Improving design and analyses of gene expression profiling approaches to produce reproducible results for translational and personalised medicine
Supervisors: Dr Chris Jones, Professor Sarah Newbury, Dr Ben Towler
Application deadline: Wednesday 11 June 2025
Funded PhD Project (UK Students Only)
About the Project
We are looking for an enthusiastic and motivated PhD student to join our team at Brighton and Sussex Medical School. The candidate will work closely with researchers with extensive expertise in genetics, molecular and developmental biology, gene expression measurement techniques, data analysis, and statistics (1-4).
Understanding the cellular pathways controlling gene expression are crucial for developing effective therapies and biomarkers in diseases such as cancer and neurodegeneration. The changes in gene expression which accompany proliferative or progressive disease are frequently assessed using techniques such as quantitative PCR (qPCR) and RNA-sequencing (RNA-seq). However, gene expression data from patient material can be extremely variable, due to variations in sample collection/preparation and genetic variation between patients. Moreover, methodologies such as long read (Nanopore) and short read (Illumina) sequencing can have their own biases due to particular chemistries involved. This variability means that it can be difficult to identify the true underlying cellular basis of the disease/mechanism being studied, and/or the biomarkers which are reproducible in prognosis and diagnosis of disease. This means that many published results are not reproducible (5).
The aim of the project is to use experimental techniques as well as existing datasets to understand experimental and biological biases lead to unreproducible results, and how analyses can be conducted robustly. Although the research in this PhD is applicable to many diseases and syndromes, the two diseases to be focussed on are myeloma (a blood cancer) and motor neurone disease (ALS; amyotrophic lateral sclerosis). Focus on these diseases will allow extension of our existing collaborative work and access to patient data and materials. The project will involve performing qPCR and short/long read RNA-seq experiments in the lab using state-of-the-art equipment (Illumina NextSeq and Nanopore PromethION 2 solo), statistical modelling/simulations, as well as bioinformatic analyses. An increasing number of datasets are available in online repositories, and these will be used to assess and compare experimental designs and analyses. The available information will be used to improve design, analysis and presentation of gene expression experiments, including analysing the effects of robust experimental designs for qPCR, RNA-seq and other high-throughput methods. The improved design will then be used to re-analyse existing datasets to discover novel cellular pathways and biomarkers.
The student will receive training in experimental techniques such as qPCR and RNA-seq as well as bioinformatic and statistical analyses and novel computer simulations to model real-world and ideal experimental conditions (in Python/R/Stata). The simulations will involve creating datasets representative of real populations and then drawing samples from these to simulate performing experiments with differing designs. The student will develop expertise in statistical approaches across different research areas, with a unique understanding of biological/patient sample preparation and laboratory techniques. This exceptional and state-of the-art training will put the student in an excellent position to apply for future positions in molecular, clinical, and statistical research areas in NHS or University settings.
The student will be based in the BSMS Primary Care and Public Health department alongside interdisciplinary statisticians and health researchers, and will gain hands-on experience in generating data using the relevant laboratory techniques in the Newbury/Towler labs. They will have the opportunity to join the Sussex RNA and Cancer Research Centres and collaborate with the NIHR Laboratory Studies group. The supervisory team have extensive experience in molecular techniques, statistics, and bioinformatics and work closely with clinical academics studying the genetic basis of cancers such as myeloma and glioma. The research carried out will be of fundamental importance in the increasing use of genomics and personalised medicine for the prognosis and diagnosis of human diseases, including cancer.
Entry requirements
This studentship is suitable for those with a background in lab science or statistics (experience is not required in both). We invite applications from students who have received or are on target to achieve a relevant undergraduate degree with minimum 2:1 classification (or equivalent). Previous laboratory or statistical experience is desirable but not essential.
How to apply
Applicants must apply through the University of Brighton application portal where they can submit a CV and complete the application form. The deadline for applications is 11 June 2025. Interviews will be held 26–27June 2025.
Informal enquiries are welcome and should be submitted to Dr Chris Jones: c.i.jones@bsms.ac.uk.
Funding Notes
This is a 3-year PhD studentship funded by Brighton and Sussex Medical funded, starting on 1st October 2025. Funding will cover tuition fees for UK students (at the Home rate), a stipend at the UKRI rate and a research allowance which will cover research running costs. International applicants are welcome to apply but will be required to cover the difference between Home and International fees.
References
Jones CI, Pashler AL, Towler BP, Robinson SR, Newbury SF (2016) RNA-seq reveals post-transcriptional regulation of Drosophila insulin-like peptide dilp8 and the neuropeptide-like precursor Nplp2 by the exoribonuclease Pacman/XRN1. Nucleic Acids Research, 44 (1). pp. 267-280. DOI: https://doi.org/10.1093/nar/gkv1336
Towler BP, Pashler AL, Haime HJ, Przybyl KM, Viegas SC, Matos, RG, Morley SJ, Arraiano CM, Newbury SF (2020). “Dis3L2 regulates cell proliferation and tissue growth through a conserved mechanism”. PLoS Genetics, 16 (12): e1009297. doi: 10.1371/journal.pgen.1009297.
Pashler AL, Towler BP, Jones CI, Haime HJ, Burgess T, Newbury SF (2021) “Genome-wide analysis of XRN1-sensitive targets in osteosarcoma cells identify disease-relevant transcripts containing G-rich motifs”. RNA. 27(10) 1265-1280. doi 10.1261/rna.078872.121.
Bernard E, Towler BP, Rogoyski OM, Newbury SF (2024). “Characterisation of the in-vivo miRNA landscape in Drosophila ribonuclease mutants reveals Pacman mediated regulation of the highly conserved let-7 cluster during apoptotic processes”. Frontiers in Genetics. doi 10.3389/fgene.2024.1272689.
Ioannidis, JPA (2005) Why Most Published Research Findings Are False. PLoS Medicine 2:e124. https://doi.org/10.1371/journal.pmed.0020124