Baillie lab, Roslin Institute, University of Edinburgh

Opportunities to join us

If you like what we do, and you think you could contribute, please get in touch. Jobs and PhD studentships become available from time to time and we'll post them here, but if there are good candidates out there then it helps to know about them. We're always looking for talented people - we can help you write fellowship applications or try to find support from other sources.

Funded PhD project opportunity

Data-driven crossvalidation to identify novel therapeutic targets in a diverse range of human diseases.

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.

Example output from integrative crossvalidation, showing shared information content between 43 different experiments. Each experiment is represented by a bar on the outer edge of the circle. Bars are scaled to indicate total information content contributed by that data source.

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).

How to apply

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 (i) Personal statement about their research interests and their reasons for applying, and (ii) CV.

The deadline for applications is Friday, April 06, 2018.

Pairwise coexpression networks derived from GWAS results. Each coloured ball indicates a transcription start region containing a GWAS-associated variant. Red - significantly coexpressed by network density analysis. Light blue - all other transcription region containing GWAS-associated variants for this phenotype. (3d visualisation by vasturiano)

Funded PhD project opportunity

Network analyses to reveal mechanisms of disease, and to develop functional genomics methods for the detection of clinically-relevant disease endotypes.

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[1], 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[2].

This discovery may indicate two distinct mechanisms underlying each disease. Alternatively, it may indicate the existence of two distinct endotypes[3] 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.

  1. 1. Forrest, A. R. R., Kawaji, H., Rehli, M., Baillie, J.K., et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
  2. 2. Baillie, J. K. et al. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease. PLOS Computational Biology 14, e1005934 (2018).
  3. 3. Russell, C. D. & Baillie, J. K. Treatable traits and therapeutic targets: Goals for systems biology in infectious disease. Current Opinion in Systems Biology 2, 139–145 (2017).

How to apply

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.

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