Graph Neural Networks for Double-Strand DNA Breaks Prediction
This work addresses the high costs and technical challenges of experimental DSB prediction methods for biomedical research, though it appears incremental as it builds on existing graph neural network techniques.
The paper tackles the problem of predicting double-strand DNA breaks (DSBs) by developing GraphDSB, a graph neural network method that uses DNA sequence features and chromosome structure information, achieving results validated on datasets from NHEK and K562 cell lines with ablation studies confirming component effectiveness.
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.