AI Based Prediction of Splice Site Selection (WUT_U26CMP)
About the Project
The central dogma of molecular biology describes the flow of genetic information from DNA to RNA and ultimately to protein. This process is not a simple transcriptional transfer, as precursor messenger RNA (pre-mRNA) undergoes splicing, during which intronic regions are removed and exonic regions are joined to form mature mRNA. Understanding how the spliceosome accurately recognises exon–intron boundaries across eukaryotic genomes remains a fundamental challenge. Numerous computational approaches have therefore been proposed to predict splice sites, demonstrating that complex splicing signals can, to a large extent, be inferred directly from primary DNA sequence alone (1).
Over the past few years, the Wu group has developed deep learning approaches for extracting evolutionary and functional signals from large scale genomic datasets (2). Building on this expertise, this PhD project aims to develop AI based models to predict splicing sites and splicing alterations. In collaboration with Dr Alper Akay (UEA) and Prof. Yiliang Ding (JIC), the project will utilise deep learning and large language models to integrate genomic sequence analysis with RNA sequencing data from wild type and spliceosome mutant Caenorhabditis elegans and human cells (3).
The project is expected to generate new insights into the mechanisms governing pre-mRNA splicing and how their disruption contributes to human disease, including cancer and inherited genetic disorders. Applicants should hold a bachelor’s degree in Computer Science, Biology, or a relevant discipline. Prior biological knowledge is not required, as training will be provided within a collaborative research environment at the Norwich Research Park.
The School of Computing Sciences (https://www.uea.ac.uk/about/school-of-computing-sciences) provides a vibrant research environment for conducting Computing and allied research and training. We collaborate with multi-national companies such as Apple, BT, the National Trust and Aviva, research institutes in the Norwich Research Park (https://www.norwichresearchpark.com), as well as other universities and industries in the UK and overseas. We are also members of the Turing University Network, a group of 65 UK universities working together to advance world-class research and build skills for the future.
The successful candidate will also be expected to contribute to Tutor activities for laboratory support on our BSc and MSc Courses in Artificial Intelligence, Data Science, Computing Sciences and Cyber Security commensurate with their core expertise, within the working hours permitted for full-time Postgraduate Researchers.
1. Jaganathan et al. (2019). Predicting splicing from primary sequence with deep learning. Cell.
2. Winder et al. (2025). Environmental adaptations in metagenomes revealed by deep learning. BMC Biology.
3. Shen et al. (2024). U6 snRNA m6A modification is required for accurate and efficient splicing of C. elegans and human pre mRNAs. Nucleic Acids Research.
Funding Notes
This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees, an annual stipend for 3 years and a research training support budget.
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