FEM

FEM integrates DNA methylation data from the Illumina Infinium HumanMethylation450 (450k) platform with gene expression data to identify functionally related epigenetic modules showing coordinated differential methylation and expression.

Key Features:

• Functional Supervised Algorithm: Employs a supervised algorithm that identifies modules of genes with coordinated differential DNA methylation and gene expression.
• Infinium 450k Integration: Integrates DNA methylation data specifically from the Illumina Infinium HumanMethylation450 (450k) platform with matched gene expression datasets.
• Human Interactome Contextualization: Operates within a human interactome framework, incorporating protein-protein interactions and molecular relationships to contextualize modules.
• Validation on Simulated and Real Data: Has been validated using both simulated datasets and real-world data, including applications to endometrial cancer.
• Discovery of Novel Epigenetic Hotspots: Identifies novel epigenetically deregulated hotspots, including regions upstream of pathways such as the progesterone receptor tumour suppressor pathway.
• Application to Cellular Differentiation and Disease: Detects key genes and signaling pathways driving cellular differentiation or disease, including endothelial cell subtype-specific markers and modules influenced by DNA hypomethylation.

Scientific Applications:

• Epigenetically Regulated Gene Modules: Facilitates discovery of modules of functionally related genes with differential methylation and expression to map epigenetic regulation.
• Pathway Analysis: Elucidates molecular pathways affected by coordinated methylation and expression changes to inform mechanistic understanding.
• Cancer Research: Pinpoints deregulated genes and novel epigenetic hotspots relevant to cancer mechanisms and potential therapeutic targets.

Methodology:

Integrates Infinium 450k DNA methylation data with gene expression datasets, assumes an inverse association between promoter DNA methylation and gene expression, uses a supervised algorithm that incorporates prior biological knowledge, and maps findings onto a human interactome (protein-protein interactions).