RT Journal Article
SR Electronic
T1 How do Protein Contaminants Influence DDA and DIA Proteomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.27.489766
DO 10.1101/2022.04.27.489766
A1 Frankenfield, Ashley M.
A1 Ni, Jiawei
A1 Ahmed, Mustafa
A1 Hao, Ling
YR 2022
UL http://biorxiv.org/content/early/2022/04/28/2022.04.27.489766.abstract
AB Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and impossible to avoid. For data-dependent acquisition (DDA) proteomics, 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 proteomics data is also unclear. In this study, we established the 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 proteomics workflow. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in different DDA and DIA data analysis platforms can be a valuable resource for proteomics researchers, which are freely accessible at https://github.com/HaoGroup-ProtContLib.Competing Interest StatementThe authors have declared no competing interest.