HaloClassifier: integrating coding-signature features and k-mer composition for plasmid-chromosome discrimination in haloarchaeal genomes
- 1Universitat de Valencia
Abstract
Background: Plasmids are key drivers of horizontal gene transfer (HGT), enabling the dissemination of accessory traits that shape microbial adaptation and ecological interactions. In Haloarchaea-dominant members of hypersaline environments-characterizing plasmidomes remains particularly challenging because most available genomes are incomplete, leaving many contigs unassigned to either chromosomal or plasmidic origin. This limitation hampers our ability to reconstruct archaeal plasmid diversity and to evaluate how plasmid-borne genes circulate within Haloarchaea and, potentially, outside this class. Results: Here, we present HaloClassifier, the first machine-learning tool specifically designed to distinguish plasmid- and chromosome-derived contigs in haloarchaeal genomes. Unlike existing bacterial-focused approaches, HaloClassifier uniquely integrates haloarchaeal-specific genomic signatures with coding-derived features-an underused but highly informative class of predictors-and incorporates a variable-selection strategy that substantially reduces model complexity and computational cost while maintaining high predictive accuracy. When trained on simulated contigs generated from complete haloarchaeal genomes, the model achieves 96.61% accuracy on extralarge contigs (40-100 kb), increasing to 98.1% at a 0.6 classification threshold, with only 3.55% of contigs left unclassified. For small contigs (1-5 kb), the model achieves 79.51% accuracy overall and 84.42% at the 0.6 classification threshold, with 17.07% of contigs left unclassified. The model also maintains robust performance in metagenomic datasets. Conclusions: By enabling reliable plasmid identification in fragmented assemblies, HaloClassifier fills a major gap and establishes the foundation for large-scale haloarchaeal plasmidome reconstruction. This framework will support future investigations into gene mobility, ecological adaptation, and the evolutionary dynamics of plasmids and HGT in Haloarchaea.
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BibTeX
@misc{lopezroig2026haloclassifier,
title = {{HaloClassifier: integrating coding-signature features and k-mer composition for plasmid-chromosome discrimination in haloarchaeal genomes}},
author = {L. Lopez-Roig and F. Gonzalez-Candelas and A. Fillol-Salom},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753098},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753098v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - HaloClassifier: integrating coding-signature features and k-mer composition for plasmid-chromosome discrimination in haloarchaeal genomes
AU - L. Lopez-Roig
AU - F. Gonzalez-Candelas
AU - A. Fillol-Salom
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753098
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753098v1
AB - Background: Plasmids are key drivers of horizontal gene transfer (HGT), enabling the dissemination of accessory traits that shape microbial adaptation and ecological interactions. In Haloarchaea-dominant members of hypersaline environments-characterizing plasmidomes remains particularly challenging because most available genomes are incomplete, leaving many contigs unassigned to either chromosomal or plasmidic origin. This limitation hampers our ability to reconstruct archaeal plasmid diversity and to evaluate how plasmid-borne genes circulate within Haloarchaea and, potentially, outside this class. Results: Here, we present HaloClassifier, the first machine-learning tool specifically designed to distinguish plasmid- and chromosome-derived contigs in haloarchaeal genomes. Unlike existing bacterial-focused approaches, HaloClassifier uniquely integrates haloarchaeal-specific genomic signatures with coding-derived features-an underused but highly informative class of predictors-and incorporates a variable-selection strategy that substantially reduces model complexity and computational cost while maintaining high predictive accuracy. When trained on simulated contigs generated from complete haloarchaeal genomes, the model achieves 96.61% accuracy on extralarge contigs (40-100 kb), increasing to 98.1% at a 0.6 classification threshold, with only 3.55% of contigs left unclassified. For small contigs (1-5 kb), the model achieves 79.51% accuracy overall and 84.42% at the 0.6 classification threshold, with 17.07% of contigs left unclassified. The model also maintains robust performance in metagenomic datasets. Conclusions: By enabling reliable plasmid identification in fragmented assemblies, HaloClassifier fills a major gap and establishes the foundation for large-scale haloarchaeal plasmidome reconstruction. This framework will support future investigations into gene mobility, ecological adaptation, and the evolutionary dynamics of plasmids and HGT in Haloarchaea.
ER -