GEM-TREND

GEM-TREND searches the Gene Expression Omnibus (GEO) for gene-expression signatures matching user-provided signatures or gene expression ratio data and evaluates statistical significance to identify biologically relevant co-expression patterns.

Key Features:

• Gene-Expression Signature Search: Accepts gene-expression signatures or gene expression ratio data and queries GEO for matching expression profiles.
• Nonparametric, Rank-Based Pattern Matching: Employs the nonparametric, rank-based pattern matching method of Lamb et al. (Science 2006) to compare query signatures to GEO profiles.
• Statistical Significance Calculation: Calculates statistical significance for matches to prioritize biologically relevant entries.
• Network Visualization and Annotation Linking: Constructs co-expression networks and links genes and annotations to external data repositories for downstream exploration.

Scientific Applications:

• Similar profile identification: Identify GEO experiments and samples with expression patterns similar to a query signature.
• Co-expression network construction: Build co-expression networks from matched GEO entries to examine gene–gene relationships.
• Genome-scale gene function inference: Support inference of gene functions across the genome by leveraging matched expression profiles.
• Biological state characterization: Characterize biological states or conditions by comparing query signatures to annotated GEO data.
• Biomarker and therapeutic target exploration: Explore potential biomarkers or therapeutic targets by finding consistent expression patterns in public datasets.

Methodology:

Compares user-provided gene-expression signatures or ratio data to GEO entries using a nonparametric, rank-based pattern matching method (Lamb et al., Science 2006) and applies statistical significance calculations to reported matches.