To quantify the amount of variation in DNA methylation explained by genomic context, we considered the correlation between genomic context and principal components (PCs) of methylation levels across all 100 samples (Figure 4). We found that many of the features derived from a CpG site’s genomic context appear to be correlated with the first principal component (PC1). The methylation status of upstream and downstream neighboring CpG sites and a co-localized DNAse I hypersensitive (DHS) site are the most highly correlated features, with Pearson’s correlation r=[0.58,0.59] (P<2.2?10 ?16 ). Ten genomic features have correlation r>0.5 (P<2.2?10 ?16 ) with PC1, including co-localized active TFBSs ELF1 (ETS-related transcription factor 1), MAZ (Myc-associated zinc finger protein), MXI1 (MAX-interacting protein 1) and RUNX3 (Runt-related transcription factor 3), and co-localized histone modification trimethylation of histone H3 at lysine 4 (H3K4me3), suggesting that they may be useful in predicting DNA methylation status (Additional file 1: Figure S3). 67,P<2.2?10 ?16 ) [53,54].
Correlation matrix out-of anticipate has actually that have basic 10 Pcs out-of methylation accounts. The fresh new x-axis represents among the many 122 provides; the fresh new y-axis means Personal computers 1 owing to ten. Shade correspond to Pearson’s correlation, as revealed on the legend. Pc, dominant parts.
Digital methylation condition forecast
These observations about patterns of DNA methylation suggest that correlation in DNA methylation is local and dependent on genomic context. Using prediction features, including neighboring CpG site methylation levels and features characterizing genomic context, we built a classifier to predict binary DNA methylation status. Lees verder