hReg-CNCC

hReg-CNCC reconstructs genome-wide regulatory networks in human cranial neural crest cells (CNCCs) by integrating paired gene expression and chromatin accessibility data to identify cis-regulatory modules, transcription factors (TFs), and target genes relevant to craniofacial development.

Key Features:

• Data integration: Integrates paired gene expression and chromatin accessibility datasets to infer regulatory interactions in CNCCs.
• Regulatory element identification: Identifies high-quality cis-regulatory modules using consensus optimization techniques.
• Network reconstruction: Builds a genome-wide regulatory network that links TFs to target genes within CNCCs.
• TF hierarchy delineation: Resolves hierarchical architecture of upstream, core, and downstream transcription factors involved in neural plate border specification, migration, and differentiation.
• Variant annotation: Maps genetic variants, including SNPs, to cis-regulatory modules, TFs, and target genes for functional interpretation.
• GWAS linkage: Associates facial GWAS signals and disease-trait variants with distal and combinatorial regulation by core TFs such as ALX1.
• Evolutionary integration: Connects evolutionary sequence annotations (ultra-conserved elements and human accelerated regions) with gene expression and phenotypic outcomes.
• Developmental-stage interpretation: Enables interpretation of variants in the context of early embryonic stages such as gastrulation.

Scientific Applications:

• CNCC regulatory biology: Elucidates transcriptional regulatory mechanisms underlying CNCC specification, migration, and differentiation.
• GWAS functional follow-up: Prioritizes and functionally links facial trait and cranial disease-associated SNPs to regulatory modules and TFs.
• Evolutionary developmental analysis: Integrates conserved and accelerated genomic elements with gene regulation to study evolutionary impacts on craniofacial phenotypes.
• Developmental genetics: Interprets how regulatory variation affects gene expression during embryogenesis, including gastrulation.

Methodology:

Integrates paired gene expression and chromatin accessibility data, applies consensus optimization to identify cis-regulatory modules, reconstructs TF–target networks with upstream/core/downstream hierarchy, and maps genetic variants and evolutionary annotations to regulatory modules and target genes.