% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Declercq:298620,
      author       = {A. Declercq and R. Devreese and J. Scheid and C. Jachmann
                      and T. Van Den Bossche and A. Preikschat and D.
                      Gómez-Zepeda$^*$ and J. B. Rijal and A. Hirschler and J. R.
                      Krieger and T. Srikumar and G. Rosenberger and C. Martelli
                      and D. Trede and C. Carapito and S. Tenzer and J. S.
                      Walz$^*$ and S. Degroeve and R. Bouwmeester and L. Martens
                      and R. Gabriels},
      title        = {{TIMS}2{R}escore: {A} {D}ata {D}ependent
                      {A}cquisition-{P}arallel {A}ccumulation and {S}erial
                      {F}ragmentation-{O}ptimized {D}ata-{D}riven {R}escoring
                      {P}ipeline {B}ased on {MS}2{R}escore.},
      journal      = {Journal of proteome research},
      volume       = {24},
      number       = {3},
      issn         = {1535-3893},
      address      = {Washington, DC},
      publisher    = {ACS Publications},
      reportid     = {DKFZ-2025-00320},
      pages        = {1067-1076},
      year         = {2025},
      note         = {HI-TRON / 2025 Mar 7;24(3):1067-1076},
      abstract     = {The high throughput analysis of proteins with mass
                      spectrometry (MS) is highly valuable for understanding human
                      biology, discovering disease biomarkers, identifying
                      therapeutic targets, and exploring pathogen interactions. To
                      achieve these goals, specialized proteomics subfields,
                      including plasma proteomics, immunopeptidomics, and
                      metaproteomics, must tackle specific analytical challenges,
                      such as an increased identification ambiguity compared to
                      routine proteomics experiments. Technical advancements in MS
                      instrumentation can mitigate these issues by acquiring more
                      discerning information at higher sensitivity levels. This is
                      exemplified by the incorporation of ion mobility and
                      parallel accumulation and serial fragmentation (PASEF)
                      technologies in timsTOF instruments. In addition, AI-based
                      bioinformatics solutions can help overcome ambiguity issues
                      by integrating more data into the identification workflow.
                      Here, we introduce TIMS2Rescore, a data-driven rescoring
                      workflow optimized for DDA-PASEF data from timsTOF
                      instruments. This platform includes new timsTOF MS2PIP
                      spectrum prediction models and IM2Deep, a new deep
                      learning-based peptide ion mobility predictor. Furthermore,
                      to fully streamline data throughput, TIMS2Rescore directly
                      accepts Bruker raw mass spectrometry data and search results
                      from ProteoScape and many other search engines, including
                      Sage and PEAKS. We showcase TIMS2Rescore performance on
                      plasma proteomics, immunopeptidomics (HLA class I and II),
                      and metaproteomics data sets. TIMS2Rescore is open-source
                      and freely available at
                      https://github.com/compomics/tims2rescore.},
      keywords     = {DDA-PASEF (Other) / machine learning (Other) / mass
                      spectrometry (Other) / peptide identification (Other) /
                      proteomics (Other) / rescoring (Other) / timsTOF (Other)},
      cin          = {TU01 / D191},
      ddc          = {540},
      cid          = {I:(DE-He78)TU01-20160331 / I:(DE-He78)D191-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39915959},
      doi          = {10.1021/acs.jproteome.4c00609},
      url          = {https://inrepo02.dkfz.de/record/298620},
}