MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies

By James Robertson, John H. E. Nash. Microbial Genomics, 2018

940 citations recorded by the source corpus.

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.

Subfields: Endocrinology, Molecular Biology, Molecular Medicine

View Paper: https://www.semanticscholar.org/paper/d75a5f10100c3586d7dbc99c08d6d8a3c19884ca

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In Endocrinology

  1. Andreas Keller
  2. Christina Backes
  3. Valentina Galata
  4. Tobias Fehlmann

In Molecular Biology

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In Molecular Medicine

  1. Andreas Keller
  2. Christina Backes
  3. Valentina Galata
  4. Tobias Fehlmann

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