基于图神经网络的双股DNA断裂预测
Graph Neural Networks for Double-Strand DNA Breaks Prediction
摘要：
双链DNA断裂（DSB）是一种DNA损伤形式，可导致异常染色体重排。针对目前基于高通量实验的技术成本高、技术难度大的问题，本文设计了一种基于图神经网络的预测DSB的方法（GraphDSB）。为了提高模型的表达能力，引入了跳跃知识结构和几种有效的结构化编码方法。通过对正常人表皮角质形成细胞（NHEK）和慢性髓性白血病细胞系（K562）数据集的实验验证了结构信息对DSB预测的贡献，并且消融研究进一步证明了所提出的GraphDSB框架中所设计的组件的有效性。最后，利用GNNExplainer分析了节点特征和拓扑结构对DSB预测的贡献，证明了5-mer DNA序列特征和两种染色质相互作用模式对DSB预测的贡献率较高。
英文摘要：
Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause abnormal chromosomal rearrangements. Recent technologies based on high-throughput experiments have obvious high costs and technical challenges.Therefore, we design a graph neural network based method to predict DSBs (GraphDSB), using DNA sequence features and chromosome structure information. In order to improve the expression ability of the model, we introduce Jumping Knowledge architecture and several effective structural encoding methods. The contribution of structural information to the prediction of DSBs is verified by the experiments on datasets from normal human epidermal keratinocytes (NHEK) and chronic myeloid leukemia cell line (K562), and the ablation studies further demonstrate the effectiveness of the designed components in the proposed GraphDSB framework. Finally, we use GNNExplainer to analyze the contribution of node features and topology to DSBs prediction, and proved the high contribution of 5-mer DNA sequence features and two chromatin interaction modes.
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