SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
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DOI：
10.1016/j.omtn.2019.08.011
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发表时间：
2019-12-06
影响因子：
8.8
通讯作者：
Lee, Gwang
中科院分区：
文献类型：
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作者：
Basith, Shaherin;Manavalan, Balachandran;Lee, Gwang
DNA N-6-腺嘌呤甲基化(6 MA)是原核生物和真核生物中的一种表观遗传修饰。鉴定水稻基因组中的6 mA位点在水稻表观遗传和育种中具有重要意义，但这些位点的非随机分布和生物学功能尚不清楚。一些机器学习工具可以识别6 mA位点，但预测精度有限，这限制了它们在表观遗传学研究中的可用性。在这里，我们开发了一种新的计算预测因子，称为基于序列的DNA N-6-甲基腺嘌呤预测因子(SDM6A)，这是一种用于识别水稻基因组中6 mA位点的两层集成方法。与基于具有基本功能的单一模型的现有方法不同，SDM6A探索了各种功能，并确定了五种适合此问题的编码方法。随后，从编码中识别出最优特征集，并使用支持向量机和极随机树分别建立相应的模型。首先，通过集成的方法将所有五个单一模型集成在一起，为每个分类器定义类。其次，将两个分类器集成在一起以生成最终预测。SDM6A在交叉验证和独立评价方面取得了较好的效果，平均准确率和马修斯相关系数分别为88.2%和0.764。相应的指标分别比现有方法高4.7%~11.0%和2.3%~5.5%。实现了一个用户友好的、可公开访问的Web服务器(http://thegleelab.org/SDM6A))来预测水稻基因组中可能的新的6 mA位点。
DNA N-6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and breeding, but non-random distribution and biological functions of these sites remain unclear. Several machine-learning tools can identify 6mA sites but show limited prediction accuracy, which limits their usability in epigenetic research. Here, we developed a novel computational predictor, called the Sequence-based DNA N-6-methyladenine predictor (SDM6A), which is a two-layer ensemble approach for identifying 6mA sites in the rice genome. Unlike existing methods, which are based on single models with basic features, SDM6A explores various features, and five encoding methods were identified as appropriate for this problem. Subsequently, an optimal feature set was identified from encodings, and corresponding models were developed individually using support vector machine and extremely randomized tree. First, all five single models were integrated via ensemble approach to define the class for each classifier. Second, two classifiers were integrated to generate a final prediction. SDM6A achieved robust performance on cross-validation and independent evaluation, with average accuracy and Matthews correlation coefficient (MCC) of 88.2% and 0.764, respectively. Corresponding metrics were 4.7%-11.0% and 2.3%-5.5% higher than those of existing methods, respectively. A user-friendly, publicly accessible web server (http://thegleelab.org/SDM6A) was implemented to predict novel putative 6mA sites in rice genome.