PhD Studentship Data-mining for disease gene identification

A PhD Studentship entitled ‘Data-mining for disease gene identification’ is available from the earliest date that the successful candidate could commence, within the Leeds Institute of Biomedical Clinical Sciences, Faculty of Medicine and Health, University of Leeds under the supervision of Dr Ian Carr, Professor David Bonthron and Dr Chris Watson. The studentship is available for UK and EU citizens only and the studentship will attract an annual tax-free stipend of £13,726 p.a. and will cover the UK/EU tuition fees. Applicants should have or expect to get at least a 2.1 Honours degree in a relevant discipline.


Massively parallel DNA sequencing analysis has rapidly become the method of choice for the detection of causative disease mutations in patients with inherited diseases. Major hurdles remain in distinguishing a single pathogenic mutation from many thousands of candidate sequence variants. Standard approaches, such as filtering against databases of known genetic variants, or according to location within known disease genes, are often insufficient.

There is great potential in the approach of identifying disease genes through recognizing functional links between candidate genes and the disease phenotype. Manual literature searches on genes or gene families can be fruitful, as can text-based searches of web portals that contain specific types of information about genes, such as expression profiles, protein domain structures, protein functions, mouse knock-out phenotypes etc. As currently performed, such analyses are tedious, error-prone and time-consuming. The parameters of success and failure are also ill-defined.

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The aim of this studentship will be to develop integrated web-based methods and resources that will allow researchers to simultaneously screen multiple data sets against disease-relevant search terms and filter the resultant record set against genes with sequence variants found in the sequence detection stage. By collating and ranking the results of the search queries on each data set it should be possible to identify strong functional candidate genes containing possible deleterious variants in the patient(s).

The successful applicant will join the Next Generation Sequencing Facility which is jointly operated by the University of Leeds and the Leeds Teaching Hospitals. Members of the facility have an international reputation for both identifying recessive disease genes and developing software designed to aid the disease gene discovery process. The studentship could be suitable either for a biological sciences graduate with an interest in computer programming, or a computing- or maths-based graduate with an interest in bioinformatics. Due to the multidisciplinary nature of the project, proven practical experience in relevant projects will be taken into consideration, as well as academic credentials.

For further information see the NGS Facility web page: and our genetics software page: or contact Dr Carr ( or Professor Bonthron (

To apply for this studentship applicants should send a CV with a cover letter to

Closing date for this studentship is August 31st 2013.

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