cGRNB

cGRNB constructs context-specific combinatorial gene regulatory networks by integrating transcription factors (TFs), microRNAs (miRNAs), seed-matching sequence information, and gene expression data.

Key Features:

• Integrated engineering approach: Dual forward- and reverse-engineering algorithms streamlined from R codes into two functional modules for network construction.
• Built-in data libraries: Curated putative TF-gene, miRNA-gene, and TF-miRNA regulatory relationships compiled from forward-engineering pipelines.
• MPGE module: Processes miRNA-perturbed gene expression (MPGE) datasets and outputs a miRNA-centered two-layer combinatorial regulatory cascade.
• Parallel expression module: Processes parallel miRNA/mRNA expression datasets to generate genome-wide networks encompassing TF-gene, TF-miRNA, and miRNA-gene regulations.

Scientific Applications:

• Next-generation sequencing analyses: Construction of combinatorial regulatory networks from parallel miRNA/mRNA expression datasets produced by next-generation sequencing.
• Gene regulation studies: Generation of detailed, context-specific networks to investigate combinatorial regulation by TFs and miRNAs.
• Disease modeling and target discovery: Elucidation of regulatory mechanisms and identification of candidate regulatory targets for disease-related studies.

Methodology:

Integration of seed-matching sequence information with gene expression data; application of dual forward- and reverse-engineering algorithms; R code implementations streamlined into two major functional modules; compilation of curated TF-gene, miRNA-gene, and TF-miRNA relationships from forward-engineering pipelines.