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@ARTICLE{Tichy:142144,
      author       = {D. Tichy$^*$ and J. M. A. Pickl$^*$ and A. Benner$^*$ and
                      H. Sültmann$^*$},
      title        = {{E}xperimental design and data analysis of
                      {A}go-{RIP}-{S}eq experiments for the identification of
                      micro{RNA} targets.},
      journal      = {Briefings in bioinformatics},
      volume       = {19},
      number       = {5},
      issn         = {1477-4054},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2018-02374},
      pages        = {918 - 929},
      year         = {2018},
      abstract     = {The identification of microRNA (miRNA) target genes is
                      crucial for understanding miRNA function. Many methods for
                      the genome-wide miRNA target identification have been
                      developed in recent years; however, they have several
                      limitations including the dependence on low-confident
                      prediction programs and artificial miRNA manipulations.
                      Ago-RNA immunoprecipitation combined with high-throughput
                      sequencing (Ago-RIP-Seq) is a promising alternative.
                      However, appropriate statistical data analysis algorithms
                      taking into account the experimental design and the inherent
                      noise of such experiments are largely lacking.Here, we
                      investigate the experimental design for Ago-RIP-Seq and
                      examine biostatistical methods to identify de novo miRNA
                      target genes. Statistical approaches considered are either
                      based on a negative binomial model fit to the read count
                      data or applied to transformed data using a normal
                      distribution-based generalized linear model. We compare them
                      by a real data simulation study using plasmode data sets and
                      evaluate the suitability of the approaches to detect true
                      miRNA targets by sensitivity and false discovery rates. Our
                      results suggest that simple approaches like linear
                      regression models on (appropriately) transformed read count
                      data are preferable.},
      cin          = {C060 / B063},
      ddc          = {004},
      cid          = {I:(DE-He78)C060-20160331 / I:(DE-He78)B063-20160331},
      pnm          = {312 - Functional and structural genomics (POF3-312)},
      pid          = {G:(DE-HGF)POF3-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28379479},
      doi          = {10.1093/bib/bbx032},
      url          = {https://inrepo02.dkfz.de/record/142144},
}