TY  - JOUR
AU  - Gómez-Zepeda, David
AU  - Michna, Thomas
AU  - Ziesmann, Tanja
AU  - Distler, Ute
AU  - Tenzer, Stefan
TI  - HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments.
JO  - Proteomics
VL  - 24
IS  - 8
SN  - 1615-9853
CY  - Weinheim
PB  - Wiley VCH
M1  - DKFZ-2023-01836
SP  - e2300134
PY  - 2024
N1  - HI-TRON / #EA:D190#LA:D191# / 2024 Apr;24(8):e2300134
AB  - 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.
KW  - LC-MS (Other)
KW  - contamination (Other)
KW  - sample preparation (Other)
KW  - software (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37679057
DO  - DOI:10.1002/pmic.202300134
UR  - https://inrepo02.dkfz.de/record/282690
ER  -