Machine learning in transcriptome analysis using long RNA sequencing data
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Hafezqorani, Saber
issued: 2024-07-29T20:19:41Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The advent of long-read RNA sequencing technologies has advanced transcriptomic research by enabling the sequencing of entire transcripts. This advancement holds the promise of uncovering novel insights into the complex nature of eukaryotic transcriptomes, characterized by phenomena such as alternative splicing and intron retention. However, the vast amounts of data generated by these technologies necessitates the development of sophisticated tools for effective data analysis. This thesis addresses these challenges through two main objectives: the development of a novel simulator for long-read RNA sequencing data, capable of accurately mimicking transcriptome-specific features and the application of deep learning techniques for the robust analysis of RNA sequencing data. The first objective addresses the need for a simulator tailored specifically for RNA sequencing data from Oxford Nanopore Technologies, aiming to create a tool that not only generates reads mimicking real sequencing outputs, but also incorporates critical transcriptomic features such as expression levels and intron retention events. This simulator serves as a resource for the development and refinement of transcriptome analysis tools, offering a cost-effective alternative to extensive sequencing experiments to generate ground-truth data for benchmarking. The second objective explores the potential of deep learning in transcriptomics, focusing on the development of a nucleotide sequence embedding method. It aims to capture the complex, long-term dependencies within sequences, a task that has proven challenging due to the variable length and intricate nature of RNA sequences. By leveraging deep learning's capacity to learn feature representations implicitly, this research seeks to enhance the accuracy and efficiency of sequence classification and clustering tasks within transcriptomic studies.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88727
note: MACHINE LEARNING IN TRANSCRIPTOME ANALYSIS USING LONG RNA SEQUENCING DATA by Saber Hafezqorani M.Sc., Middle East Technical University, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 © Saber Hafezqorani, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Machine learning in transcriptome analysis using long RNA sequencing data submitted by Saber Hafezqorani in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Inanc Birol, Professor, Medical Genetics, University of British Columbia Supervisor William Hsiao, Associate Professor, Health Sciences, Simon Fraser University Supervisory Committee Member Angela Brooks-Wilson, Professor, Medical Genetic, University of British Columbia University Examiner Judith Mank, Professor, Zoology, University of British Columbia University Examiner Additional Supervisory Committee Members: Faraz Hach, Assistant Professor, Urological Sciences, University of British Columbia Supervisory Committee Member Robert Holt, Professor, Medical Genetics, University of British Columbia Supervisory Committee Member iii Abstract The advent of long-read RNA sequencing technologies has advanced transcriptomic research by enabling the sequencing of entire transcripts. This advancement holds the promise of uncovering novel insights into the complex nature of eukaryotic transcriptomes, characterized by phenomena such as alternative splicing and intron retention. However, the vast amounts of data generated by these technologies necessitates the development of sophisticated tools for effective data analysis. This thesis addresses these challenges through two main objectives: the development of…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0444844
language: eng
degree discipline: Bioinformatics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Birol, Inanc
type: Text
identifier URI: http://hdl.handle.net/2429/88727