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@ARTICLE{Huang:128631,
      author       = {Z. Huang$^*$ and D. Jones$^*$ and Y. Wu$^*$ and P.
                      Lichter$^*$ and M. Zapatka$^*$},
      title        = {conf{F}use: {H}igh-{C}onfidence {F}usion {G}ene {D}etection
                      across {T}umor {E}ntities.},
      journal      = {Frontiers in genetics},
      volume       = {8},
      issn         = {1664-8021},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2017-04647},
      pages        = {137},
      year         = {2017},
      abstract     = {Background: Fusion genes play an important role in the
                      tumorigenesis of many cancers. Next-generation sequencing
                      (NGS) technologies have been successfully applied in fusion
                      gene detection for the last several years, and a number of
                      NGS-based tools have been developed for identifying fusion
                      genes during this period. Most fusion gene detection tools
                      based on RNA-seq data report a large number of candidates
                      (mostly false positives), making it hard to prioritize
                      candidates for experimental validation and further analysis.
                      Selection of reliable fusion genes for downstream analysis
                      becomes very important in cancer research. We therefore
                      developed confFuse, a scoring algorithm to reliably select
                      high-confidence fusion genes which are likely to be
                      biologically relevant. Results: confFuse takes multiple
                      parameters into account in order to assign each fusion
                      candidate a confidence score, of which score ≥8 indicates
                      high-confidence fusion gene predictions. These parameters
                      were manually curated based on our experience and on certain
                      structural motifs of fusion genes. Compared with alternative
                      tools, based on 96 published RNA-seq samples from different
                      tumor entities, our method can significantly reduce the
                      number of fusion candidates (301 high-confidence from 8,083
                      total predicted fusion genes) and keep high detection
                      accuracy (recovery rate $85.7\%).$ Validation of 18 novel,
                      high-confidence fusions detected in three breast tumor
                      samples resulted in a $100\%$ validation rate. Conclusions:
                      confFuse is a novel downstream filtering method that allows
                      selection of highly reliable fusion gene candidates for
                      further downstream analysis and experimental validations.
                      confFuse is available at
                      https://github.com/Zhiqin-HUANG/confFuse.},
      cin          = {B060 / B062},
      ddc          = {570},
      cid          = {I:(DE-He78)B060-20160331 / I:(DE-He78)B062-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29033976},
      pmc          = {pmc:PMC5627533},
      doi          = {10.3389/fgene.2017.00137},
      url          = {https://inrepo02.dkfz.de/record/128631},
}