Research Areas
The applied bioinformatics research group works in several areas associated with second generation sequencing and assembly, genetic and genomic variation analysis and the discovery and application of molecular genetic markers. We work with a range of species, mostly in collaboration with a broad range of national and international research groups.
Genomic Sequencing
One of our main research areas is the genomic sequencing of key agricultural species. This foundational work underpins a range of downstream analyses, including phylogenetic analysis, pangenome construction and annotation, and trait association studies. For making this data available to other researchers, we maintain a number of different sites relating to the below species:
This list is not exhaustive; we carry out genome sequencing on a variety of other plants and animal species.
Machine learning in Plant Biology
Deep learning is a rapidly evolving technique with the ability to automatically extract features and analyse complex multi-dimensional datasets with high scalability to big data.
It can be applied to a wide variety of plant phenotyping tasks, such as the identification of abiotic stress signs caused by climate fluctuations, helping improve crop monitoring and breeding aspects.
Climate change has affected the yield of several major crops worldwide, for example as temperature rises, wheat yield is expected to reduce 5-6% per 1 °C of temperature increase. These environmental changes will cause significant abiotic stress to crops causing yield and quality decline, urging crop breeders to develop better adapted varieties in order to withstand the abiotic stresses.
Pan-Genome Generation
Our research explores the power of pan-genomics to deepen our understanding of plant biology and evolution.
By analysing the full spectrum of genetic diversity across multiple individuals of a species, we move beyond a single reference genome to uncover structural variations, gene presence/absence patterns, and lineage-specific adaptations.
This approach allows us to identify key genetic factors involved in stress tolerance, disease resistance, and crop performance. Leveraging pan-genome data helps us build more accurate models of gene function and paves the way for more informed breeding strategies aimed at enhancing plant resilience and productivity.
Genome annotation and visualisation.
Our research focuses on gene annotation and visualization in plants to enhance our understanding of genome structure and function.
Accurate gene annotation is critical for interpreting plant genomes, identifying functional elements, and linking genes to phenotypes. We develop and apply computational pipelines to refine gene models, integrate transcriptomic evidence, and predict functional domains.
Alongside this, we design intuitive visualization tools to explore complex genomic features and relationships across plant species. These efforts support downstream analyses in plant biology and breeding by making genomic data more accessible, interpretable, and actionable.