  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.
