Computational identification of within-host diversity of SARS-CoV-2 and its benefits for mRNA vaccine design

Cai, Xinhui

Computational identification of within-host diversity of SARS-CoV-2 and its benefits for mRNA vaccine design

Cai, Xinhui

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Cai, Xinhui
dc.date.accessioned	2023-09-06T03:44:34Z
dc.date.available	2023-09-06T03:44:34Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/171947
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The emergence of COVID-19 in late 2019 in Wuhan has had a profound global impact. Despite extensive research on the causative virus, SARS-CoV-2, some questions regarding its origin remain unanswered. This paper investigates the sequencing reads and assembled SARS-CoV-2 reference genome to shed light on these gaps. By retracing the assembly process, evidence of the coexistence of multiple SARS-CoV-2 strains within patients is revealed. The assembly tool, MEGAHIT, tends to overlook alternative assembly routes, necessitating the development of a workflow to rectify this issue and identify multiple strains in COVID-19 patient samples. This workflow includes error correction, relevant read extraction, strain identification, phylogenetic analysis, and protein structure examination. Results indicate the presence of multiple SARS-CoV-2 strains in the sample used for the reference sequence, with differing binding affinity and phylogenetic relationships to the published SARS-CoV-2 reference genome, SARS-CoV, and other variants. These strains exhibit structural differences that impact the protein binding affinity with human ACE2. Consequently, this workflow highlights the importance of considering these variations when designing mRNA vaccines, as the coexisting strains possess different nucleotide sequences from the assembled reference sequence.	en_US.UTF-8
dc.format	Thesis (MAnalytics)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/171947/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2022 Xinhui Cai
dc.rights	au.edu.uts.lib/cph
dc.title	Computational identification of within-host diversity of SARS-CoV-2 and its benefits for mRNA vaccine design	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The emergence of COVID-19 in late 2019 in Wuhan has had a profound global impact. Despite extensive research on the causative virus, SARS-CoV-2, some questions regarding its origin remain unanswered. This paper investigates the sequencing reads and assembled SARS-CoV-2 reference genome to shed light on these gaps. By retracing the assembly process, evidence of the coexistence of multiple SARS-CoV-2 strains within patients is revealed. The assembly tool, MEGAHIT, tends to overlook alternative assembly routes, necessitating the development of a workflow to rectify this issue and identify multiple strains in COVID-19 patient samples. This workflow includes error correction, relevant read extraction, strain identification, phylogenetic analysis, and protein structure examination. Results indicate the presence of multiple SARS-CoV-2 strains in the sample used for the reference sequence, with differing binding affinity and phylogenetic relationships to the published SARS-CoV-2 reference genome, SARS-CoV, and other variants. These strains exhibit structural differences that impact the protein binding affinity with human ACE2. Consequently, this workflow highlights the importance of considering these variations when designing mRNA vaccines, as the coexisting strains possess different nucleotide sequences from the assembled reference sequence.

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http://hdl.handle.net/10453/171947