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PreprintCC BY 4.0bioRxiv

A community audit of error control in crosslinking mass spectrometry

14 more authors
18 affiliations
  1. 1Technische Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany.
  2. 2Si-M/“Der Simulierte Mensch”, a Science Framework of Technische Universität Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany.
  3. 3Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
  4. 4Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria.
  5. 5University of Washington, Seattle, WA, USA.
  6. 6Carlos Chagas Institute, Fiocruz Paraná, Curitiba, Brazil
  7. 7Department of Pharmaceutical Chemistry, University of California, San Francisco, USA.
  8. 8Department of Chemistry, Unicamp, São Paulo, Brazil
  9. 9Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, and Netherlands Proteomics Center, Utrecht, the Netherlands.
  10. 10Institute for Systems Biology, Seattle, WA, USA.
  11. 11Department of Physical and Chemical Sciences, University of L’Aquila, L’Aquila, Italy.
  12. 12Leibniz-Forschungsinstitut für Molekulare Pharmakologie im Forschungsverbund Berlin e.V. (FMP) 12489 Berlin, Germany.
  13. 13Research Institute of Molecular Pathology (IMP), The Vienna Biocenter, Dr. Bohrgasse 7, Vienna, A-1030, Austria.
  14. 14Institute of Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.
  15. 15Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria.
  16. 16current affiliation: Max Planck Institute of Molecular Plant Physiology, Potsdam Science Park, Potsdam-Golm, Germany
  17. 17Arafel Consulting LLC, Seattle, Washington. USA.
  18. 18Thermo Fisher Scientific, San Jose, CA, USA.

Abstract

In a community effort, we audited error control in twelve crosslinking mass spectrometry pipelines using two ground-truth designs that expose a lower bound on false identifications. Some pipelines passed, whereas for others these verifiably false identifications alone exceeded the requested false discovery rate up to 29-fold at the protein–protein interaction level. The audit also tracked improvement between software versions, supporting its use for regression testing.

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BibTeX
@misc{fischer2026community,
  title = {{A community audit of error control in crosslinking mass spectrometry}},
  author = {Lutz Fischer and Bruno C. Amaral and Micha Johannes Birklbauer and James E. Bruce and Paulo C Carvalho and Robert J. Chalkley and Milan A Clasen and Viktoria Dorfer and Fabio C Gozzo and A.J.R Heck and Michael R. Hoopmann and Claudio Iacobucci and Andrew Keller and Lars Kolbowski and Swantje Lenz and Diogo Borges Lima and Fan Liu and Karl Mechtler and Robert L. Moritz and Fränze Müller and Julia Ruta and David C. Schriemer and David D. Shteynberg and Michael J. Trnka and Rosa Viner and Juri Rappsilber},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.1101/2025.04.27.649519},
  url = {https://doi.org/10.1101/2025.04.27.649519},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - A community audit of error control in crosslinking mass spectrometry
AU  - Lutz Fischer
AU  - Bruno C. Amaral
AU  - Micha Johannes Birklbauer
AU  - James E. Bruce
AU  - Paulo C Carvalho
AU  - Robert J. Chalkley
AU  - Milan A Clasen
AU  - Viktoria Dorfer
AU  - Fabio C Gozzo
AU  - A.J.R Heck
AU  - Michael R. Hoopmann
AU  - Claudio Iacobucci
AU  - Andrew Keller
AU  - Lars Kolbowski
AU  - Swantje Lenz
AU  - Diogo Borges Lima
AU  - Fan Liu
AU  - Karl Mechtler
AU  - Robert L. Moritz
AU  - Fränze Müller
AU  - Julia Ruta
AU  - David C. Schriemer
AU  - David D. Shteynberg
AU  - Michael J. Trnka
AU  - Rosa Viner
AU  - Juri Rappsilber
PY  - 2026
DA  - 2026/10/28
PB  - bioRxiv
DO  - 10.1101/2025.04.27.649519
UR  - https://doi.org/10.1101/2025.04.27.649519
AB  - In a community effort, we audited error control in twelve crosslinking mass spectrometry pipelines using two ground-truth designs that expose a lower bound on false identifications. Some pipelines passed, whereas for others these verifiably false identifications alone exceeded the requested false discovery rate up to 29-fold at the protein–protein interaction level. The audit also tracked improvement between software versions, supporting its use for regression testing.
SN  - 2692-8205
ER  -