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@ARTICLE{Daenekas:287227,
      author       = {B. Daenekas and E. Pérez and F. Boniolo and S. Stefan and
                      S. Benfatto and M. Sill$^*$ and D. Sturm$^*$ and D. T. W.
                      Jones$^*$ and D. Capper$^*$ and M. Zapatka$^*$ and V.
                      Hovestadt},
      title        = {{C}onumee 2.0: {E}nhanced copy-number variation analysis
                      from {DNA} methylation arrays for humans and mice.},
      journal      = {Bioinformatics},
      volume       = {40},
      number       = {2},
      issn         = {0266-7061},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2024-00176},
      pages        = {btae029},
      year         = {2024},
      note         = {2024 Feb 1;40(2):btae029},
      abstract     = {Copy-number variations (CNVs) are common genetic
                      alterations in cancer and their detection may impact tumor
                      classification and therapeutic decisions. However, detection
                      of clinically relevant large and focal CNVs remains
                      challenging when sample material or resources are limited.
                      This has motivated us to create a software tool to infer
                      CNVs from DNA methylation arrays which are often generated
                      as part of clinical routines and in research settings.We
                      present our R package, conumee 2.0, that combines tangent
                      normalization, an adjustable genomic binning heuristic, and
                      weighted circular binary segmentation to utilize DNA
                      methylation arrays for CNV analysis and mitigate technical
                      biases and batch effects. Segmentation results were
                      validated in a lung squamous cell carcinoma dataset from
                      TCGA (n = 367 samples) by comparison to segmentations
                      derived from genotyping arrays (Pearson's correlation
                      coefficient of 0.91). We further introduce a segmented block
                      bootstrapping approach to detect focal alternations that
                      achieved $60.9\%$ sensitivity and $98.6\%$ specificity for
                      deletions affecting CDKN2A/B $(60.0\%$ and $96.9\%$ for RB1,
                      respectively) in a low-grade glioma cohort from TCGA (n =
                      239 samples). Finally, our tool provides functionality to
                      detect and summarize CNVs across large sample
                      cohorts.Conumee 2.0 is available under open-source license
                      at: https://github.com/hovestadtlab/conumee2.Supplementary
                      data are available at Bioinformatics online.},
      cin          = {B062 / HD01 / B360 / BE01 / B060},
      ddc          = {570},
      cid          = {I:(DE-He78)B062-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B360-20160331 / I:(DE-He78)BE01-20160331 /
                      I:(DE-He78)B060-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38244574},
      doi          = {10.1093/bioinformatics/btae029},
      url          = {https://inrepo02.dkfz.de/record/287227},
}