DISTILLER

DISTILLER integrates diverse experimental datasets to infer transcriptional module networks and identify regulatory relationships in gene expression data.

Key Features:

• Data Integration: Combines multiple types of experimental data to construct comprehensive models of transcriptional regulation.
• Network Inference: Infers networks representing transcriptional modules, defined as groups of genes co-regulated by common regulatory elements.
• Experimental Validation: Predicts regulatory targets that have been validated through empirical experiments.
• Modularity Analysis: Assesses the modularity of inferred networks and compares modularity against existing databases such as RegulonDB.
• Regulatory Complexity Analysis: Evaluates regulatory complexity, indicating that complex regulatory programs can alter expected network modularity.

Scientific Applications:

• Regulatory Network Analysis: Construction and analysis of transcriptional networks to study gene co-regulation within organisms.
• Condition Dependency Studies: Exploration of how regulatory networks change under different environmental or experimental conditions.

Methodology:

DISTILLER employs a systematic data integration approach to infer transcriptional module networks and has been applied to Escherichia coli studies focusing on the fumarate nitrate reductase regulator with predicted targets validated experimentally.