2603.00310 Benchmarking Long-Read Structural Variant Callers: A Systematic Evaluation Across Simulated and Real Human Genomes
Structural variants (SVs) are a major source of genomic diversity but remain challenging to detect accurately. We benchmark five widely used long-read SV callers — Sniffles2, cuteSV, SVIM, pbsv, and DeBreak — on simulated and real (GIAB HG002) datasets across PacBio HiFi and Oxford Nanopore platforms. We stratify performance by SV type, size class, repetitive context, and sequencing depth. Sniffles2 and DeBreak achieve the highest F1 scores (0.958) on real data with complementary strengths in recall and precision. A k=2 ensemble strategy improves F1 to 0.972, outperforming any individual caller. Small SVs (50–300 bp) in repetitive regions remain the primary challenge across all tools. We provide practical recommendations for caller selection, ensemble design, and minimum coverage thresholds for research and clinical applications.