Modelling the Human Cancer Genome

The project:

Following on from work on integrative methods for machine learning, and with collaborators such as Tom Gaunt at the University of Bristol, we have been developing integrative methods for identifying disease-driver variants in the human cancer genome (see [1] for a review, also [2]). Our most well-known method is FATHMM-MKL. From 2015-2022, this method was the mutation impact predictor at COSMIC in Cambridge, the world's largest cancer genome archive (Google `FATHMM-MKL cosmic’ and ‘cosmic cancer’): it predicted which single nucleotide variants in the human cancer genome are drivers of unregulated cell proliferation, with an associated probability. There are many papers using these methods. We are working on a data resource, DrivR-Base [3], for building enhanced and more accurate prediction tools. There are innumerable possible projects in this research area, from cancer-type specific predictors, annotators (does a variant in the human genome cause gain or loss of function?), cancer gene prediction, and tools for handling variants within functional segments of the non-coding genome. There are also linked therapeutics-oriented projects. The PhD programme will start with a prediction tool which uses haplo-insufficiency data and, thereafter, will be flexible according to the student's interest, though remaining within the general area of variant impact prediction using machine learning methods.

• [1] MF Rogers et al. Briefings in Bioinformatics (OUP). Volume 122, pages: 1467–1476 (2020), bbaa250,
• [2] https://seis.bristol.ac.uk/~enicgc/software.htm
• [3] Amy Francis, Colin Campbell, Tom R Gaunt, DrivR-Base: a feature extraction toolkit for variant effect prediction model construction. Bioinformatics (2024) doi: 10.1093/bioinformatics/btae197.

Candidate requirements:   

Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree. 

If English is not your first language, you need to meet this profile level: Profile E 

Further information about English language requirements and profile levels. 

Contacts:  

For questions about the research topic, please contact the project supervisor.

For questions about eligibility and the application process please contact Engineering Postgraduate Research Admissions admissions-engpgr@bristol.ac.uk 

How to apply:  

Prior to submitting an online application, you will need to contact the project supervisor to discuss. 

Online applications are made at http://www.bris.ac.uk/pg-howtoapply. Please select Engineering Mathematics (PhD) on the Programme Choice page. You will be prompted to enter details of any studentship you would like to be considered for in the Funding and Research Details sections of the form.