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@ARTICLE{Chen:307384,
      author       = {Y. Chen$^*$ and A. Preikschat and A. Arnold$^*$ and R.
                      Pecori$^*$ and D. Gomez-Zepeda$^*$ and S. Tenzer$^*$},
      title        = {{B}enchmarking {S}oftware for {DDA}-{PASEF}
                      {I}mmunopeptidomics.},
      journal      = {Molecular $\&$ cellular proteomics},
      volume       = {nn},
      issn         = {1535-9476},
      address      = {Bethesda, Md.},
      publisher    = {The American Society for Biochemistry and Molecular
                      Biology},
      reportid     = {DKFZ-2025-03028},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:D191#LA:D191# / epub},
      abstract     = {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.},
      cin          = {D191 / D150},
      ddc          = {610},
      cid          = {I:(DE-He78)D191-20160331 / I:(DE-He78)D150-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41423049},
      doi          = {10.1016/j.mcpro.2025.101492},
      url          = {https://inrepo02.dkfz.de/record/307384},
}