ArticleBriefings in bioinformatics2024
Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Structural Variation and Its Roles in Plant Genomes.Plants (Basel, Switzerland) · 2026Review
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Copy Number Variant Duplications Associated with Essential Tremor.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Recommendations for bioinformatics in clinical practice.Genome medicine · 2025Review
- Benchmarking copy number variation detection with low-coverage whole-genome sequencing.Briefings in bioinformatics · 2025Article
- Comparative analysis of hybrid-SNP microarray and nanopore sequencing for detection of large-sized copy number variants in the human genome.Molecular cytogenetics · 2025Article
- Enhancing interpretation of clinical disease-associated copy number variations from multiple sequencing strategies with CNVSeeker.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.
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Registered trials
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