CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure

@article{citeulike:1004974, abstract = {CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90\&\#37; of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease.}, author = {Bock, Christoph and Paulsen, Martina and Tierling, Sascha and Mikeska, Thomas and Lengauer, Thomas and Walter, J\&\#246;rn }, citeulike-article-id = {1004974}, comment = {DNA methylation prediction is conceptually easier than the prediction of more volatile epigenetic mechanisms because DNA methylation patterns exhibit relatively low tissue specificity compared to other epigenetic information. Therefore, it is not surprising that similar approaches applied to DNA methylation data for blood (Bock et al., 2006) and brain tissue (Das et al., 2006; Fang et al., 2006) yielded comparable results. In all three cases, machine-learning methods were used to derive a classifier for presence or absence of DNA methylation in a given region. Prediction accuracies were high, and the most predictive attributes included CpG-rich sequence patterns (Bock et al., 2006; Das et al., 2006; Fang et al., 2006), specific DNA structure properties and repetitive DNA elements (Bock et al., 2006) as well as certain transcription factor binding sites (Fang et al., 2006). Interestingly, a similar method could also predict which genomic regions are prone to becoming methylated in a cell line overexpressing the DNA methyltransferase DNMT1 (Feltus et al., 2003). - Bock el al 2007 }, doi = {http://dx.doi.org/10.1371/journal.pgen.0020026}, journal = {PLoS Genetics}, keywords = {integration, methylation, prediction}, month = {March}, number = {3}, pages = {e26+}, posted-at = {2008-01-19 17:15:29}, priority = {2}, title = {CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure}, url = {http://dx.doi.org/10.1371/journal.pgen.0020026}, volume = {2}, year = {2006} }

TY - JOUR ID - citeulike:1004974 L3 - citeulike-article-id:1004974 N2 - CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease. IS - 3 JF - PLoS Genetics TI - CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure VL - 2 SP - e26 KW - integration KW - methylation KW - prediction N1 - DNA methylation prediction is conceptually easier than the prediction of more volatile epigenetic mechanisms because DNA methylation patterns exhibit relatively low tissue specificity compared to other epigenetic information. Therefore, it is not surprising that similar approaches applied to DNA methylation data for blood (Bock et al., 2006) and brain tissue (Das et al., 2006; Fang et al., 2006) yielded comparable results. In all three cases, machine-learning methods were used to derive a classifier for presence or absence of DNA methylation in a given region. Prediction accuracies were high, and the most predictive attributes included CpG-rich sequence patterns (Bock et al., 2006; Das et al., 2006; Fang et al., 2006), specific DNA structure properties and repetitive DNA elements (Bock et al., 2006) as well as certain transcription factor binding sites (Fang et al., 2006). Interestingly, a similar method could also predict which genomic regions are prone to becoming methylated in a cell line overexpressing the DNA methyltransferase DNMT1 (Feltus et al., 2003). - Bock el al 2007 AU - Bock, Christoph AU - Paulsen, Martina AU - Tierling, Sascha AU - Mikeska, Thomas AU - Lengauer, Thomas AU - Walter, Jörn PY - 2006/03/01/ UR - http://dx.doi.org/10.1371/journal.pgen.0020026 ER -