Abstract
BackgroundThe computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.ResultsWe defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation.ConclusionOur approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.
Highlights
The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes
20% of the CpG dinucleotides (CpGs) islands (CGIs) are located in the gene-body and we found that these CGIs were unequally distributed, since there were approximately 30% more CD8 T lymphocytes; (CDS)-overlapping CGIs than those located in introns
Though the majority of the differentially methylated CGIs that were conserved overlapped with the CDS (Table 9), we found that they were the only class of CGI that was significantly enriched in highly conserved non-coding elements (HCNEs) [47] (Additional file 5)
Summary
The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. The methylation of DNA provokes a localized restriction of transcription that can be used for the selective silencing of genes This form of transcriptional control is mediated by regulatory regions termed CpG islands (CGIs) which overlap the promoter of all human housekeeping genes and over half of all tissue-specific genes [4,5,6,7]. The characteristics that make a sequence susceptible or resistant to methylation are not completely understood
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