ArticleCommunications biology2025
Definition of the microbial rare biosphere through unsupervised machine learning.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Environmental variables associated with Arbuscular Mycorrhizal Fungi diversity in Ohio soybean agricultural systems.Mycorrhiza · 2026Article
- Temporal Dependence of the Phylogenetic Diversity-Habitat Size Relationship: Evidence from a Closed Fermentation System.Microorganisms · 2026Article
- Rare Taxa and Stochastic Drift Drive the Microbiome Assembly of the Invasive Pest Drosophila suzukii, While Host Filtering Structures the Grape Sour Rot Community.Microbial ecology · 2026Article
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Quantifying the contribution of the rare biosphere and functional potential to the compacted clay baseliner of sanitary landfill.World journal of microbiology & biotechnology · 2026Article
- Comparative analysis of prokaryotic communities, hydrography, and biogeochemistry in Atlantic vs non-Atlantic influenced Svalbard fjords.BMC microbiology · 2026Article
- Metagenomic insights into oral microbiota dynamics in diabetic and non-diabetic periodontal disease: a pilot study.Frontiers in microbiology · 2026Article
- Integrating Life-Cycle Assessment (LCA) and Artificial Neural Networks (ANNs) for Optimizing the Inclusion of Supplementary Cementitious Materials (SCMs) in Eco-Friendly Cementitious Composites: A Literature Review.Materials (Basel, Switzerland) · 2025Review
- Definition of the microbial rare biosphere through unsupervised machine learning.Communications biology · 2025Article
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The microbial rare biosphere, composed of low-abundance microorganisms in a community, lacks a standardized delineation method for its definition. Currently, most studies rely on arbitrary thresholds to define the microbial rare biosphere (e.g., 0.1% relative abundance per sample), hampering comparisons across studies. To address this challenge, we present ulrb (Unsupervised Learning based Definition of the Rare Biosphere), available as an R package. ulrb uses unsupervised machine learning to optimally classify taxa into abundance categories (e.g., rare, intermediate, or abundant) within microbial communities. We show that ulrb is more consistent than threshold-based approaches and can be applied to data derived from common microbial ecology protocols and non-microbial studies. ulrb can be used to identify different types of rarity and is statistically valid for the analysis of various dataset sizes. In conclusion, ulrb discerns rare from abundant organisms in a user-independent manner, finding applicability in selected ecological datasets.
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