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@ARTICLE{Teschner:300630,
      author       = {D. Teschner and D. Gómez-Zepeda$^*$ and M. K. Łącki and
                      T. Kemmer and A. Busch and S. Tenzer$^*$ and A. Hildebrandt},
      title        = {{R}ustims: {A}n {O}pen-{S}ource {F}ramework for {R}apid
                      {D}evelopment and {P}rocessing of tims{TOF}
                      {D}ata-{D}ependent {A}cquisition {D}ata.},
      journal      = {Journal of proteome research},
      volume       = {24},
      number       = {5},
      issn         = {1535-3893},
      address      = {Washington, DC},
      publisher    = {ACS Publications},
      reportid     = {DKFZ-2025-00844},
      pages        = {2358-2368},
      year         = {2025},
      note         = {2025 May 2;24(5):2358-2368},
      abstract     = {Mass spectrometry is essential for analyzing and
                      quantifying biological samples. The timsTOF platform is a
                      prominent commercial tool for this purpose, particularly in
                      bottom-up acquisition scenarios. The additional ion mobility
                      dimension requires more complex data processing, yet most
                      current software solutions for timsTOF raw data are
                      proprietary or closed-source, limiting integration into
                      custom workflows. We introduce rustims, a framework
                      implementing a flexible toolbox designed for processing
                      timsTOF raw data, currently focusing on data-dependent
                      acquisition (DDA-PASEF). The framework employs a
                      dual-language approach, combining efficient, multithreaded
                      Rust code with an easy-to-use Python interface. This allows
                      for implementations that are fast, intuitive, and easy to
                      integrate. With imspy as its main Python scripting interface
                      and sagepy for Sage search engine bindings, rustims enables
                      fast, integrable, and intuitive processing. We demonstrate
                      its capabilities with a pipeline for DDA-PASEF data
                      including rescoring and integration of third-party tools
                      like the Prosit intensity predictor and an extended ion
                      mobility model. This pipeline supports tryptic proteomics
                      and nontryptic immunopeptidomics data, with benchmark
                      comparisons to FragPipe and PEAKS. Rustims is available on
                      GitHub under the MIT license, with installation packages for
                      multiple platforms on PyPi and all analysis scripts
                      accessible via Zenodo.},
      keywords     = {DDA-PASEF (Other) / Python (Other) / framework (Other) /
                      ion mobility (Other) / mass spectrometry (Other) /
                      open-source (Other) / proteomics (Other) / rust-lang (Other)
                      / timsTOF (Other)},
      cin          = {D190 / D191},
      ddc          = {540},
      cid          = {I:(DE-He78)D190-20160331 / 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:40260647},
      doi          = {10.1021/acs.jproteome.4c00966},
      url          = {https://inrepo02.dkfz.de/record/300630},
}