PhD Studentship: Project title: Evolutionary Dynamics of Transposable Elements in the Genome Eecosystem of Eukaryotes

Transposable elements (TEs) are small pieces of DNA that move (transpose) and replicate their sequences independently of typical cell division during mitosis and meiosis. As a result of their activity over evolutionary time, TEs comprise the majority of eukaryotic DNA, for example ~50% of the human genome and up to 80-90% of the genome of plants. Initially considered as junk DNA, it is now known that TEs fundamentally shape genome function and evolution by affecting gene expression, rewiring regulatory networks, and driving change in chromosome size and structure.

In recent years, research on TEs has accelerated with long-read sequencing and the exponential release of high-quality genomes, allowing comparisons at genome-wide level across the Tree of Life. In this project and by focusing on plants, the student will explore for the first time the relationship between different TE classes on the ‘genome ecosystem’ of their plant hosts at scale. TEs, like any other group of organisms, have a complex phylogenetic classification, and we aim to elucidate cases of conflict or cooperation as these arise when different TEs colonise the various niches of the genome, e.g. gene-rich regions, heterochromatic areas, or centromeres, and how this impacts genome function itself. Centromeres drive chromosome segregation during cell division, and are of particular interest because they form hotspots of TE invasion. How TEs invade centromeres is poorly understood, so we will analyse the sequence and folding of key TE genes across a large number of centrophilic and centrophobic TEs, aiming to pinpoint what drives convergent and recurrent centrophilia across TEs and hosts. The insights of this project are fundamental and central to further our understanding of the function and evolution of eukaryotic genomes.

This project will rely heavily on computational analyses, so experience on bioinformatics/informatics is required. The student will receive extensive training in big-data analysis, genome-wide/species-wide computational approaches, and the art of data visualization in complex but meaningful ways. The student will become an expert in using programming languages like Python and R. We work closely with highly successful groups in UK and abroad, and the student will participate in these meetings as normal. The student will regularly present their findings to the group and at scientific conferences.

Interested students can contact Dr Bousios directly via email (ab35@sussex.ac.uk) providing a CV and a cover letter explaining their interest and motivation in the project.

Advert information