Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes

Abstract

Methylation of the CpG-rich region (CpG island) overlapping a gene’s promoter is a generally accepted mechanism for silencing expression. While recent technological advances have enabled measurement of DNA methylation and expression changes genome-wide, only modest correlations between differential methylation at gene promoters and expression have been found. We hypothesize that stronger associations are not observed because existing analysis methods oversimplify their representation of the data and do not capture the diversity of existing methylation patterns. Recently, other patterns such as CpG island shore methylation and long partially hypomethylated domains have also been linked with gene silencing. Here, we detail a new approach for discovering differential methylation patterns associated with expression change using genome-wide high-resolution methylation data: we represent differential methylation as an interpolated curve, or signature, and then identify groups of genes with similarly shaped signatures and corresponding expression changes. Our technique uncovers a diverse set of patterns that are conserved across embryonic stem cell and cancer data sets. Overall, we find strong associations between these methylation patterns and expression. We further show that an extension of our method also outperforms other approaches by generating a longer list of genes with higher quality associations between differential methylation and expression.

Epigenetic silencing of Na,K-ATPase β1subunit geneATP1B1by methylation in clear cell renal cell carcinoma

“…The importance of so-called CpG island shore methylation in human cancer has become well recognized in recent years. 34,35 We proposed earlier that Na,K-β expression could be used as a biomarker whose expression is reduced in ccRCC. 17 Since the morphologically normal tissues adjacent to advanced tumors showed ATP1B1 promoter hypermethylation by MSP, and significant differences between normal and tumor tissue were found from the TCGA data, the methylation pattern of ATP1B1 promoter may have potential as an early biomarker for the diagnosis of ccRCC.…”

Genome Distribution of Replication-independent Histone H1 Variants Shows H1.0 Associated with Nucleolar Domains and H1X Associated with RNA Polymerase II-enriched Regions

“…Repetitive sequences were taken from RepBase database (47). The mean methylation levels at individual CpG islands were calculated by assessing the overlap between the methylation levels from Vanderkraats et al (48) and the CpG islands using BedTools (49). The genomic locations of hyper-and hypomethylated regions from the T47D cell line were recalculated as in Ref.…”

“…RNA-sequencing data from the T47D cell line was taken from Vanderkraats et al (48). Reads were mapped to the hg18 genome using the TopHat algorithm (version 2.0.12) (51).…”

A Biologically Informed Method for Detecting Associations with Rare Variants

“…Biofilter would map relevant data to the root of the module (genomic location), i.e. hypermethlyation patterns on the CpG island overlapping CDH4 promoter [151], pertinent expression data (for CDH4…”