@article{PlutaHoffjanZimmeretal.2022,
  author    = {Pluta, Natalie and Hoffjan, Sabine and Zimmer, Frederic and K{\"o}hler, Cornelia and L{\"u}cke, Thomas and Mohr, Jennifer and Vorgerd, Matthias and Nguyen, Hoa Huu Phuc and Atlan, David and Wolf, Beat and Zaum, Ann-Kathrin and Rost, Simone},
  title     = {Homozygous inversion on chromosome 13 involving SGCG detected by short read whole genome sequencing in a patient suffering from limb-girdle muscular dystrophy},
  series = {Genes},
  volume    = {13},
  journal   = {Genes},
  number    = {10},
  issn      = {2073-4425},
  doi       = {10.3390/genes13101752},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288122},
  year      = {2022},
  abstract  = {New techniques in molecular genetic diagnostics now allow for accurate diagnosis in a large proportion of patients with muscular diseases. Nevertheless, many patients remain unsolved, although the clinical history and/or the muscle biopsy give a clear indication of the involved genes. In many cases, there is a strong suspicion that the cause must lie in unexplored gene areas, such as deep-intronic or other non-coding regions. In order to find these changes, next-generation sequencing (NGS) methods are constantly evolving, making it possible to sequence entire genomes to reveal these previously uninvestigated regions. Here, we present a young woman who was strongly suspected of having a so far genetically unsolved sarcoglycanopathy based on her clinical history and muscle biopsy. Using short read whole genome sequencing (WGS), a homozygous inversion on chromosome 13 involving SGCG and LINC00621 was detected. The breakpoint in intron 2 of SGCG led to the absence of γ-sarcoglycan, resulting in the manifestation of autosomal recessive limb-girdle muscular dystrophy 5 (LGMDR5) in the young woman.},
  language  = {en}
}
@article{WolfKuonenDandekaretal.2015,
  author    = {Wolf, Beat and Kuonen, Pierre and Dandekar, Thomas and Atlan, David},
  title     = {DNAseq workflow in a diagnostic context and an example of a user friendly implementation},
  series = {BioMed Research International},
  journal   = {BioMed Research International},
  number    = {403497},
  doi       = {10.1155/2015/403497},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144527},
  year      = {2015},
  abstract  = {Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.},
  language  = {en}
}
@article{KunzWolfSchulzeetal.2016,
  author    = {Kunz, Meik and Wolf, Beat and Schulze, Harald and Atlan, David and Walles, Thorsten and Walles, Heike and Dandekar, Thomas},
  title     = {Non-Coding RNAs in Lung Cancer: Contribution of Bioinformatics Analysis to the Development of Non-Invasive Diagnostic Tools},
  series = {Genes},
  volume    = {8},
  journal   = {Genes},
  number    = {1},
  doi       = {10.3390/genes8010008},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147990},
  pages     = {8},
  year      = {2016},
  abstract  = {Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.},
  language  = {en}
}