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• W777600181 abstract "Vast amounts of molecular data are being generated every day. However, how to properly harness the data remains often a challenge for many biologists. Firstly, due to the typical large dimension of the molecular data, analyses can either require exhaustive amounts of computer memory or be very time-consuming, or both. Secondly, biological problems often have their own special features, which put demand on specially designed software to obtain meaningful results from statistical analyses without imposing too much requirements on the available computing resources. Finally, the general complexity of many biological research questions necessitates joint use of many different methods, which requires a considerable expertise in properly understanding the possibilities and limitations of the analysis tools. In the first part of this thesis, we discuss three general Bayesian classification/clustering frameworks, which in the considered applications are targeted towards clustering of DNA sequence data, in particular in the context of bacterial population genomics and evolutionary epidemiology. Based on more generic Bayesian concepts, we have developed several statistical tools for analyzing DNA sequence data in bacterial metagenomics and population genomics. In the second part of this thesis, we focus on discussing how to reconstruct bacterial evolutionary history from a combination of whole genome sequences and a number of core genes for which a large set of samples are available. A major problem is that for many bacterial species horizontal" @default.
• W777600181 created "2016-06-24" @default.
• W777600181 creator A5089651936 @default.
• W777600181 date "2013-10-04" @default.
• W777600181 modified "2023-09-24" @default.
• W777600181 title "Bayesian methods in bacterial population genomics" @default.
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• W777600181 hasPublicationYear "2013" @default.
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