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EASTBIO Rapid mobile sequencing and bioinformatics for transboundary diseases

  • Full or part time
    Dr M Bronsvoort
  • Application Deadline
    Sunday, January 05, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Royal (Dick) School of Veterinary Studies / The Roslin Institute

Transboundary diseases are a major concern for livestock keepers globally causing direct loses in countries where they are endemic (mainly LMICs) and both indirect and direct loses in free countries as they try to maintain their status or control outbreaks such as the FMD outbreaks in the UK in 2001 and 2007 or the current ASF pandemic in Europe and Asia. However, understanding of transmission patterns and the scale of populations needed to support persistence in these settings in sub-Saharan Africa (SSA) is almost non-existent, and consequently it is difficult to develop evidence based interventions and strategies for control at the livestock/wildlife interface.
Rapid on-site sequencing would be a major step forward, and small portable sequencing devices exist such as the nanopore/Minion but new field PCR tools are coming on line as well as new lateral flow technologies (Global Dx) that could provide a step change in affordability for surveillance in LMICs. The nanopore technology has been successfully used in the Ebola [1] and Zika outbreaks but critically it was already known which strains to target. For livestock diseases a more agnostic approach is needed, and obtaining low error rate sequences would allow transmission networks (who infected whom) to be inferred – these are valuable tools for understanding where to direct resources for the control of infectious diseases [2] and having the capability for LMICs and the UK would be a major improvement for government field veterinarians and epidemiologists.

Transmission networks can be inferred from pathogen sequence data due to the fast evolution and generation times, although their fidelity depends upon the number of (non-error) mutations observable at the appropriate temporal and spatial scales.
In this project you will investigate the use of nanopore sequencers with field samples and compare to next generation sequencing. You will develop and evaluate algorithms to extract the most out of the data – minimising sequencing error and inferring transmission networks (including under missing data scenarios), considering a variety of algorithm types including Bayesian inference, machine learning or deep learning [3].

Initial pathogen samples will include samples from an ongoing disease surveillance and genetic improvement programme in Uganda (PigBoost), but there will be opportunity to collect additional samples and try your protocols and algorithms in the field in later years. In particular this project will build upon existing Roslin-African collaborations including the National ANIMAL Disease Diagnostics and Epidemiology Centre (NADDEC)

Ministry of Agriculture, Animal industry and Fisheries, Uganda.
This project combines sequencing technology, field epidemiology and computer science / maths & statistics, and the student will be trained in laboratory, field work, computer programming and data science.

All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree. To qualify for full funding students must be UK or EU citizens who have been resident in the UK for 3 years prior to commencement.

Funding Notes

Completed application form along with your supporting documents should be sent to our PGR student team at

Please send the reference request form to two referees. Completed forms for University of Edinburgh, Royal (Dick) School of Veterinary Studies and the Roslin Institute project should be returned to by the closing date: 5th January 2020.

It is your responsibility to ensure that references are provided by the specified deadline.
Download application and reference forms via:
View Website


[1] Real-time, portable genome sequencing for Ebola surveillance, Quick et al, Nature 530, 228–232 (2016)
[2] Supersize me: how whole-genome sequencing and big data are transforming epidemiology, Kao et al, Trends Microbiol. 2014 May;22(5):282-91
[3] Emerging Concepts of Data Integration in Pathogen Phylodynamics, Baele et al, Syst Biol (2016)

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