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HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments
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  • David Gomez-Zepeda,
  • Thomas Michna,
  • Tanja Ziesmann,
  • Ute Distler,
  • Stefan Tenzer
David Gomez-Zepeda
Helmholtz-Institute for Translational Oncology Mainz (HI-TRON), Germany
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Thomas Michna
University Medical Center of the Johannes Gutenberg University Mainz
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Tanja Ziesmann
University Medical Center of the Johannes Gutenberg University Mainz
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Ute Distler
University Medical Center of the Johannes Gutenberg University Mainz
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Stefan Tenzer
University Medical Center of the Johannes Gutenberg University Mainz

Corresponding Author:[email protected]

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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 taking corrective or preventive actions to minimize instrument downtime.
24 Jul 2023Submitted to PROTEOMICS
26 Jul 2023Review(s) Completed, Editorial Evaluation Pending
26 Jul 2023Submission Checks Completed
26 Jul 2023Assigned to Editor
26 Jul 2023Reviewer(s) Assigned
18 Aug 2023Editorial Decision: Revise Minor
22 Aug 2023Review(s) Completed, Editorial Evaluation Pending
22 Aug 20231st Revision Received
24 Aug 2023Editorial Decision: Accept