PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids in Escherichia coli.

ABSTRACT: Accurate reconstruction of Escherichia coli 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 E. coli 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 combine

SUBMITTER: Paganini JA 

PROVIDER: S-EPMC10926690 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature