Funded PhD studentship in XFEL Science
Combining multiomics to develop new bioinformatics approaches for personalized medicine
Applications are invited for a prestigious EPSRC research studentship leading to the award of a PhD degree, under the supervision of Dr. Alessia David (Dept of Life Science) and Prof Michael Johnson (Dept of Medicine), to develop new bioinformatics approaches for the identification of new genes and genetic variants responsible for human disease. The post is supported by a bursary and fees (UK student rate only) funded by the EPSRC. The duration of the studentship is 36 months, starting October 2023.
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 with AlphaFold, and the unprecedent wealth of information now available on gene expression in human and model organisms, are allowing an expanded use of multiomics for developing new prediction algorithms that will identify genes and genetic variants, which may disrupt protein function/structure and biological pathways.
In this project you will exploit available information on gene expression, protein-protein interaction, and three-dimensional protein structure data experimentally determined or modelled by the in-house Phyre2 homology modeling software and/or the deep learning algorithm AlphaFold. You will integrate these with multi omics data to develop new robust bioinformatics algorithms for predicting the effect of genetic variants and identifying new candidate genes for human disease. You will apply these new bioinformatics approaches to genomic and clinical data to interpret their biological relevance. You will have the opportunity to develop effective visualisation web tools to make these new algorithms available to the biomedical community. You will work closely with clinicians to design bespoke approaches to ensure the effective clinical utilization of these new bioinformatic resources.
The Department of Bioinformatics in the Department of Life Sciences at Imperial College has strong expertise in developing robust bioinformatics resources for the biomedical community, e.g. the Missense3D prediction algorithm suite for the interpretation of genetic variants (> 8000 users and > 250 citations since its release in 2019) and the Missense3D-DB database, a large catalog of precomputed predictions for 4 million human genetic variant, which is included in DECIPHER at the European Bioinformatics Institute. The group lead by Prof Michael Johnson, Department of Medicine, has extensive expertise in using cell transcriptomics for the identification of new genetic mechanisms underlying neurological disorders.
You will receive training in the use of genomics, proteomics and 3D protein structures to develop new bioinformatics resources to analyze genetic patient data.
Requirements and eligibility
The studentship provides 3 years of funding, starting October 2023. Applicants should have a BSc honours degree (at least 2.1 or equivalent) in computing, physics, applied mathematics, computational biology, or bioengineering with additional interest in biology. Applicants with a Masters degree (at Merit level or better) in addition to the BSc will be given preference. Interdisciplinarity may be given preference. Enthusiasm and self-motivation are essential.
Funding provides full support for tuition fees for the three-year duration of the studentship, and an annual tax-free stipend of approx. £19,000 per year.
How to apply:
Please direct informal enquiries and requests for further information to Dr. Alessia David (email@example.com). Please email a single PDF file including: a brief cover letter describing your relevant interests and research experience, your C.V. and names and contact information of three referees. Applications will be considered as they are received, so early applications are encouraged.