Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.
Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.
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利用人类 DNA 组成和序列复杂性对 DNA 甲基化进行全基因组预测
DOI：
10.3390/ijms18020420
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发表时间：
2017-02-16
影响因子：
5.6
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Wu C;Yao S;Li X;Chen C;Hu X
DNA甲基化通过抑制活性在转录调控中发挥重要作用。DNA甲基化水平的变化是影响靶基因表达及下游表型的一个重要因素。由于目前的实验技术只能检测人类基因组中一小部分的CpG位点，因此迫切需要开发可靠的计算模型来预测全基因组的DNA甲基化情况。在此，我们提出了一种新颖的算法，该算法能准确提取序列复杂度特征（7种特征），并利用人类胚胎干细胞的甲基化图谱，结合已报道的DNA组成特征（三核苷酸频率和GC含量，65种特征），开发出一种基于支持向量机的预测模型。对人类22条染色体采用不同大小窗口的预测结果表明，600碱基对的窗口实现了94.7%的最佳平均准确率。此外，与两种现有方法的比较进一步显示了我们模型的优越性，对小鼠数据的跨物种预测也证明了我们的模型具有一定的泛化能力。最后，对ChromHMM注释的功能区域的实验数据和预测数据进行统计检验，发现10个区域中有6个是一致的，这意味着对未检测的CpG位点的预测是可靠的。因此，我们相信我们的新模型在预测DNA甲基化方面将是有用且可靠的。
DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.