Computational prediction of methylation status in human genomic sequences.
Proc Natl Acad Sci U S A
; 103(28): 10713-6, 2006 Jul 11.
Article
em En
| MEDLINE
| ID: mdl-16818882
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
DNA
/
Genoma Humano
/
Biologia Computacional
/
Metilação de DNA
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2006
Tipo de documento:
Article