About the Project
Accurate genomic variant reporting is essential for rare disease diagnosis, yet scientific papers, clinical reports, and database submissions routinely contain erroneous variant descriptions and omit crucial clinical evidence such as ACMG scores and pathogenicity classifications. These gaps limit the findability of supporting evidence, reduce diagnostic yield, and add to the genomic medicine diagnostic odyssey. With a new global professional standard for interpreted genomic variation due in…
2026, there is an urgent need for tools that help clinicians and healthcare scientists efficiently create reports that meet this standard across both research and clinical workflows.
This PhD project will develop an integrated AI-driven text reading and variant visualisation platform that supports accurate, complete, and reproducible curation of genomic variant information. The student will build upon two key community resources, LOVD HGVS Syntax Checker and VariantValidator, which together provide unmatched coverage and correction of HGVS variant descriptions. Modern clinically developed AI systems, including VariantGPT, will be used to extract and interpret variant descriptions from manuscripts, preprints, and clinical texts. VariantGPT will be extended to feed extracted data into LOVD SC and VariantValidator, automatically inserting corrected or missing information back into the text and prompting the author when required.
To address missing clinical evidence, the student will integrate the NHS All Wales ACMG classification tool to generate standards-compliant ACMG classifications and supporting evidence directly from the text.
A key outcome of the project will be a next-generation variant visualisation interface that displays corrected variant descriptions, evidence, and classifications in an intuitive and clinically meaningful way. Together with a real-time document-editing API, which will additionally be made accessible via a document editor plugin, this system will act as a “genomic spell checker” supporting authors, reviewers, and clinical scientists.
All curated outputs will be deposited into partner databases such as LOVD, ensuring they remain findable, reproducible, and accessible across research and clinical genomics.
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
We strongly recommend that you contact the supervisors for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
Funding
This 3.5 year PhD is for self-funded students or those with external funding.
We recommend that you apply early as the advert may be removed before the deadline.
How to apply
You will need to submit an online application through our website here: https://uom.link/pgr-apply
When you apply, you will be asked to upload the following supporting documents:
- Final Transcript and certificates of all awarded university level qualifications
- Interim Transcript of any university level qualifications in progress
- CV
- You will be asked to supply contact details for two referees on the application form (please make sure that the contact email you provide is an official university/ work email address as we may need to verify the reference)
- Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it.
- English Language certificate (if applicable). If you require an English qualification to study in the UK, you can apply now and send this in at a later date.
If you have any queries regarding making an application please contact our admissions team FSE.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. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Funding Notes
This 3.5 year PhD is for self-funded students or those with external funding.
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