I-DNAN6mA：使用碱基配对图和深度学习准确识别 DNA N6-甲基腺嘌呤位点,Journal of Chemical Information and Modeling

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I-DNAN6mA：使用碱基配对图和深度学习准确识别 DNA N6-甲基腺嘌呤位点
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2023-02-01 , DOI: 10.1021/acs.jcim.2c01465
Xue-Qiang Fan ₁ , Bing Lin ₁ , Jun Hu ₂ , Zhong-Yi Guo ₁

Affiliation

最近发现的大量 DNA N ⁶ -甲基腺嘌呤 (6mA) 位点改变了我们对 6mA 在生物体中的作用的看法。然而，我们无法通过现有实验方法快速且经济高效地识别 6mA 站点，从而阻碍了我们理解它们的能力。开发一种快速准确识别 6mA 位点的新方法对于加快其功能检测和理解的进程至关重要。在这项研究中，我们提出了一种称为 I-DNAN6mA 的新型计算方法，通过利用碱基配对规则和精心设计的具有成对输入的三阶段深度学习模型，来识别 6mA 位点并很好地补充实验方法。我们提出的方法的性能在四个物种上进行了基准测试和评估，即拟南芥、果蝇、水稻和蔷薇科。实验结果表明，I-DNAN6mA 的受试者工作特征曲线下面积值分别为 0.967、0.963、0.947、0.976 和 0.990，准确度分别为 91.5、92.7、88.2、0.938 和 96.2%，Mathew 相关系数值为 0.855 、0.831、0.763、0.877 和 0.924 分别在五个基准数据集上，并且优于几种现有的最先进方法。据我们所知，I-DNAN6mA 是第一种使用 DNA 序列的新型图像表示和具有成对输入的深度学习模型来识别 6mA 位点的方法。I-DNAN6mA 有望用于定位 DNA 的功能区域。

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I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning

The recent discovery of numerous DNA N⁶-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew’s correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.

更新日期：2023-02-01

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