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

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

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...

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Main Authors: Qimeng Yang, Jianfeng Sun, Xinyu Wang, Jiong Wang, Quanzhong Liu, Jinlong Ru, Xin Zhang, Sizhe Wang, Ran Hao, Peipei Bian, Xuelei Dai, Mian Gong, Zhuangbiao Zhang, Ao Wang, Fengting Bai, Ran Li, Yudong Cai, Yu Jiang
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-57756-z
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https://doi.org/10.1038/s41467-025-57756-z

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