Baillie lab, Roslin Institute, University of Edinburgh
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Supervisors: Dr JK Baillie, Dr M Gutman (School of Informatics), Prof R Fitzgerald (Microbiology)
A common problem in modern biology is combining data, in the form of lists of genes or proteins, from a range of different experiments conducted in different laboratories and under different conditions. In the post-genome era there is an unmet need for a simple, intuitive method to cross-validate and merge different sources of data in order to prioritise targets for future study.
This project will build on a novel method to combine data from disparate sources by sytematically evaluating relevant information content in each source. The approach works by iteratively evaluating a weighting factor for each data source. This weighting is determined by how frequently the results of each experiment is replicated in all of the other experiments.
In this cross-disciplinary project you will develop and improve this method, putting into practice some existing plans and your own ideas, and then systematically evaluate the method by comparing it with real and permuted data. The method will be made publicly available as downloadable software from github, and through a web interface hosted by the Roslin Institute.
Concurrently with improving and optimising the method, you will apply it to prioritise gene and protein targets for therapy in a range of applications, including host macrophage responses to life-threatening infection, development of new antibiotics to target specific bacteria, and other applications.
You will work between two world-class research centres at the University of Edinburgh: the Roslin Institute and the School of Informatics. You will be jointly supervised by Ken Baillie (translational genomics, sepsis, intensive care medicine), Michael Gutmann (machine learning, multidimensional data analysis) and Ross Fitzgerald (microbiology, antimicrobial resistance).
The studentship will be awarded competitively. Applicants should hold at least an upper second class degree or equivalent in a relevant discipline (eg informatics, mathematics, computer science, statistics). Applicants should submit the following documents to email@example.com: (i) Personal statement about their research interests and their reasons for applying, and (ii) CV.
The deadline for applications is Friday, April 06, 2018.
Supervisors: Dr JK Baillie and Prof C Haley (Institute for Genetics and Molecular Medicine)
This project has been selected for a funded PhD position on the prestigious MRC Precision Medicine Doctoral training programme. This offers a 4-year funded PhD with individually-tailored taught components in data science, statistics, and biology.
In this cross-disciplinary project, you will build on an extensive program of work to create and apply analytic methods that translate disease-associated genetic variants into meaningful biological understanding. You will apply these methods to a very broad range of disease processes and other phenotypes, with a primary focus on poorly-understood life-threatening processes such as influenza and sepsis.
Building on previous work in which we demonstrated that many biological pathways can be detected from expression patterns in high-resolution transcriptomic data, we have shown that GWAS hits for a given disease tend to be near promoter/enhancer elements with similar expression profiles, which enables us to find more hits, fine map probable causative SNPs, and implicate cell types in pathogenesis. Surprisingly, we also observe several separate groupings among the promoter/enhancer elements associated with some diseases.
This discovery may indicate two distinct mechanisms underlying each disease. Alternatively, it may indicate the existence of two distinct endotypes of each condition. It is likely that this discovery will have direct therapeutic relevance - that is, that patients with a preponderance of genetic variants in one group will respond differently to specific therapies.
Please click here to apply to the MRC Precision Medicine PhD programme.
The deadline for applications is 5pm on Monday 16th April 2018.
We are very grateful to recieve funding from the following sources: Wellcome Trust, BBSRC, Intensive Care Society. MRC, NIH.