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Avant-garde: an automated data-driven DIA data curation tool


Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.

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Fig. 1: Role of AvG in data analysis and schematic diagram of the modules.
Fig. 2: AvG improves quantitative figures of merit in a calibration curve.
Fig. 3: AvG equals the performances obtained by expert visual inspection and manual validation.
Fig. 4: Evaluation of AvG with LFQBench data.
Fig. 5: Detection of differentially expressed peptides in unoptimized and curated data.

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Data availability

The original mass spectra have been deposited in the public proteomics repository MassIVE and are accessible at Source data are provided with this paper.

Code availability

Avant-garde is an open-source software tool available as an R package and as a Skyline External tool at Avant-garde can be directly downloaded from the tool Store interface within Skyline or from the Skyline tool Store at


  1. 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 

  2. 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 

  3. Egertson, J. D., MacLean, B., Johnson, R., Xuan, Y. & MacCoss, M. J. Multiplexed peptide analysis using data-independent acquisition and Skyline. Nat. Protoc. 10, 887–903 (2015).

    Article  Google Scholar 

  4. Chapman, J. D., Goodlett, D. R. & Masselon, C. D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

    Article  CAS  Google Scholar 

  5. 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 

  6. Silva, J. C. et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 77, 2187–2200 (2005).

    Article  CAS  Google Scholar 

  7. Silva, J. C. et al. Simultaneous qualitative and quantitative analysis of the Escherichia coli proteome: a sweet tale. Mol. Cell Proteom. 5, 589–607 (2006).

    Article  CAS  Google Scholar 

  8. 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 

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

    Article  CAS  Google Scholar 

  10. Bilbao, A. et al. Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15, 964–980 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. 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 

  13. 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 

  14. Jaffe, J. D., Feeney, C. M., Patel, J., Lu, X. & Mani, D. R. Transitioning from targeted to comprehensive mass spectrometry using genetic algorithms. J. Am. Soc. Mass Spectrom. 27, 1745–1751 (2016).

    Article  CAS  Google Scholar 

  15. Tsou, C.-C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12, 258–264 (2015).

    Article  CAS  Google Scholar 

  16. Searle, B. C. et al. Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat. Commun. 9, 5128 (2018).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. 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 

  19. Abelin, J. G. et al. Reduced-representation phosphosignatures measured by quantitative targeted MS capture cellular states and enable large-scale comparison of drug-induced phenotypes. Mol. Cell Proteom. 15, 1622–1641 (2016).

    Article  CAS  Google Scholar 

  20. Rosenberger, G. et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat. Methods 14, 921–927 (2017).

    Article  CAS  Google Scholar 

  21. Röst, H. L. et al. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat. Methods 13, 777–783 (2016).

    Article  Google Scholar 

  22. Ramus, C. et al. Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset. J. Proteom. 132, 51–62 (2016).

    Article  CAS  Google Scholar 

  23. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  Google Scholar 

  24. Röst, H., Malmström, L. & Aebersold, R. A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol. Cell Proteom. 11, 540–549 (2012).

    Article  Google Scholar 

  25. Keller, A., Bader, S. L., Shteynberg, D., Hood, L. & Moritz, R. L. Automated validation of results and removal of fragment ion interferences in targeted analysis of data-independent acquisition mass spectrometry (MS) using SWATHProphet. Mol. Cell Proteom. 14, 1411–1418 (2015).

    Article  CAS  Google Scholar 

  26. Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).

    Article  CAS  Google Scholar 

  27. Teo, G. et al. mapDIA: preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J. Proteom. 129, 108–120 (2015).

    Article  CAS  Google Scholar 

  28. Tsai, T.-H. et al. Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments. Mol. Cell Proteom. 19, 944–959 (2020).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

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We thank N. Pythoud, J. Bons, A. Burel and C. Carapito for beta-testing the software. This work was funded by U54 HG008097 to J.D.J. This work was also supported in part by grants from the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium grants NIH/NCI U24-CA210986 and NIH/NCI U01 CA214125 (to S.A.C.) and NIH/NCI U24-CA210979 to D.R. Mani, principal computational scientist in the Proteomics Platform at the Broad Institute of MIT and Harvard.

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Authors and Affiliations



A.S.V.J. conceived the study, designed and performed experiments, collected the data, authored software, and wrote the manuscript. R.P. provided help with the data analysis of the benchmarking datasets. N.S. and B.M. adapted Skyline to facilitate the use of Avant-garde as an External tool. B.M. performed data processing and validation of the LFQBench and Extended Benchmark datasets. K.K. provided help with the statistical analysis of the benchmarking datasets. K.C.D. and A.O. carried out experiments and collected data for the P100 dataset. K.C.D., A.O. and K.E.C. beta-tested the software and provided help with the data analysis. M.J.M. and S.A.C. provided laboratory resources and guidance on the manuscript. J.D.J. provided laboratory resources, provided input on the software, provided guidance on the experimental design and wrote the manuscript.

Corresponding authors

Correspondence to Alvaro Sebastian Vaca Jacome or Jacob D. Jaffe.

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Competing interests

The MacCoss Lab at the University of Washington (members N.S., B.M. and M.J.M.) has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. Additionally, M.J.M. is a paid consultant for Thermo Fisher Scientific. J.D.J. is employed by Inzen Therapeutics and declares that he has no conflict of interest. The remaining authors declare no competing interests.

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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.

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Vaca Jacome, A.S., Peckner, R., Shulman, N. et al. Avant-garde: an automated data-driven DIA data curation tool. Nat Methods 17, 1237–1244 (2020).

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