Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics


As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (, a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo–tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification–MS data.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Untargeted and targeted data analysis strategies and DIA-Umpire hybrid framework.
Figure 2: DIA-Umpire signal-processing algorithms.
Figure 3: Untargeted peptide and protein identification with DDA and DIA data from UPS2, E. coli and human cell lysate samples.
Figure 4: Comparative analysis of peptide identifications from DDA and DIA data from human cell lysate samples.
Figure 5: Application of the entire DIA-Umpire workflow to an AP-SWATH interactome data set.


  1. Bantscheff, M., Lemeer, S., Savitski, M.M. & Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965 (2012).

    Article  CAS  Google Scholar 

  2. Nesvizhskii, A.I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteomics 73, 2092–2123 (2010).

    Article  CAS  Google Scholar 

  3. Bailey, D.J., McDevitt, M.T., Westphall, M.S., Pagliarini, D.J. & Coon, J.J. Intelligent data acquisition blends targeted and discovery methods. J. Proteome Res. 13, 2152–2161 (2014).

    Article  CAS  Google Scholar 

  4. Weisbrod, C.R., Eng, J.K., Hoopmann, M.R., Baker, T. & Bruce, J.E. Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J. Proteome Res. 11, 1621–1632 (2012).

    Article  CAS  Google Scholar 

  5. Michalski, A., Cox, J. & Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res. 10, 1785–1793 (2011).

    Article  CAS  Google Scholar 

  6. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  Google Scholar 

  7. Tate, S., Larsen, B., Bonner, R. & Gingras, A.C. Label-free quantitative proteomics trends for protein-protein interactions. J. Proteomics 81, 91–101 (2013).

    Article  CAS  Google Scholar 

  8. Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A. & Yates, J.R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    Article  CAS  Google Scholar 

  9. Silva, J.C., Gorenstein, M.V., Li, G.Z., Vissers, J.P. & Geromanos, S.J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156 (2006).

    Article  CAS  Google Scholar 

  10. Panchaud, A. et al. Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 81, 6481–6488 (2009).

    Article  CAS  Google Scholar 

  11. Geiger, T., Cox, J. & Mann, M. Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol. Cell. Proteomics 9, 2252–2261 (2010).

    Article  CAS  Google Scholar 

  12. Egertson, J.D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods 10, 744–746 (2013).

    Article  CAS  Google Scholar 

  13. Distler, U. et al. Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat. Methods 11, 167–170 (2014).

    Article  CAS  Google Scholar 

  14. Purvine, S., Eppel, J.T., Yi, E.C. & Goodlett, D.R. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3, 847–850 (2003).

    Article  CAS  Google Scholar 

  15. Colangelo, C.M., Chung, L., Bruce, C. & Cheung, K.H. Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 61, 287–298 (2013).

    Article  CAS  Google Scholar 

  16. Röst, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).

    Article  Google Scholar 

  17. Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014).

    Article  CAS  Google Scholar 

  18. Li, G.Z. et al. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9, 1696–1719 (2009).

    Article  CAS  Google Scholar 

  19. Pak, H. et al. Clustering and filtering tandem mass spectra acquired in data-independent mode. J. Am. Soc. Mass Spectrom. 24, 1862–1871 (2013).

    Article  CAS  Google Scholar 

  20. Craig, R., Cortens, J.P. & Beavis, R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004).

    Article  CAS  Google Scholar 

  21. Eng, J.K., Jahan, T.A. & Hoopmann, M.R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    Article  CAS  Google Scholar 

  22. Kim, S. et al. The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search. Mol. Cell. Proteomics 9, 2840–2852 (2010).

    Article  CAS  Google Scholar 

  23. Keller, A., Nesvizhskii, A.I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

    Article  CAS  Google Scholar 

  24. Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 10, M111.007690 (2011).

    Article  Google Scholar 

  25. Nesvizhskii, A.I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

    Article  CAS  Google Scholar 

  26. Lambert, J.P. et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat. Methods 10, 1239–1245 (2013).

    Article  CAS  Google Scholar 

  27. Lam, H. et al. Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods 5, 873–875 (2008).

    Article  CAS  Google Scholar 

  28. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8, 430–435 (2011).

    Article  CAS  Google Scholar 

  29. Liu, Y. et al. Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Mol. Cell. Proteomics 13, 1753–1768 (2014).

    Article  CAS  Google Scholar 

  30. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  31. Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics 11, M111.013987 (2012).

    Article  Google Scholar 

  32. Collins, B.C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 10, 1246–1253 (2013).

    Article  CAS  Google Scholar 

  33. Nesvizhskii, A.I. Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments. Proteomics 12, 1639–1655 (2012).

    Article  CAS  Google Scholar 

  34. Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011).

    Article  CAS  Google Scholar 

  35. Choi, H., Glatter, T., Gstaiger, M. & Nesvizhskii, A.I. SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments. J. Proteome Res. 11, 2619–2624 (2012).

    Article  CAS  Google Scholar 

  36. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823 (2013).

