Inferring temporal dynamics of gene expression from RNA editing profiles

Thesis

Human gene expression involves the continuous synthesis and decay of mRNA. The expressed genes of a cell at any given point in time can be measured by RNA sequencing (RNA-seq). However, RNA-seq only provides an instantaneous snapshot of the state of the cell and dedicated experimental protocols are required to measure the synthesis and decay kinetics. In this thesis, I develop new computational methods of Endogenous RNA Age (ERA) that extract temporal information from RNA-seq data. By measuring the underlying rate of A-to-I editing at all editing sites in the transcriptome, I show that these rates are robust, and that A-to-I edits encode the ages of human transcripts. The mean age of a population of transcripts can be estimated from short-read RNA-seq data, whilst long-read data can be used to further infer the ages of individual transcripts. Crucially, in contrast to current metabolic labelling methods, ERA requires no dedicated experimental protocols provided that the editing rates are known. I show that transcripts ages provide information on the kinetics of mRNA decay and - when combined with abundance measurements - on synthesis rates and past changes to transcription. This thesis presents the first characterisation of endogenous A-to-I editing rates in human cells and new methods for measuring mRNA kinetics from bulk and single-cell RNA sequencing data.

Authors: Bayne, JAC

• DOI: 10.5287/ora-b7bwmnzzk
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Subjects: Monocytes; RNA editing; Computational biology