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-NN: neural networks and interference correction enable deep proteome coverage in high throughput


We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Fig. 1: DIA-NN workflow and its performance on conventional and short chromatographic gradients.
Fig. 2: LFQbench test performance of DIA-NN.

Data availability

The newly generated mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE32 partner repository with the dataset identifier PXD014690; previously published data were also used to benchmark the software (repositories with identifiers PXD005573, PXD002952, PXD010529 and PXD006722). All the precursor and protein identification and quantification information has been uploaded to the OSF repository (

Code availability

DIA-NN (1.6.0) is open-source and is freely available at under a permissive licence.


  1. 1.

    Yates, J. R., Ruse, C. I. & Nakorchevsky, A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11, 49–79 (2009).

    CAS  Article  Google Scholar 

  2. 2.

    Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  Article  Google Scholar 

  3. 3.

    Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 13, 942 (2017).

    Article  Google Scholar 

  4. 4.

    Zelezniak, A. et al. Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts. Cell Syst. 7, 269–283 (2018).

    CAS  Article  Google Scholar 

  5. 5.

    Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteom. 14, 1400–1410 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Meier, F., Geyer, P. E., Virreira Winter, S., Cox, J. & Mann, M. BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat. Methods 15, 440–448 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    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).

    CAS  Article  Google Scholar 

  8. 8.

    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. Proteom. 11, O111.016717 (2012).

    Article  Google Scholar 

  9. 9.

    Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for qualitative and quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

    Article  Google Scholar 

  10. 10.

    Collins, B. C. et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performances of SWATH-mass spectrometry. Nat. Commun. 8, 291 (2017).

    Article  Google Scholar 

  11. 11.

    Vowinckel, J. et al. Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. Sci. Rep. 8, 4346 (2018).

    Article  Google Scholar 

  12. 12.

    Bruderer, R. et al. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteom. 16, 2296–2309 (2017).

    CAS  Article  Google Scholar 

  13. 13.

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

    CAS  Article  Google Scholar 

  14. 14.

    Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    CAS  Article  Google Scholar 

  15. 15.

    Ting, Y. S. et al. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat. Methods 14, 903–908 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Wang, J. et al. MSPLIT-DIA: sensitive peptide identification for data-independent acquisition. Nat. Methods 12, 1106–1108 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

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

    CAS  Article  Google Scholar 

  20. 20.

    Peckner, R. et al. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat. Methods 15, 371–378 (2018).

    CAS  Article  Google Scholar 

  21. 21.

    Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Sun, S. et al. MS-Simulator: predicting y-ion intensities for peptides with two charges based on the intensity ratio of neighboring ions. J. Proteome Res. 11, 4509–4516 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. Preprint at arXiv (2014).

  24. 24.

    Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 479–498 (2002).

    Article  Google Scholar 

  25. 25.

    Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).

    Article  Google Scholar 

  26. 26.

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

    CAS  Article  Google Scholar 

  27. 27.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).

  28. 28.

    Parker, S. J. et al. Indentification of a set of conserved eukaryotic internal retention time standards for data-independent acquisition mass spectrometry. Mol. Cell. Proteom. 14, 2800–2813 (2015).

    CAS  Article  Google Scholar 

  29. 29.

    Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeting proteomics workflows. EMBO Rep. 9, 429–434 (2008).

    CAS  Article  Google Scholar 

  30. 30.

    Teleman, J. et al. DIANA—algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics 31, 555–562 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Mülleder, M., Campbell, K., Matsarskaia, O., Eckerstorfer, F. & Ralser, M. Saccharomyces cerevisiae single-copy plasmids for auxotrophy compensation, multiple marker selection, and for designing metabolically cooperating communities. F1000Res 5, 2351 (2016).

    Article  Google Scholar 

  32. 32.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  Article  Google Scholar 

Download references


We thank R. Bruderer (Biognosys) for providing the spectral libraries. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001134), the UK Medical Research Council (FC001134), and the Wellcome Trust (FC001134), and received specific funding from the BBSRC (BB/N015215/1 and BB/N015282/1) and the Wellcome Trust (200829/Z/16/Z) as well as a Crick Idea to Innovation (i2i) initiative (grant number 10658).

Author information




V.D., M.R. and K.S.L. designed the study, V.D. and M.R. wrote the first manuscript draft, V.D. designed and implemented the algorithms, C.B.M., V.D. and S.I.V. performed the experiments, and all authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Markus Ralser.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Demichev, V., Messner, C.B., Vernardis, S.I. et al. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17, 41–44 (2020).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing