Abstract
The birth of the third-generation sequencing technology provides a large number of long-read data for calling structural variations (SVs). However, the existing calling tools for these long-read data have high precision but low recall. Therefore, to solve this problem, a new method called GcnSV is proposed in this paper. Firstly, GcnSV maps all reads in the genome sequencing data into corresponding graphs as the input of the graph neural network. Then, it uses these graphs to train the graph neural network in order to learn the characteristics of variations themselves and their upstream and downstream. Finally, a clustering algorithm is designed to obtain the final calling results. On the simulated and real data, we give the evaluation results of GcnSV and other calling tools. The experimental results show that GcnSV has higher recall and F1-score on different coverage depths and different variant lengths.
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Huang, M., Wang, H., Gao, J. (2023). GcnSV: A Method Based on Deep Learning of Calling Structural Variations from the Third-Generation Sequencing Data. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1813. Springer, Singapore. https://doi.org/10.1007/978-981-99-2449-3_35
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DOI: https://doi.org/10.1007/978-981-99-2449-3_35
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