Computational methods for annotation and analysis of RNA splicing in development and disease

Download files
Access & Terms of Use
open access
Copyright: Signal, Bethany
Altmetric
Abstract
RNA splicing is a key regulatory mechanism required for correct processing of multi-exonic genes. Through alternative splicing, it also enables diversification of information encoded by a single gene and acts as an additional layer of regulatory control. Despite technology and software now allowing splicing to be quantified in concert with gene expression, splicing is rarely investigated, due in part to difficulties in interpretation of differential splicing events. In addition, splicing is rarely investigated in clinical variant annotation pipelines, despite an estimated 15% of genetic diseases caused through alterations to splicing. Chapter 2 addresses the current lack of annotation of a core splicing element — the branchpoint. We use experimental annotations to develop a machine-learning model which expands annotations from covering 17% to 85% of human introns, and show that branchpoint identity and number are related to splicing patterns. Chapter 3 addresses gene expression and splicing dynamics in multiple biological contexts. We show that splicing is indeed a dynamically regulated process involved in the control of cellular responses, although more loosely controlled and affecting a different subset of genes than differential gene expression. This work highlighted the need for interpretive tools to discern which events are capable of producing a functional change to gene products and to characterise such changes. In Chapters 4 and 5 we developed methods to simulate the consequences of alternative splicing events in silico and provide automated comparisons of transcript isoforms. Through application of these tools, we showed that splicing affects different gene sets in different manners, and can aid in the interpretation of such results. Lastly, in Chapter 6, we use RNA-Sequencing to identify splicing variants of clinical relevance. Using methods developed in the previous chapters, we identify genetic variants that fall at splice elements, quantify splicing to identify aberrant splicing events, and characterise the effects these may have on transcripts — leading to the identification of a causal variant in 1/5 cases. Together, the work presented in this thesis comprises a significant advance in the way that splicing is investigated, and illustrates the importance of exploring splicing patterns to better understand development and disease.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Author(s)
Signal, Bethany
Supervisor(s)
Dinger, Marcel
Gloss, Brian
Creator(s)
Editor(s)
Translator(s)
Curator(s)
Designer(s)
Arranger(s)
Composer(s)
Recordist(s)
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
2019
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
Files
download public version.pdf 17.95 MB Adobe Portable Document Format
Related dataset(s)