Dissemin is shutting down on January 1st, 2025

Links

Tools

Export citation

Search in Google Scholar

Charting the methylome

Proceedings article published in 2011 by Geert Trooskens, Tim De Meyer, Simon Denil, Wim Van Criekinge
This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

Full text: Unavailable

Question mark in circle
Preprint: policy unknown
Question mark in circle
Postprint: policy unknown
Question mark in circle
Published version: policy unknown

Abstract

Epigenetics, with DNA-methylation as its most stable feature, translates the genetic background into a particular phenotype. Massively parallel sequencing technologies opened up new possibilities for genome-wide profiling of DNA-methylation. Particularly Methyl Binding Domain capturing based Sequencing (MethylCap-Seq) is a low-cost, high-resolution technology to uncover DNA-methylation in a truly genome-wide manner and is becoming increasingly popular. Methods : To chart the map of the methylome, we used raw MethylCap-Seq data of 80 different samples, including different healthy tissues, cell lines and tumor samples. Since no normalization procedures are applied, artefacts are avoided. A Poisson background model is used to identify significantly methylated regions. A conservative set of rules was derived that identifies adjacent methylation prone regions in a single region.
 Results : Based on this methodology, we provide a reference map of ~1.5 million methylation cores. Together they make up about 10.4% of the human genome and 40% of the approximately 28 million human CpGs dinucleotides. Validation by a different MBD kit and targeted bisulfite sequencing data indicates that the Map of the Human Methylome is approximately 95% complete. Conclusions : We found that although CpG-islands (CGIs) and exon regions are higly enriched in methylation cores with high methylation levels , they show less variability between samples compared to promotor, intergenic and intronic regions. The accuracy of the methylome map will increase with more samples from different tissues and diseases. Comparing the map of the methylome with expression and other data such as histone marks will enable functional annotation of the methylation prone regions, providing a better understanding of the mechanisms involved in epigenetic regulation. This approach is a flexible methodology that can be ported to other genome wide high- throughput methods such as third generation sequencing technologies.