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A systems-level integrative framework from genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control

, , and . Bioinformatics 30 (16): 2360-2366 (May 2014)

Abstract

MOTIVATION: There is a growing number of studies generating matched Illumina Infinium HumanMethylation450 and gene expression data, yet there is a corresponding shortage of statistical tools aimed at their integrative analysis. Such integrative tools are important for the discovery of epigenetically regulated gene modules or molecular pathways, which play key roles in cellular differentiation and disease. RESULTS: Here, we present a novel functional supervised algorithm, called Functional Epigenetic Modules (FEM), for the integrative analysis of Infinium 450k DNA methylation and matched or unmatched gene expression data. The algorithm identifies gene modules of coordinated differential methylation and differential expression in the context of a human interactome. We validate the FEM algorithm on simulated and real data, demonstrating how it successfully retrieves an epigenetically deregulated gene, previously known to drive endometrial cancer development. Importantly, in the same cancer, FEM identified a novel epigenetically deregulated hotspot, directly upstream of the well-known progesterone receptor tumour suppressor pathway. In the context of cellular differentiation, FEM successfully identifies known endothelial cell subtype-specific gene expression markers, as well as a novel gene module whose overexpression in blood endothelial cells is mediated by DNA hypomethylation. The systems-level integrative framework presented here could be used to identify novel key genes or signalling pathways, which drive cellular differentiation or disease through an underlying epigenetic mechanism. AVAILABILITY AND IMPLEMENTATION: FEM is freely available as an R-package from http://sourceforge.net/projects/funepimod.

Description

This article discusses Functional Epigenetic Modules (FEM), which are used to study gene expression in studies of DNA methylation. Teschendorff et al. used this algorithm to identify important signalling pathways in their breast cancer dataset.

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jiao2014systemslevel
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