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Article Dans Une Revue Bioinformatics Année : 2018

panISa: Ab initio detection of insertion sequences in bacterial genomes from short read sequence data

Résumé

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.
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Dates et versions

hal-02438015 , version 1 (14-01-2020)

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Panisa Treepong, Christophe Guyeux, Alexandre Meunier, Charlotte Couchoud, Didier Hocquet, et al.. panISa: Ab initio detection of insertion sequences in bacterial genomes from short read sequence data. Bioinformatics, 2018, 34 (22), pp.3795-3800. ⟨10.1093/bioinformatics/bty479⟩. ⟨hal-02438015⟩
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