PuSH - Publication Server of Helmholtz Zentrum München

Yang, Q.* ; Sun, J.* ; Wang, X.* ; Wang, J.* ; Liu, Q.* ; Ru, J. ; Zhang, X.* ; Wang, S.* ; Hao, R.* ; Bian, P.* ; Dai, X.* ; Gong, M.* ; Zhang, Z.* ; Wang, A.* ; Bai, F.* ; Li, R.* ; Cai, Y.* ; Jiang, Y.*

SVLearn: A dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Nat. Commun. 16:2406 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Spectrum
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 16, Issue: 1, Pages: , Article Number: 2406 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Virology (VIRO)
Grants High-Performance Computing platform of Northwest AF University
Shaanxi Livestock and Poultry Breeding Double-chain Fusion Key Project
National Natural Science Foundation of China
National Key R&D Program of China
This work was supported by grants from the National Key RD Program of China (2023YFD1300402, 2022YFF1000100), National Natural Science Foundation of China (U21A20120) and Shaanxi Livestock and Poultry Breeding Double-chain Fusion Key Project (2022GD-TSLD-