ArticleNature methods2023
BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
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- Metabolomic and biochemometric profiling of Tecoma stans with mechanistic insights of its in vitro cytotoxic activity.Scientific reports · 2026Article
- ENVnet provides a global molecular resource of dissolved organic matter.Nature biotechnology · 2026Article
- Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.bioRxiv : the preprint server for biology · 2026Article
- Activity-guided substructure prioritization accelerates discovery of gut microbiota-derived immune-regulating metabolites.Nature communications · 2026Article
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Pan-Metabolomics Repository Mapping of the Carnitine Landscape.bioRxiv : the preprint server for biology · 2026Article
- De Novo Structure Prediction from Tandem Mass Spectra: Algorithms, Benchmarks, and Limitations.Molecules (Basel, Switzerland) · 2026Review
- Navigating the conjugated metabolome.bioRxiv : the preprint server for biology · 2026Article
- Article
- ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.Briefings in bioinformatics · 2026Article
- FIDDLE: a deep learning method for chemical formulas prediction from tandem mass spectra.Nature communications · 2025Article
- Decomposition causes short-term increases in functional molecular diversity of dissolved organic matter.Nature communications · 2025Article
- Automated metabolite formula ranking using formula subset analysis for LC-MS/MS-based metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Machine Learning-Assisted Recognition of Environmental Sulfur-Containing Chemicals in Nontargeted Mass Spectrometry Analysis of Inadequate Mass Resolution.ACS environmental Au · 2025Article
- Targeted Isolation of Prenylated Flavonoids fromMetabolites · 2025Article
- Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics.Nature communications · 2025Article
- MMEASE: enhanced analytical workflow for single-cell metabolomics.Nucleic acids research · 2025Article
- SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation.Briefings in bioinformatics · 2025Article
- MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers.Nature communications · 2025Article
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5 authors.
Funding
Abstract
A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).
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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.