ISMARA

ISMARA reconstructs regulatory networks from genome-wide gene expression and chromatin modification data by modeling the activities of transcription factor and microRNA binding sites across the genome.

Key Features:

• Automated analysis: Accepts gene expression or chromatin state data across samples and automates the modeling of regulatory influences.
• Regulatory site prediction: Leverages genome-wide predictions of binding sites for transcription factors (TFs) and microRNAs (miRNAs) to model regulation.
• Modeling of expression and chromatin dynamics: Models dynamics of gene expression and chromatin states based on constellations of predicted regulatory sites.
• Identification of key regulators: Identifies TFs and miRNAs that drive changes in expression or chromatin states across samples.
• Predicted regulator activities: Predicts activities of TFs and miRNAs across different samples.
• Genome-wide target prediction: Provides predictions of genome-wide targets of identified regulators.
• Enriched gene categories: Identifies enriched gene categories among predicted targets.
• Regulatory interaction prediction: Predicts direct interactions between key regulators.

Scientific Applications:

• Innate immunity: Discovery of regulatory interactions involved in innate immune responses.
• Mucociliary differentiation: Identification of a master regulator involved in mucociliary differentiation.
• Cancer research: Detection of transcription factors consistently deregulated in cancer.
• Chromatin modifications: Elucidation of transcription factors mediating specific chromatin modifications.

Methodology:

Automated modeling of genome-wide expression data or chromatin modifications using genome-wide predictions of TF and miRNA binding sites; analysis of regulatory-site constellations to infer regulator activities, genome-wide targets, enriched gene categories, and regulator–regulator interactions.