Evolution, systems and genomics
This domain is concerned with the understanding of gene and genome function, structure, sequence variation and long-term evolution.
There are 130 research groups associated with the domain, with almost 700 researcher members and approximately £50 million in live grant funding at any one time.
The domain encompasses the broad scope of genetic diversity from viruses and bacteria, fungi and protozoans, plants and animals – including humans. Genetic and genomic work includes neonatal testing through the women and children domain of the Manchester Academic Health Science Centre (MAHSC) and working with data from millions of completed genomes. We are also closely linked with the Manchester Centre for Genomic Medicine.
The domain is not only concerned with basic science. It also considers the practical applications of this understanding as it relates to issues of global importance such as the spread of antibiotic resistance, the impact of human genetic variation, and the underlying causes of disease.
Methodological approaches range from studies on the control and functions of individual genes to models of how whole systems operate and evolve. We use highly interdisciplinary experimental, statistical and computational methods. We use massively parallel computing infrastructures for genome assembly, annotation and analysis, and develop algorithms and computer code for data analytics.
Our three priority areas are:
- Gene discovery and diagnosis
- Research to improve clinical care
- Implementation of novel therapeutics
- Education and training
Dynamic evolutionary systems
- Molecular epidemiology
- Anti-microbial resistance
- Organismal behaviour and evolution
- Protein structure evolution
- Secondary metabolite evolution (including directed evolution)
- Evolutionary medicine
- Big data analysis
- Software of the future for biomedicine and biology
- Bioinformatics for cancer
- Bioinformatics for single cell sequencing
- Algorithm development
- Bioinformatics for diagnostics
- Integration of bioinformatics with patient records