ArticleCell reports methods2025
Accurate prediction of absolute prokaryotic abundance from DNA concentration.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Interpreting oral microbiome research in clinical dentistry: terminology, limitations and clinical implications.British dental journal · 2026Review
- Differential effects of two common GVHD prophylaxis regimens on the gut microbiome: Results from the BMT CTN 1801 study.bioRxiv : the preprint server for biology · 2026Article
- Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.mSystems · 2025Article
- Accurate prediction of absolute prokaryotic abundance from DNA concentration.Cell reports methods · 2025Article
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absolute microbial abundance, they are technically challenging and costly, presenting an opportunity for machine learning. Here, we observe a strong correlation between DNA concentration and the absolute number of 16S ribosomal RNA copies as measured by digital droplet PCR in clinical stool samples from individuals undergoing hematopoietic cell transplantation (BMT CTN 1801). Based on this correlation and additional measurements, we trained an accurate yet simple machine learning model for the prediction of absolute prokaryotic load, which showed exceptional prediction accuracy on an external cohort that includes people living with Parkinson's disease and healthy controls. We propose that, with further validation, this model has the potential to enable accurate absolute abundance estimation based on readily available sample measurements.
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Registered trials
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