Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers |
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| Authors: | Yao-zhong Zhang Seiya Imoto Satoru Miyano Rui Yamaguchi |
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| Affiliation: | 1. Division of Health Medical Intelligence, Institute of Medical Science, the University of Tokyo, Tokyo, Japan ; 2. M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan ; 3. Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan ; 4. Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan ; Princeton University, UNITED STATES |
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| Abstract: | Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV. |
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