PlaScope: a targeted approach to assess the plasmidome from genome assemblies at the species level

Abstract

Plasmid prediction may be of great interest when studying bacteria of medical importance such as Enterobacteriaceae as well as Staphylococcus aureus or Enterococcus. Indeed, many resistance and virulence genes are located on such replicons with major impact in terms of pathogenicity and spreading capacities. Beyond strain outbreak, plasmid outbreaks have been reported in particular for some extended-spectrum beta-lactamase- or carbapenemase-producing Enterobacteriaceae. Several tools are now available to explore the ‘plasmidome’ from whole-genome sequences with various approaches, but none of them are able to combine high sensitivity and specificity. With this in mind, we developed PlaScope, a targeted approach to recover plasmidic sequences in genome assemblies at the species or genus level. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, PlaScope classifies contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, PlasFlow and cBar, it achieves better recall (0.87), specificity (0.99), precision (0.96) and accuracy (0.98) on a dataset of 70 genomes of Escherichia coli containing plasmids. In a second part, we identified 20 of the 21 chromosomal integrations of the extended-spectrum beta-lactamase coding gene in a clinical dataset of E. coli strains. In addition, we predicted virulence gene and operon locations in agreement with the literature. We also built a database for Klebsiella and correctly assigned the location for the majority of resistance genes from a collection of 12 Klebsiella pneumoniae strains. Similar approaches could also be developed for other well-characterized bacteria.

Phylogenetic background and habitat drive the genetic diversification ofEscherichia coli

“…Mobile genetic elements encoding the mechanisms for transmission between genomes (using virions or conjugation) or within genomes (insertion sequences, integron cassettes) are known to transfer at high rates and be rapidly lost 49–51 . We detected prophages using VirSorter 52 , plasmids using PlaScope 53 , and conjugative systems using ConjScan 54 (Supplementary Figs. 11-13).…”

“…Plasmids : In the RefSeq dataset, all the extrachromosomal replicons were considered as plasmids. In the Australian dataset, plasmid sequences were identified using PlaScope v.1.3 53 with the database dedicated to E. coli . PlaScope provides a method for plasmid and chromosome classification of E. coli contigs.…”

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores

“…To tackle this issue, many new bioinformatic tools have recently been developed, following different approaches: (i) Recycler and plasmidSPAdes [15,16] exploit coverage variations of sequenced DNA fragments within a genome; (ii) PLACNET investigates paired-end reads linking contig ends [17]; (iii) PlasmidFinder searches for plasmid specific motifs, i.e. incompatibility groups [18]; (iv) cBar, PlasFlow and mlPlasmids use machine learning methods to classify k-mer frequencies [19][20][21]; (v) PlaScope and PlasmidSeeker perform fast k-mer-based database searches of known plasmid sequences [22,23]; Recycler additionally exploits information on circularization [15]. Overall, each approach provides unique advantages and drawbacks.…”

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein-sequence-based replicon distribution scores

“…(v) PlaScope and PlasmidSeeker perform fast k-mer-based database searches of known plasmid sequences [22,23] ; Recycler additionally exploits information on circularization [15] . Overall, each approach provides unique advantages and drawbacks.…”

“…Unfortunately, not all currently available bioinformatics software tools are suitable for high-throughput analysis, let alone the technical integration into larger analysis pipelines [25][26][27] due to interactive designs or web-based implementations [17,18,21,28] . Taxon-specific database designs also pose additional barriers as users might not have sufficient computational resources or bioinformatics support to build customized or large multi-taxon databases [20,22] . Furthermore, dependence on raw or intermediate data such as sequence reads and assembly graphs might impede analyses as such data might not be available [15,16] .…”