HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer

Ji, Yongxin; Shang, Jiayu; Tang, Xubo; Sun, Yanni

doi:10.1093/bioinformatics/btad283

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…This was done to include Escherichia coli (as in the "ESKAPEE") [26,27] as well as to ensure that contigs without complete taxonomic annotations but which are likely derived from the ESKAPE organisms would be captured. This is common for contigs originating from ESKAPE-associated plasmids and MGEs [28,29]. MetaCompare 2.0 follows the computational approach adopted in MetaCompare 1.0, with some modifications.…”

Section: Overview Of Pipelinementioning

confidence: 99%

MetaCompare 2.0: Differential ranking of ecological and human health resistome risks

Rumi,

Oh,

Davis

et al. 2024

Preprint

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Background: While there is increasing recognition of numerous environmental contributions to the spread of antibiotic resistance, quantifying the relative contributions of various sources remains a fundamental challenge. Similarly, there is a need to differentiate acute human health risks corresponding to exposure to a given environment, versus broader ecological risk of evolution and spread of antibiotic resistance genes (ARGs) across microbial taxa. Recent studies have proposed various methods of harnessing the rich information housed by metagenomic data for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon the original MetaCompare pipeline by differentiating indicators of human health resistome risk (i.e., potential for human pathogens to acquire ARGs) from ecological resistome risk (i.e., overall mobility of ARGs across a given microbiome). Results: To demonstrate the sensitivity of the MetaCompare 2.0 pipeline, we analyzed publicly available metagenomes representing a broad array of environments, including wastewater, surface water, soil, sediment, and human gut. We also assessed the effect of sequence assembly methods on the risk scores. We further evaluated the robustness of the pipeline to sequencing depth, contig count, and metagenomic library coverage bias through comparative analysis of a range of subsamples extracted from a set of deeply sequenced wastewater metagenomes. The analysis utilizing samples from different environments demonstrated that MetaCompare 2.0 consistently produces lower risk scores for environments with little human influence and higher risk scores for human contaminated environments affected by pollution or other stressors. We found that the ranks of risk scores were not measurably affected by different assemblers employed. The MetaCompare 2.0 risk scores were remarkably consistent despite varying sequencing depth, contig count, and coverage. Conclusion: MetaCompare 2.0 successfully ranked a wide array of environments according to both human health and ecological resistome risks, with both scores being strongly impacted by anthropogenic stress. We packaged the improved pipeline into a publicly-available web service that provides an easy-to-use interface for computing resistome risk scores and visualizing results. The web service is available at http://metacompare.cs.vt.edu/

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Section: Overview Of Pipelinementioning

confidence: 99%

MetaCompare 2.0: Differential ranking of ecological and human health resistome risks

Rumi,

Oh,

Davis

et al. 2024

Preprint

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“…For example, PlasmidHostFinder, a trained random-forest model, shows advantages over PlasmidFinder on incomplete plasmid sequences [23]. The newly released HOTSPOT [24] incorporates a language model, Transformer, and takes all predicted proteins into account, outperforming MOB-typer [18], PlasmidHostFinder [23], and PlasFlow [25].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Suggests That Small Size Helps Broaden Plasmid Host Range

Wang,

Finazzo,

Artsimovitch

2023

Genes

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Plasmids mediate gene exchange across taxonomic barriers through conjugation, shaping bacterial evolution for billions of years. While plasmid mobility can be harnessed for genetic engineering and drug-delivery applications, rapid plasmid-mediated spread of resistance genes has rendered most clinical antibiotics useless. To solve this urgent and growing problem, we must understand how plasmids spread across bacterial communities. Here, we applied machine-learning models to identify features that are important for extending the plasmid host range. We assembled an up-to-date dataset of more than thirty thousand bacterial plasmids, separated them into 1125 clusters, and assigned each cluster a distribution possibility score, taking into account the host distribution of each taxonomic rank and the sampling bias of the existing sequencing data. Using this score and an optimized plasmid feature pool, we built a model stack consisting of DecisionTreeRegressor, EvoTreeRegressor, and LGBMRegressor as base models and LinearRegressor as a meta-learner. Our mathematical modeling revealed that sequence brevity is the most important determinant for plasmid spread, followed by P-loop NTPases, mobility factors, and β-lactamases. Ours and other recent results suggest that small plasmids may broaden their range by evading host defenses and using alternative modes of transfer instead of autonomous conjugation.

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Shotgun metagenomic analysis reveals the emergence of plasmid-encoded mcr-5.1 gene in hospital wastewater in Bangladesh

Khan,

Afrin,

Ahmed

et al. 2024

Journal of Global Antimicrobial Resistance

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HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer

Cited by 7 publications

References 35 publications

MetaCompare 2.0: Differential ranking of ecological and human health resistome risks

MetaCompare 2.0: Differential ranking of ecological and human health resistome risks

Machine Learning Suggests That Small Size Helps Broaden Plasmid Host Range

Shotgun metagenomic analysis reveals the emergence of plasmid-encoded mcr-5.1 gene in hospital wastewater in Bangladesh

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