ArticleNature methods2015
DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.
Article in Nature methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 339 papers.
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339 citing papers in PubMed.
- Preprocessing Large-Scale Proteomics Data.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Improving lipid identification and quantification: chromatogram deconvolution for LC-MS/MS workflows.Bioinformatics (Oxford, England) · 2026Article
- A Spatiotemporal Atlas of the Androgen Receptor Proximal Interactome.bioRxiv : the preprint server for biology · 2026Article
- Review
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- Regulation between LRRK2 and PP2A signaling in cellular models of Parkinson's disease.The Biochemical journal · 2026Article
- Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Designer Aromatic Cations for Photoinduced Protein Ligation, Imaging, and Intracellular Labeling at Extended Wavelengths.Journal of the American Chemical Society · 2026Article
- Harnessing Proteogenomics to Advance Precision Oncology: From Melanoma and Hepatocellular Carcinoma Perspective.Current oncology reports · 2026Review
- Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.Molecular & cellular proteomics : MCP · 2026Article
- Early-activated extracellular matrix proteins shape the metabolic and spatial dynamics of the kidney fibrotic microenvironment.Nature metabolism · 2026Article
- Proteoforms as the true units of physiological function.European journal of applied physiology · 2026Review
- xTracer: Integrating chromatogram and mobiligram correlations for untargeted peptide identification in SLIM-based PAMAF data.bioRxiv : the preprint server for biology · 2026Article
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis.Scientific reports · 2026Article
- Tissue and extracellular matrix remodeling of the subchondral bone during osteoarthritis of knee joints as revealed by spatial mass spectrometry imaging.Bone research · 2026Article
- MHC1-TIP enables single-tube multimodal immunopeptidome profiling and uncovers intratumoral heterogeneity in antigen presentation.Communications biology · 2026Article
- Frag'n'Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments.BMC bioinformatics · 2026Article
- Data-independent acquisition-based quantitative proteomics in plants.Frontiers in plant science · 2026Review
- A comprehensive proteomic analysis uncovers novel molecular subtypes of gastric signet ring cell carcinoma: Identification of potential prognostic biomarkers and therapeutic targets.Genes & diseases · 2026Article
279 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.
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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.