Cluefish

Cluefish applies over-representation analysis on pre-clustered protein-protein interaction networks to identify specific biological functions in transcriptomic data series.

Key Features:

• Pre-clustered network analysis: Uses clusters from protein-protein interaction networks as anchors to identify smaller, more specific biological functions.
• Cluster merging: Enables aggregation of related clusters for more complete exploration of the data.
• Isolated gene recovery: Recovers isolated genes through shared biological contexts to improve data coverage.
• Integration with DRomics: Combines with dose-response analysis to identify gene clusters deregulated at low doses and linked to overlooked biological functions.

Scientific Applications:

• Toxicology and dose-response studies: Identifies sensitive pathways and biological functions affected by chemical exposure, such as retinoid signaling disruption by dibutyl phthalate in zebrafish development.
• Multi-organism transcriptomics: Applicable to diverse organisms for comprehensive biological interpretation of data series.
• Hypothesis generation: Provides clues for biological hypothesis generation and experimental validation from untargeted transcriptomic data.

Methodology:

Cluefish is implemented as an R workflow that applies over-representation analysis on pre-clustered protein-protein interaction networks, with cluster merging and recovery of isolated genes through shared biological contexts.