The Hughes group is interested in how mammalian genes are regulated and how their deregulation is linked with human disease. The ~22 thousand genes in the mammalian genome are present in the DNA of every cell but are used in complex patterns in different cell types and organs. This system to turn genes off or on, modulating their levels of activity in different cell types is central to maintaining the complex biological system that is a multicellular organism.
What has become clear from large-scale genetic studies of human predisposition to common disease is that it is the control of the use of genes, rather than the genes themselves, that is frequently damaged. It is now known that functional elements other that genes exist in our DNA and these elements act as molecular switches which interact with the genes and control their use, however the mechanisms involved are not well understood. The Hughes group integrates both bench technologies and computational approaches to try and understand how these regulatory switches or enhancer elements work and how variations in their activity in our genomes leads to increased risk of developing common diseases, such as anemia, cancer, diabetes and autoimmune diseases.
How human genome variation affects gene expression? This project will use variants of both general and cutting edge genomics methods such as ATAC-seq, ChIP-seq, RNA-seq and Capture-C and computational analysis to understand the principles of how population variation in the human genome affect gene regulation and predisposes to common human diseases. As part of this project the student will be given bioinformatics training to analyse their own data and will learn to perform theses genomics methods on human samples, recalled by genotype.
It is currently unclear how the function of an enhancer is encoded into DNA sequences and how this combination of DNA sequence confers ability to regulate genes and the tissue specificity of that function. This project will combined convoluted neural network machine learning, synthetic biology and genome engineering approaches to unpick this code. As part of this project the student with learn coding and bioinformatics, advanced machine learning techniques and genome engineering approaches.
This project is focused on how gene regulation is deregulated and coopted in cancer cells. The regulatory landscape of cancer cells is very different from the non-malignant cells that they derive from and it is these processes that drive changes in the behavior of the cells that produce malignancy and bypass the protective mechanisms that would normally destroy them. This joint project with the Milne lab will use cutting edge epigenetic assays, bioinformatics, machine learning and genome engineering approaches to interrogate how these processes and altered in cancer and reform the regulatory landscape of the cancer genome. As part of this project the student with learn how to perform genomics assays, bioinformatics, machine learning techniques and genome engineering approaches.
All member of the Hughes group are encouraged to learn bioinformatics and develop coding skills. Students will be expected and supported in learning the skills to analyse their own results. Students from a computational background will get the opportunity to get very close to and even participate in the generation of the data and so gain a real understanding of the types of data the will be using in the projects. The Hughes group is expert in next generation sequencing based genomics assays and also develops such assays when needed. They have a long history in genome engineering approaches and now use a variety of CRISPR/Cas9 based approaches in their work.
As well as the specific training detailed above, students will have access to high-quality training in scientific and generic skills, as well as access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.
The Department has a successful mentoring scheme, open to graduate students. We hold an Athena SWAN Silver Award.
Funding for this project is available through the RDM Scholars Programme or the WIMM Prize Studentship. Both programmes offer funding to outstanding candidates from any country. Successful candidates will have all tuition and college fees paid and will receive a stipend of £18,000 per annum.
Applications must be received, including all relevant supporting materials, by Friday 11th January 2019 at 12 noon (midday).
Please visit our website for more information on how to apply.
Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints. Schwessinger R, Suciu MC, McGowan SJ, Telenius J, Taylor S, Higgs DR, Hughes JR. Genome Res. 2017 Oct;27(10):1730-1742.
Robust detection of chromosomal interactions from small numbers of cells using low-input Capture-C. Oudelaar AM, Davies JOJ, Downes DJ, Higgs DR, Hughes JR. Nucleic Acids Res. 2017 Dec 15;45(22):e184.
How best to identify chromosomal interactions: a comparison of approaches. Davies JO, Oudelaar AM, Higgs DR, Hughes JR. Nat Methods. 2017 Jan 31;14(2):125-134.
Genetic dissection of the α-globin super-enhancer in vivo. Hay D, Hughes JR, Babbs C, Davies JOJ, Graham BJ, Hanssen L, Kassouf MT, Marieke Oudelaar AM, Sharpe JA, Suciu MC, Telenius J, Williams R, Rode C, Li PS, Pennacchio LA, Sloane-Stanley JA, Ayyub H, Butler S, Sauka-Spengler T, Gibbons RJ, Smith AJH, Wood WG, Higgs DR. Nat Genet. 2016 Aug;48(8):895-903.
Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Davies JO, Telenius JM, McGowan SJ, Roberts NA, Taylor S, Higgs DR, Hughes JR. Nat Methods. 2016 Jan;13(1):74-80.
A tissue-specific self-interacting chromatin domain forms independently of enhancer-promoter interactions. Brown JM, Roberts NA, Graham B, Waithe D, Lagerholm C, Telenius JM, De Ornellas S, Oudelaar AM, Scott C, Szczerbal I, Babbs C, Kassouf MT, Hughes JR, Higgs DR, Buckle VJ. Nat Commun. 2018 Sep 21;9(1):3849.
How good is research at University of Oxford in Clinical Medicine?
FTE Category A staff submitted: 238.51
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