ArticleNature methods2020
Avant-garde: an automated data-driven DIA data curation tool.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Label-Free Targeted Proteomics Data Analysis Workflow Selection - Benchmarking AI-based and Data-Driven Approaches.Molecular & cellular proteomics : MCP · 2026Article
- Accurate quantification in proteomics with QuantUMS.Nature biotechnology · 2026Article
- Comparative Proteomics of Salinity Stress Responses in Fish and Aquatic Invertebrates.Proteomics · 2025Review
- Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer.Nature communications · 2024Article
- Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.Journal of proteome research · 2024Article
- Targeted proteomics data interpretation with DeepMRM.Cell reports methods · 2023Article
- Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes.Molecular & cellular proteomics : MCP · 2023Article
- Skyline Batch: An Intuitive User Interface for Batch Processing with Skyline.Journal of proteome research · 2022Article
- MaxDIA enables library-based and library-free data-independent acquisition proteomics.Nature biotechnology · 2021Article
- Deep representation features from DreamDIACommunications biology · 2021Article
- Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds.Scientific data · 2021Article
- Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study.Journal of clinical medicine · 2021Article
- An Introduction to Advanced Targeted Acquisition Methods.Molecular & cellular proteomics : MCP · 2021Review
- Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.Molecular & cellular proteomics : MCP · 2020Article
- Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition.Molecular & cellular proteomics : MCP · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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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Registered trials
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