bioRxivBioinformaticsPreprint
ISdetector: precise mapping of insertion sequences and associated structural variations from short-read sequencing data
Abstract
MotivationInsertion sequences (ISs) are key drivers of genomic plasticity in bacteria and archaea. Determining their exact insertion coordinates is critical for understanding drug resistance, virulence, and pathogen epidemiology. However, accurately mapping ISs from high-throughput short-read sequencing data remains challenging due to the repetitive nature of these elements and accompanying structural variations, which frequently confound standard alignment-based algorithms. As whole-genome sequencing becomes the standard for population-level studies, there is a need for robust, scalable, and specialized pipelines to detect ISs. ResultsWe present ISdetector, a bioinformatics pipeline that detects precise insertion sites of specific ISs using an IS-clean reference strategy combined with clustering of IS-relevant signals from soft-clipped reads. Compared with existing tools, including ISMapper and MGEFinder, ISdetector demonstrates higher accuracy and robustness, achieving high F1 scores in both high-GC-content genomes (e.g., Mycobacterium tuberculosis, F1=0.91) and high-IS-burden genomes (e.g., Shigella sonnei, F1=0.85). Furthermore, ISdetector identifies IS movements accompanied by structural variations, such as large-scale deletions, which are often missed by existing methods. Implemented with multi-threading, ISdetector shows near-linear decreases in running time with increasing thread counts, making it highly scalable and efficient for processing large numbers of samples in population-level studies. AvailabilityISdetector is an open-source pipeline implemented in Python. It integrates standard bioinformatics tools, including BWA, SAMtools, and BLAST+, and uses the Biopython and Pysam libraries for data processing. The source code, documentation, and usage instructions are freely available at https://github.com/carolynzy/ISdetector. CONTACTZHOUYANG@CHINACDC.CN
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
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