Language
English
Publication Date
1-5-2026
Journal
Nature Communications
DOI
10.1038/s41467-025-67096-7
PMID
41491162
PMCID
PMC12770363
PubMedCentral® Posted Date
1-5-2025
PubMedCentral® Full Text Version
Post-print
Abstract
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.
Keywords
Humans, Gene Rearrangement, Genomics, Sequence Alignment, Genome, Human, Computational models, Software
Published Open-Access
yes
Recommended Citation
Ding, Hongyu; Sedlazeck, Fritz J; Proukakis, Christos; et al., "VACmap: An Accurate Long-Read Aligner for Unraveling Complex Genomic Rearrangements" (2026). Faculty, Staff and Students Publications. 6257.
https://digitalcommons.library.tmc.edu/baylor_docs/6257