ArticleNature methods2025
Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.Nature communications · 2026Article
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- End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.Scientific reports · 2026Article
- Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform.Nature communications · 2026Article
- Proteoforms as the true units of physiological function.European journal of applied physiology · 2026Review
- Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.Analytical chemistry · 2026Article
- A Functional Metabolomics Framework to Track Microbiome Drug Metabolism.bioRxiv : the preprint server for biology · 2026Article
- Proteome profiling reveals early diagnostic biomarker candidates for colorectal cancer.BMC cancer · 2026Article
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6 authors.
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Abstract
A critical challenge in mass spectrometry proteomics is accurately assessing error control, especially given that software tools employ distinct methods for reporting errors. Many tools are closed-source and poorly documented, leading to inconsistent validation strategies. Here we identify three prevalent methods for validating false discovery rate (FDR) control: one invalid, one providing only a lower bound, and one valid but under-powered. The result is that the proteomics community has limited insight into actual FDR control effectiveness, especially for data-independent acquisition (DIA) analyses. We propose a theoretical framework for entrapment experiments, allowing us to rigorously characterize different approaches. Moreover, we introduce a more powerful evaluation method and apply it alongside existing techniques to assess existing tools. We first validate our analysis in the better-understood data-dependent acquisition setup, and then, we analyze DIA data, where we find that no DIA search tool consistently controls the FDR, with particularly poor performance on single-cell datasets.
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