Transcriptional Noise as a Biomarker of Healthy Ageing: A Genome Informatics Approach

About the Project

According to the central dogma of molecular biology, genetic information flows from DNA to RNA to protein in a precise and regulated manner. However, this process is far from perfect. We now understand that transcription—the conversion of DNA information into RNA—is inherently noisy, particularly in complex organisms such as humans. Errors in transcription, together with post-transcriptional events such as RNA splicing and RNA editing, can introduce significant variability in the transcriptome.

Emerging evidence suggests that this transcriptional noise is not merely random background activity but may carry biological meaning, especially in the context of ageing. Different forms of transcriptional noise—such as aberrant splicing, RNA editing errors, and fusion transcript formation—may reflect cellular stress, decline in molecular fidelity, or compensatory adaptive mechanisms associated with the ageing process.

Our group has previously demonstrated that aberrant fusion transcripts accumulate in the human brain with age, and that such signatures could potentially serve as biomarkers of healthy ageing. Building on these insights, this PhD project aims to systematically characterise different types of transcriptional noise across the human lifespan using large-scale transcriptomic datasets.

The student will employ genome informatics and computational transcriptomics approaches to:

• Quantify different forms of transcriptional noise (e.g. splicing errors, editing variability, chimeric transcripts) across tissues and age groups;
• Explore associations between noise patterns and known hallmarks of ageing;
• Identify potential transcriptional noise signatures that could serve as biomarkers of healthy ageing.

This project is ideal for candidates with an interest in computational biology, RNA biology, and the molecular mechanisms of ageing. The work will primarily involve computational analysis of publicly available genomic and transcriptomic data (e.g. GTEx, ENCODE, and ageing cohorts), and there will be opportunities to collaborate with a wide network within academia and industry.

Applicants should have a strong background in genetics, molecular biology, bioinformatics, or a related discipline. Experience with programming (e.g. Python, R, or similar) and data analysis will be advantageous. The ideal candidate will be someone who can work independently, is able to generate ideas and willing to take a lead role in establishing analysis pipelines using existing open access tools.

This research will contribute to our understanding of how transcriptional fidelity changes with age and whether this variability can act as a predictive marker for healthy ageing. The findings could inform novel approaches for early detection of age-related decline and contribute to the broader field of Precision Health for healthy living.

Supervisor

Professor Arijit Mukhopadhyay