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@ARTICLE{Slynko:144015,
      author       = {A. Slynko and A. Benner$^*$},
      title        = {{S}tatistical methods for classification of 5hm{C} levels
                      based on the {I}llumina {I}nifinium {H}uman{M}ethylation450
                      (450k) array data, under the paired bisulfite ({BS}) and
                      oxidative bisulfite (ox{BS}) treatment.},
      journal      = {PLOS ONE},
      volume       = {14},
      number       = {6},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {DKFZ-2019-01566},
      pages        = {e0218103 -},
      year         = {2019},
      abstract     = {Hydroxymethylcytosine (5hmC) methylation is a well-known
                      epigenetic mark that is involved in gene regulation and may
                      impact genome stability. To investigate a possible role of
                      5hmC in cancer development and progression, one must be able
                      to detect and quantify its level first. In this paper, we
                      address the issue of 5hmC detection at a single base
                      resolution, starting with consideration of the
                      well-established 5hmC measure Δβ and, in particular, with
                      an analysis of its properties, both analytically and
                      empirically. Then we propose several alternative
                      hydroxymethylation measures and compare their properties
                      with those of Δβ. In the absence of a gold standard, the
                      (pairwise) resemblance of those 5hmC measures to Δβ is
                      characterized by means of a similarity analysis and relative
                      accuracy analysis. All results are illustrated on matched
                      healthy and cancer tissue data sets as derived by means of
                      bisulfite (BS) and oxidative bisulfite converting (oxBS)
                      procedures.},
      cin          = {C060},
      ddc          = {610},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31194780},
      doi          = {10.1371/journal.pone.0218103},
      url          = {https://inrepo02.dkfz.de/record/144015},
}