Combining multiomics to develop new bioinformatics approaches for personalized medicine

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The identification of genes and genetic variants responsible for human disease and the interpretation of their function within a biological network is a crucial step towards personalized medicine and the discovery of novel biomarkers. DNA sequencing projects from individual research groups and the 100K Genomes Project at Genomics England are providing an unprecedented amount of DNA sequencing data that need to be analysed, to derive biologically meaningful information. This task cannot be performed manually and requires computational resources.Recent major developments in the field of 3D protein modeling withAlphaFold, and the unprecedent wealth of information now available on gene expression in human and model organisms, are allowing an expandeduse of multiomics for developing new prediction algorithms that will identify genes and genetic variants, which may disrupt protein function/structureand biological pathways.In this project you will exploit available information on gene expression,protein-protein interaction, and three-dimensional protein structure dataexperimentally determined or modelled by the in-house Phyre2 homologymodeling software and/or the deep learning algorithm AlphaFold. You willintegrate these with multi omics data to develop new robust bioinformaticsalgorithms for predicting the effect of genetic variants and identifying newcandidate genes for human disease. You will apply these new bioinformaticsapproaches to genomic and clinical data to interpret their biologicalrelevance. You will have the opportunity to develop effective visualisationweb tools to make these new algorithms available to the biomedicalcommunity. You will work closely with clinicians to design bespokeapproaches to ensure the effective clinical utilization of these newbioinformatic resources.