This project aims to develop a novel method to harness the utility of bacterial sex (recombination) in shuffling those gene linkages periodically by using datasets sampled across multi-year timepoints to try to capture that shuffling. That shuffling will allow more accurate recognition of the causal genes in phenotypes by decoupling them. Methods developed to achieve bacterial GWAS results of high confidence could be extremely useful in determining the genetic elements associated with phenotypes of public health importance, such as invasiveness, disease severity, antibiotic resistance or host niche.

The aims of the studentship will be:

• To develop a novel approach of longitudinal and spatial sampling in big data that offers an opportunity to resolve genuine associations from spurious one’s due to linkage.

• This will be tested by using datasets that are deeply sampled longitudinally and testing time periods individually to see if association gene lists change over time and if certain genes remain constant in their assigned significance. In recombining bacteria, sampling over time causes linkage between loci to decline.

• Simulations will be run using the state-of-the-art computational simulation tool Fwdpp to gauge the optimum time period needed to allow enough recombination to happen to decrease spurious linkage enough to make GWAS accurate. The student will aim to estimate; the minimum number of required genomes and frequency of sampling when given the recombination rate of the species.

• We will also develop machine learning algorithms trained on associated data to predict associations from unanalysed data, to provide a prediction mechanism for important infectious disease phenotypes.

• Systematic testing will be performed in a variety of large genomic datasets to develop a robust protocol to provide high confidence results. To limit the complexity of linkage disequilibrium and mirror the success of GWAS in humans (with very low levels of linkage disequilibrium), highly recombinogenic bacteria such as Streptococcus pneumoniae and Neisseria gonorrhoeae datasets will be used in initial strategies.

This studentship will use state of the art genomics and cutting-edge computational tools to wrangle big data to understand phenotypes of bacteria of public health importance.


Applicants for a studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an area appropriate to the skills requirements of the project.

IMPORTANT: In order to apply for this project, you should apply using the DTP’s online application form:

You do NOT need to apply to the University of Bath at this stage – only those applicants who are successful in obtaining an offer of funding form the DTP will be required to submit an application to study at Bath.

More information on the application process may be found here:


Funding Notes

A full studentship will cover UK/EU tuition fees, a Research and Training Support Grant of £2-5k per annum and a stipend (£15,009 per annum for 2019/20, updated each year) for 3.5 years.

UK and EU applicants who have been residing in the UK since September 2017 will be eligible for a full award; a limited number of studentships may be available to EU applicants not meeting the residency requirement. Applicants who are classed as Overseas for tuition fee purposes are not eligible for funding.

More information on eligibility may be found here: View Website

How good is research at University of Bath in Biological Sciences?

FTE Category A staff submitted: 24.50

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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