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Record W4416947312 · doi:10.1038/s41597-025-06191-2

Reconstruction of 1,979 prokaryotic metagenome-assembled genomes from 37 global cave environments

2025· article· en· W4416947312 on OpenAlexaff
Huihong Li, Yuping Cao, Xueke Liu, Zunfu Ke, Liang Chen, Bupe A. Siame, Sima Yaron, Ka Yin Leung

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsTrinity Western UniversityWestern University
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaLi Ka Shing Foundation
KeywordsCavePhylumGenomeMetagenomicsArchaeaPhylogeneticsExtremophileGeneBiodiversity

Abstract

fetched live from OpenAlex

Cave microorganisms represent unique extremophiles that have evolved in isolated, nutrient-limited environments and harbor exceptional metabolic capabilities. However, knowledge of cave microbial diversity at genomic level remains limited. Previous studies have focused on individual caves and do not give a global picture. Here, we present the first prokaryotic cave metagenomic catalog from 37 geographical diverse cave environments. We employed an optimized genome reconstruction pipeline to recover 3,837 medium-to-high quality cave metagenome-assembled genomes (MAGs). These MAGs were dereplicated into 1,979 species-level representative clusters that spanned 67 phyla of Bacteria (n = 1,858) and Archaea (n = 121) domains. Classification of representative species showed that 98.7% did not match any existing genome taxonomy classification of named species at ≥ 95% average nucleotide identity (ANI). Most representative genomes harbored putative biosynthetic gene clusters (BGCs) (98.0%) and enzymatic antibiotic resistance genes (ARGs) (95.0%). This comprehensive MAGs catalog provides a foundational resource for exploring cave microbial diversity, secondary metabolism, and the evolutionary origins of antibiotic resistance in subterranean ecosystems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.223
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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