PhD Studentship: Developing a variant effect predictor for multiple missense variants in the same protein molecule to aid clinical interpretation
University of Exeter
Context: Genome sequencing is becoming increasingly widespread in both research and diagnostics, and over a million people have already had their genomes sequenced. However, interpreting the effect of genetic variants is challenging and frequently delays diagnosis in patients with rare genetic diseases. The number of variants of uncertain significance (VUS) in clinical databases is growing exponentially, and >70% of them are missense variants, i.e. where one amino acid in a protein sequence is replaced with anot...
Context: Genome sequencing is becoming increasingly widespread in both research and diagnostics, and over a million people have already had their genomes sequenced. However, interpreting the effect of genetic variants is challenging and frequently delays diagnosis in patients with rare genetic diseases. The number of variants of uncertain significance (VUS) in clinical databases is growing exponentially, and >70% of them are missense variants, i.e. where one amino acid in a protein sequence is replaced with anot...
Opportunity details
Context: Genome sequencing is becoming increasingly widespread in both research and diagnostics, and over a million people have already had their genomes sequenced. However, interpreting the effect of genetic variants is challenging and frequently delays diagnosis in patients with rare genetic diseases. The number of variants of uncertain significance (VUS) in clinical databases is growing exponentially, and >70% of them are missense variants, i.e. where one amino acid in a protein sequence is replaced with another. Numerous computational variant effect predictors (VEPs) have been developed to predict the effect of missense variants and are increasingly being used in genome sequencing analysis and diagnostic interpretation. However, despite every human having ~10,000 missense variants in their genome, and ~1000 genes on average with more than one missense, genetic variant interpretation still largely considers these variants individually. Although some computational models can predict the effects of multiple amino acid changes, these have mainly been developed for protein-engineering purposes. Key question: The aim of this PhD project is to develop and validate a new VEP designed to predict the effect of multiple amino acid changes in the same protein to aid clinical interpretation. Interdisciplinarity: This is a highly interdisciplinary project that involves in silico 3D protein structure modelling and machine learning methods, working with genetic (e.g. clinical, population), biochemical (e.g. AlphaFold) and functional (e.g. MAVEs) datasets. We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagn...
Eligibility
We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagnosis and gene-disease discovery in rare disease - Edinburgh (Marsh): comparing and validating VEPs against different datasets - Imperial (David): designing and developing VEPs using 3D protein structure Methodology: Review and benchmark existing approaches for predicting multiple missense variants, including evolutionary, protein language and 3D structure-based models.
Requirements
We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagnosis and gene-disease discovery in rare disease - Edinburgh (Marsh): comparing and validating VEPs against different datasets - Imperial (David): designing and developing VEPs using 3D protein structure Methodology: Review and benchmark existing approaches for predicting multiple missense variants, including evolutionary, protein language and 3D structure-based models.
Funding and benefits
Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..
How to apply
We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagnosis and gene-disease discovery in rare disease - Edinburgh (Marsh): comparing and validating VEPs against different datasets - Imperial (David): designing and developing VEPs using 3D protein structure Methodology: Review and benchmark existing approaches for predicting multiple missense variants, including evolutionary, protein language and 3D structure-based models.
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Recorded source check: October 28, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..
Eligibility summary
We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagnosis and gene-disease discovery in rare disease - Edinburgh (Marsh): comparing and validating VEPs against different datasets - Imperial (David): designing and developing VEPs using 3D protein structure Methodology: Review and benchmark existing approaches for predicting multiple missense variants, including evolutionary, protein language and 3D structure-based models.
Requirements
We have therefore assembled a team of supervisors with experience in genetics, bioinformatics and protein structure, who provide complementary expertise: - Exeter (Wright, Leslie): applying and assessing VEPs for clinical diagnosis and gene-disease discovery in rare disease - Edinburgh (Marsh): comparing and validating VEPs against different datasets - Imperial (David): designing and developing VEPs using 3D protein structure Methodology: Review and benchmark existing approaches for predicting multiple missense variants, including evolutionary, protein language and 3D structure-based models.
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