{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/333933"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/333933","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Examining bacterial variation with genome graphs and Nanopore sequencing","abstract":"A bacterial species' genetic content can be remarkably fluid. The collection of genes found within a given species is called the pan-genome and is generally much larger than the gene repertoire of a single cell. A consequence of this pan-genome is that bacterial genomes are highly adaptable and thus variable. The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome, which serves as a coordinate system. As an introduction to this thesis, we outline why this approach is inadequate for bacteria and describe a new approach using genome graphs. In the first chapter, we present algorithms for de novo variant discovery within such genome graphs and evaluate their performance with empirical data. The remaining chapters address a question relating to a critical bacterial pathogen: can Nanopore sequencing of Mycobacterium tuberculosis provide high-quality public health information? We collect data from Madagascar, South Africa, and England to help answer this question. First, we assess outbreaks identified using single-reference and genome graph methods. Second, we evaluate antimicrobial resistance predictions and introduce a framework for using genome graphs to improve current methods. Lastly, we train an M. tuberculosis-specific Nanopore basecalling model with considerable accuracy improvement. Together, this thesis provides general methods for uncovering bacterial variation and applies them to an important global public health question.","abstract_html":"A bacterial species' genetic content can be remarkably fluid. The collection of genes found within a given species is called the pan-genome and is generally much larger than the gene repertoire of a single cell. A consequence of this pan-genome is that bacterial genomes are highly adaptable and thus variable. The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome, which serves as a coordinate system. As an introduction to this thesis, we outline why this approach is inadequate for bacteria and describe a new approach using genome graphs. In the first chapter, we present algorithms for de novo variant discovery within such genome graphs and evaluate their performance with empirical data. The remaining chapters address a question relating to a critical bacterial pathogen: can Nanopore sequencing of Mycobacterium tuberculosis provide high-quality public health information? We collect data from Madagascar, South Africa, and England to help answer this question. First, we assess outbreaks identified using single-reference and genome graph methods. Second, we evaluate antimicrobial resistance predictions and introduce a framework for using genome graphs to improve current methods. Lastly, we train an M. tuberculosis-specific Nanopore basecalling model with considerable accuracy improvement. 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We collect data from Madagascar, South Africa, and England to help answer this question. First, we assess outbreaks identified using single-reference and genome graph methods. Second, we evaluate antimicrobial resistance predictions and introduce a framework for using genome graphs to improve current methods. Lastly, we train an M. tuberculosis-specific Nanopore basecalling model with considerable accuracy improvement. 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The dominant paradigm for analysing genetic variation relies on a central idea: all genomes in a species can be described as minor differences from a single reference genome, which serves as a coordinate system. As an introduction to this thesis, we outline why this approach is inadequate for bacteria and describe a new approach using genome graphs. In the first chapter, we present algorithms for de novo variant discovery within such genome graphs and evaluate their performance with empirical data. The remaining chapters address a question relating to a critical bacterial pathogen: can Nanopore sequencing of Mycobacterium tuberculosis provide high-quality public health information? We collect data from Madagascar, South Africa, and England to help answer this question. First, we assess outbreaks identified using single-reference and genome graph methods. Second, we evaluate antimicrobial resistance predictions and introduce a framework for using genome graphs to improve current methods. Lastly, we train an M. tuberculosis-specific Nanopore basecalling model with considerable accuracy improvement. 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