ArticleNature communications2025
SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research evolution of flat peach (Frontiers in plant science · 2025Pooled it
- Molecular QTL are enriched for structural variants in a cattle long-read cohort.Communications biology · 2026Article
- SVhet: towards accurate detection of germline heterozygous deletions using short reads.BMC bioinformatics · 2025Article
- Article
- Review
- Predicting PROTAC off-target effects via warhead involvement levels in drug-target interactions using graph attention neural networks.Computational and structural biotechnology journal · 2025Article
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Authors and funding
18 authors.
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Abstract
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here, we introduce SVLearn, a machine-learning approach for genotyping bi-allelic SVs. It exploits a dual-reference strategy to engineer a curated set of genomic, alignment, and genotyping features based on a reference genome in concert with an allele-based alternative genome. Using 38,613 human-derived SVs, we show that SVLearn significantly outperforms four state-of-the-art tools, with precision improvements of up to 15.61% for insertions and 13.75% for deletions in repetitive regions. On two additional sets of 121,435 cattle SVs and 113,042 sheep SVs, SVLearn demonstrates a strong generalizability to cross-species genotype SVs with a weighted genotype concordance score of up to 90%. Notably, SVLearn enables accurate genotyping of SVs at low sequencing coverage, which is comparable to the accuracy at 30× coverage. Our studies suggest that SVLearn can accelerate the understanding of associations between the genome-scale, high-quality genotyped SVs and diseases across multiple species.
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Registered trials
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