College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, and School of Artificial Intelligence, Anhui University, Hefei, China.
BMC Genomics. 2023 Aug 9;24(1):448. doi: 10.1186/s12864-023-09217-4.
Previous studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops.
We proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC [Formula: see text]).
The results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.
先前的研究已经确定染色体结构在基因调控中起着非常重要的作用。转录因子 Yin Yang 1（YY1）是一种多功能 DNA 结合蛋白，它可以形成二聚体来介导染色质环和活跃的增强子-启动子相互作用。YY1 缺失或 YY1 结合位点的点突变会显著抑制增强子-启动子相互作用，从而影响基因表达。迄今为止，仅有少数计算方法可用于识别 YY1 介导的染色质环。
我们提出了一种名为 CapsNetYY1 的新模型，该模型基于胶囊网络架构来识别一对 YY1 基序是否可以形成染色质环。首先，我们使用独热编码方法对 DNA 序列进行编码。其次，使用多尺度卷积层提取序列的局部特征，并使用双向门控循环单元学习跨时间步的特征。最后，胶囊网络（卷积胶囊层和数字胶囊层）用于提取更高层次的特征并识别 YY1 介导的染色质环。与唯一预测 YY1 介导的染色质环的 DeepYY1 相比，我们的模型 CapsNetYY1 在独立数据集上实现了更好的性能（AUC [公式]）。
结果表明，CapsNetYY1 是一种识别 YY1 介导的染色质环的优秀方法。我们相信，CapsNetYY1 方法将用于其他 DNA 序列的预测分类。
