Exemplar Project:

Genomic data analysis is a major contributor to the turnaround time for genomic tests, with significant backlogs, particularly in routine Whole Genome Sequencing (WGS). The analysis of large volumes of complex genomic data is challenging and there is an opportunity to improve efficiencies through implementation of new bioinformatics techniques that could help alleviate the manual curation effort.

Project Overview  

This proof-of-concept project aims to leverage AI through the development and evaluation of GenePy, a tool designed to simplify genomic data by assigning a single pathogenicity score to each gene per individual. By doing so, the project seeks to enhance diagnostic accuracy, reduce manual curation, and accelerate processing times. Key deliverables include optimising the GenePy pipeline for large-scale use, testing its impact on retrospective and prospective genomic data, benchmarking against current methods, and exploring broader adoption with commercial partners.

National benefit

The evaluation of GenePy will lead to an increased understanding of the benefits and challenges of using AI in Genomic analysis. This project aims to optimise the code for the GenePy algorithm on massive data and package the pipeline in an accessible workflow for wider application and deployment across a wider NHS audience. 

For more information, contact the team:

Sarah Ennis, Project Lead – s.ennis@soton.ac.uk