iDREM

iDREM reconstructs dynamic regulatory networks by integrating high-throughput time series data (gene expression, miRNA expression, proteomics, epigenomics, single-cell RNA-Seq) with static protein-DNA interaction datasets to elucidate temporal regulator activity.

Key Features:

• Integration of Multiple Data Types: Integrates gene expression, miRNA expression, proteomics, epigenomics, and single-cell RNA-Seq time series with static protein-DNA interaction data for combined analysis.
• Dynamic Network Reconstruction: Reconstructs temporal regulatory networks to identify the timing and function of regulators within dynamic biological processes.
• Interactive Visualization and Querying: Provides multiple analytical views—gene-centric, transcription factor (TF)-centric, pathway-centric, and model-centric—for exploration and querying of reconstructed models.
• Extension of DREM: Incorporates and extends the Dynamic Regulatory Event Miner (DREM) framework to enhance integration of diverse time series and interaction datasets.
• Application to Real Datasets: Has been applied to microglia developmental data from multiple laboratories to demonstrate handling of diverse datasets and extracting biological insights.

Scientific Applications:

• Gene regulation analysis: Dissects regulatory mechanisms by linking temporal expression changes to protein-DNA interactions.
• Temporal dynamics of cellular responses: Investigates timing and sequence of regulatory events during cellular responses.
• Developmental biology: Analyzes developmental processes such as microglia development using integrated time series and interaction data.
• Hypothesis generation about regulators: Generates hypotheses on the roles and timing of specific regulators in complex networks.

Methodology:

Integrates static protein-DNA interaction data with time series datasets (gene expression, miRNA expression, proteomics, epigenomics, single-cell RNA-Seq) to reconstruct dynamic regulatory networks and produces multiple analytical views for querying the reconstructed models.