Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA.
Genome Res. 2022 May;32(5):930-944. doi: 10.1101/gr.275870.121. Epub 2022 Apr 8.
Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach called GraphReg that exploits 3D interactions from chromosome conformation capture assays to predict gene expression from 1D epigenomic data or genomic DNA sequence. By using graph attention networks to exploit the connectivity of distal elements up to 2 Mb away in the genome, GraphReg more faithfully models gene regulation and more accurately predicts gene expression levels than the state-of-the-art deep learning methods for this task. Feature attribution used with GraphReg accurately identifies functional enhancers of genes, as validated by CRISPRi-FlowFISH and TAP-seq assays, outperforming both convolutional neural networks (CNNs) and the recently proposed activity-by-contact model. Sequence-based GraphReg also accurately predicts direct transcription factor (TF) targets as validated by CRISPRi TF knockout experiments via in silico ablation of TF binding motifs. GraphReg therefore represents an important advance in modeling the regulatory impact of epigenomic and sequence elements.
将远端增强子与基因联系起来，并对其影响靶基因表达的方式进行建模，这是调控基因组学中长期未解决的问题，对于解释非编码遗传变异至关重要。在这里，我们提出了一种新的深度学习方法，称为 GraphReg，它利用来自染色体构象捕获测定的 3D 相互作用，从 1D 表观基因组数据或基因组 DNA 序列预测基因表达。通过使用图注意网络来利用基因组中可达 2Mb 远的远端元件的连通性，GraphReg 比该任务的最先进的深度学习方法更忠实地模拟基因调控，并更准确地预测基因表达水平。与 GraphReg 一起使用的特征归因方法可以准确识别基因的功能增强子，这通过 CRISPRi-FlowFISH 和 TAP-seq 测定得到了验证，优于卷积神经网络 (CNNs) 和最近提出的活性接触模型。基于序列的 GraphReg 还可以通过 CRISPRi TF 敲除实验通过在计算机上切除 TF 结合基序来准确预测直接转录因子 (TF) 靶标。因此，GraphReg 代表了在建模表观基因组和序列元件的调控影响方面的重要进展。
