Authors
Hua Shi, Yihang Lin, Dachen Liu, Hongfeng Wu, Ashidi Nor Mat Isa, Leyi Wei, Quan Zou, Xiang Chen
Published in
Briefings in bioinformatics. Volume 27. Issue 5. Sep 01, 2026.
Abstract
Detecting structural variations (SVs) via long-read sequencing remains difficult due to algorithmic variations and genomic complexity, alongside a shortage of comprehensive benchmarks for somatic variants. We present a unified benchmarking framework covering both germline and somatic SV detection, which evaluates 14 long-read callers across 20 core datasets from Pacific Biosciences (PacBio) Continuous Long Reads (CLR), Circular Consensus Sequencing (CCS), and Oxford Nanopore Technologies (ONT) platforms. Performance was analyzed across 12 dimensions using metrics including baseline artefact rate, Mendelian discordance rate (MDR), and Mendelian inheritance error rate (MIER). For germline SVs, DeBreak, cuteSV2, and SVDF showed stable and accurate detection. cuteSV2 and SVHunter maintained consistent genotyping accuracy across sequencing depths, whereas Severus and cuteSV2 performed well in identifying complex SVs such as inversions, duplications, and translocations. In tumor datasets, tools designed specifically for somatic SVs outperformed germline callers. Severus and SAVANA showed strong overall somatic performance, and nanomonsv showed distinct advantages at low coverage. These results offer practical references for tool selection under various sequencing scenarios, supporting future algorithmic development. The code and resources are available at https://github.com/model-lab/LR-SV-Benchmark.
PMID:
42803633
Bibliographic data and abstract were imported from PubMed on 28 Sep 2026.
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