School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China.
The Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Methods. 2022 Aug;204:142-150. doi: 10.1016/j.ymeth.2022.04.011. Epub 2022 Apr 25.
DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and with growth as well as development of plants. Precisely detecting DNA 6mA sites is of great importance to exploration of 6mA functions. Although many computational methods have been presented for DNA 6mA prediction, there is still a wide gap in the practical application. We presented a convolution neural network (CNN) and bi-directional long-short term memory (Bi-LSTM)-based deep learning method (Deep6mAPred) for predicting DNA 6mA sites across plant species. The Deep6mAPred stacked the CNNs and the Bi-LSTMs in a paralleling manner instead of a series-connection manner. The Deep6mAPred also employed the attention mechanism for improving the representations of sequences. The Deep6mAPred reached an accuracy of 0.9556 over the independent rice dataset, far outperforming the state-of-the-art methods. The tests across plant species showed that the Deep6mAPred is of a remarkable advantage over the state of the art methods. We developed a user-friendly web application for DNA 6mA prediction, which is freely available at http://106.13.196.152:7001/ for all the scientific researchers. The Deep6mAPred would enrich tools to predict DNA 6mA sites and speed up the exploration of DNA modification.
DNA N6-甲基腺嘌呤（6mA）是一种关键的 DNA 修饰，在细胞过程中发挥着多种作用，包括基因表达调控、DNA 修复和 DNA 复制。DNA 6mA 与哺乳动物中的许多疾病以及植物的生长和发育密切相关。准确检测 DNA 6mA 位点对于探索 6mA 功能非常重要。尽管已经提出了许多用于预测 DNA 6mA 的计算方法，但在实际应用中仍然存在很大的差距。我们提出了一种卷积神经网络（CNN）和基于双向长短时记忆（Bi-LSTM）的深度学习方法（Deep6mAPred），用于预测植物物种中的 DNA 6mA 位点。Deep6mAPred 以并行方式而不是串联方式堆叠 CNN 和 Bi-LSTMs。Deep6mAPred 还采用了注意力机制来提高序列的表示能力。Deep6mAPred 在独立的水稻数据集上的准确率达到了 0.9556，远远超过了最先进的方法。在跨植物物种的测试中，Deep6mAPred 显示出明显优于最先进方法的优势。我们开发了一个用户友好的 DNA 6mA 预测网络应用程序，该程序可在 http://106.13.196.152:7001/ 上免费供所有科研人员使用。Deep6mAPred 将丰富预测 DNA 6mA 位点的工具，并加速 DNA 修饰的探索。
