MGraph

MGraph reconstructs genetic regulatory networks and analyzes gene expression profiles from DNA microarray data using probabilistic graphical models for the interpretation of regulatory interactions and experimental-factor effects.

Key Features:

• MATLAB implementation: Implemented as a MATLAB toolbox for computational analysis of gene expression data.
• DNA microarray data support: Operates on DNA microarray expression data to infer regulatory relationships and assess condition-specific expression changes.
• Graphical Gaussian Model (GGM): Predicts genetic regulatory networks by capturing dependencies between genes based on expression levels and has been used to reconstruct four MAPK pathways in yeast.
• Graphical Log-Linear Model (GLM): Quantifies effects of experimental conditions on gene expression and decomposes transcriptional variance into contributions from factors such as sex, genotype, and age, exemplified in Drosophila melanogaster.
• Dual analytic capability: Supports both network reconstruction and investigation of experimental factors affecting gene expression.

Scientific Applications:

• Genetic regulatory network reconstruction: Infers gene–gene dependency structures to reconstruct pathways, including MAPK pathway reconstruction in yeast.
• Experimental-factor analysis: Decomposes transcriptional variance to quantify contributions of factors such as sex, genotype, and age in studies of Drosophila melanogaster.
• Gene expression profiling: Analyzes DNA microarray datasets to study gene responses across conditions relevant to developmental biology and disease pathology.

Methodology:

Uses probabilistic graphical models—specifically Graphical Gaussian Models (GGM) and Graphical Log-Linear Models (GLM)—to capture gene dependencies from DNA microarray expression data and to decompose transcriptional variance into factor-specific contributions.