Related Experiment Video
Updated: Oct 11, 2025
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning
Linda van der Graaf-van Bloois1,2, Jaap A Wagenaar1,2,3, Aldert L Zomer1,2
1Faculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Abstract:
Antimicrobial-resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know whether the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole-genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like k-mer composition, circularity of the DNA molecule, copy number or sequence identity to plasmid replication genes, all of which have their drawbacks, especially when faced with large single-copy plasmids, which often carry resistance genes. With our newly developed prediction tool RFPlasmid, we use a combination of multiple features, including k-mer composition and databases with plasmid and chromosomal marker proteins, to predict whether the likely source of a contig is plasmid or chromosomal. The tool RFPlasmid supports models for 17 different bacterial taxa, including Campylobacter, Escherichia coli and Salmonella, and has a taxon agnostic model for metagenomic assemblies or unsupported organisms. RFPlasmid is available both as a standalone tool and via a web interface.
Insights
Identifying antimicrobial-resistance (AMR) genes on plasmids versus chromosomes is crucial. RFPlasmid is a new tool that accurately distinguishes plasmid and chromosomal DNA in bacterial genomes, aiding molecular epidemiology and risk assessment.
Area of Science:
- Microbiology
- Genomics
- Bioinformatics
Background:
- Antimicrobial-resistance (AMR) genes are frequently located on bacterial plasmids, facilitating their spread.
- Distinguishing between plasmid-borne and chromosomal AMR genes is vital for molecular epidemiology and risk assessment.
- Draft bacterial genome assemblies pose challenges in accurately differentiating plasmid and chromosomal contigs.
Purpose of the Study:
- To develop a novel computational tool, RFPlasmid, for accurate prediction of plasmid versus chromosomal contigs in bacterial genomes.
- To overcome limitations of existing methods that rely on single features and struggle with large, single-copy plasmids.
Main Methods:
- RFPlasmid utilizes a combination of features, including k-mer composition and curated databases of plasmid and chromosomal marker proteins.
- The tool incorporates predictive models for 17 specific bacterial taxa.
- A taxon-agnostic model is available for metagenomic assemblies and unsupported organisms.
Main Results:
- RFPlasmid demonstrates improved accuracy in distinguishing plasmid and chromosomal contigs compared to single-feature methods.
- The tool effectively handles complex genomic data, including large single-copy plasmids.
- Validated models for key bacterial species like *Campylobacter*, *Escherichia coli*, and *Salmonella*.
Conclusions:
- RFPlasmid provides a robust and versatile solution for classifying contigs from draft bacterial genomes.
- Accurate plasmid/chromosome discrimination enhances AMR surveillance, molecular epidemiology, and risk assessment.
- The tool is accessible as a standalone application and through a web interface.
Related Concept Videos
03:36Genome Annotation and Assembly
02:54Evolutionary Relationships through Genome Comparisons