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

Area of Science:

• Genomics
• Microbial Evolution
• Bioinformatics

Background:

• Insertion sequence (IS) elements are crucial for bacterial genome organization and evolution.
• Their repetitive nature complicates detection in short-read sequencing data.
• Next-generation sequencing advances bacterial genome analysis.

Purpose of the Study:

• To develop a novel software pipeline, PanISa, for ab initio identification of IS insertions in bacterial genomes.
• To overcome limitations of existing IS detection methods, particularly their reliance on databases.
• To analyze IS dynamics in a high-risk clone lineage of *Pseudomonas aeruginosa*.

Main Methods:

• PanISa utilizes a database-free approach to detect IS insertions.
• The pipeline identifies insertions by analyzing read-mapping patterns at potential insertion sites.
• It is implemented in Python and designed for short-read sequencing data.

Main Results:

• PanISa demonstrated high sensitivity and precision in identifying IS insertions.
• Applied to *Pseudomonas aeruginosa*, PanISa detected 43 insertions of five distinct IS elements, including three novel ones.
• A significant burst of ISPa1635 insertions was observed in a hypermutator isolate.

Conclusions:

• PanISa offers an effective, database-free solution for IS element detection in bacterial genomes.
• The tool enhances the study of bacterial genome evolution and dynamics.
• The findings highlight the role of IS elements in the evolution of *Pseudomonas aeruginosa*.