The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics
Open accessPublished version of a preprint
Abstract
Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.
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“Contaminant libraries were used to exclude background signals67.”
From Investigating the neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies (2025) · cites this paper as [67]
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Metadata
- Authors
- Frankenfield, A. M., Ni, J., Ahmed, M., & Hao, L.
- Affiliations
- George Washington University (US)
- Corresponding author
- Hao, Ling
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-07-06
- Volume
- 21
- Issue
- 9
- Pages
- 2104-2113
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC10040255
- OpenAlex
- W4284882692
- Cited by
- 321
- References
- 46
- Citations by year
- 2026: 114 · 2025: 115 · 2024: 58 · 2023: 31 · 2022: 3
- FWCI
- 19.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Proteome Research (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS121608)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- protein contaminants, data-dependent acquisition, data-independent acquisition, false discovery rate, protein identification, shotgun proteomics
- MeSH terms
- Software, Mass Spectrometry, Proteome, Proteomics
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