ArticleNature genetics2026
Population-level structural variant characterization using pangenome graphs.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Resolving structural variants in pangenome graphs with Swave.Nature reviews. Genetics · 2026Article
- Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.Briefings in bioinformatics · 2026Article
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Revealing the Shared Genetic Basis of Thermal Adaptation and Abdominal Adiposity in Wenchang Chicken Using Whole-Genome Structural Variation Analysis.Animals : an open access journal from MDPI · 2026Article
- [Applications and Challenges of Deep Learning in Human Genome Research].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Population-level structural variant (SV) profiling is crucial in the era of pangenomes. However, identifying SVs from genome assemblies and pangenome graphs remains a substantial challenge. Here we present Swave, a sequence-to-image, deep learning-based method that accurately resolves both simple and complex SVs, along with their population characteristics, from assembly-derived pangenome graphs. Swave introduces 'projection waves' to summarize the dotplot images that capture mapping patterns between reference and SV-indicating alleles in the pangenome. Then, a recurrent neural network distinguishes true SV signals from background noise introduced by genomic repeats. Swave demonstrates superior performance in both SV-type classification and genotyping compared with existing methods. When applied to healthy cohorts and rare-disease cohorts, Swave reveals complex and polymorphic SV patterns across human populations and identifies potentially pathogenic SVs. These advancements will facilitate the creation of comprehensive population-level SV catalogs, deepening our understanding of SVs in genetic diversity and disease associations.
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Registered trials
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