ELMER

ELMER links DNA methylation and gene expression data to identify transcriptional enhancers and infer upstream transcription factor regulatory networks.

Key Features:

• Enhancer Identification: Uses DNA methylation patterns to identify enhancers genome-wide and correlates enhancer methylation with expression of nearby genes to assign candidate transcriptional targets.
• Transcription Factor Network Inference: Performs transcription factor motif analysis within identified enhancers and integrates motif occurrences with transcription factor expression to infer upstream regulators and construct regulatory networks.
• Scalability with TCGA: Processes large sample cohorts, demonstrated on The Cancer Genome Atlas (TCGA) datasets including analyses of over 2,000 tumor samples for large-scale genomic studies.

Scientific Applications:

• Cancer transcriptional network elucidation: Identifies regulatory networks involving known cancer drivers such as GATA3 and FOXA1 in breast cancer, SOX17 and FOXA2 in endometrial cancer, and NFE2L2, SOX2, and TP63 in squamous cell lung cancer.
• Discovery of novel regulatory associations: Reveals previously unreported regulatory links with potential prognostic relevance, for example RUNX1 involvement in kidney cancer.
• Cis-regulatory interaction mapping: Maps cis-regulatory interactions between transcription factors and their target genes in normal and disease states using integrated methylation and expression data.

Methodology:

ELMER (R-based) identifies enhancers from DNA methylation patterns, correlates enhancer methylation states with gene expression to pinpoint candidate targets, and performs motif analysis within enhancers integrated with transcription factor expression to infer upstream regulators.