ArticleNature communications2023
PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Translocation-induced enhancer rewiring reveals cryptic oncogenic circuits in multiple myeloma.Science advances · 2026Article
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Pan-genome based on chromosome sequences of wild and cultivated Agaricus bisporus.Scientific data · 2026Article
- The Impact of Structural Variation on Alzheimer's Disease in the Alzheimer's Disease Sequencing Project.Research square · 2026Article
- 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
- Structural variation detection and association analysis of whole-genome-sequence data from 16,543 Alzheimer's disease sequencing project subjects.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- 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
- Phased T2T genome assemblies facilitate the mining of disease-resistance genes inHorticulture research · 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
- Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men.Research square · 2024Article
- Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .
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