ACCIO: An Assembly-Based Tool Enabling Plasmid Detection.

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Plasmids, mobile genetic elements carrying antimicrobial resistance genes, are crucial for tracking disease spread.
• Current plasmid epidemiology faces challenges due to limitations in whole genome sequence data resolution.
• Existing genomic surveillance infrastructure struggles to accurately identify and track complete plasmids.

Purpose of the Study:

• To develop a bioinformatics tool, ACCIO, for accurate plasmid identification and grouping from various sequencing data types.
• To overcome technological and infrastructural barriers in plasmid epidemiology.
• To improve the tracking of plasmid evolution and spread, particularly those carrying antimicrobial resistance genes.

Main Methods:

• ACCIO creates a reference plasmid database for inferring plasmid presence and related groupings in assemblies.
• The tool processes Illumina, Nanopore, and hybrid assemblies.
• Validation involved internal and external datasets, and benchmarking against MOB-suite.

Main Results:

• ACCIO demonstrated high sensitivity and positive predictive value (PPV) across different assembly types (e.g., 100% sensitivity, 92.1% PPV for hybrid assemblies).
• The tool showed consistently high performance on external datasets.
• ACCIO outperformed MOB-suite in plasmid reconstruction and typing across various assembly types and plasmid grouping levels.

Conclusions:

• ACCIO effectively addresses limitations in sequencing technologies for plasmid epidemiology.
• The integrated workflow of ACCIO supports all major sequencing platforms, facilitating broader adoption.
• ACCIO is poised to advance plasmid epidemiology and the surveillance of antimicrobial resistance.