Lead Institute / Faculty: Faculty of Medicine
Main Supervisor: Professor Diana Baralle
Other members of the supervisory team: Prof Niranjan Mahesan, Dr Jenny Lord
Duration of the award: 3 years full time
Due to recent advances in sequencing technologies, we can generate unprecedented amounts of genomic data, but clinical interpretation of that data has lagged behind, and is one of the major challenges in healthcare at present. For patients with rare disorders, even after whole genome sequencing, generally half of patients do not get a molecular diagnosis. With estimates that up to 50% of disease causing mutations affect splicing, gaining a better understanding of how splicing operates under normal conditions, as well as the types of mutations that can perturb the process has huge potential for immediate impact.
This project will help establish what the “best practices” are for interpreting mutations affecting splicing, and how this can be applied in a clinical setting. Optimal in silico methodologies will be developed and applied to genetic data from patients with rare diseases in the 100,000 genomes project to help improve diagnostic rates. Machine learning techniques will be applied to large genomic and transcriptomic datasets to “learn” the genetic code determining normal and disease causing aberrant splicing.
The student will work in a cross-disciplinary, highly collaborative and supportive environment with wet lab scientists, computational biologists, clinicians and computer scientists. They will learn computational approaches to biological problem solving (including bioinformatics, DNA and RNA sequencing data analysis methods, and machine learning), as well as developing a firm understanding of splicing, clinical informatics, and medical genetics. Dependent on experience, the student will be supported to attend any relevant training courses and present at conferences useful to themselves and the project.
The ideal candidate will have a strong background in genetics, bioinformatics, computer science, or a related field, and have a strong interest in the other disciplines.
Please contact: Professor Baralle, [email protected]
Person Specification: See below https://jobs.soton.ac.uk/Upload/vacancies/files/20783/03%20Doctoral%20Researcher%20Person%20Specification_UoS_FoM_PhD.docx
The successful candidate is likely to have the following qualifications:
• A 1stor 2:1 degree in a relevant discipline and/or second degree with a related Masters
Administrative contact and how to apply:
Please complete the University’s online application form, which you can find at https://studentrecords.soton.ac.uk/BNNRPROD/bzsksrch.P_Login?pos=7209&majr=7209&term=201920
You should enter Diana Baralle as your proposed supervisor. To support your application provide an academic CV (including contact details of two referees), official academic transcripts and a personal statement (outlining your suitability for the studentship, what you hope to achieve from the PhD and your research experience to date).
Informal enquiries relating to the project or candidate suitability should be directed to Professor Baralle ([email protected]
Closing date: 26/04/2019
Interview date: 09/05/2019