1887

Abstract

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.

Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000206
2018-07-27
2019-12-14
Loading full text...

Full text loading...

/deliver/fulltext/mgen/4/8/mgen000206.html?itemId=/content/journal/mgen/10.1099/mgen.0.000206&mimeType=html&fmt=ahah

References

  1. Arredondo-Alonso S, Willems RJ, van Schaik W, Schürch AC. On the (im)possibility of reconstructing plasmids from whole-genome short-read sequencing data. Microb Genom 2017;3:e000128 [CrossRef][PubMed]
    [Google Scholar]
  2. Wick RR, Judd LM, Gorrie CL, Holt KE. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb Genom 2017;3:e000132 [CrossRef][PubMed]
    [Google Scholar]
  3. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 2017;13:e1005595 [CrossRef][PubMed]
    [Google Scholar]
  4. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014;58:3895–3903 [CrossRef][PubMed]
    [Google Scholar]
  5. von Wintersdorff CJ, Penders J, van Niekerk JM, Mills ND, Majumder S et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol 2016;7:173 [CrossRef][PubMed]
    [Google Scholar]
  6. Mulvey MR, Mataseje LF, Robertson J, Nash JH, Boerlin P et al. Dissemination of the mcr-1 colistin resistance gene. Lancet Infect Dis 2016;16:289–290 [CrossRef][PubMed]
    [Google Scholar]
  7. Shintani M, Sanchez ZK, Kimbara K. Genomics of microbial plasmids: classification and identification based on replication and transfer systems and host taxonomy. Front Microbiol 2015;6:242 [CrossRef][PubMed]
    [Google Scholar]
  8. Ramsay JP, Kwong SM, Murphy RJ, Yui Eto K, Price KJ et al. An updated view of plasmid conjugation and mobilization in Staphylococcus. Mob Genet Elements 2016;6:e1208317 [CrossRef][PubMed]
    [Google Scholar]
  9. Garcillán-Barcia MP, Alvarado A, de La Cruz F. Identification of bacterial plasmids based on mobility and plasmid population biology. FEMS Microbiol Rev 2011;35:936–956 [CrossRef][PubMed]
    [Google Scholar]
  10. Ilangovan A, Connery S, Waksman G. Structural biology of the Gram-negative bacterial conjugation systems. Trends Microbiol 2015;23:301–310 [CrossRef][PubMed]
    [Google Scholar]
  11. Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol 2017;8:182 [CrossRef][PubMed]
    [Google Scholar]
  12. Alvarado A, Garcillán-Barcia MP, de La Cruz F. A degenerate primer MOB typing (DPMT) method to classify gamma-proteobacterial plasmids in clinical and environmental settings. PLoS One 2012;7:e40438 [CrossRef][PubMed]
    [Google Scholar]
  13. Rozwandowicz M, Brouwer MSM, Fischer J, Wagenaar JA, Gonzalez-Zorn B et al. Plasmids carrying antimicrobial resistance genes in Enterobacteriaceae. J Antimicrob Chemother 2018;73:1121–1137 [CrossRef][PubMed]
    [Google Scholar]
  14. Carattoli A, Bertini A, Villa L, Falbo V, Hopkins KL et al. Identification of plasmids by PCR-based replicon typing. J Microbiol Methods 2005;63:219–228 [CrossRef][PubMed]
    [Google Scholar]
  15. Bradley P, den BH, Rocha E, McVean G, Iqbal Z. Real-time search of all bacterial and viral genomic data. bioRxiv 2017;234955
    [Google Scholar]
  16. Laczny CC, Galata V, Plum A, Posch AE, Keller A. Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates. Brief Bioinform 2017; [CrossRef][PubMed]
    [Google Scholar]
  17. Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 2018;46:e35 [CrossRef][PubMed]
    [Google Scholar]
  18. Page AJ, Wailan A, Shao Y, Judge K, Dougan G et al. PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data. Microb Genom 2018; [CrossRef][PubMed]
    [Google Scholar]
  19. Clausen PT, Zankari E, Aarestrup FM, Lund O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother 2016;71:2484–2488 [CrossRef][PubMed]
    [Google Scholar]
  20. Zhou F, Xu Y. cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data. Bioinformatics 2010;26:2051–2052 [CrossRef][PubMed]
    [Google Scholar]
  21. Antipov D, Hartwick N, Shen M, Raiko M, Lapidus A et al. plasmidSPAdes: assembling plasmids from whole genome sequencing data. Bioinformatics 2016;32:3380–3387 [CrossRef][PubMed]
    [Google Scholar]
  22. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016;17:132 [CrossRef][PubMed]
    [Google Scholar]
  23. Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 2011;12:385 [CrossRef][PubMed]
    [Google Scholar]
  24. Müllner D. fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. J Stat Softw 2013;53: [CrossRef]
    [Google Scholar]
  25. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 2014;9:e112963 [CrossRef][PubMed]
    [Google Scholar]
  26. Hunt M, Silva ND, Otto TD, Parkhill J, Keane JA et al. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biol 2015;16:294 [CrossRef][PubMed]
    [Google Scholar]
  27. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res 2006;34:D32–D36 [CrossRef][PubMed]
    [Google Scholar]
  28. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009;10:421 [CrossRef][PubMed]
    [Google Scholar]
  29. Han E, Sinsheimer JS, Novembre J. Characterizing bias in population genetic inferences from low-coverage sequencing data. Mol Biol Evol 2014;31:723–735 [CrossRef][PubMed]
    [Google Scholar]
  30. Achtman M, Wain J, Weill FX, Nair S, Zhou Z et al. Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLoS Pathog 2012;8:e1002776 [CrossRef][PubMed]
    [Google Scholar]
  31. Maiden MC, Jansen van Rensburg MJ, Bray JE, Earle SG, Ford SA et al. MLST revisited: the gene-by-gene approach to bacterial genomics. Nat Rev Microbiol 2013;11:728–736 [CrossRef][PubMed]
    [Google Scholar]
  32. Sheppard SK, Jolley KA, Maiden MC. A gene-by-gene approach to bacterial population genomics: whole genome MLST of Campylobacter. Genes 2012;3:261–277 [CrossRef][PubMed]
    [Google Scholar]
  33. Alikhan NF, Zhou Z, Sergeant MJ, Achtman M. A genomic overview of the population structure of Salmonella. PLoS Genet 2018;14:e1007261 [CrossRef][PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000206
Loading
/content/journal/mgen/10.1099/mgen.0.000206
Loading

Data & Media loading...

Supplements

Supplementary File 1

PDF

Supplementary File 2

Supplementary File 3

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error