MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach
MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach
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DOI：
10.1093/bib/bbac195
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发表时间：
2022-05-18
影响因子：
9.5
通讯作者：
Luo, Junwei
中科院分区：
文献类型：
--
作者：
Ding, Hongyu;Luo, Junwei
结构变异在人类遗传多样性中起着重要作用，缺失和插入是两种常见的结构变异类型，已被证明与遗传疾病有关。因此，准确检测和分型SV对疾病研究具有重要意义。尽管长读段测序技术已经改善了SV检测和基因分型领域，但仍然存在一些挑战，无法获得令人满意的结果。在本文中，我们提出了MAMnet，这是一种基于长读段的快速可扩展SV检测和基因分型方法，并结合了卷积神经网络和长短期网络。MAMnet使用深度神经网络通过一种新的预测策略实现敏感的SV检测。在真实的长读段测序数据集上，我们证明MAMnet在F1得分方面优于Sniffles，SVIM，cuteSV和PBSV，同时实现了更好的缩放性能。源代码可从https://github.com/micahvista/MAMnet获得。
Structural variations (SVs) play important roles in human genetic diversity; deletions and insertions are two common types of SVs that have been proven to be associated with genetic diseases. Hence, accurately detecting and genotyping SVs is significant for disease research. Despite the fact that long-read sequencing technologies have improved the field of SV detection and genotyping, there are still some challenges that prevent satisfactory results from being obtained. In this paper, we propose MAMnet, a fast and scalable SV detection and genotyping method based on long reads and a combination of convolutional neural network and long short-term network. MAMnet uses a deep neural network to implement sensitive SV detection with a novel prediction strategy. On real long-read sequencing datasets, we demonstrate that MAMnet outperforms Sniffles, SVIM, cuteSV and PBSV in terms of their F1 scores while achieving better scaling performance. The source code is available from https://github.com/micahvista/MAMnet.