CpG Island Mapping by Epigenome Prediction

Abstract
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. A key challenge for bioinformatic research is the identification of regulatory regions in the human genome. Regulatory regions are DNA elements that control gene expression and thereby contribute to the organism's phenotype. An important class of regulatory regions consists of so-called CpG islands, which are characterized by frequent occurrence of the CG sequence pattern. CpG islands are strongly associated with open and transcriptionally competent chromatin structure, they play a critical role in gene regulation, and they are involved in the epigenetic causes of cancer. In this article we make several conceptual improvements to the definition and mapping of CpG islands. First, we show that the traditional distinction between CpG islands and non-CpG islands is too harsh, and instead we propose a quantitative measure of CpG island strength to gradually distinguish between stronger and weaker regulatory regions. Second, by genome-wide comparison of multiple epigenome datasets we identify high correlation between features of the genome's DNA sequence and the epigenome, indicating strong functional interdependence. Third, we develop and apply a novel method for predicting the strength of all CpG islands in the human genome, giving rise to an improved and more accurate CpG island mapping.