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@ARTICLE{GmezZepeda:282690,
      author       = {D. Gómez-Zepeda$^*$ and T. Michna and T. Ziesmann and U.
                      Distler and S. Tenzer$^*$},
      title        = {{H}ow{D}irty: {A}n {R} package to evaluate molecular
                      contaminants in {LC}-{MS} experiments.},
      journal      = {Proteomics},
      volume       = {24},
      number       = {8},
      issn         = {1615-9853},
      address      = {Weinheim},
      publisher    = {Wiley VCH},
      reportid     = {DKFZ-2023-01836},
      pages        = {e2300134},
      year         = {2024},
      note         = {HI-TRON / #EA:D190#LA:D191# / 2024 Apr;24(8):e2300134},
      abstract     = {Contaminants derived from consumables, reagents, and sample
                      handling often negatively affect LC-MS data acquisition. In
                      proteomics experiments, they can markedly reduce
                      identification performance, reproducibility, and
                      quantitative robustness. Here, we introduce a data analysis
                      workflow combining MS1 feature extraction in Skyline with
                      HowDirty, an R-markdown-based tool, that automatically
                      generates an interactive report on the molecular contaminant
                      level in LC-MS data sets. To facilitate the interpretation
                      of the results, the HTML report is self-contained and
                      self-explanatory, including plots that can be easily
                      interpreted. The R package HowDirty is available from
                      https://github.com/DavidGZ1/HowDirty. To demonstrate a
                      showcase scenario for the application of HowDirty, we
                      assessed the impact of ultrafiltration units from different
                      providers on sample purity after filter-assisted sample
                      preparation (FASP) digestion. This allowed us to select the
                      filter units with the lowest contamination risk. Notably,
                      the filter units with the lowest contaminant levels showed
                      higher reproducibility regarding the number of peptides and
                      proteins identified. Overall, HowDirty enables the efficient
                      evaluation of sample quality covering a wide range of common
                      contaminant groups that typically impair LC-MS analyses,
                      facilitating corrective or preventive actions to minimize
                      instrument downtime.},
      keywords     = {LC-MS (Other) / contamination (Other) / sample preparation
                      (Other) / software (Other)},
      cin          = {D190 / D191},
      ddc          = {540},
      cid          = {I:(DE-He78)D190-20160331 / I:(DE-He78)D191-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37679057},
      doi          = {10.1002/pmic.202300134},
      url          = {https://inrepo02.dkfz.de/record/282690},
}