ArticleNature methods2017
Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.
Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
114 citing papers in PubMed.
- Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.Molecular & cellular proteomics : MCP · 2019Trial
- Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability.Molecular systems biology · 2026Article
- Ammonification Process Is Limited by Transformation of Medium Molecular Weight Nitrogenous Organics but Not Hydrolysis of Proteins.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrative Multi-Omics Analysis of Multiple Sclerosis Reveals Cell-Type-Specific Regulatory Landscapes and Discordant Methylation-Expression Coupling.International journal of molecular sciences · 2026Article
- Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Generation of peptide detectability datasets from single DIA experiment for prediction model fine-tuning.Bioinformatics advances · 2026Article
- Data-independent acquisition-based quantitative proteomics in plants.Frontiers in plant science · 2026Review
- Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry.Journal of mass spectrometry and advances in the clinical lab · 2025Article
- Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data.Communications chemistry · 2025Article
- Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability.bioRxiv : the preprint server for biology · 2025Article
- HLA Ligand Atlas DIA: extending the benign immunopeptidomics resource with increased sensitivity through data-independent acquisition mass spectrometry.Journal for immunotherapy of cancer · 2025Article
- Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.Journal of translational medicine · 2025Article
- Unifying the analysis of bottom-up proteomics data with CHIMERYS.Nature methods · 2025Article
- Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.Briefings in bioinformatics · 2025Review
- Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics.ACS omega · 2024Article
- An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.Journal of proteome research · 2024Article
- Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states.Nature communications · 2024Article
- Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples.Journal of proteome research · 2024Article
- Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.Nature communications · 2024Article
54 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, as exemplified by the technique SWATH-MS, has emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale data sets. We demonstrate that statistical concepts developed for discovery proteomics based on spectrum-centric scoring can be adapted to large-scale DIA experiments that have been analyzed with peptide-centric scoring strategies, and we provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent the accumulation of false positives across large-scale data sets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for the detected peptide queries, peptides and inferred proteins.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.