Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, as exemplified by the technique SWATH-MS, has emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale data sets. We demonstrate that statistical concepts developed for discovery proteomics based on spectrum-centric scoring can be adapted to large-scale DIA experiments that have been analyzed with peptide-centric scoring strategies, and we provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent the accumulation of false positives across large-scale data sets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for the detected peptide queries, peptides and inferred proteins.

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Please note that M.H., C.L.H., Y.L., M.J.M., B.X.M., A.I.N., P.G.A.P., L.R., H.L.R., S.T. and Y.S.T. were added to the author list in alphabetical order. We thank the authors of the SWATH-MS interlaboratory study and of the human blood plasma data set for providing the data to conduct this study. We also thank the Scientific IT Support (ID SIS) and the high-performance computing (HPC) teams of ETH Zurich for support and maintenance of the computing infrastructure. M.H. was supported by a grant from the Institut Mérieux; A.I.N. was funded by the US National Institutes of Health (NIH; grant R01GM094231); H.L.R. was funded by the Swiss National Science Foundation (SNSF; grant P2EZP3 162268); B.C.C. was supported by a SNSF Ambizione grant (PZ00P3_161435); and R.A. was supported by ERC Proteomics v3.0 (AdG-233226 Proteomics v.3.0) and AdG-670821 Proteomics 4D), the PhosphonetX project of SystemsX.ch and the Swiss National Science Foundation (SNSF) grant 31003A_166435.

Author information

Author notes

    • George Rosenberger
    •  & Isabell Bludau

    These authors contributed equally to this work.


  1. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.

    • George Rosenberger
    • , Isabell Bludau
    • , Moritz Heusel
    • , Yansheng Liu
    • , Patrick G A Pedrioli
    • , Hannes L Röst
    • , Ben C Collins
    •  & Ruedi Aebersold
  2. PhD Program in Systems Biology, University of Zurich and ETH Zurich, Zurich, Switzerland.

    • George Rosenberger
    •  & Isabell Bludau
  3. ID Scientific IT Services, ETH Zurich, Zurich, Switzerland.

    • Uwe Schmitt
  4. PhD program in Molecular and Translational Biomedicine, Competence Center Personalized Medicine (CC-PM), ETH Zurich and University of Zurich, Zurich, Switzerland.

    • Moritz Heusel
  5. SCIEX, Redwood City, California, USA.

    • Christie L Hunter
  6. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

    • Michael J MacCoss
    • , Brendan X MacLean
    •  & Ying S Ting
  7. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.

    • Alexey I Nesvizhskii
  8. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.

    • Alexey I Nesvizhskii
  9. Biognosys, Schlieren, Switzerland.

    • Lukas Reiter
  10. SCIEX, Concord, Ontario, Canada.

    • Stephen Tate
  11. Faculty of Science, University of Zurich, Zurich, Switzerland.

    • Ruedi Aebersold


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G.R., I.B. and R.A. wrote the paper with feedback from all authors; G.R. and B.C.C. developed the methods; I.B. analyzed the data set; U.S. and G.R. implemented the PyProphet extension; M.H., C.L.H., Y.L., M.J.M., B.X.M., A.I.N., P.G.A.P., L.R., H.L.R., S.T. and Y.S.T. provided critical input on the project; and B.C.C. and R.A. designed and supervised the study.

Competing interests

C.L.H. and S.T. are employees of SCIEX, which operates in the field of quantitative proteomics by data-independent acquisition covered by the article. M.J.M. is a paid consultant for Thermo Fisher Scientific, which operates in the field of quantitative proteomics by data-independent acquisition covered by the article. L.R. is employee of Biognosys AG, which operates in the field of quantitative proteomics by data-independent acquisition covered by the article. R.A. holds shares of Biognosys AG.

Corresponding authors

Correspondence to Ben C Collins or Ruedi Aebersold.

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