Journal Article

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

Panisa Treepong, Christophe Guyeux, Alexandre Meunier, Charlotte Couchoud, Didier Hocquet and Benoit Valot

Edited by Inanc Birol

in Bioinformatics

Volume 34, issue 22, pages 3795-3800
Published in print November 2018 | ISSN: 1367-4803
Published online June 2018 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/bty479
panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data

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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.

Journal Article.  4402 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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