ArticleAmerican journal of human genetics2022
StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 69 citations in OpenAlex.
- Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation.Global medical genetics · 2026Article
- Structure Variations and 3D Genome Disruption: Implications in Safety of hPSC-Based Cell Therapy.International journal of molecular sciences · 2026Review
- Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Structural variants in the 3D genome as drivers of disease.Nature reviews. Genetics · 2025Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings.Bioinformatics (Oxford, England) · 2025Article
- Long-Read Sequencing and Structural Variant Detection: Unlocking the Hidden Genome in Rare Genetic Disorders.Diagnostics (Basel, Switzerland) · 2025Review
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease.Genome research · 2025Article
- Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men.Nature communications · 2025Article
- Systematic assessment of structural variant annotation tools for genomic interpretation.Life science alliance · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Case report: Deciphering the clinical significance of a novel partialFrontiers in oncology · 2025Article
- Identification of novel variants underlying non-syndromic primary ovarian insufficiency using a targeted NGS gene panel.Frontiers in endocrinology · 2025Article
- Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Nature communications · 2024Article
- CNVoyant a machine learning framework for accurate and explainable copy number variant classification.Scientific reports · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men.Research square · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and SV detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. To address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. We anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in probands where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is publicly available.
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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.