BioProfiling.de

BioProfiling.de performs statistical enrichment and network-based profiling of gene and protein lists to interpret functional annotations, pathway relationships, protein interactions, in silico predicted microRNA–gene associations, and text-mining–derived biological associations.

Key Features:

• Input types: Accepts gene and protein lists as the primary input for analysis.
• Enrichment analysis: Applies statistical enrichment frameworks to identify overrepresented functional annotations and pathways.
• Network-based statistics: Uses network-based statistical frameworks to analyze relationships among genes and proteins.
• Gene function integration: Incorporates detailed gene function annotations for contextual profiling.
• Protein interactions: Integrates protein–protein interaction information to inform network analyses.
• Pathway relationships: Utilizes pathway relationship data to connect genes and proteins to biological pathways.
• microRNA associations: Includes in silico predicted microRNA-to-gene associations for regulatory inference.
• Text-mining associations: Leverages text-mining–derived associations from the literature for additional evidence.
• Organism and identifier support: Supports multiple model organisms and a wide range of gene identifiers for cross-study compatibility.

Scientific Applications:

• Functional interpretation: Interprets gene and protein lists to reveal enriched biological functions and processes.
• Pathway analysis: Identifies pathway-level relationships and enrichment among queried genes or proteins.
• Interaction network analysis: Analyzes protein–protein interaction networks to uncover connectivity and modules.
• microRNA-target hypothesis generation: Prioritizes putative microRNA–gene regulatory relationships using in silico predictions.
• Literature-based association discovery: Discovers associations supported by text-mining evidence from scientific literature.
• Cross-species analyses: Enables analyses across multiple model organisms through broad identifier support.

Methodology:

Computational methods explicitly include statistical enrichment and network-based statistical frameworks integrating gene function annotations, protein–protein interactions, pathway relationships, in silico predicted microRNA–gene associations, and text-mining–derived associations.