ArticleGigaScience2017
A recurrence-based approach for validating structural variation using long-read sequencing technology.
Article in GigaScience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed.
- A Personalized Haplotype-Resolved Near-Gapless Genome Framework for Somatic Variant Discovery in Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Structure Variations and 3D Genome Disruption: Implications in Safety of hPSC-Based Cell Therapy.International journal of molecular sciences · 2026Review
- Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease.bioRxiv : the preprint server for biology · 2026Article
- A Murine Database of Structural Variants Identifies A Candidate Gene for a Spontaneous Murine Lymphoma Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Murine Database of Structural Variants Enables the Genetic Architecture of a Spontaneous Murine Lymphoma to be Characterized.bioRxiv : the preprint server for biology · 2025Article
- Adaptive functions of structural variants in human brain development.Science advances · 2024Article
- Article
- SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph.Bioinformatics (Oxford, England) · 2023Article
- Article
- TT-Mars: structural variants assessment based on haplotype-resolved assemblies.Genome biology · 2022Article
- Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data.BMC genomics · 2022Article
- Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain.Nature neuroscience · 2022Article
- Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants.Genomics, proteomics & bioinformatics · 2022Article
- Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data.BMC genomics · 2021Article
- Cas9 targeted enrichment of mobile elements using nanopore sequencing.Nature communications · 2021Article
- Expectations and blind spots for structural variation detection from long-read assemblies and short-read genome sequencing technologies.American journal of human genetics · 2021Article
- Haplotype-resolved diverse human genomes and integrated analysis of structural variation.Science (New York, N.Y.) · 2021Article
- Article
- Identification and characterization of occult human-specific LINE-1 insertions using long-read sequencing technology.Nucleic acids research · 2020Article
- Hecaton: reliably detecting copy number variation in plant genomes using short read sequencing data.BMC genomics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Although numerous algorithms have been developed to identify structural variations (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. This is significant as the accurate identification of structural variation is still an outstanding but important problem in genomics. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of the region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as visual inspection of each region. Here we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long-read sequencing data. We assessed the performance of VaPoR on SVs in both simulated and real genomes and report a high-fidelity rate for overall accuracy across different levels of sequence depths. We show that VaPoR can interrogate a much larger range of SVs while still matching existing methods in terms of false positive validations and providing additional features considering breakpoint precision and predicted genotype. We further show that VaPoR can run quickly and efficiency without requiring a large processing or assembly pipeline. VaPoR provides a long read-based validation approach for genomic SVs that requires relatively low read depth and computing resources and thus will provide utility with targeted or low-pass sequencing coverage for accurate SV assessment. The VaPoR Software is available at: https://github.com/mills-lab/vapor.
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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 OpenQuestion graph.