基于卷积的水稻基因组i6mA-CNN N6-methyladenine位点识别方法
i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome
摘要：
DNA腺嘌呤核苷酸的N6甲基化（6 mA）是一种复制后修饰，参与多种生物学功能。用于全基因组6 mA位点检测的实验方法是昂贵且手工劳动密集的过程。自动化和准确的计算方法可以帮助识别长基因组中的6 mA位点，节省大量的时间和金钱。我们的研究开发了一个基于卷积神经网络的工具i6 mA-CNN，能够识别水稻基因组中的6 mA位点。我们的模型在多种类型的特征之间进行协调，例如PseAAC启发的定制特征向量，多个单热表示和二核苷酸理化性质。它实现了0.98的受试者工作特征曲线下面积，使用基准数据集的5倍交叉验证的总体准确度为0.94。最后，我们评估我们的模型上的其他两个植物基因组6 mA位点识别数据集除了水稻。结果表明，我们提出的工具是能够概括其能力的6 mA网站识别植物基因组，无论植物物种。这项研究的网络工具可以在https://cutt.ly/Co6KuWG上找到。补充数据（基准数据集、独立测试数据集、比较目的数据集、训练模型、物理化学性质值、基序发现的注意机制细节）可在https://cutt.ly/PpDdeDH上获得。
英文摘要：
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions. Experimental methods for genome wide 6mA site detection is an expensive and manual labour intensive process. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves area under the receiver operating characteristic curve of 0.98 with an overall accuracy of 0.94 using 5 fold cross validation on benchmark dataset. Finally, we evaluate our model on two other plant genome 6mA site identification datasets besides rice. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. Web tool for this research can be found at: https://cutt.ly/Co6KuWG. Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at https://cutt.ly/PpDdeDH.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
