ArticleScientific reports2024
Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 22 citations in OpenAlex.
- Structural Variant benchmarking frameworks: Parameterization, matching logic, and evaluation assumptions presented through HG002 and NA12878.PLoS computational biology · 2026Article
- Resolving CYP2D6 Structural Complexity with Long-Read Sequencing: Implications for Tamoxifen Precision Dosing in Thai Breast Cancer Patients.Clinical pharmacology and therapeutics · 2026Article
- Long-Read Sequencing in CKD Diagnostics: Breaking Genomic Barriers and Expanding Global Inclusion.Kidney international reports · 2026Review
- Feasibility of long-read nanopore sequencing for methylation-based classification of posterior fossa ependymomas.Brain tumor pathology · 2026Article
- ANOMALY: a Snakemake pipeline for identifying NuMTs from long-read sequencing data.NAR genomics and bioinformatics · 2026Article
- SnakeBITE: A SNAKEmake-Based Interface for Third-Generation Sequencing Data Analysis.Biomolecules · 2026Article
- Multiplatform comparisons and annotation of structural variants highlight the utility of the T2T reference genome in human diagnostics.GigaScience · 2026Article
- VACmap: an accurate long-read aligner for unraveling complex genomic rearrangements.Nature communications · 2026Article
- Insights into natural neocentromere evolution from a cattle T2T X chromosome.Nature communications · 2025Article
- Comprehensive detection of structural variations in long and short reads dataset of French cattle.Scientific reports · 2025Article
- Pangenomic structural variant patterns reflect evolutionary diversification in Brassica napus.Genome biology · 2025Article
- Long read whole genome sequencing-based discovery of structural variants and their role in aetiology of non-syndromic autism spectrum disorder in India.BMC medical genomics · 2025Article
- A Hitchhiker Guide to Structural Variant Calling: A Comprehensive Benchmark Through Different Sequencing Technologies.Biomedicines · 2025Article
- Review
- Interlaboratory evaluation of high molecular weight DNA extraction methods for long-read sequencing and structural variant analysis.BMC genomics · 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
- A prospective trial comparing programmable targeted long-read sequencing and short-read genome sequencing for genetic diagnosis of cerebellar ataxia.Genome research · 2025Article
- Artificial intelligence in variant calling: a review.Frontiers in bioinformatics · 2025Review
- Combining long-read DNA and RNA sequencing to enhance molecular understanding of structural variations leading to copy gains.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural variants (SVs) are one of the significant types of DNA mutations and are typically defined as larger-than-50-bp genomic alterations that include insertions, deletions, duplications, inversions, and translocations. These modifications can profoundly impact the phenotypic characteristics and contribute to disorders like cancer, response to treatment, and infections. Four long-read aligners and five SV callers have been evaluated using three Oxford Nanopore NGS human genome datasets in terms of precision, recall, and F1-score statistical metrics, depth of coverage, and speed of analysis. The best SV caller regarding recall, precision, and F1-score when matched with different aligners at different coverage levels tend to vary depending on the dataset and the specific SV types being analyzed. However, based on our findings, Sniffles and CuteSV tend to perform well across different aligners and coverage levels, followed by SVIM, PBSV, and SVDSS in the last place. The CuteSV caller has the highest average F1-score (82.51%) and recall (78.50%), and Sniffles has the highest average precision value (94.33%). Minimap2 as an aligner and Sniffles as an SV caller act as a strong base for the pipeline of SV calling because of their high speed and reasonable accomplishment. PBSV has a lower average F1-score, precision, and recall and may generate more false positives and overlook some actual SVs. Our results are valuable in the comprehensive evaluation of popular SV callers and aligners as they provide insight into the performance of several long-read aligners and SV callers and serve as a reference for researchers in selecting the most suitable tools for SV detection.
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