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@ARTICLE{Afzal:124312,
      author       = {S. Afzal$^*$ and S. Wilkening$^*$ and C. von Kalle$^*$ and
                      M. Schmidt$^*$ and R. Fronza$^*$},
      title        = {{GENE}-{IS}: {T}ime-{E}fficient and {A}ccurate {A}nalysis
                      of {V}iral {I}ntegration {E}vents in {L}arge-{S}cale {G}ene
                      {T}herapy {D}ata.},
      journal      = {Molecular Therapy / Nucleic Acids},
      volume       = {6},
      issn         = {2162-2531},
      address      = {New York, NY},
      publisher    = {Nature Publ. Group},
      reportid     = {DKFZ-2017-01208},
      pages        = {133 - 139},
      year         = {2017},
      abstract     = {Integration site profiling and clonality analysis of viral
                      vector distribution in gene therapy is a key factor to
                      monitor the fate of gene-corrected cells, assess the risk of
                      malignant transformation, and establish vector biosafety. We
                      developed the Genome Integration Site Analysis Pipeline
                      (GENE-IS) for highly time-efficient and accurate detection
                      of next-generation sequencing (NGS)-based viral vector
                      integration sites (ISs) in gene therapy data. It is the
                      first available tool with dual analysis mode that allows IS
                      analysis both in data generated by PCR-based methods, such
                      as linear amplification method PCR (LAM-PCR), and by rapidly
                      evolving targeted sequencing (e.g., Agilent SureSelect)
                      technologies. GENE-IS makes use of trimming strategies,
                      customized reference genome, and soft-clipped information
                      with sequential filtering steps to provide annotated IS with
                      clonality information. It is a scalable, robust, precise,
                      and reliable tool for large-scale pre-clinical and clinical
                      data analysis that provides users complete flexibility and
                      control over analysis with a broad range of configurable
                      parameters. GENE-IS is available at
                      https://github.com/G100DKFZ/gene-is.},
      cin          = {G010 / G100},
      ddc          = {610},
      cid          = {I:(DE-He78)G010-20160331 / I:(DE-He78)G100-20160331},
      pnm          = {317 - Translational cancer research (POF3-317)},
      pid          = {G:(DE-HGF)POF3-317},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28325279},
      pmc          = {pmc:PMC5363413},
      doi          = {10.1016/j.omtn.2016.12.001},
      url          = {https://inrepo02.dkfz.de/record/124312},
}