A data-driven approach for uncovering translational signatures of short open reading frames using ribosome profiling and RNA-Seq data

Yu, Boyu (2026) A data-driven approach for uncovering translational signatures of short open reading frames using ribosome profiling and RNA-Seq data. PhD thesis, University of Glasgow.

Abstract

Upstream open reading frames (uORFs) are emerging as critical regulatory elements in gene expression, influencing diverse cellular processes and disease mechanisms. Despite their significance, current ORF detection workflows are inadequate for capturing uORFs due to several limitations: most existing tools are optimised for long, main coding regions, often overlook non-AUG initiation, and fail to accurately identify uORFs that are short and exhibit low ribosome occupancy. This thesis addresses these challenges by developing an advanced computational workflow specifically tailored for the detection and characterisation of uORFs using ribosome profiling (Ribo-Seq) and RNA sequencing (RNA-Seq) data.

The workflow standardises the raw sequencing data pipeline, enhances the detection rate of translated ORFs, and constructs a feature matrix for detailed uORF characterisation. It also includes a series of downstream analyses to investigate uORF functionality within experimental setups. Using this workflow, significant findings were uncovered, including codon usage biases embedded within uORFs across different cell cycle-related gene sets, homologous uORF sequences, and the potential regulatory effects of uORFs on downstream coding sequences (CDSs).

This research not only improves the accuracy of uORF detection but also provides deeper insights into their regulatory roles, bridging key gaps in current genomic annotations. The developed framework advances our understanding of non-canonical translation and offers valuable tools for future studies exploring the impact of uORFs in gene regulation and disease pathogenesis.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from Cancer Research UK (CRUK).
• Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Cancer Sciences
• Funder's Name: Cancer Research UK (CRUK)
• Supervisor's Name: Miller, Professor Crispin and Bushell, Professor Martin
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86143
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 17 Jul 2026 14:02
• Last Modified: 17 Jul 2026 14:04
• URI: https://theses.gla.ac.uk/id/eprint/86143