Getting Personal with Epigenetics: Towards Machine-Learning-Assisted Precision Epigenomics
Getting Personal with Epigenetics: Towards Machine-Learning-Assisted Precision Epigenomics
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DOI：
10.1101/2022.02.11.479115
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发表时间：
2022-02
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通讯作者：
Alex Hawkins-Hooker;G. Visonà;Tanmayee Narendra;Mateo Rojas-Carulla;B. Scholkopf;G. Schweikert
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文献类型：
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作者：
Alex Hawkins-Hooker;G. Visonà;Tanmayee Narendra;Mateo Rojas-Carulla;B. Scholkopf;G. Schweikert
表观遗传修饰是参与基因表达调控的动态控制机制。与DNA序列本身不同，它们不仅在个体之间存在差异，而且在同一个体的不同细胞类型之间也存在差异。暴露于环境因素、体细胞突变和衰老导致表观基因组随时间发生变化，这可能构成疾病的早期标志或致病因素。表观遗传变化是可逆的，因此是有希望的治疗靶点。然而，确定个体细胞类型特异性表观基因组的作图工作受到实验成本的限制。我们开发了eDICE，一种基于注意力的深度学习模型，用于估算表观基因组轨迹。与参考Roadmap表观基因组上的先前模型相比，eDICE实现了改进的整体性能。此外，我们提出了一个概念证明，用于对ENTEx数据集进行个性化表观基因组测量，其中eDICE正确预测了个体和细胞类型特异性表观遗传模式。该案例研究是朝着稳健地采用基于机器学习的方法进行个性化表观基因组学迈出的重要一步。
Epigenetic modifications are dynamic control mechanisms involved in the regulation of gene expression. Unlike the DNA sequence itself, they vary not only between individuals but also between different cell types of the same individual. Exposure to environmental factors, somatic mutations, and ageing contribute to epigenomic changes over time, which may constitute early hallmarks or causal factors of disease. Epigenetic changes are reversible and, therefore, promising therapeutic targets. However, mapping efforts to determine an individual’s cell-type-specific epigenome are constrained by experimental costs. We developed eDICE, an attention-based deep learning model, to impute epigenomic tracks. eDICE achieves improved overall performance compared to previous models on the reference Roadmap epigenomes. Furthermore, we present a proof of concept for the imputation of personalised epigenomic measurements on the ENTEx dataset, where eDICE correctly predicts individual- and cell-type-specific epigenetic patterns. This case study constitutes an important step towards robustly employing machine-learning-based approaches for personalised epigenomics.