Deciphering epigenomic code for cell differentiation using deep learning
Deciphering epigenomic code for cell differentiation using deep learning
复制标题
利用深度学习破解细胞分化的表观基因组密码
DOI：
10.1186/s12864-019-6072-8
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发表时间：
2019-09-12
期刊：
影响因子：
4.4
通讯作者：
Su, Zhengchang
中科院分区：
文献类型：
--
作者：
Ni, Pengyu;Su, Zhengchang
背景资料：虽然DNA序列在建立细胞类型的独特表观基因组中起着至关重要的作用，但对导致细胞分化期间产生的不同细胞类型的独特表观基因组的序列决定因素知之甚少。为了填补这一空白，我们采用了两种类型的深度卷积神经网络（CNN），为每种差异相关的细胞类型和细胞中测量的每种组蛋白标记构建，以了解每种细胞类型中各种组蛋白修饰模式的序列决定因素。结果：我们将我们的模型应用于四种差异相关的人类CD 4 + T细胞类型和每种细胞类型中测量的六种组蛋白标记。细胞模型可以准确地预测每种细胞类型中的组蛋白标记，而标记模型也可以基于单个标记准确地预测细胞类型。通过细胞或标记模型学习的序列基序与已知在CD4+ T细胞分化中起重要作用的转录因子的已知结合基序高度相似。每种细胞类型中独特的组蛋白标记模式和不同细胞类型中相同组蛋白标记的不同模式都由一组具有独特组合的基序决定。有趣的是，在不同的细胞模型中学习到的共享基序的水平反映了细胞的谱系关系，而在不同的组蛋白标记模型中学习到的共享基序的水平反映了它们的功能关系。这些模型也可以使学习的图案和它们的相互作用，在确定特定的组蛋白标记模式在celltypes.Conclusion的重要性的预测：序列决定因素的各种组蛋白修饰模式在不同的细胞类型可以揭示通过比较分析的图案学习的CNN模型中的多个细胞类型和组蛋白标记。学习的基序是可解释的，并且可以提供对在不同细胞类型中建立独特表观基因组的潜在分子机制的见解。因此，我们的研究结果支持了这样的假设，即DNA序列最终通过与转录因子、表观基因组重塑系统和细胞分化过程中的细胞外信号的相互作用来决定不同细胞类型的独特表观基因组。
Background: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type.Results: We applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types.Conclusion: Sequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation.