Modeling complex patterns of differential DNA methylation that associate with gene expression changes.
Nucleic Acids Res
; 45(9): 5100-5111, 2017 May 19.
Article
en En
| MEDLINE
| ID: mdl-28168293
ABSTRACT
Numerous genomic studies are underway to determine which genes are abnormally regulated by DNA methylation in disease. However, we have a poor understanding of how disease-specific methylation changes affect expression. We thus developed an integrative analysis tool, Methylation-based Gene Expression Classification (ME-Class), to explain specific variation in methylation that associates with expression change. This model captures the complexity of methylation changes around a gene promoter. Using 17 whole-genome bisulfite sequencing and RNA-seq datasets from different tissues from the Roadmap Epigenomics Project, ME-Class significantly outperforms standard methods using methylation to predict differential gene expression change. To demonstrate its utility, we used ME-Class to analyze 32 datasets from different hematopoietic cell types from the Blueprint Epigenome project. Expression-associated methylation changes were predominantly found when comparing cells from distantly related lineages, implying that changes in the cell's transcriptional program precede associated methylation changes. Training ME-Class on normal-tumor pairs from The Cancer Genome Atlas indicated that cancer-specific expression-associated methylation changes differ from tissue-specific changes. We further show that ME-Class can detect functionally relevant cancer-specific, expression-associated methylation changes that are reversed upon the removal of methylation. ME-Class is thus a powerful tool to identify genes that are dysregulated by DNA methylation in disease.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Regulación de la Expresión Génica
/
Metilación de ADN
/
Modelos Genéticos
Tipo de estudio:
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos