ArticleGenome biology2024
Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.Briefings in bioinformatics · 2026Article
- Characteristics of fusion genes in breast cancer.Cancer cell international · 2026Review
- Complementarity of Long-Reads and Optical Mapping in Parkinson's Disease for Structural Variants.Annals of clinical and translational neurology · 2026Article
- NanoVar: a comprehensive workflow for structural variant detection to uncover the genome's hidden patterns.Nature protocols · 2026Review
- SVNeoPP: A Workflow for Structural-Variant-Derived Neoantigen Prediction and Prioritization Using Multi-Omics Data.Biology · 2026Article
- Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing.Frontiers in genetics · 2026Review
- SDFA: a standardized decomposition format and toolkit for efficient analysis of structural variants in large-scale population genomic studies.Genome biology · 2025Article
- Hidden in the Genome: The First Italian Family with North Carolina Macular Dystrophy Carrying a NovelBiomedicines · 2025Article
- Accurate, Scalable Structural Variant Genotyping in Complex Genomes at Population Scales.Molecular biology and evolution · 2025Article
- Long-Read Sequencing and Structural Variant Detection: Unlocking the Hidden Genome in Rare Genetic Disorders.Diagnostics (Basel, Switzerland) · 2025Review
- The nature of complex structural variations in tomatoes.Horticulture research · 2025Article
- Review
- Systematic benchmarking of tools for structural variation detection using short- and long-read sequencing data in pigs.iScience · 2025Article
- Benchmarking, detection, and genotyping of structural variants in a population of whole-genome assemblies using the SVGAP pipeline.bioRxiv : the preprint server for biology · 2025Article
- Extrachromosomal circular DNA drives dynamic genome plasticity: emerging roles in disease progression and clinical potential.Theranostics · 2025Review
- Long-Read Sequencing for the Rapid Response to Infectious Diseases Outbreaks.Current clinical microbiology reports · 2025Review
- Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.Genome biology · 2024Article
- Structural variations in livestock genomes and their associations with phenotypic traits: a review.Frontiers in veterinary science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundStructural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.
resultsThis study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking .
conclusionsThis study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.