Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes
| Autor: | Keith F. Decker, Nathan D. VanderKraats, John R. Edwards, Jeffrey F. Hiken |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Regulation of gene expression
Genetics 0303 health sciences Computational Biology Genomics Methylation DNA Methylation Biology 03 medical and health sciences 0302 clinical medicine Epigenetics of physical exercise Differentially methylated regions Gene Expression Regulation 030220 oncology & carcinogenesis DNA methylation Humans Illumina Methylation Assay Transcription Initiation Site Promoter Regions Genetic RNA-Directed DNA Methylation 030304 developmental biology Epigenomics |
| Zdroj: | Nucleic Acids Research |
| ISSN: | 1362-4962 0305-1048 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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