@article{SchmidtHaywardCoelhoetal.2019,
  author    = {Schmidt, Thomas S. B. and Hayward, Matthew R. and Coelho, Luiis P. and Li, Simone S. and Costea, Paul I. and Voigt, Anita Y. and Wirbel, Jakob and Maistrenko, Oleksandr M. and Alves, Renato J. C. and Bergsten, Emma and de Beaufort, Carine and Sobhani, Iradj and Heintz-Buschart, Anna and Sunagawa, Shinichi and Zeller, Georg and Wilmes, Paul and Bork, Peer},
  title     = {Extensive transmission of microbes along the gastrointestinal tract},
  series = {eLife},
  volume    = {8},
  journal   = {eLife},
  doi       = {10.7554/eLife.42693},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228954},
  pages     = {e42693, 1-18},
  year      = {2019},
  abstract  = {The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.},
  subject      = {Barrier},
  language  = {en}
}
@phdthesis{Yu2019,
  author    = {Yu, Sung-Huan},
  title     = {Development and application of computational tools for RNA-Seq based transcriptome annotations},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176468},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {In order to understand the regulation of gene expression in organisms, precise genome annotation is essential. In recent years, RNA-Seq has become a potent method for generating and improving genome annotations. However, this Approach is time consuming and often inconsistently performed when done manually. In particular, the discovery of non-coding RNAs benefits strongly from the application of RNA-Seq data but requires significant amounts of expert knowledge and is labor-intensive. As a part of my doctoral study, I developed a modular tool called ANNOgesic that can detect numerous transcribed genomic features, including non-coding RNAs, based on RNA-Seq data in a precise and automatic fashion with a focus on bacterial and achaeal species. The software performs numerous analyses and generates several visualizations. It can generate annotations of high-Resolution that are hard to produce using traditional annotation tools that are based only on genome sequences. ANNOgesic can detect numerous novel genomic Features like UTR-derived small non-coding RNAs for which no other tool has been developed before. ANNOgesic is available under an open source license (ISCL) at https://github.com/Sung-Huan/ANNOgesic. My doctoral work not only includes the development of ANNOgesic but also its application to annotate the transcriptome of Staphylococcus aureus HG003 - a strain which has been a insightful model in infection biology. Despite its potential as a model, a complete genome sequence and annotations have been lacking for HG003. In order to fill this gap, the annotations of this strain, including sRNAs and their functions, were generated using ANNOgesic by analyzing differential RNA-Seq data from 14 different samples (two media conditions with seven time points), as well as RNA-Seq data generated after transcript fragmentation. ANNOgesic was also applied to annotate several bacterial and archaeal genomes, and as part of this its high performance was demonstrated. In summary, ANNOgesic is a powerful computational tool for RNA-Seq based annotations and has been successfully applied to several species.},
  subject      = {Genom},
  language  = {en}
}