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Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics.
- Source :
-
Molecular & cellular proteomics : MCP [Mol Cell Proteomics] 2023 Apr; Vol. 22 (4), pp. 100515. Date of Electronic Publication: 2023 Feb 14. - Publication Year :
- 2023
-
Abstract
- Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA-peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.<br />Competing Interests: Conflict of interest A. W. P. is a scientific advisor for Bioinformatics Solutions Inc (the provider of PEAKS software). There are no other conflicts of interest declared by the authors.<br /> (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1535-9484
- Volume :
- 22
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Molecular & cellular proteomics : MCP
- Publication Type :
- Academic Journal
- Accession number :
- 36796644
- Full Text :
- https://doi.org/10.1016/j.mcpro.2023.100515