Avant-garde: An automated data-driven DIA data curation tool.

Multiple challenges remain in Data-Independent Acquisition (DIA) data analysis, like confidently identifying peptides, defining integration boundaries, removing interferences, and controlling false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We developed Avant-garde as a tool to refine DIA (and PRM) data. Avant-garde uses a novel data-driven scoring strategy; signals are refined by learning from the data itself, using all measurements in all samples to achieve the best optimization. We evaluated Avant-garde’s performance with benchmarking DIA datasets. We showed that it can determine the quantitative suitability of a peptide peak, and reaches 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 MS data.