TEAK

TEAK identifies biologically relevant KEGG subpathways from gene expression and molecular profiling data to reveal activated metabolic and signaling subpathways.

Key Features:

• TEAK module: Extracts activated KEGG subpathways from molecular profiling and gene expression datasets.
• Query Structure Enrichment Analysis (QSEA): Enables hierarchical querying of KEGG pathway structures to explore pathway relationships.
• Algorithmic approach: Implements a novel in-house algorithm and a tailor-made Clique Percolation Method to extract both linear and nonlinear subpathways from KEGG.
• Scoring mechanisms: Applies the Bayesian Information Criterion for context-specific analyses and the Kullback-Leibler divergence for case-control datasets.
• Empirical applications: Applied to microarray datasets profiling Saccharomyces cerevisiae stress responses, identifying linear sphingolipid metabolic subpathways activated under nitrogen stress, nonlinear glycerophospholipid subpathways involving SLC1 linked to filamentous growth, and previously unreported fitness defects in specific mutant strains under nitrogen limitation.

Scientific Applications:

• Systems biology: Maps topology-aware subpathway activation within KEGG to interpret system-level metabolic and signaling responses.
• Functional genomics: Connects gene expression changes to subpathway activation and downstream phenotypic consequences.
• Yeast stress-response research: Elucidates pathway-level mechanisms in Saccharomyces cerevisiae under nitrogen stress, supporting identification of genetic interactions and fitness phenotypes.

Methodology:

Computational methods explicitly include a novel in-house algorithm, a tailor-made Clique Percolation Method, the Bayesian Information Criterion, the Kullback-Leibler divergence, and hierarchical querying of KEGG pathways to extract linear and nonlinear subpathways.