ArticleNature communications2023
PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Computational Tools for LC-IMS-MS Data Processing in Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- Review
- Enabling malic acid production from corn-stover hydrolysate in Lipomyces starkeyi via metabolic engineering and bioprocess optimization.Microbial cell factories · 2025Article
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.Journal of the American Society for Mass Spectrometry · 2024Article
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Mapping microhabitats of lignocellulose decomposition by a microbial consortium.Nature chemical biology · 2024Article
- Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.Proteomics · 2024Review
- Ion Mobility Mass Spectrometry for Large Synthetic Molecules: Expanding the Analytical Toolbox.Journal of the American Chemical Society · 2024Review
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
- PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.Nature communications · 2023Article
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Registered trials
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