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@ARTICLE{Lienhard:124376,
      author       = {M. Lienhard and S. Grasse and J. Rolff and S. Frese and U.
                      Schirmer$^*$ and M. Becker and S. Börno and B. Timmermann
                      and L. Chavez$^*$ and H. Sültmann$^*$ and G. Leschber and
                      I. Fichtner and M. R. Schweiger and R. Herwig},
      title        = {{QSEA}-modelling of genome-wide {DNA} methylation from
                      sequencing enrichment experiments.},
      journal      = {Nucleic acids symposium series},
      volume       = {45},
      number       = {6},
      issn         = {0261-3166},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2017-01255},
      pages        = {e44 - e44},
      year         = {2017},
      abstract     = {Genome-wide enrichment of methylated DNA followed by
                      sequencing (MeDIP-seq) offers a reasonable compromise
                      between experimental costs and genomic coverage. However,
                      the computational analysis of these experiments is complex,
                      and quantification of the enrichment signals in terms of
                      absolute levels of methylation requires specific
                      transformation. In this work, we present QSEA, Quantitative
                      Sequence Enrichment Analysis, a comprehensive workflow for
                      the modelling and subsequent quantification of MeDIP-seq
                      data. As the central part of the workflow we have developed
                      a Bayesian statistical model that transforms the enrichment
                      read counts to absolute levels of methylation and, thus,
                      enhances interpretability and facilitates comparison with
                      other methylation assays. We suggest several calibration
                      strategies for the critical parameters of the model, either
                      using additional data or fairly general assumptions. By
                      comparing the results with bisulfite sequencing (BS)
                      validation data, we show the improvement of QSEA over
                      existing methods. Additionally, we generated a clinically
                      relevant benchmark data set consisting of methylation
                      enrichment experiments (MeDIP-seq), BS-based validation
                      experiments (Methyl-seq) as well as gene expression
                      experiments (RNA-seq) derived from non-small cell lung
                      cancer patients, and show that the workflow retrieves
                      well-known lung tumour methylation markers that are
                      causative for gene expression changes, demonstrating the
                      applicability of QSEA for clinical studies. QSEA is
                      implemented in R and available from the Bioconductor
                      repository 3.4 (www.bioconductor.org/packages/qsea).},
      cin          = {B063 / B062},
      ddc          = {540},
      cid          = {I:(DE-He78)B063-20160331 / 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:27913729},
      pmc          = {pmc:PMC5389680},
      doi          = {10.1093/nar/gkw1193},
      url          = {https://inrepo02.dkfz.de/record/124376},
}