ArticleBMC genomics2024
Comparison of structural variant callers for massive whole-genome sequence data.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Diagnostic discovery of structural variants causing foveal hypoplasia using SVRare and long-read nanopore sequencing.European journal of human genetics : EJHG · 2026Article
- Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies.Genome medicine · 2026Article
- Untangling the Copy Number Variation at the Basis of Belted Phenotypes in Cattle Using Long-Read Sequencing.Animal genetics · 2026Article
- Invas: an inversion-aware method for transcriptome assembly.Nature communications · 2026Article
- GrassSV - hybrid method to detect structural variants in high throughput DNA-seq data.PLoS computational biology · 2026Article
- Resolving repetitive and telomeric regions through a high-contiguity genome assembly of the African Turquoise killifish.Genes & genomics · 2026Article
- SVCROWS: a user-defined tool for interpreting significant structural variants in heterogeneous datasets.Nucleic acids research · 2026Article
- Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing.Frontiers in genetics · 2026Review
- Novel Strategy for Structural Variant Genotyping by Short-Read Genomic Sequencing From Restriction-Circles: Experimental and Bioinformatics Proof-of-Concept.Human mutation · 2026Article
- Article
- High-quality phased genome assemblies of line-bred Korean Hanwoo cattle.Scientific data · 2025Article
- A Prospective Evaluation of the Diagnostic Utility for Low-Coverage Genome Sequencing in Prenatal Samples: A Comparison With Chromosomal Microarray Analysis.Prenatal diagnosis · 2025Article
- A Hitchhiker Guide to Structural Variant Calling: A Comprehensive Benchmark Through Different Sequencing Technologies.Biomedicines · 2025Article
- Review
- Comparative analysis of hybrid-SNP microarray and nanopore sequencing for detection of large-sized copy number variants in the human genome.Molecular cytogenetics · 2025Article
- Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.PloS one · 2025Article
- Visual Inspection of Sequencing Data for Diagnosis: Practical Guide to Structural Variant Analysis Using Integrative Genomics Viewer (IGV).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Artificial intelligence in variant calling: a review.Frontiers in bioinformatics · 2025Review
- Enhanced detection and characterization of germline structural variants in cancer predisposition genes via genome sequencing.Genetics in medicine open · 2025Article
- A large structural variant collection in Holstein cattle and associated database for variant discovery, characterization, and application.BMC genomics · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundDetecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.
resultsNotably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions.
conclusionsOur findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.
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