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PhD studentship in Machine learning and Network analysis applied to Single-Cell and Population Genomics

   MRC London Institute of Medical Sciences (LMS)

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  Dr M Spivakov, Dr N Skene  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

This computational PhD project will leverage single-cell genomics, machine learning and formal network analysis methods to interpret data arising from genome-wide association studies (GWAS). These studies detect genetic variants on the DNA (“mutations”) that distinguish people with a specific disease from the control population. However, GWAS findings remain difficult to interpret, because the majority of the identified variants are not within genes, and because multiple weak signals need to be integrated in a meaningful way to understand the underlying disease mechanisms. 

GWAS interpretation can be significantly improved using prior knowledge about the function of the genomic regions containing GWAS signals and their functional relationships with each other. To address this, the proposed project will leverage single-cell multiomics data, 3D chromosome conformation capture data and the network analysis approach. The proposal is to use these data to reconstruct ‘cis-regulatory’ networks underpinning specific biological processes aided by machine learning, then map the locations of genetic variants onto these networks and interrogate them jointly using state-of-the-art network analysis methods.

The focus will be on autoimmune diseases and data from single-cell multiomics assays in human primary monocytes stimulated with cytokines. Predictions from the analyses will be validated experimentally in collaboration with wet-lab researchers in the host lab and through our clinical collaborator. The computational methodologies arising from this project will be implemented in publicly available software tools, enabling their application in a broad range of other biological systems and associated diseases.

The project will provide ample training opportunities in single-cell genomics, epigenomic data analysis, epigenomic and GWAS data integration, and network analysis. Some experience in genomics data integration and/or network analysis is however desirable. The student will join the collaborative and interdisciplinary environments of the two host labs based on Imperial College’s White City/Hammersmith Hospital campus in West London, working alongside computational and experimental researchers. 

To apply, please visit the link below;

Funding Notes

The funding for this studentship includes full (Home rate) tuition fees for 3.5 years as well as a generous stipend amounting to £21,000pa paid directly to the student for the same duration.
Please note that we are only able to accept applications from Home Fee rate students, which includes students with settled status or pre-settled status covering the duration of the whole studentship (until June 2026).


Relevant review articles:
1. Cano-Gamez E and Trynka G. From GWAS to function: Using functional genomics to identify the mechanisms underlying complex diseases. 2020. Front Genet, 11:424.
2. Leiserson MDM et al. Network analysis of GWAS data. 2013. Curr Opin Genet Devel, 23:602-610.
3. Cowen et al. Network propagation: a universal amplifier of genetic associations. 2017. Nature Rev Genet, 18:551-562.
4. Ray-Jones H and Spivakov M. Transcriptional enhancers and their communication with gene promoters. 2021. Cell Mol Life Sci, 78:6453-6485.
Relevant research papers from the supervisors:
1. Javierre et al. Lineage-specific genome architecture links enhancers and
non-coding disease variants to target gene promoters. 2016. Cell 167: 1369-1384.
2. Skene et al. Genetic identification of brain cell types underlying schizophrenia. 2018. Nat Genet, 50:825-833.
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