MassIVE MSV000088714

Complete Public PXD031139

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics

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Description

Mass spectrometry-based proteomics is challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and almost impossible to avoid, reducing the sensitivity, specificity and reproducibility of protein identification and quantification. For data-dependent acquisition (DDA) proteomics, exclusion lists and a contaminant FASTA library 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) proteomics. In this study, we established custom FASTA and spectral libraries for common protein contaminants in bottom-up proteomics, and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We found that including our contaminant library can reduce false identifications, increase protein IDs, and modestly reduce quantification variations. We also compared various DIA and DDA data analysis platforms, and provided practical suggestions on how to best incorporate the contaminant library for different software platforms. Therefore, we recommend using our contaminant library for both DDA and DIA proteomics workflows. With the increasing popularity of DIA proteomics, our contaminant FASTA and spectral libraries can be an especially useful resource for the proteomics community. [doi:10.25345/C53285] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Protein Contaminant ; Contamination ; DIA ; DDA ; DIA-NN ; Spectronaut ; DirectDIA

Contact

Principal Investigators:
(in alphabetical order) 		Ling Hao, The George Washington University, United States of America
Submitting User: haolab

Publications

Frankenfield AM, Ni J, Ahmed M, Hao L.
Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.
J Proteome Res. 2022 Sep 2;21(9):2104-2113. Epub 2022 Jul 6.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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