Genexpi

Genexpi infers and validates gene regulatory networks, emphasizing identification of sigma factor regulons by integrating time-series expression data (microarrays, RNA-seq), ChIP-seq binding data, and literature mining to improve network accuracy.

Key Features:

• Integration of Diverse Data Sources: Combines time-series expression data from microarrays and RNA-seq with static binding data such as ChIP-seq and literature mining to inform network inference.
• Time Series Analysis: Handles gene expression time-course data to capture temporal dynamics of regulatory responses.
• Sigma Factor Regulon Identification: Focuses on identifying regulons associated with sigma factors by integrating binding and expression evidence.
• Biological Validation: Validated using real bacterial regulon datasets to ensure biological relevance of inferred interactions.
• Gene Regulatory Network Inference: Infers potential regulatory interactions by correlating multiple evidence types to produce biologically interpretable networks.

Scientific Applications:

• Bacterial regulon characterization: Characterizes sigma factor regulons and other bacterial regulatory interactions using combined expression and ChIP-seq evidence.
• Dynamic regulatory analysis: Elucidates temporal regulatory dynamics from time-course microarray or RNA-seq experiments.
• Network inference workflows: Integrates into gene network inference workflows to generate and validate biologically interpretable regulatory networks.

Methodology:

Identifies candidate sigma factors from ChIP experiments or literature mining and correlates these candidates with time-course gene expression data to infer potential regulatory networks by examining temporal expression changes.