tacs

tacs analyzes time-series gene expression profiles to infer temporal control strength and identify activation periods and state transitions of gene regulations using gene regulatory network information.

Key Features:

• Temporal Resolution: Extracts information from single-time-point samples within gene expression time courses to resolve the timing of regulatory events.
• Gene Regulatory Network Integration: Integrates a gene regulatory network to report activation periods for each regulatory interaction observed in expression profiles.
• Dynamic Analysis Capability: Identifies transitions between activation and inactivation states of gene regulations to enable dynamic analyses of regulatory programs.

Scientific Applications:

• Model Organism Studies: Applied to the diauxic shift in Escherichia coli, identifying three distinct periods: glucose consumption, nutrient source transition, and lactose consumption.
• Cell Differentiation Analysis: Applied to mouse adipocyte differentiation from embryonic stem cells (ES) over 62 time points, revealing four regulatory periods including two known regulatory waves and a final differentiation completion period.
• Disease Research Potential: By identifying transitions in gene regulations, the method holds potential for informing studies of diseases such as diabetes and osteoporosis.

Methodology:

Extracts information from single-time-point samples within time-course expression data, integrates a gene regulatory network to assign activation periods to regulatory interactions, and identifies transitions between regulatory states.