iPAGE

iPAGE performs integrative, information-theoretic analysis of whole-genome expression and genomic sequence data, operating synergistically with FIRE (Functional Inference from Expression data) to identify perturbed pathways and associated cis-regulatory elements in cancer.

Key Features:

• Pathway Perturbation Analysis: Identifies pathway perturbations contributing to cancer by analyzing global gene expression profiles from whole-genome datasets.
• Information-Theoretic Approach: Applies information-theoretic methods to analyze expression levels, pathways, and genomic sequences.
• De Novo Motif Discovery: Performs de novo motif discovery linking pathways to transcription-factor binding sites and miRNA targets, including associations with E2F, NF-Y, p53, and let-7.
• Validation of Predictions: Produces cis-regulatory interaction predictions that have been validated by follow-up in vivo experiments.
• Discovery Beyond Known Pathways: Detects perturbations associated with putative cis-regulatory elements that fall outside known cancer pathways.

Scientific Applications:

• Cancer Biology Research: Systems-level dissection of regulatory perturbations in cancer to inform mechanistic hypotheses of neoplastic transformation.
• Drug Target Discovery: Identification of novel regulatory elements and pathways as potential targets for cancer therapy.

Methodology:

Integrates global gene expression data with genomic sequences using information-theoretic analysis, performs de novo motif discovery to associate pathways with transcription-factor binding sites and miRNA targets, and operates synergistically with FIRE (Functional Inference from Expression data).