Memorial Sloan Kettering Cancer Center, New York, USA.
University of Washington, Seattle, USA.
Genome Biol. 2023 Jun 6;24(1):134. doi: 10.1186/s13059-023-02934-9.
Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and or even capture differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from widely available epigenomic tracks. Epiphany uses bidirectional long short-term memory layers to capture long-range dependencies and optionally a generative adversarial network architecture to encourage contact map realism. Epiphany shows excellent generalization to held-out chromosomes within and across cell types, yields accurate TAD and interaction calls, and predicts structural changes caused by perturbations of epigenomic signals.
最近的深度学习模型可以根据 DNA 序列预测 Hi-C 接触图谱，取得了有前景的准确性，但不能推广到新的细胞类型，甚至不能捕捉到训练细胞类型之间的差异。我们提出了 Epiphany，这是一种从广泛可用的表观基因组轨迹预测细胞类型特异性 Hi-C 接触图谱的神经网络。Epiphany 使用双向长短期记忆层来捕捉远程依赖关系，并可选地使用生成对抗网络架构来鼓励接触图谱的真实性。Epiphany 在细胞内和细胞间的保留染色体上表现出出色的泛化能力，产生准确的 TAD 和相互作用调用，并预测由表观基因组信号扰动引起的结构变化。
