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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/28448

Title: Profile analysis and prediction of tissue-specific CpG island methylation classes
Authors: Previti, Christopher
Harari, Oscar
Zwir, Igor
Val, Coral del
Issue Date: 2009
Abstract: Background The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.
Results We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation.
Conclusion Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.
Sponsorship: This work was supported in part by the Spanish Ministry of Science and Technology (MEC) under project TIN-2006-12879 and the Consejeria de Innovacion, Investigacion y Ciencia de la Junta de Andalucia under project TIC-02788. C. Previti was supported by a grant from the German Academic Exchange Service (DAAD). O. Harari acknowledges the doctoral MAEC- AECI fellowship. I. Zwir is a senior research scientist supported by the Howard Hughes Medical Institute and the "Ramon y Cajal" program of the MEC, C. del Val was supported by the "Programa de Retorno de Investigadores" from the Junta de Andalucia.
Publisher: Biomed Central
Keywords: Computational biology
CpG islands
DNA methylation
Databases
Epigenesis
Genome
URI: http://hdl.handle.net/10481/28448
ISSN: 1471-2105
Citation: Previti, C.; et al. Profile analysis and prediction of tissue-specific CpG island methylation classes. BMC Bioinformatics, 10: 116 (2009). [http://hdl.handle.net/10481/28448]
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