TY  - JOUR
AU  - Chen, Yannic
AU  - Preikschat, Annica
AU  - Arnold, Annette
AU  - Pecori, Riccardo
AU  - Gomez-Zepeda, David
AU  - Tenzer, Stefan
TI  - Benchmarking Software for DDA-PASEF Immunopeptidomics.
JO  - Molecular & cellular proteomics
VL  - nn
SN  - 1535-9476
CY  - Bethesda, Md.
PB  - The American Society for Biochemistry and Molecular Biology
M1  - DKFZ-2025-03028
SP  - nn
PY  - 2025
N1  - #EA:D191#LA:D191# / epub
AB  - Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition (DDA)-based software platforms for immunopeptidomics: MaxQuant, FragPipe, PEAKS and MHCquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-DDA-PASEF method. We assessed each software's ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1
LB  - PUB:(DE-HGF)16
C6  - pmid:41423049
DO  - DOI:10.1016/j.mcpro.2025.101492
UR  - https://inrepo02.dkfz.de/record/307384
ER  -