ArticleScientific reports2023
Environment and taxonomy shape the genomic signature of prokaryotic extremophiles.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Genome-wide pervasiveness and localized variation of [Formula: see text]-mer-based genomic signatures in eukaryotes.Scientific reports · 2026Article
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- Life at the extremes: maximally divergent microbes with similar genomic signatures linked to extreme environments.NAR genomics and bioinformatics · 2025Article
- Genomic Characterization of MarineMicroorganisms · 2025Article
- A composite universal DNA signature for the tree of life.Nature ecology & evolution · 2025Article
- Genomic and metabolic network properties in thermophiles and psychrophiles compared to mesophiles.Scientific reports · 2025Article
- Lessons from Extremophiles: Functional Adaptations and Genomic Innovations across the Eukaryotic Tree of Life.Genome biology and evolution · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [Formula: see text] extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, [Formula: see text]. The supervised learning resulted in high accuracies for taxonomic classifications at [Formula: see text], and medium to medium-high accuracies for environment category classifications of the same datasets at [Formula: see text]. For [Formula: see text], our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.
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