    Article  CAS  Google Scholar 

  37. Jeronimo, C. et al. Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. Mol. Cell 27, 262–274 (2007).

    Article  CAS  Google Scholar 

  38. Prakash, A. et al. Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis. J. Proteome Res. 13, 5415–5430 (2014).

    Article  CAS  Google Scholar 

  39. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

  40. Chambers, M.C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  Google Scholar 

  41. Tautenhahn, R., Bottcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).

    Article  Google Scholar 

  42. Nesvizhskii, A.I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 5, 652–670 (2006).

    Article  CAS  Google Scholar 

  43. Kryuchkov, F., Verano-Braga, T., Hansen, T.A., Sprenger, R.R. & Kjeldsen, F. Deconvolution of mixture spectra and increased throughput of peptide identification by utilization of intensified complementary ions formed in tandem mass spectrometry. J. Proteome Res. 12, 3362–3371 (2013).

    Article  CAS  Google Scholar 

  44. Deutsch, E.W. et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics 10, 1150–1159 (2010).

    Article  CAS  Google Scholar 

  45. Tsou, C.C. et al. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol. Cell. Proteomics 9, 131–144 (2010).

    Article  CAS  Google Scholar 

  46. Lam, H., Deutsch, E.W. & Aebersold, R. Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics. J. Proteome Res. 9, 605–610 (2010).

    Article  CAS  Google Scholar 

  47. Cox, J., Michalski, A. & Mann, M. Software lock mass by two-dimensional minimization of peptide mass errors. J. Am. Soc. Mass Spectrom. 22, 1373–1380 (2011).

    Article  CAS  Google Scholar 

  48. Barsnes, H. et al. compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics 12, 70 (2011).

    Article  Google Scholar 

  49. Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012).

    Article  CAS  Google Scholar 

  50. Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014).

    Article  Google Scholar 

Download references


We thank B. MacLean for help with Skyline, H. Röst and B. Collins for help with OpenSWATH and S. Tate for useful discussions. We also thank S. Danielson at Thermo Scientific for access to the Q Exactive Plus and Z.-Y. Lin for the acquisition of the DIA samples for MEPCE, EIF4A2 and GFP. This work was supported by US National Institutes of Health grants 5R01GM94231 (to A.I.N. and A.-C.G.), R01GM107148 and U24DK097153 (to A.I.N.); the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-069 to A.-C.G., A.I.N. and H.C.) and the Canadian Institutes of Health Research (MOP-84314 and MOP-123322 to A.-C.G.); and Singapore Ministry of Education grant R-608-000-088-112 (to H.C.).

Author information

Authors and Affiliations



A.I.N. and C.-C.T. conceived the project and developed the algorithm. C.-C.T. implemented the software. D.A. assisted with the OpenSWATH analysis and contributed to the algorithm and software development. B.L. and M.T. acquired mass spectrometry data. H.C. assisted with SAINT scoring and contributed to the development of protein quantification strategies. A.-C.G., C.-C.T., B.L. and A.I.N. designed experiments and analyzed data. A.I.N. and A.-C.G. supervised the project. C.-C.T., A.I.N. and A.-C.G. wrote the manuscript with input from B.L. and D.A.

Corresponding authors

Correspondence to Anne-Claude Gingras or Alexey I Nesvizhskii.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–29 (PDF 7641 kb)

Supplementary Table 1

Number of protein and peptide ion identifications from DDA MS/MS and DIA pseudo-MS/MS spectra obtained with three different search engines (X!Tandem, Comet and MSGF+), as well as with all three search engines combined (XLSX 12 kb)

Supplementary Table 2

Protein identification and quantification results (protein, peptide ion and fragment ion levels), human cell lysate samples (XLSX 30210 kb)

Supplementary Table 3

Protein identification and quantification results (protein, peptide ion and fragment ion levels), E. coli cell lysate samples (XLSX 20544 kb)

Supplementary Table 4

Protein identification and quantification results (protein, peptide ion and fragment ion levels), UPS2 samples (XLSX 3843 kb)

Supplementary Table 5

Peptide ion identifications from DDA, DIA with DIA-Umpire and DIA with OpenSWATH, human cell lysate samples (XLSX 2051 kb)

Supplementary Table 6

Peptide ion identifications from DDA, DIA with DIA-Umpire and DIA with OpenSWATH, E. coli cell lysate samples (XLSX 1394 kb)

Supplementary Table 7

Protein identification and quantification results (protein, peptide ion and fragment ion levels), DIA (SWATH) glycoproteomics data set (XLSX 19282 kb)

Supplementary Table 8

Protein identification and quantification results (protein, peptide ion and fragment ion levels), AP-SWATH interactome data set (XLSX 42939 kb)

Supplementary Table 9

SAINT results, AP-SWATH interactome data set (XLSX 119 kb)

Supplementary Table 10

Number of peptide ions and proteins for a representative DIA (SWATH) run identified using different thresholds for precursor-fragment grouping (XLSX 12 kb)

Supplementary Table 11

List of the raw mass spectrometry files (XLSX 12 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsou, CC., Avtonomov, D., Larsen, B. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12, 258–264 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research