The timely identification of pathogens responsible for disease outbreaks is crucial for implementing effective control measures and minimizing the spread of infectious diseases. Conventional methods of identification are limited to specific pathogen species because they require prior knowledge and pure cultures of the pathogen. Therefore, these methods cannot detect new pathogens responsible for newly emerging diseases. Computational methods that rely on sequencing data have the potential to overcome these limitations. However, the diverse phenotypes among microbial species and strains within the same species pose a challenge in accurately identifying the specific pathogen responsible for the disease. This dissertation highlights the importance of strain-level detection for the identification and characterization of pathogens by employing computational methods that rely on genomic and metagenomic sequencing data. To realize that computational goal, a comparison of different tools, currently used for metagenome classification, was done to illustrate effective detection of bacterial pathogens. To develop computational methods for characterization, genomes of the plant pathogen Ralstonia solanacearum were studied to understand the basis of virulence at cool temperatures. Finally, a new tool was developed that combines accurate detection and characterization at the strain level, through the use of taxonomic databases constructed using genome similarity thresholds. This dissertation work is a contribution to the development of improved approaches to detect and contain disease outbreaks in plants with possible applications in human and animal diseases as well. / Doctor of Philosophy / Detecting and identifying pathogens is crucial for controlling disease outbreaks in humans, animals, and plants. However, currently used methods are limited to identifying only those pathogens that can be grown in a lab. An ideal method for pathogen detection should be broadly applicable to many pathogens. A newer technique called metagenome sequencing allows us to identify known as well as unknown pathogens, including the ones that cannot be grown in a lab. This makes it possible to detect new pathogens from newly emerging diseases. Computational tools that accurately analyze the sequencing data are needed.
This dissertation highlights the importance of accurately identifying specific strains of pathogens using computational techniques based on genomic and metagenomic sequencing data. As a result, different tools were evaluated for classifying metagenomes for the successful detection of bacterial pathogens. For the characterization of specific traits responsible for causing disease, genomes of Ralstonia solanacearum, a plant pathogen, were studied to understand how some strains remain harmful at lower temperatures. The dissertation also introduces a novel metagenomic classification tool that combines accurate detection and characterization of pathogen strains by using genome similarity thresholds to create taxonomic databases. This approach improves our ability to identify and understand pathogens at a more specific level.
Overall, this research aims to enhance our ability to identify and understand pathogens, allowing for more effective measures to control and prevent disease outbreaks.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/116063 |
| Date | 18 August 2023 |
| Creators | Sharma, Parul |
| Contributors | Genetics, Bioinformatics, and Computational Biology, Vinatzer, Boris A., Heath, Lenwood S., Allen, Caitilyn, Jelesko, John G. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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