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Volume 10, Issue 2

PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids in Open Access

Abstract

Accurate reconstruction of antibiotic resistance gene (ARG) plasmids from Illumina sequencing data has proven to be a challenge with current bioinformatic tools. In this work, we present an improved method to reconstruct plasmids using short reads. We developed plasmidEC, an ensemble classifier that identifies plasmid-derived contigs by combining the output of three different binary classification tools. We showed that plasmidEC is especially suited to classify contigs derived from ARG plasmids with a high recall of 0.941. Additionally, we optimized gplas, a graph-based tool that bins plasmid-predicted contigs into distinct plasmid predictions. Gplas2 is more effective at recovering plasmids with large sequencing coverage variations and can be combined with the output of any binary classifier. The combination of plasmidEC with gplas2 showed a high completeness (median=0.818) and F1-Score (median=0.812) when reconstructing ARG plasmids and exceeded the binning capacity of the reference-based method MOB-suite. In the absence of long-read data, our method offers an excellent alternative to reconstruct ARG plasmids in .

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antibiotic resistance , assembly graph , bioinformatics , Escherichia coli , Illumina , plasmids , short reads and WGS
Funding
This study was supported by the:
  • H2020 Marie Skłodowska-Curie Actions (Award 801,133)
    • Principle Award Recipient: SergioArredondo-Alonso
  • Health~Holland (Award LSHM19138)
    • Principle Award Recipient: AnitaC Schürch
  • NCOH
    • Principle Award Recipient: AnitaC Schürch
  • zonmw (Award 541 003 005)
    • Principle Award Recipient: AnitaC Schürch
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2024-02-20
2024-05-08
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PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids in Escherichia coli
M Gen 10, 001193 (2024); https://doi.org/10.1099/mgen.0.001193
/content/journal/mgen/10.1099/mgen.0.001193
/content/journal/mgen/10.1099/mgen.0.001193
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