Vaca Jacome AS, Peckner R, Shulman N, Krug K, DeRuff KC, Officer A, Christianson KE, MacLean B, MacCoss MJ, Carr SA, Jaffe JD. Avant-garde: an automated data-driven DIA data curation tool.
Nat Methods 2020;
17:1237-1244. [PMID:
33199889 PMCID:
PMC7723322 DOI:
10.1038/s41592-020-00986-4]
[Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/25/2020] [Indexed: 12/03/2022]
Abstract
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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