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| Home > Publications database > HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments. > print |
| 001 | 282690 | ||
| 005 | 20240418143016.0 | ||
| 024 | 7 | _ | |a 10.1002/pmic.202300134 |2 doi |
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| 037 | _ | _ | |a DKFZ-2023-01836 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 540 |
| 100 | 1 | _ | |a Gómez-Zepeda, David |0 P:(DE-He78)4569ef2919d2438765ad71515f53646b |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments. |
| 260 | _ | _ | |a Weinheim |c 2024 |b Wiley VCH |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a HI-TRON / #EA:D190#LA:D191# / 2024 Apr;24(8):e2300134 |
| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a LC-MS |2 Other |
| 650 | _ | 7 | |a contamination |2 Other |
| 650 | _ | 7 | |a sample preparation |2 Other |
| 650 | _ | 7 | |a software |2 Other |
| 700 | 1 | _ | |a Michna, Thomas |b 1 |
| 700 | 1 | _ | |a Ziesmann, Tanja |b 2 |
| 700 | 1 | _ | |a Distler, Ute |b 3 |
| 700 | 1 | _ | |a Tenzer, Stefan |0 P:(DE-He78)74e391c68d7926be83d679f3d8891e33 |b 4 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1002/pmic.202300134 |g p. 2300134 |0 PERI:(DE-600)2037674-1 |n 8 |p e2300134 |t Proteomics |v 24 |y 2024 |x 1615-9853 |
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