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

Research output: Contribution to journalArticlepeer-review

Weiwei Zhang, Tim D Spector, Panos Deloukas, Jordana T Bell, Barbara E Engelhardt

Original languageEnglish
Article number14
JournalGENOME BIOLOGY
Volume16
DOIs
Accepted/In press2 Jan 2015
Published24 Jan 2015

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  • s13059_015_0581_9

    s13059_015_0581_9.pdf, 3.5 MB, application/pdf

    Uploaded date:08 Jan 2016

    Version:Final published version

    Licence:CC BY

King's Authors

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 critical to enable genome-wide analyses, but current approaches tackle average methylation within a 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 methylation levels at CpG site resolution using features including neighboring CpG site methylation levels and genomic distance, co-localization with coding regions, CpG islands (CGIs), and regulatory elements from the ENCODE project. Our approach achieves 92% prediction accuracy of genome-wide methylation levels at single-CpG-site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs and is robust across platform and cell-type heterogeneity. Our classifier outperforms other types of classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation, CGIs, co-localized DNase I hypersensitive sites, transcription factor binding sites, and histone modifications were found to be most predictive of methylation levels.

CONCLUSIONS: Our observations of DNA methylation patterns led us to develop a classifier to predict DNA methylation levels at CpG site resolution with high accuracy. Furthermore, our method identified genomic features that interact with DNA methylation, suggesting mechanisms involved in DNA methylation modification and regulation, and linking diverse epigenetic processes.

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