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| 001 | 307384 | ||
| 005 | 20251223120207.0 | ||
| 024 | 7 | _ | |a 10.1016/j.mcpro.2025.101492 |2 doi |
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| 100 | 1 | _ | |a Chen, Yannic |0 P:(DE-He78)e82233886826e6243af5e60717e5fb8a |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Benchmarking Software for DDA-PASEF Immunopeptidomics. |
| 260 | _ | _ | |a Bethesda, Md. |c 2025 |b The American Society for Biochemistry and Molecular Biology |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a #EA:D191#LA:D191# / epub |
| 520 | _ | _ | |a 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% 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, supporting the immunopeptidomics/MS community in determining the right choice of software. |
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| 700 | 1 | _ | |a Preikschat, Annica |b 1 |
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| 700 | 1 | _ | |a Pecori, Riccardo |0 P:(DE-He78)a8b399fa71eacddc353846ca1d9d2127 |b 3 |u dkfz |
| 700 | 1 | _ | |a Gomez-Zepeda, David |0 P:(DE-He78)4569ef2919d2438765ad71515f53646b |b 4 |u dkfz |
| 700 | 1 | _ | |a Tenzer, Stefan |0 P:(DE-He78)74e391c68d7926be83d679f3d8891e33 |b 5 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1016/j.mcpro.2025.101492 |g p. 101492 - |0 PERI:(DE-600)2071375-7 |p nn |t Molecular & cellular proteomics |v nn |y 2025 |x 1535-9476 |
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