Machine-learning Analysis and Modelling of Histone Chaperone Function

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

This project aims to elucidate the mechanisms by which histone chaperones function. In the process of transcription (in which DNA is transcribed into messenger RNA by RNA polymerases) histone-comprised nucleosomes must be evicted before polymerase can advance along the DNA and replaced in their original location when polymerase has passed. The chaperoning process is required to be highly accurate as the nucleosomes can carry signals whose positions must be maintained. Aberrant nucleosome signals and misregulated transcription are hallmarks of many diseases including Alzheimer’s and cancer, making the study of these processes highly relevant to medical science, in addition to its fundamental relevance to biology.

Despite decades of study, the mechanistic details of the process remain poorly understood. Sophisticated experimental techniques that allow for the measurement of histone chaperone positions on the DNA are available and intriguing patterns of chaperone occupation have been observed1, manifesting as sawtooth shapes in plots of chaperone location versus extent of protection of DNA. In a previous collaboration1, a combination of mathematical modelling and experimental chaperone revealed an “inchworm” mechanism in which histone chaperones load onto a nucleosome and then extend to the next one before retracting. However, that study (along with many others in the field) assumed that the action of histone chaperones was identical at all units of transcription and the average over all observed patterns was used in the analysis.

One key reason for the use of average patterns is that measurements on individual transcription units tend to be quite noisy. This makes identification of distinct behaviours a significant challenge. Preliminary work undertaken as part of MSc projects, and working on a dataset reduced to look only at locations of chaperone occupancy and not extents, hinted that the patterns seen on average could be formed from several distinct patterning behaviours. The chaperone occupancy datasets will be taken from their raw form and reprocessed to allow study of individual genes with occupancy and extent. Machine-learning methods, including k-means clustering, will be employed to test the hypothesis that patterns can be delineated subgroups of units with sufficiently stratified behaviour such that the averages of the subgroups can be used to investigate distinctions in the mechanisms of histone chaperones by class of transcriptional unit.

Once distinct groups have been identified, histone chaperone occupancy patterns will be compared to additional transcription data (including NET-seq nascent transcription measurements and ChIP-seq histone modification measurements) using further machine-learning methods, including random-forest models, to reveal which additional factors could be playing a role in the function of the chaperones. Mechanisms of histone chaperone function will be modelled with stochastic processes and fit to the pattern subgroups to elucidate the most likely underlying mechanisms. Alongside the direct benefit to the understanding of histone chaperone function, this project will provide insight into the use of machine-learning methods in this kind of biological data, something that will be of significant use to the field.

Informal enquiries can be made by contacting Dr A Angel (andrew.angel@abdn.ac.uk).

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in physics, mathematics, bioinformatics or related discipline.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Degree of Doctor of Philosophy in Physics to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the project.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts .

Please note: you do not need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk