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@ARTICLE{Schrter:271003,
      author       = {J. Schröter and T. Dattner and J. Hüllein and A. Jayme
                      and V. Heuveline and G. F. Hoffmann and S. Kölker and D.
                      Lenz and T. Opladen and B. Popp and C. P. Schaaf and C.
                      Staufner and S. Syrbe and S. Uhrig and D. Hübschmann$^*$
                      and H. Brennenstuhl},
      title        = {a{R}gus: {M}ultilevel visualization of non-synonymous
                      single nucleotide variants $\&$ advanced pathogenicity score
                      modeling for genetic vulnerability assessment.},
      journal      = {Computational and structural biotechnology journal},
      volume       = {21},
      issn         = {2001-0370},
      address      = {Gotenburg},
      publisher    = {Research Network of Computational and Structural
                      Biotechnology (RNCSB)},
      reportid     = {DKFZ-2023-00356},
      pages        = {1077 - 1083},
      year         = {2023},
      abstract     = {The widespread use of high-throughput sequencing techniques
                      is leading to a rapidly increasing number of
                      disease-associated variants of unknown significance and
                      candidate genes. Integration of knowledge concerning their
                      genetic, protein as well as functional and conservational
                      aspects is necessary for an exhaustive assessment of their
                      relevance and for prioritization of further clinical and
                      functional studies investigating their role in human
                      disease. To collect the necessary information, a multitude
                      of different databases has to be accessed and data
                      extraction from the original sources commonly is not
                      user-friendly and requires advanced bioinformatics skills.
                      This leads to a decreased data accessibility for a relevant
                      number of potential users such as clinicians, geneticist,
                      and clinical researchers. Here, we present aRgus
                      (https://argus.urz.uni-heidelberg.de/), a standalone webtool
                      for simple extraction and intuitive visualization of
                      multi-layered gene, protein, variant, and variant effect
                      prediction data. aRgus provides interactive exploitation of
                      these data within seconds for any known gene of the human
                      genome. In contrast to existing online platforms for
                      compilation of variant data, aRgus complements visualization
                      of chromosomal exon-intron structure and protein domain
                      annotation with ClinVar and gnomAD variant distributions as
                      well as position-specific variant effect prediction score
                      modeling. aRgus thereby enables timely assessment of protein
                      regions vulnerable to variation with single amino acid
                      resolution and provides numerous applications in variant and
                      protein domain interpretation as well as in the design of in
                      vitro experiments.},
      keywords     = {Computational genetics (Other) / Pathogenicity scores
                      (Other) / Variant assessment (Other) / Variant effect
                      prediction (Other)},
      cin          = {HD01},
      ddc          = {570},
      cid          = {I:(DE-He78)HD01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36789265},
      pmc          = {pmc:PMC9900257},
      doi          = {10.1016/j.csbj.2023.01.027},
      url          = {https://inrepo02.dkfz.de/record/271003},
}