Journal
NUCLEIC ACIDS RESEARCH
Volume 45, Issue 9, Pages 5100-5111Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkx078
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Funding
- Siteman Cancer Center, U.S. Department of Defense Congressionally Directed Medical Research Program for Breast Cancer [W81XWH-11-1-0401]
- National Institutes of Health [NIGMS 5R01GM108811, NLM R21LM011199]
- National Institutes of Health T32 Genome Analysis Training Program [2T32HG000045-16]
- NIH [R01 GM108811]
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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.
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