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@PHDTHESIS{Gusmao:681125,
      author       = {Gusmao, Eduardo G.},
      othercontributors = {Berlage, Thomas and Zenke, Martin and Decker, Stefan Josef},
      title        = {{C}omputational footprinting methods for next-generation
                      sequencing experiments},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2017-00003},
      pages        = {1 Online-Ressource (xv, 128 Seiten) : Diagramme},
      year         = {2016},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2017; Dissertation, RWTH Aachen University, 2016},
      abstract     = {Transcriptional regulation orchestrates the proper temporal
                      and spatial expression of genes. The identification of
                      transcriptional regulatory elements, such as transcription
                      factor binding sites (TFBSs), is crucial to understand
                      regulatory networks driving cellular processes such as cell
                      development and the onset of diseases.The standard
                      computational approach is to use sequence-based methods,
                      which search over the genome’s DNA for sequences
                      representing the DNA binding affinity sequence of
                      transcription factors (TFs). However, this approach is not
                      able to predict active binding sites, i.e. binding sites
                      that are being currently bound by TFs at a particular cell
                      state. This happens as the sequence-based methods do not
                      account for the fact that the chromatin dynamically changes
                      its state between an open form (and accessible to TF
                      binding) and closed (not accessible by TFs).Advances in
                      next-generation sequencing techniques have enabled the
                      measurement of such open chromatin regions in a genome-wide
                      manner with assays such as the chromatin immunoprecipitation
                      followed by massive sequencing (ChIP-seq) and DNase I
                      digestion followed by massive sequencing (DNase-seq).
                      Current research has proven that such open chromatin
                      genome-wide assays improve sequence-based detection of
                      active TFBSs. The rationale is to restrict the
                      sequence-based search of binding sites to genomic regions
                      where these assays indicate the chromatin is open and
                      accessible for TF binding, in a cell-specific manner.We
                      propose the first computational framework which integrates
                      both DNase-seq and ChIP-seq data to perform predictions of
                      active TFBSs. We have previously observed that there is a
                      distinctive pattern at active TFBSs regarding both DNase-seq
                      and ChIP-seq data. Our framework treats these data using
                      signal normalization strategies and searches for these
                      distinctive patterns, the so-called “footprints”, by
                      segmenting the genome using hidden Markov models (HMMs).
                      Given that, our framework - termed HINT (HMM-based
                      identification of TF footprints) - is categorized as a
                      “computational footprinting method”.We evaluate our
                      computational footprinting method by comparing the footprint
                      predictions to experimentally verified active TFBSs. Our
                      evaluation approach creates statistics which enables the
                      comparison between our method and competing computational
                      footprinting methods. Our comparative experiment is the most
                      complete so far, with a total of 14 computational
                      footprinting methods and 233 TFs evaluated.Furthermore, we
                      successfully applied our computational footprinting method
                      HINT in two different biological studies to identify
                      regulatory elements involved in specific biological
                      conditions. HINT has proven to be a useful computational
                      framework in biological studies involving regulatory
                      genomics.},
      cin          = {122620 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)122620_20140620$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:82-rwth-2017-000039},
      doi          = {10.18154/RWTH-2017-00003},
      url          = {https://publications.rwth-aachen.de/record/681125},
}