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.

本研究设计了基于图神经网络的 GraphDSB 方法，使用了不同的有效结构编码方法和结构信息来预测双链 DNA 断裂，无高昂成本和技术挑战。在 NHEK 和 K562 数据集上，验证了结构信息对 DSBs 预测的贡献，并通过剥离实验进一步证明了所设计组件在 GraphDSB 框架中的有效性。最后，我们使用 GNNExplainer 来分析节点特征和拓扑对 DSBs 预测的贡献，并证明了 5-mer DNA 序列特征和两种染色质相互作用模式的高贡献性。