Please use this identifier to cite or link to this item:
Authors: Gomez-Zepeda, David
Michna, Thomas
Ziesmann, Tanja
Distler, Ute
Tenzer, Stefan
Title: HowDirty: an R package to evaluate molecular contaminants in LC-MS experiments
Online publication date: 8-Dec-2023
Year of first publication: 2023
Language: english
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 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.
DDC: 610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC BY-NC
Information on rights of use:
Journal: Proteomics
Version of Record (VoR)
Pages or article number: 2300134
Publisher: Wiley-VCH
Publisher place: Weinheim
Issue date: 2023
ISSN: 1615-9853
Publisher DOI: 10.1002/pmic.202300134
Appears in collections:DFG-491381577-H

Files in This Item:
  File Description SizeFormat
howdirty__an_r_package_to_eva-20231208102956316.pdf810.37 kBAdobe PDFView/Open