New bioinformatics and statistical methods for the analysis and visualisation of foot and mouth disease virus sequences
Start date1 October 2019
Eligible students will receive a minimum annual stipend of of £15,009 and university registration fees will be paid.
Funding sourceThis has been generously funded by the legacy of Mr Kenneth Longhurst and the Pirbright Institute
The foot-and-mouth disease virus (FMDV) severely affects hooved livestock, with a major economic impact particularly on low-income countries in Asia and Africa. Informed decisions about controlling FMDV outbreaks can only be made by exploiting the relation between the strain responsible for the outbreak and known viral strains. The FMDV World Reference Laboratory at The Pirbright Institute (TPI) has sequenced an extensive catalogue of representative viruses, sampling infected animals and vaccine strains across a broad geographic and temporal range. Given the complex population structure and molecular biology of FMDV, sophisticated data analysis methods are necessary to extract and provide reliable information on new viral strains.
The FMDV Toolkit portal was recently initiated by the Integrative Biology and Bioinformatics group at TPI. It aims to empower the FMDV research community around the world, and especially researchers in low-income countries, by democratising access to such analysis methods, and making them available through a user-friendly web interface. The main goal of the proposed PhD project will be the development of novel statistical and visualisation techniques for the FMDV Toolkit to provide useful and timely information to aid the control of outbreaks in resource-poor countries.
You will have full flexibility into the direction of the project, as multiple sources of information will require integration of diverse statistical, computational and bioinformatic approaches. Some of the areas we expect you to work on are as follows:
- Phylogenetic analysis and phylogeographic inference with multiple tools, both based on known software such as BEAST and newer approaches leading to a comparative analysis of how different phylodynamic and phylogeographic approaches work for FMDV. Reconstruction of mutational trajectories and their fitness/immunity properties will be based on a combination of standard phylogenetic tools (for example RaXML) and ad-hoc methods for FMDV based on recent work from the literature on different viruses.
- Biochemical and immunological properties of mutations to be inferred using machine learning approaches that are already being tested on serology data being available to the FMDV reference lab and other labs at TPI.
- For the integration of different sources of information, different general-purpose statistical approaches (PCA/CA/PLS, clustering and classification methods) could be applied alongside statistical machine learning techniques when possible. This could include explicit models, if they become available, and Bayesian techniques which will be used to build an extended phylogeographic and phylodynamic approach including a fitness landscape synthesis of the structural and immunological information.
You will mainly focus on the back-end of the FMDV Toolkit portal, implementing some of the methods described above. You will also have the opportunity to implement part of the front-end, for example, building a visual interface for the results of the computation.
The project is co-funded by TPI and will be carried out in collaboration with Dr Paolo Ribeca, Head of Integrative Biology and Bioinformatics and Dr Yasaman Kalantar-Motamedi, Bioinformatician. The student will based at the Pirbright Institute and spend time at the University of Surrey’s School of Veterinary Medicine during the project.
Related linksThe Pirbright Institute Integrative Biology and Bioinformatics group at Pirbright Veterinary epidemiology and public health research at Surrey
- Ferretti et al. Viruses 10 (5), 221 (2018)
- Marco-Sola et al. Nature Methods 9 (12), 1185-U76 (2012)
- Horton et al. PloS NTD 9 (3) (2015)
- Ludi et al. J. Gen Virol 95, 384-392 (2014)
Due to the multidisciplinary nature of this project, applicants are required to hold an undergraduate degree in either Bioinformatics, Biological Sciences, Computer Sciences, Statistics or related subject with at least a 2:1 or equivalent. Coding skills are essential. Experience in molecular biology, bioinformatics and/or epidemiology is desirable. To be eligible, you must also be a UK or EU student.
Students without English as a first language must provide evidence that they meet the English language requirement, with an IELTS score of 7.0 and no less than 6.5 in any other section.
How to apply
You can apply for this studentship via The Pirbright Institute, where you will find an application form and guidance on what documentation is needed to apply for this studentship.