VACmap: an accurate long-read aligner for unraveling complex genomic rearrangements

Sequence alignment is essential for genomic research and clinical diagnostics, yet detecting complex rearrangements such as inversions, duplications, and gene conversions remains challenging due to allele complexity and limitations of current methods. We introduce VACmap, a non-linear mapping approach to enhance the detection and representation of all genetic variations. VACmap improves duplication detection from 20% to 90% in the Challenging Medically-Relevant Genes (CMRG) benchmark and improves characterization of complex inversions in repetitive regions and gene conversion events. It improves resolving clinically significant loci, including the LPA gene (with repetitive KIV-2 units linked to coronary heart disease), GBA1 and STRC genes (risk factors for Parkinson’s disease and hearing loss, respectively, affected by pseudogene recombination with GBAP1 and STRCP1). Here, we show that VACmap delivers better alignment accuracy and SV detection, providing a robust tool for genomic analysis and clinical insights, with potential to advance understanding of genetic diversity and disease mechanisms. Here the authors introduce VACmap, a nonlinear long-read aligner that improves detection of complex structural variations like duplications, inversions, and gene conversions. It enhances SV callers’ performance on benchmarks and resolves clinically relevant loci in LPA, GBA1, and STRC genes.