MOBFinder: a tool for MOB typing for plasmid metagenomic fragments based on language model

Abstract

Background MOB typing is a classification scheme that classifies plasmid genomes based on their relaxase gene. The host range of plasmids of different MOB categories are diverse and MOB typing is crucial for investigating the mobilization of plasmid, especially the transmission of resistance genes and virulence factors. However, MOB typing of plasmid metagenomic data is challenging due to the highly fragmented characteristic of metagenomic contigs. Results We developed MOBFinder, an 11-class classifier to classify the plasmid fragments into 10 MOB categories and a non-mobilizable category. We first performed the MOB typing for classifying complete plasmid genomes using the relaxes information, and constructed the artificial benchmark plasmid metagenomic fragments from these complete plasmid genomes whose MOB types are well annotated. Based on natural language models, we used the word vector to characterize the plasmid fragments. Several random forest classification models were trained and integrated for predicting plasmid fragments with different lengths. Evaluating the tool over the benchmark dataset, MOBFinder demonstrates higher performance compared to the existing tool, with an overall accuracy of approximately 59% higher than the MOB-suite. Moreover, the balanced accuracy, harmonic mean and F1-score could reach 99% in some MOB types. In an application focused on a T2D cohort, MOBFinder offered insights suggesting that the MOBF type might accelerate the antibiotic resistance transmission in patients suffering from T2D. Conclusions To the best of our knowledge, MOBFinder is the first tool for MOB tying for plasmid metagenomic fragments. MOBFinder is freely available at https://github.com/FengTaoSMU/MOBFinder.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* https://github.com/FengTaoSMU/MOBFinder