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Updated: Nov 9, 2025

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Detection of plasmid contigs in draft genome assemblies using customized Kraken databases.

Ryota Gomi^1,2, Kelly L Wyres¹, Kathryn E Holt^1,3

¹Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia.

Microbial Genomics

|April 7, 2021

Summary

We developed a new method using the Kraken classifier to accurately identify plasmid DNA in fragmented bacterial genomes. This approach improves the detection of important genes, such as those for antimicrobial resistance, in bacterial evolution studies.

Keywords:

Klebsiella pneumoniae antimicrobial resistance gene plasmid detection whole-genome sequencing

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Last Updated: Nov 9, 2025

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Published on: January 13, 2017

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

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Published on: August 20, 2021

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Published on: January 25, 2019

Area of Science:

Bacterial genomics
Molecular biology
Bioinformatics

Background:

Plasmids are crucial for bacterial evolution, mediating gene transfer for virulence and antimicrobial resistance.
Short-read sequencing generates fragmented bacterial genomes, complicating plasmid identification.
Existing tools for plasmid identification in draft assemblies often involve a trade-off between sensitivity and specificity.

Purpose of the Study:

To evaluate the Kraken classifier, with a custom database, for identifying plasmid-derived contigs in draft genome assemblies of Klebsiella pneumoniae species complex (KpSC).
To compare Kraken's performance against other established plasmid identification tools.

Main Methods:

Utilized the Kraken classifier with a custom database of KpSC chromosomal and plasmid sequences.
Assessed performance on 82 Illumina-based KpSC draft genome assemblies with known complete genomes as ground truth.
Benchmarked Kraken against Centrifuge, RFPlasmid, mlplasmids, PlaScope, and Platon.

Main Results:

Kraken demonstrated balanced sensitivity (90.8%) and high specificity (>99.4%) for contig count.
Kraken achieved the highest accuracy (96.8%) and F1-score (94.5%) for contig count compared to other tools.
Performance was consistent across the KpSC genome collection, with potential for improvement by expanding the Kraken database.

Conclusions:

Kraken, with a tailored database, is a highly accurate and efficient tool for identifying plasmid contigs in fragmented bacterial genomes.
This methodology offers a robust solution for analyzing plasmid-borne genes, including antimicrobial resistance, in bacterial species.
The approach is adaptable for other bacterial species with sufficient completed genome data.