@phdthesis{Fasemore2023,
  author    = {Fasemore, Akinyemi Mandela},
  title     = {Genomic and internet based analysis of \(Coxiella\) \(burnetii\)},
  doi       = {10.25972/OPUS-29663},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-296639},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Coxiella burnetii, a Gram negative obligate intracellular bacterium, is the causative agent of Q fever. It has a world wide distribution and has been documented to be capable of causing infections in several domestic animals, livestock species, and human beings. Outbreaks of Q fever are still being observed in livestock across animal farms in Europe, and primary transmission to humans still oc- curs especially in animal handlers. Public health authorities in some countries like Germany are required by law to report human acute cases denoting the significance of the challenge posed by C. burnetii to public health. In this thesis, I have developed a platform alongside methods to address the challenges of genomic analyses of C. burnetii for typing purposes. Identification of C. burnetii isolates is an important task in the laboratory as well as in the clinics and genotyping is a reliable method to identify and characterize known and novel isolates. Therefore, I designed and implemented several methods to facilitate the genotyping analyses of C. burnetii genomes in silico via a web platform. As genotyping is a data intensive process, I also included additional features such as visualization methods and databases for interpretation and storage of obtained results. I also developed a method to profile the resistome of C. burnetii isolates using a machine learning approach. Data about antibiotic resistance in C. burnetii are scarce majorly due to its lifestyle and the difficulty of cultivation in laboratory media. Alternative methods that rely on homology identification of resistance genes are also inefficient in C. burnetii, hence, I opted for a novel approach that has been shown to be promising in other bacteria species. The applied method relied on an artificial neural network as well as amino acid composition of position specific scoring matrix profile for feature extraction. The resulting model achieved an accuracy of ≈ 0.96 on test data and the overall performance was significantly higher in comparison to existing models. Finally, I analyzed two new C. burnetii isolates obtained from an outbreak in Germany, I compared the genome to the RSA 493 reference isolate and found extensive deletions across the genome landscape. This work has provided a new digital infrastructure to analyze and character- ize C. burnetii genomes that was not in existence before and it has also made a significant contribution to the existing information about antibiotic resistance genes in C. burnetii.},
  language  = {en}
}