VariantVisualiser: Ensuring precise diagnoses of genetic disorders through the development of intuitive software that facilitates the integration and visualisation of complex informatics with biological and clinical data

Rare diseases affect <1-in-2,000 people, but ~8,000 rare genetic diseases impact ~10% of births globally. These arise from variations in patients’ DNA sequences. To reach a diagnosis, an ecosystem of clinicians, journal publishers and databases must understand one another and agree on the specific variant(s) they are talking about. A standardised name is given to each variant so that associated diagnostic evidence can be shared. However, dataflow into clinical databases is hindered by incorrect naming of variants in the scientific literature which renders evidence undiscoverable. Additionally, there is no standardised way to record diagnostic evidence that clinicians use to classify variants as disease-causing, yielding disparities in the reliability of evidence. These seemingly minor issues contribute to ~3,000 children born annually in the UK who never receive a diagnosis. Consequently, families cannot access support they need, and up to 3,000 children die undiagnosed each year.

This proposal builds on substantial efforts of a Human Genome Organization committee which has proposed a unified professional-standard for representing genomic data which, when finalised, will be adopted world-wide. We will create visualisation software to enable the professional-standard in a framework intended to better communicate genetic data throughout the human genomics cimmunity. The framework captures precise names for variants and documents the clinical evidence used to classify them. We will upgrade the VariantValidator infrastructure to create the framework. We will then replace the existing VariantValidator interface with a new display, facilitating precise communication and interpretation of the stored clinical genomic data. Translation into tangible benefits will be facilitated by integrating of the resources into NHS training, and embedding resources into clinical pipelines, literature management systems and clinical databases. This holistic intervention will increase availability of diagnostic data enabling hundreds of thousands of children globally each year to receive a diagnosis.

Training/techniques to be provided

VariantValidator and the VariantValidator resources form part of the NHS Scientist Training Programmes in genomics and genomic bioinformatics. The student will be trained to teach trainees in the Co-Applicants digital and face-2-face training environments. Teaching is a vital part of the clinical informaticians toolkit and this training will set the student in good stead for career development and moving towards obtaining an academic position.

Entry requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in an area/subject related to Bioinformatics or Computer Science. Programming skills in Python are essential, as is experience in SQL databases. An ideal candidate will also have experience or an interest in HTML, JavaScript, React JS.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Bioinformatics.

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit https://www.bmh.manchester.ac.uk/study/research/programmes/integrated-teaching/

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/