Abstract
BackgroundEpigenetic modifications, including CG methylation (a major form of DNA methylation) and histone modifications, interact with each other to shape their genomic distribution patterns. However, the entire picture of the epigenetic crosstalk regulating the CG methylation pattern is unknown especially in cells that are available only in a limited number, such as mammalian oocytes. Most machine learning approaches developed so far aim at finding DNA sequences responsible for the CG methylation patterns and were not tailored for studying the epigenetic crosstalk.ResultsWe built a machine learning model named epiNet to predict CG methylation patterns based on other epigenetic features, such as histone modifications, but not DNA sequence. Using epiNet, we identified biologically relevant epigenetic crosstalk between histone H3K36me3, H3K4me3, and CG methylation in mouse oocytes. This model also predicted the altered CG methylation pattern of mutant oocytes having perturbed histone modification, was applicable to cross-species prediction of the CG methylation pattern of human oocytes, and identified the epigenetic crosstalk potentially important in other cell types.ConclusionsOur findings provide insight into the epigenetic crosstalk regulating the CG methylation pattern in mammalian oocytes and other cells. The use of epiNet should help to design or complement biological experiments in epigenetics studies.
Highlights
IntroductionEpigenetic modifications, including CG methylation (a major form of DNA methylation) and histone modifications, interact with each other to shape their genomic distribution patterns
Epigenetic modifications, including CG methylation and histone modifications, interact with each other to shape their genomic distribution patterns
Outline of epiNet: prediction of CG methylation patterns based on other epigenetic features To aid in the identification of epigenetic features regulating CG methylation patterns, we developed a convolutional neural network-based regression model named epiNet, which predicts CG methylation patterns based on other available genome-wide epigenetic features
Summary
Epigenetic modifications, including CG methylation (a major form of DNA methylation) and histone modifications, interact with each other to shape their genomic distribution patterns. Reverse genetics approaches began to reveal the impact of histone modifications, such as H3K36me, on the establishment of CG methylation in mouse oocytes [6], but the entire picture of the epigenetic crosstalk regulating CG methylation patterns is unknown. This is partly due to the scarcity of oocyte samples available for molecular studies and the high cost required for a genome-wide epigenetic modification analysis. Most of the existing approaches aim at finding DNA sequences responsible for the CG methylation patterns and were not tailored for studying the epigenetic crosstalk
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