CyTargetLinker

CyTargetLinker extends biological networks in Cytoscape by integrating regulatory interactions such as microRNA-target, transcription factor-target, and drug-target interactions to support network-based interpretation of molecular systems.

Key Features:

• Regulatory interaction integration: Incorporates microRNA-target, transcription factor-target, and drug-target interactions into existing networks using linksets.
• Linkset support: Accepts prebuilt and custom linksets to add regulatory edges linking network nodes to external interaction data.
• Identifier mapping: Harmonizes disparate identifier types to enable integration of interaction data from diverse resources.
• Programmatic access and automation: Supports command-line execution and integration with external programming environments such as R, Jupyter, and Python for automated workflows.
• Visualization for interpretation: Leverages Cytoscape graphical capabilities to visualize extended regulatory networks for biological interpretation.

Scientific Applications:

• Systems biology network integration: Integrates multiple regulator types to support systems-level interpretation of molecular processes.
• Protein–protein interaction extension: Extends PPI networks with compound-target interactions and disease-gene annotations.
• Differential expression contextualization: Loads differentially expressed genes and extends them with gene-pathway associations.
• Regulatory meta-network construction: Builds meta-networks by integrating transcription factor, microRNA, and drug regulation.
• Case-specific enrichment: Examples include extending a mouse molecular interaction network with validated and predicted microRNAs linked to diabetes mellitus and enriching a DNA repair gene network with ENCODE transcription factor data.
• Non-biological network construction: Can be applied to construct networks such as author-article-journal relationships using the same linkset mechanism.

Methodology:

Operates within the Cytoscape framework, applies linksets to add regulatory interactions to networks, performs identifier mapping to harmonize identifiers, and provides programmatic execution via command-line tools and integration with R, Jupyter, and Python; example computational use cases include extending PPI networks with compound-target and disease-gene annotations, extending lists of differentially expressed genes with gene-pathway associations, and integrating transcription factor, microRNA, and drug regulation to create regulatory meta-networks.