Abstract
Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called HDFINDER) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using HDFINDER, we have depicted the entire genomic methylation patterns for all 22 human autosomes.
Original language | English (US) |
---|---|
Pages (from-to) | 10713-10716 |
Number of pages | 4 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 103 |
Issue number | 28 |
DOIs | |
State | Published - Jul 11 2006 |
Externally published | Yes |
Keywords
- CpG islands
- DNA methylation
- Epigenomics
- Methylation prediction
ASJC Scopus subject areas
- General