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Computational approaches for rapid pathogen genomic sequence analysis


   Institute of Microbiology and Infection

  Dr Nicole Wheeler  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Research interests/description of main research theme:

In order to better guard against the rapid spread of high-risk strains of infectious diseases in the future, we must better leverage the large scale collection and processing of genomic data that is possible with today’s technologies. A number of international initiatives are currently exploring the implementation of genomic surveillance to track the prevalence of existing pathogens and identify emerging pathogens. These programs will produce enormous amounts of data which must be processed quickly to discover concerning trends.

This project aims to develop computational approaches to better link genotype to phenotype and detect concerning patterns in pathogen spread and evolution over time and space.

Person Specification

Applicants should have a background in bioinformatics, biology, biochemistry, physics, mathematics, computer science, or a related subject. The project will involve computational analysis of DNA sequence data and require programming skills. Prior proficiency in Python is preferred. An ideal candidate will have experience analyzing microbial genomic data or working with kmer and/or graph-structured data.

Applicants should hold or realistically expect to obtain at least an Upper Second Class Honours Degree in a relevant subject (see above).

How to apply

Informal enquiries should be directed to Nicole Wheeler

Applications should be directed to Dr Nicole Wheeler (). To apply, please send:

•             A detailed CV, including your nationality and country of birth;

•             Names and addresses of two referees;

•             A covering letter highlighting your research experience/capabilities;

•             Copies of your degree certificates with transcripts;

•             Evidence of your proficiency in the English language, if applicable.

 


Funding Notes

• Self-funded PhD students only

References

Jaillard M, Lima L, Tournoud M, Mahé P, van Belkum A, Lacroix V, et al. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events. PLoS Genet. 2018;14: e1007758.Wheeler, N.E., Gardner, P.P. and Barquist, L. (2018) ‘Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica’, PLoS genetics, 14(5), p. e1007333.
Wheeler, N.E. et al. (2019) ‘Contrasting approaches to genome-wide association studies impact the detection of resistance mechanisms in Staphylococcus aureus’, BioRxiv. doi:10.1101/758144.

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