ArticleNature communications2022
DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Sex-dependent mild cardiac dysfunction and neurometabolic abnormalities in a model of Huntington's disease.Disease models & mechanisms · 2026Article
- Gallbladder removal induces hepatic transcriptional and metabolic shifts with cholesterol dysregulation as a key feature.Scientific reports · 2026Article
- Article
- NMR metabolomics reveals metabolic alterations in a novel mouse model of neurodegeneration.Frontiers in neuroscience · 2026Article
- LC-MS Data Processing Using OpenMS.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PPARγ controls ESCRT-dependent fibroblast-like synoviocyte exosome biogenesis and alleviates chondrocyte osteoarthritis mediated by exosomal ANXA1.Journal of orthopaedic translation · 2025Article
- Perspectives in computational mass spectrometry: recent developments and key challenges.Bioinformatics advances · 2025Article
- Phytochemistry, quality control and biosynthesis in ginseng research from 2021 to 2023: A state-of-the-art review concerning advances and challenges.Chinese herbal medicines · 2024Review
- Article
- MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.Nature communications · 2024Article
- LC-MS/DIA-based strategy for comprehensive flavonoid profiling: anRSC advances · 2024Article
- Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data.Briefings in bioinformatics · 2024Article
- Ion entropy and accurate entropy-based FDR estimation in metabolomics.Briefings in bioinformatics · 2024Article
- MetaboLights: open data repository for metabolomics.Nucleic acids research · 2024Article
- Recent advances in mass spectrometry-based computational metabolomics.Current opinion in chemical biology · 2023Review
- PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.Nature communications · 2023Article
- A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.Nature communications · 2023Article
- Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.Handbook of experimental pharmacology · 2023Article
- The critical role that spectral libraries play in capturing the metabolomics community knowledge.Metabolomics : Official journal of the Metabolomic Society · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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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.