JPOST Repositoryapplication/xmlhttps://storage.jpostdb.org/JPST003768/files/PEAKS11_Online_HLA1.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS_X_Pro_HLA2_FDR.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS_X_Pro_HLA1.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS_X_Pro_HLA1_entrapment.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS11_pro_HLA1_transcriptome.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS11_Online_HLA2_FDR.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS11_pro_HLA1_de_novo.csvhttps://storage.jpostdb.org/JPST003768/files/PEAKS11_Online_HLA1_entrapment.csvhttps://storage.jpostdb.org/JPST003768/files/G220824_013_Slot2-31_1_6738.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_008_Slot2-29_1_6733.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_019_Slot2-30_1_6744.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_014_Slot2-28_1_6739.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_015_Slot2-29_1_6740.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_018_Slot2-29_1_6743.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_020_Slot2-31_1_6745.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_007_Slot2-28_1_6732.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_017_Slot2-28_1_6742.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_009_Slot2-30_1_6734.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_010_Slot2-31_1_6735.d.ziphttps://storage.jpostdb.org/JPST003768/files/G220824_012_Slot2-30_1_6737.d.zipprimary200OKProteomicsStefan TenzerHomo Sapiens (human)https://repository.jpostdb.org/entry/JPST003768HI-TRON, DKFZjPOSTMass 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-based software platforms for immunopeptidomics: MaxQuant (https://maxquant.org/), FragPipe (https://fragpipe.nesvilab.org/), PEAKS (https://www.bioinfor.com/peaks-software/) and major histocompatibility complexquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-data-dependent acquisition-parallel accumulation and serial fragmentation 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% false discovery rate control, achieving medium to high identification confidence correlations. The largest number of immunopeptides was identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.Benchmarking Software for DDA-PASEF Immunopeptidomics.Chen Yannic Y, Preikschat Annica A, Arnold Annette A, Pecori Riccardo R, Gomez-Zepeda David D, Tenzer Stefan SfalseBenchmarking Software for DDA-PASEF ImmunopeptidomicsMass 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. This distinction necessitates searching without the constraints of proteolytic specificity, dramatically expanding the search space and 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 several 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 identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate (FDR) control, achieving medium to high identification confidence correlations. The largest number of immunopeptides were identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, helping to determine the right choice of software.Fri Jun 26 00:00:00 BST 2026PXD065501960641423049