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

ABSTRACT: Vaca S, Peckner R, Shulman N, Krug K, DeRuff KC, Officer A, MacCoss MJ, Carr SA, Jaffe JD, Nature Methods 2020. 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. Avant-garde is a new tool to refine DIA (and PRM) data. Unlike other tools where MS experiments are scored independently from each other, 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 envisioned as a tool complementary to existing DIA analysis engines and aims to establish the strongest foundation for subsequent analysis of quantitative MS data.

INSTRUMENT(S): Q Exactive HF, Q Exactive HFX

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606) Saccharomyces Cerevisiae (ncbitaxon:4932)

SUBMITTER: Jacob D. Jaffe

PROVIDER: MSV000085540 | MassIVE | Wed Jun 03 06:16:00 BST 2020

REPOSITORIES: MassIVE

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