CpG Island Mapping by Epigenome Prediction

CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1) reliance on arbitrary threshold parameters that bear little biological justification, (2) failure to account for widespread heterogeneity among CpG islands, and (3) apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of ��CpG island strength�� that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted ��bona fide�� CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to their characteristic epigenetic and functional states. And it is superior to purely experimental epigenome mapping for CpG island detection since it abstracts from specific properties that are limited to a single cell type or tissue. In addition, using computational epigenetics methods we could identify high correlation between the epigenome and characteristics of the DNA sequence, a finding which emphasizes the need for a better understanding of the mechanistic links between genome and epigenome.CpG岛（CpG islands）最初通过表观遗传与功能特性被鉴定，即不存在DNA甲基化且常与启动子相关联。然而，这一概念很快被简化的DNA序列标准所取代，后者可在缺乏大规模表观遗传数据集的情况下实现CpG岛的全基因组注释。尽管当前CpG岛标准已被广泛使用，但仍存在显著弊端：(1) 依赖几乎无生物学依据的任意阈值参数；(2) 无法涵盖CpG岛间广泛存在的异质性；(3) 应用于人类基因组时明显缺乏特异性。本研究的核心思路为：整合表观遗传与功能特性的“CpG岛强度”定量评分，可助力解决上述弊端。我们构建了一条表观基因组预测流程，可将CpG岛的DNA序列与其表观遗传状态（包括DNA甲基化、组蛋白修饰及染色质可及性）相关联。我们基于人类21号与22号染色体上CpG岛的表观遗传数据训练支持向量机（support vector machines），鉴定出与开放/致密染色质结构相关的有效DNA特征。利用这些DNA特征，我们可对全基因组范围内所有CpG岛的表观遗传状态进行预测。结合多个表观遗传特征的预测结果，我们可估算人类基因组中每个CpG岛的固有CpG岛强度，即其呈现开放且具备转录活性的染色质结构的内在倾向。我们通过独立数据集对研究结果进行了广泛验证，结果表明CpG岛强度预测可适用于不同组织与细胞类型且具备参考价值；同时我们获得了优化后的预测“真正的（bona fide）”CpG岛图谱。通过表观基因组预测进行CpG岛定位，在概念上优于基于通用序列标准的CpG岛鉴定方法，因为前者将CpG岛检测与其典型的表观遗传及功能状态相关联。同时，该方法也优于仅依靠实验手段进行CpG岛检测的表观基因组定位方法，因为它规避了仅局限于单一细胞类型或组织的特定特性带来的局限。此外，通过计算表观遗传学方法，我们发现表观基因组与DNA序列特征之间存在高度相关性，这一发现凸显了深入解析基因组与表观基因组间机制性关联的必要性。