CyGenexpi

CyGenexpi infers and validates genetic regulatory networks from time-series expression data using ordinary differential equation (ODE) models to elucidate gene regulation dynamics.

Key Features:

• ODE-based dynamical modeling: Uses ordinary differential equation models to represent temporal gene regulation dynamics.
• Time-series expression integration: Integrates time-series expression data as primary input for network inference.
• Support for microarray and RNA-seq: Accepts time-series data from microarrays and RNA-seq experiments.
• Integration of static binding data: Incorporates static binding evidence such as ChIP-seq to inform regulatory relationships.
• Literature mining integration: Uses literature-mined evidence as an additional data source for network inference and validation.
• Cytoscape integration and CyDataseries: Implements as a Cytoscape plugin and includes CyDataseries for structured management of time-series data within the environment.
• Regulon identification and validation: Provides computational support for identifying and validating regulons, including those associated with sigma factors.
• Biologically interpretable outputs: Produces results aimed at biological interpretability of inferred regulon composition and function.

Scientific Applications:

• Sigma factor regulon discovery: Identification and validation of regulons associated with bacterial sigma factors using integrated temporal and binding data.
• Bacterial gene regulatory network inference: Inferring regulatory interactions and network structure in bacterial systems from time-series data.
• Integrative evidence-based network validation: Combining expression time courses, ChIP-seq binding data, and literature evidence to validate inferred regulatory interactions.

Methodology:

Computationally applies ordinary differential equation (ODE) modeling and integrates time-series expression (microarray, RNA-seq), static binding data (ChIP-seq), and literature-mined evidence, using CyDataseries to manage time-series data.