ModuleMaster

ModuleMaster identifies cis-regulatory modules (CRMs) within sets of co-expressed genes by integrating transcription factor binding information and multivariate functional relationships between regulators and their target genes.

Key Features:

• Multivariate regulatory analysis: Applies multivariate analyses to capture complex interactions between transcription factors and gene targets, improving CRM detection.
• Transcription factor binding integration: Incorporates transcription factor binding information into CRM identification.
• Microarray and clustering data processing: Processes data from microarray experiments and subsequent clustering analyses for co-expressed gene sets.
• Algorithms and datasets for CRM pipeline: Includes algorithms and datasets required to execute the CRM identification pipeline.
• GraphML visualization and R network analysis: Generates GraphML-based visualizations and supports use of R for detailed regulatory network analysis.
• SBML export: Produces SBML files for further analytical processing and dynamic modeling of biological systems.

Scientific Applications:

• Cis-regulatory module discovery: Identification of CRMs in co-expressed gene sets to reveal regulatory elements.
• Regulatory mechanism analysis: Investigation of regulatory mechanisms governing gene expression patterns across conditions or developmental stages.
• Genetic network exploration: Construction and analysis of genetic regulatory networks.
• Disease mechanism elucidation: Application to studies aimed at elucidating regulatory changes associated with disease.
• Advancing personalized medicine: Supporting analyses that inform personalized medicine approaches through regulatory insight.
• Systems biology and dynamic modeling: Providing outputs (SBML) for dynamic modeling and systems biology analyses.

Methodology:

Integrates transcription factor binding information and multivariate analyses of regulator–target relationships to identify CRMs in co-expressed genes, processes microarray data and clustering results, generates GraphML visualizations, enables R-based network analysis, exports SBML files, and includes the algorithms and datasets required for the CRM identification pipeline.