Masters Degrees (Centre for Bioinformatics & Computational Biology)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/130968

Browse

Browsing Masters Degrees (Centre for Bioinformatics & Computational Biology) by Title

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Enhancing genomic visualisations for epidemics to inform the public health response
    (Stellenbosch : Stellenbosch University, 2024-03) Sitharam, Nikita; De Oliveira, Tulio ; Baxter, Cheryl; Xavier, Joicymara; Stellenbosch University. Faculty of Medicine and Health Sciences. Centre for Bioinformatics & Computational Biology.
    ENGLISH ABSTRACT: Introduction: Infectious disease prevention relies on effective disease surveillance and communication in preventing and controlling epidemics. An important tool that is utilised for communication, in public health informatics, is a computational dashboard. Public health dashboards are an effective way of presenting complex biological data in a concise and visual manner. This decreases the knowledge gap between researchers and the public, and allows for stakeholders and policy-makers to make informed decisions. Genomic surveillance of arthropod-borne viruses has increased due to the advancements in sequencing technologies. However, the African continent remains challenged in controlling the spread of these viruses and its vectors, due to constrained resources. Climate Amplified Diseases and Epidemics (CLIMADE) Africa is a consortium that aims to increase the genomic surveillance of arboviruses in Africa through the sharing of protocols, software applications, and capacity building. Purpose: The purpose of this study was to build the CLIMADE Africa dashboard to communicate genomic data for the three most medically-prevalent arboviruses on the African continent, Dengue Virus, Chikungunya Virus, and Zika Virus. Communicating this data could help guide the public health response in Africa. The SARS-CoV-2 Africa dashboard was enhanced by the addition of an interactive phylogenetic visualisation. This increased the amount of complex information provided on the dashboard, in a clear and user-friendly manner. Methodology: The CLIMADE Africa dashboard was produced by adapting the computational architecture of the SARS-CoV-2 Africa dashboard. The arboviral data presented was sourced from the Bacterial and Viral Bioinformatics Resource Center. The data was processed using Pandas in Python, and implemented into the dashboard using the Pycharm application. The Nextstrain SARS-CoV-2 phylogenetic build, for the SARS-CoV-2 Africa dashboard, was produced using Nextstrain’s ncov workflow. This was implemented into the filter panel on the dashboard. Results: The CLIMADE Africa dashboard was created as an easy-to-use tool that showcases the genomic epidemiology of the three most prevalent arboviruses (Dengue Virus, Chikungunya Virus, and Zika Virus) in Africa, in a digestible manner and in near-real time. This dashboard is open source and the code is available at this GitHub repository. The phylogenetic Nextstrain build presented on the SARS-CoV-2 Africa dashboard, presents a full picture of the genomic landscape of the pandemic on the African continent. Conclusion: The information extracted from the CLIMADE Africa dashboard highlights the difference between reported outbreaks and genomic surveillance on the African continent. This could be used to improve surveillance, which could then potentially improve the public health response to these arboviruses. Dashboards need to be maintained, updated, and enhanced in order to be effective at communicating accurate and up-to-date scientific information. Thus, introducing a Nextstrain build for the SARS-CoV-2 Africa dashboard, allows for more complex information to be presented in a digestible way. Future work for this study could include fully automating the data acquisition and curation for the CLIMADE Africa dashboard and fully embedding the Africa-specific Nextstrain phylogenetic build in the SARS- CoV-2 Africa dashboard.
  • Thumbnail Image
    Item
    ReciMap: a pipeline to identify rearrangement borders between closely related genomes
    (Stellenbosch : Stellenbosch University, 2024-03) Schutte, Casper Jan Hendrik; Patterton, Hugh-George; Stellenbosch University. Faculty of Medicine and Health Sciences. Centre for Bioinformatics & Computational Biology.
    ENGLISH ABSTRACT: Large genomic rearrangement events play a pivotal role in the evolutionary dynamics of genomes and the process of speciation. Recognizing the necessity for a robust tool, this thesis introduces ReciMap, a command line bioinformatics pipeline explicitly created to precisely identify the borders of genomic rearrangement events between closely related genomes. The pipeline leverages the reciprocal mapping of short, synthetic reads as a methodological approach. The development of ReciMap is extensively detailed in this thesis. We demonstrate the pipeline’s efficacy in accurately pinpointing the borders of rearrangement events with a resolution of approximately 4 base pairs (bp). To validate the pipeline’s accuracy, we conduct thorough comparisons of genomes with increasing evolutionary divergence, up to fifty thousand generations apart. Moreover, the versatility of ReciMap is showcased in its capability to incorporate novel methods for the identification of synteny blocks. This feature broadens the utility of the pipeline, allowing for a more comprehensive analysis of genomic architecture. ReciMap is introduced as an open–source, command line–based tool, accessible to researchers and practitioners alike. The repository for ReciMap is publicly available at the following URL: https://github.com/casper-schutte/recimap This research not only contributes a valuable computational resource to the field of bioinformatics but also presents a novel approach to border identification in genomic rearrangement events.