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

• Published in: Genome Biology 2015-01, Vol.16 (1), p.14-14, Article 14
• Main Authors: Zhang, Weiwei, Spector, Tim D, Deloukas, Panos, Bell, Jordana T, Engelhardt, Barbara E
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Binding sites; Bioinformatics; Brain; Cell division; Computer applications; CpG islands; CpG Islands - genetics; Deoxyribonuclease; Deoxyribonucleic acid; DNA; DNA methylation; DNA Methylation - genetics; Epigenetics; Female; Gene expression; Genome, Human; Genomes; Genomics - methods; Humans; Localization; Male; Middle Aged; Phenotypes; Principal Component Analysis; Regulatory sequences; Regulatory Sequences, Nucleic Acid - genetics; Sequence Analysis, DNA; Studies; Sulfites
• ISSN: 1465-6906; 1474-7596; 1474-760X; 1465-6906
• DOI: 10.1186/s13059-015-0581-9

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. 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. 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.