ArticleFrontiers in microbiology2020
A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data.
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed.
- Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.Microorganisms · 2026Review
- Article
- Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data.Journal of microbiology (Seoul, Korea) · 2025Review
- PredCMB: predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data.Bioinformatics (Oxford, England) · 2024Article
- DNA Barcoding Using 18S rRNA Gene Fragments for Identification of Tick-Borne Protists in Ticks in the Republic of Korea.Pathogens (Basel, Switzerland) · 2024Article
- Gut microbiome-metabolome interactions predict host condition.Microbiome · 2024Article
- Immunostimulating Commensal Bacteria and Their Potential Use as Therapeutics.International journal of molecular sciences · 2023Review
- Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.Nature machine intelligence · 2023Article
- Yeast Cell Wall Compounds on The Formation of Fermentation Products and Fecal Microbiota in Cats: An In Vivo and In Vitro Approach.Animals : an open access journal from MDPI · 2023Article
- Quantitative analysis of disease-related metabolic dysregulation of human microbiota.iScience · 2023Article
- Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids.Computational and structural biotechnology journal · 2023Review
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
- Microbiome as an immune regulator in health, disease, and therapeutics.Advanced drug delivery reviews · 2022Review
- Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience.Emerging topics in life sciences · 2022Review
- Towards a Deeper Understanding ofInternational journal of molecular sciences · 2022Article
- Mass spectrometry-based metabolomics in microbiome investigations.Nature reviews. Microbiology · 2022Review
- Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care.Pathogens (Basel, Switzerland) · 2022Review
- Strain level and comprehensive microbiome analysis in inflammatory bowel diseaseFrontiers in microbiology · 2022Article
- Integration of the Human Gut Microbiome and Serum Metabolome Reveals Novel Biological Factors Involved in the Regulation of Bone Mineral Density.Frontiers in cellular and infection microbiology · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.
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