Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

£3.6 million in funding awarded by the Wellcome Trust to combine cutting-edge 3D genome technologies with machine learning approaches to decipher the role of the non-coding genome in disease.

This week it was announced that Professors Jim Hughes and James Davies from the MRC Molecular Haematology Unit, in collaboration with Professor Cecelia Lindgren, Director of the Big Data Institute, have secured a Wellcome Discovery Award to expand their work on the genetics of common disease.

The prestigious 5-year grant aims to support ‘established researchers and teams from any discipline who want to pursue bold and creative research ideas to deliver significant shifts in understanding that could improve human life, health and wellbeing’.

“This Wellcome discovery award funds a multidisciplinary team from the WIMM and BDI to use cutting edge molecular, computational and machine learning approaches to map and model the regulatory wiring of the human genome at unprecedented detail.  Our aim is to make the 98% of the human genome that is non-coding as interpretable as the well-characterised 2% that encodes for protein.

This will ultimately allow us to decode the genetics, genes and pathways associated with the common diseases that affect us all and facilitate the development of genetically guided therapies in the future” says Professor Jim Hughes.

Their approach will train Artificial Intelligence algorithms on super-resolution maps of functional interactions between coding genes and their regulatory “switches” buried in the non-coding genome. These algorithms will then be able to determine the effect of sequence differences found in all our genomes on the activity of these genes and their links to human disease.

The researchers aim to use Micro Capture-C and Deep Neural Network Machine Learning to analyse vast quantities of genetic data. Micro Capture-C offer base-pair resolution maps showing the networks of genes and gene regulators in different types of cells. The power of this approach was demonstrated last year when the researchers uncovered gene regulatory regions associated with increased risk of death from COVID-19.

This project will make large steps towards mapping and deciphering the cell type-specific grammar and language of the non-coding genome, to understand the biology of common diseases and also for rare diseases for which there is no known molecular basis.

 

 

We want to hear about your news!

Publishing a paper? Just won an award? Get in touch with communications@rdm.ox.ac.uk

 

Similar stories

New study reveals role of lymphatic system in bone healing

Bones were thought to lack lymphatic vessels, but new research from the Kusumbe Group published in Cell not only locates them within bone tissue, but demonstrates their role in bone and blood cell regeneration and reveals changes associated with aging.

Anjali Kusumbe receives Women in Cell Biology Early Career Medal

Founded in 2015 to mark the 50th anniversity of the founding of the British Society for Cell Biology, the award recognises outstanding early career biologists.