MINDY

MINDY identifies genome-wide, context-specific posttranscriptional and posttranslational modulators that influence transcription factor activity by quantifying conditional multivariate statistical dependencies between transcription factors and their target genes.

Key Features:

• Information Theoretic Approach: Employs an information-theoretic method to quantify multivariate statistical dependencies between a transcription factor and its target genes conditional on the presence or absence of candidate modulator genes.
• Context-Specific Analysis: Accounts for cellular context including genetic and epigenetic factors such as microRNA-mediated control, posttranslational modifications (phosphorylation and acetylation), cofactor interactions, and mRNA/protein stability.
• Comprehensive Modulator Identification: Performs genome-wide identification of both posttranscriptional and posttranslational modulators of transcription factor activity.

Scientific Applications:

• Systems Biology: Elucidates context-specific regulatory networks and network dynamics governing gene expression.
• Oncology: Identifies modulators of transcription factors relevant to tumor biology and disease mechanisms.
• Developmental Biology: Analyzes how transcription factor regulation varies across developmental contexts.
• Pharmacogenomics: Studies how context-specific modulation of transcription factors affects drug response and pharmacogenomic variation.

Methodology:

Uses information theory to quantify conditional multivariate statistical dependencies between transcription factors and target genes genome-wide, evaluating candidate modulators by their presence or absence.