MARQ

MARQ compares a user-provided query gene signature against a database of gene expression signatures derived from GEO across multiple organisms and experimental platforms to identify similar or opposite expression patterns for exploration of gene expression programs and biological relationships.

Key Features:

• Gene Expression Comparison: Compares input gene sets (for example, over-expressed or under-expressed genes) against stored gene signatures to identify experiments with similar or opposite expression patterns.
• GEO-derived Signature Database: Uses a comprehensive database of gene signatures constructed from Gene Expression Omnibus (GEO) datasets spanning diverse organisms and experimental platforms.
• Discovery of Biological Relationships: Identifies conditions with analogous or contrasting gene expression profiles to reveal relationships among diseases, drugs, and biological pathways.
• Meta-analysis Pipeline: Implements a meta-analysis pipeline to identify genes differentially expressed across multiple experiments.

Scientific Applications:

• Disease Modeling: Locate GEO experiments that replicate or contrast disease-associated gene expression signatures to support disease modeling.
• Drug Discovery: Identify drugs or perturbations that produce similar or opposite gene expression signatures for repurposing or mechanistic insight.
• Pathway Analysis: Reveal biological pathways associated with query signatures by matching to experiments with relevant expression patterns.
• Experimental Validation: Validate experimental findings by finding matching or contrasting expression profiles in public GEO datasets.
• Therapeutic Target Exploration: Explore potential therapeutic targets by identifying consistently altered genes across related experiments.
• Investigation of Molecular Mechanisms: Investigate molecular underpinnings of complex diseases by comparing query signatures to a broad compendium of GEO-derived signatures.

Methodology:

Constructs a comprehensive database of gene signatures from GEO across organisms and platforms, compares user-provided query signatures to that database to identify similar or opposite expression patterns, and applies algorithms plus a meta-analysis pipeline to detect genes differentially expressed across experiments.