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

Abstract

Plasmids are extrachromosomal genetic elements replicating independently of the chromosome which play a vital role in the environmental adaptation of bacteria. Due to potential mobilization or conjugation capabilities, plasmids are important genetic vehicles for antimicrobial resistance genes and virulence factors with huge and increasing clinical implications. They are therefore subject to large genomic studies within the scientific community worldwide. As a result of rapidly improving next generation sequencing methods, the amount of sequenced bacterial genomes is constantly increasing, in turn raising the need for specialized tools to (i) extract plasmid sequences from draft assemblies, (ii) derive their origin and distribution, and (iii) further investigate their genetic repertoire. Recently, several bioinformatic methods and tools have emerged to tackle this issue; however, a combination of both high sensitivity and specificity in plasmid sequence identification is rarely achieved in a taxon-independent manner. In addition, many software tools are not appropriate for large high-throughput analyses or cannot be included into existing software pipelines due to their technical design or software implementation. In this study, we investigated differences in the replicon distributions of protein-coding genes on a large scale as a new approach to distinguish plasmid-borne from chromosome-borne contigs. We defined and computed statistical discrimination thresholds for a new metric: the replicon distribution score (RDS) which achieved an accuracy of 96.6%. The final performance was further improved by the combination of the RDS metric with heuristics exploiting several plasmid specific higher-level contig characterizations. We implemented this workflow in a new high-throughput taxon-independent bioinformatics software tool called Platon for the recruitment and characterization of plasmid-borne contigs from short-read draft assemblies. Compared to PlasFlow, Platon achieved a higher accuracy (97.5%) and more balanced predictions (F1=82.6%) tested on a broad range of bacterial taxa and better or equal performance against the targeted tools PlasmidFinder and PlaScope on sequenced E. coli isolates. Platon is available at: platon.computational.bio

RFPlasmid: Predicting plasmid sequences from short read assembly data using machine learning

“…Integrated plasmids, such as the IncQ1 plasmid in the external dataset in E. coli isolate H69 show that some predictions will remain difficult. Other improvements could be the detection of rRNA operons, as these are usually chromosomally encoded or circularization detection for the detection of smaller plasmids (Schwengers et al, 2020). An evaluation of the combination of above mentioned features with species specific models would be interesting for future research…”

“…To investigate the performance of RFPlasmid on non-simulated datam, we downloaded the Illumina and Nanopore reads of 24 multidrug-resistant Escherichia coli genomes from ENA from Bioprojects PRJNA505407 and PRJNA387731 which were also used by Schwengers et al (Schwengers et al, 2020). We performed both hybrid assembly using Unicycler v0.4.9b (Wick et al, 2017) and short read-only assembly with SPAdes (v13.3.0).…”

“…To investigate the performance of RFPlasmid on non-simulated data we also used 22 E. coli genomes, previously used by Schwenger et al (Schwengers et al, 2020) . The error rate of RFPlasmid with non-simulated data is very low; only 0.52% of basepairs (85 contigs out of 2832 contigs) were incorrectly predicted with most of them (62 contigs out of 85 contigs) being small (<3kb) ( Figure 6AB).…”

“…Multiple bioinformatic methods have been described to predict plasmids in silico, e.g. cBar (Zhou & Xu, 2010), PlasmidSPAdes (Antipov et al, 2016), Recycler (Rozov et al, 2017), PlasmidFinder (Carattoli et al, 2014), PLACNET (Lanza et al, 2014), PLAScope (Royer et al, 2018), MLPlasmids (Arredondo-Alonso et al, 2018) and Platon (Schwengers et al, 2020). The predictions with some methods suffer from a low sensitivity or specificity (Arredondo-Alonso et al, 2017), or are optimized for one specific bacterial genus and cannot be used for metagenomics.…”

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

“…Due to the presence of repeat sequences, de novo assembly of bacterial genomes from short reads results in fragmented assemblies, generally comprising hundreds of contigs of unknown origins (chromosome or plasmid). 2 Recently, several tools have been developed to identify plasmid sequences in such assemblies (or metagenomic sequence data), namely cBar, 3 PlasFlow, 4 Platon 5 , plasmidSPAdes, 6 RFPlasmid, 7 mlplasmids, 8 and PlaScope. 9 These tools can be divided into taxon-independent approaches (cBar, PlasFlow, Platon, plasmidSPAdes) and taxon-dependent approaches (mlplasmids, PlaScope) with the exception of RFPlasmid, which includes both taxon-dependent and taxon-independent models ( Table 1 ).…”