eLaRodON: identification of large genomic rearrangements in Oxford Nanopore sequencing data

Long-read sequencing enables more accurate detection of large genomic rearrangements (LGRs) compared to short-read technologies. However, existing tools for LGR calling continue to evolve and require further optimization. In this study, we present eLaRodON, a novel tool for identifying LGRs in Oxford Nanopore sequencing data. Using publicly available datasets—including Mycobacterium tuberculosis genomes with extensive complete genome references and the human cell line NA12878—we demonstrate that eLaRodON outperforms existing tools (NanoSV, Sniffles2, NanoVar, and SVIM), achieving an AUC of 0.61 (vs. 0.13–0.43) for M. tuberculosis and 0.86 (vs. 0.40–0.72) for the human genome. Validation against gold-standard LGR sets (derived from de novo Flye assemblies and Mauve alignments) confirmed the tool's high accuracy. Notably, we identified recurrent false-positive LGR patterns across diverse datasets (M. tuberculosis, NA12878, and λ phage controls). Orthogonal validation by targeted NGS and Sanger sequencing yielded a variant verification rate of 67–100% for different LGR types—including those supported by a single read. These results represent a significant advancement in LGR detection accuracy, with implications for genomic research and clinical applications. eLaRodON is an open-source program, and its code can be freely accessed on GitHub: https://github.com/aakechin/eLaRodON/.