Wang Tong, Gao Mengqi
School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China.
PLoS One. 2025 Apr 9;20(4):e0320085. doi: 10.1371/journal.pone.0320085. eCollection 2025.
An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significant for comprehending gene expression regulatory systems. While some useful predictive models have been proposed, there are still deficiencies in these models. To address current limitations, we propose a model, DNABERT2-Enhancer, based on transformer architecture and deep learning, designed for the recognition of enhancers (classified as either enhancer or non-enhancer) and the identification of their activity (strong or weak enhancers). More specifically, DNABERT2-Enhancer is composed of a BERT model for extracting features and a CNN model for enhancers classification. Parameters of the BERT model are initialized by a pre-training DNABERT-2 language model. The enhancer recognition task is then fine-tuned through transfer learning to convert the original sequence into feature vectors. Subsequently, the CNN network is employed to learn the feature vector generated by BERT and produce the prediction results. In comparison with existing predictors utilizing the identical dataset, our approach demonstrates superior performance. This suggests that the model will be a useful instrument for academic research on the enhancer recognition.
增强子是一种特定的DNA序列，通常位于基因的上游或下游位置，是真核基因转录调控中的关键元件。因此，识别增强子对于理解基因表达调控系统具有重要意义。虽然已经提出了一些有用的预测模型，但这些模型仍存在不足。为了解决当前的局限性，我们提出了一种基于Transformer架构和深度学习的模型DNABERT2-Enhancer，用于识别增强子（分为增强子或非增强子）并确定其活性（强或弱增强子）。更具体地说，DNABERT2-Enhancer由用于提取特征的BERT模型和用于增强子分类的CNN模型组成。BERT模型的参数由预训练的DNABERT-2语言模型初始化。然后通过迁移学习对增强子识别任务进行微调，将原始序列转换为特征向量。随后，使用CNN网络学习由BERT生成的特征向量并产生预测结果。与使用相同数据集的现有预测器相比，我们的方法表现出卓越的性能。这表明该模型将成为增强子识别学术研究的有用工具。
