Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

TL;DR

This study develops a highly accurate random forest classifier to predict genome-wide DNA methylation at single CpG sites using genomic features, surpassing previous methods and revealing key regulatory elements involved in methylation regulation.

Contribution

We introduce a novel classifier that predicts CpG methylation levels genome-wide with high accuracy and identify key genomic features influencing methylation.

Findings

Achieves 91-94% accuracy at single CpG site level

Predicts 98% accuracy within CpG islands

Identifies genomic features like TFBS and DHS as predictive factors

Abstract

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes.