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

Zhang W, Spector TD, Deloukas P, Bell JT, Engelhardt BE - Genome Biol. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA. wz31@duke.edu.

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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Prediction performance on WGBS data and cross-platform prediction. Precision–recall curves for cross-platform and WGBS prediction. Each precision–recall curve represents the average precision–recall for prediction on the held-out sets for each of the ten repeated random subsamples. WGBS, whole-genome bisulfite sequencing.
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Fig6: Prediction performance on WGBS data and cross-platform prediction. Precision–recall curves for cross-platform and WGBS prediction. Each precision–recall curve represents the average precision–recall for prediction on the held-out sets for each of the ten repeated random subsamples. WGBS, whole-genome bisulfite sequencing.

Mentions: Trained on 450K array data and tested on WGBS 450K sites, our RF classifier achieved an accuracy of 89.3%; trained on 450K array data and tested on WGBS non 450K sites, our RF classifier achieved an accuracy of 92.2% (Figure 6; Table 2). Training and testing exclusively on WGBS data showed a similar performance, with an accuracy of 90.0% for CpG sites in the 450K sites and 92.4% for CpG sites in the non 450K sites (Figure 6). Predictions for CpG sites in non 450K sites had lower precision at high recall rates because it is more difficult to predict unmethylated sites in the sequencing data as there are many more unmethylated CpG sites. These results suggest that our RF classifier is able to generalize across platforms and methylation assay types.Figure 6


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

Zhang W, Spector TD, Deloukas P, Bell JT, Engelhardt BE - Genome Biol. (2015)

Prediction performance on WGBS data and cross-platform prediction. Precision–recall curves for cross-platform and WGBS prediction. Each precision–recall curve represents the average precision–recall for prediction on the held-out sets for each of the ten repeated random subsamples. WGBS, whole-genome bisulfite sequencing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389802&req=5

Fig6: Prediction performance on WGBS data and cross-platform prediction. Precision–recall curves for cross-platform and WGBS prediction. Each precision–recall curve represents the average precision–recall for prediction on the held-out sets for each of the ten repeated random subsamples. WGBS, whole-genome bisulfite sequencing.
Mentions: Trained on 450K array data and tested on WGBS 450K sites, our RF classifier achieved an accuracy of 89.3%; trained on 450K array data and tested on WGBS non 450K sites, our RF classifier achieved an accuracy of 92.2% (Figure 6; Table 2). Training and testing exclusively on WGBS data showed a similar performance, with an accuracy of 90.0% for CpG sites in the 450K sites and 92.4% for CpG sites in the non 450K sites (Figure 6). Predictions for CpG sites in non 450K sites had lower precision at high recall rates because it is more difficult to predict unmethylated sites in the sequencing data as there are many more unmethylated CpG sites. These results suggest that our RF classifier is able to generalize across platforms and methylation assay types.Figure 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA. wz31@duke.edu.

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.

Show MeSH
Related in: MedlinePlus