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.

Data availability

The original mass spectra have been deposited in the public proteomics repository MassIVE and are accessible at ftp://MSV000085540@massive.ucsd.edu. 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 https://github.com/SebVaca/Avant_garde. Avant-garde can be directly downloaded from the tool Store interface within Skyline or from the Skyline tool Store at https://skyline.ms/tool-AvG.url.


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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.

Author information




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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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). https://doi.org/10.1038/s41592-020-00986-4

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