PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data

Detection of extended-spectrum beta-lactamase (ESBL) genes and plasmid replicons in Enterobacteriaceae using PlasmidSPAdes assembly of short-read sequence data

Knowledge of the epidemiology of plasmids is essential for understanding the evolution and spread of antimicrobial resistance. PlasmidSPAdes attempts to reconstruct plasmids using short-read sequence data. Accurate detection of extended-spectrum beta-lactamase (ESBL) genes and plasmid replicon genes is a prerequisite for the use of plasmid assembly tools to investigate the role of plasmids in the spread and evolution of ESBL production in Enterobacteriaceae. This study evaluated the performance of PlasmidSPAdes plasmid assembly for Enterobacteriaceae in terms of detection of ESBL-encoding genes, plasmid replicons and chromosomal wgMLST genes, and assessed the effect of k-mer size. Short-read sequence data for 59 ESBL-producing Enterobacteriaceae were assembled with PlasmidSPAdes using different k-mer sizes (21, 33, 55, 77, 99 and 127). For every k-mer size, the presence of ESBL genes, plasmid replicons and a selection of chromosomal wgMLST genes in the plasmid assembly was determined. Out of 241 plasmid replicons and 66 ESBL genes detected by whole-genome assembly, 213 plasmid replicons [88 %; 95 % confidence interval (CI): 83.9-91.9] and 43 ESBL genes (65 %; 95 % CI: 53.1-75.6) were detected in the plasmid assemblies obtained by PlasmidSPAdes. For most ESBL genes (83.3 %) and plasmid replicons (72.0 %), detection results did not differ between the k-mer sizes used in the plasmid assembly. No optimal k-mer size could be defined for the number of ESBL genes and plasmid replicons detected. For most isolates, the number of chromosomal wgMLST genes detected in the plasmid assemblies decreased with increasing k-mer size. Based on our findings, PlasmidSPAdes is not a suitable plasmid assembly tool for short-read sequence data for ESBL-encoding plasmids of Enterobacteriaceae.

Reconsidering plasmid maintenance factors for computational plasmid design

Plasmids are genetic parasites of microorganisms. The genomes of naturally occurring plasmids are expected to be polished via natural selection to achieve long-term persistence in the microbial cell population. However, plasmid genomes are extremely diverse, and the rules governing plasmid genomes are not fully understood. Therefore, computationally designing plasmid genomes optimized for model and nonmodel organisms remains challenging. Here, we summarize current knowledge of the plasmid genome organization and the factors that can affect plasmid persistence, with the aim of constructing synthetic plasmids for use in gram-negative bacteria. Then, we introduce publicly available resources, plasmid data, and bioinformatics tools that are useful for computational plasmid design.

MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies

Large-scale bacterial population genetics studies are now routine due to cost-effective Illumina short-read sequencing. However, analysing plasmid content remains difficult due to incomplete assembly of plasmids. Bacterial isolates can contain any number of plasmids and assembly remains complicated due to the presence of repetitive elements. Numerous tools have been developed to analyse plasmids but the performance and functionality of the tools are variable. The MOB-suite was developed as a set of modular tools for reconstruction and typing of plasmids from draft assembly data to facilitate characterization of plasmids. Using a set of closed genomes with publicly available Illumina data, the MOB-suite identified contigs of plasmid origin with both high sensitivity and specificity (95 and 88 %, respectively). In comparison, plasmidfinder demonstrated high specificity (99 %) but limited sensitivity (50 %). Using the same dataset of 377 known plasmids, MOB-recon accurately reconstructed 207 plasmids so that they were assigned to a single grouping without other plasmid or chromosomal sequences, whereas plasmidSPAdes was only able to accurately reconstruct 102 plasmids. In general, plasmidSPAdes has a tendency to merge different plasmids together, with 208 plasmids undergoing merge events. The MOB-suite reduces the number of errors but produces more hybrid plasmids, with 84 plasmids undergoing both splits and merges. The MOB-suite also provides replicon typing similar to plasmidfinder but with the inclusion of relaxase typing and prediction of conjugation potential. The MOB-suite is written in Python 3 and is available from https://github.com/phac-nml/mob-suite.