ChromNet

ChromNet infers chromatin interaction networks by estimating conditional-dependence relationships among ChIP-seq datasets to map interactions among transcription factors and histone modifications across genomic regions.

Key Features:

• Statistical Methodology: Employs a statistical approach to infer conditional-dependence relationships among ChIP-seq datasets and construct a chromatin interaction network.
• Comprehensive Data Integration: Applied to 1451 ChIP-seq datasets from the ENCODE Project to integrate large-scale epigenomic data.
• Interaction Detection: Demonstrates improved recovery of known physical interactions within chromatin networks compared to alternative approaches.
• Experimental Validation: Predicted previously unreported interactions such as MYC-HCFC1 that were experimentally validated.

Scientific Applications:

• Regulatory network mapping: Infers interactions among transcription factors, histone modifications, and genomic regions using ChIP-seq data.
• Epigenetics and gene regulation: Elucidates chromatin-based regulatory mechanisms relevant to gene expression studies.
• Cancer, developmental biology, and disease pathology: Identifies chromatin interactions and novel regulatory relationships relevant to cancer research, developmental biology, and disease pathology.

Methodology:

Inference of conditional-dependence relationships from ChIP-seq data and construction of a chromatin interaction network applied to 1451 ENCODE ChIP-seq datasets.