Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions

Miaomiao Tan, Zhinan Lin, Zhuofu Chen, Haonan Zhou, Junseok Park, Ziting He, Eunjung A. Lee, Zhipeng Gao and Xiaowei Zhu ()
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Miaomiao Tan: Zhejiang Shuren University
Zhinan Lin: City University of Hong Kong
Zhuofu Chen: City University of Hong Kong
Haonan Zhou: City University of Hong Kong
Junseok Park: 3 Blackfan Circle
Ziting He: City University of Hong Kong
Eunjung A. Lee: 3 Blackfan Circle
Zhipeng Gao: Zhejiang University
Xiaowei Zhu: City University of Hong Kong

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Active mobile elements in the human genome can create novel mobile element insertions (MEIs) in somatic tissues. Detection of somatic MEIs, particularly those with low mosaicism, remains a significant challenge due to sequencing artifacts and alignment errors. Existing methods lack sensitivity or require biased manual inspection. Here we present RetroNet, a deep learning algorithm that encodes sequencing reads into images to identify somatic MEIs with as few as two reads. Trained on diverse datasets, RetroNet outperforms previous methods and eliminates the need for manual examinations. RetroNet achieves high precision (0.885) and recall (0.579) on a cancer cell line, detecting insertions in just 1.79% of cells. RetroNet is also effective for degraded DNA, like circulating tumor DNA. This tool is applicable to the rapidly generated short-read sequencing data and has the potential to provide further insights into the functional and pathological implications of somatic retrotranspositions.

Date: 2025
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DOI: 10.1038/s41467-025-64237-w

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