Panisa Treepong1,2, Christophe Guyeux1, Alexandre Meunier3,4, Charlotte Couchoud3, Didier Hocquet3,4, Benoit Valot4. 1. Département DISC, UMR CNRS 6174 Institut FEMTO-ST, Université de Bourgogne, Franche-Comté, Besançon, France. 2. Faculty of Technology and Environment, Prince of Songkla University, Phuket, Thailand. 3. Laboratoire d'Hygiène Hospitalière, Centre Hospitalier Régional Universitaire, Besançon, France. 4. UMR CNRS 6249, Chrono-environnement, Université de Bourgogne Franche-Comté, Besançon, France.
Abstract
Motivation: The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results: PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic species Pseudomonas aeruginosa, and report 43 insertions of five different ISs (among which three are new) and a burst of ISPa1635 in a hypermutator isolate. Availability and implementation: PanISa is implemented in Python and released as an open source software (GPL3) at https://github.com/bvalot/panISa. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results: PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic species Pseudomonas aeruginosa, and report 43 insertions of five different ISs (among which three are new) and a burst of ISPa1635 in a hypermutator isolate. Availability and implementation: PanISa is implemented in Python and released as an open source software (GPL3) at https://github.com/bvalot/panISa. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Nickolas G Kessler; David M Caraballo Delgado; Neel K Shah; Jeff A Dickinson; Sean D Moore Journal: J Bacteriol Date: 2021-03-22 Impact factor: 3.490
Authors: Zena Lapp; Jennifer H Han; Jenna Wiens; Ellie J C Goldstein; Ebbing Lautenbach; Evan S Snitkin Journal: mSystems Date: 2021-03-16 Impact factor: 6.496