4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction
4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction
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4mCPred：DNA N-4-甲基胞嘧啶位点预测的机器学习方法
DOI：
10.1093/bioinformatics/bty668
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发表时间：
2019-02-15
期刊：
影响因子：
5.8
通讯作者：
Zou, Quan
中科院分区：
文献类型：
--
作者：
He, Wenying;Jia, Cangzhi;Zou, Quan
动机：N-4-甲基胞嘧啶（4 mC）是一种重要的表观遗传修饰，在DNA修复、表达和复制中起着重要作用。4 mC位点的准确鉴定有助于深入研究其生物学功能和机制。因为，4 mC位点的实验鉴定是耗时和昂贵的，特别是考虑到基因序列的快速积累。补充与有效的计算方法是迫切需要的。结果：在这项研究中，我们开发了一个新的工具，4 mCPred，预测4 mC网站在秀丽隐杆线虫，果蝇，拟南芥，大肠杆菌，Geoalkalibacter subjumeeus和Geophylla pickeringii。4 mCPred由两个独立的模型组成，4mCPred_I和4mCPred_II。独立和跨物种测试的预测结果表明，4mCPred_I的性能是一个有用的工具。为了确定位置特异性三核苷酸倾向（PSTNP）和电子-离子相互作用电位的功能，我们使用F-评分方法来构建预测模型，并比较它们的PSTNP功能。与其他现有的预测器相比，4 mCPred在严格的刀切和独立测试中获得了更高的准确性。我们还详细分析了不同特征的重要性。
Motivation: N-4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed.Results: In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail.