StemCellNet

StemCellNet enables generation and analysis of molecular networks to investigate physical and regulatory interactions underlying stem cell self-renewal and differentiation.

Key Features:

• Interactive Network Analysis: Generates focused networks from datasets of physical and regulatory interactions identified in human and murine stem cells.
• Integration and Prioritization: Integrates user-provided expression data to select and prioritize network components.
• Screening Against Stemness-Associated Genes: Screens networks against a compendium of stemness-associated genes to identify their presence and enrichment.
• Novel Candidate Gene Identification: Evaluates connectivity patterns within networks to suggest novel candidate genes involved in stem cell biology.
• Extended Versatility with Generic Interactions Dataset: Includes an optional generic interactions dataset covering the human and mouse proteome to extend analyses beyond stem-cell-specific interactions.

Scientific Applications:

• Network analysis of stem cell states: Dissects molecular networks that govern self-renewal and differentiation in stem cells.
• Contextualization of expression datasets: Prioritizes network components by integrating transcriptomic data provided by users.
• Candidate discovery and validation support: Identifies and ranks novel candidate genes via connectivity analyses and screening against stemness-associated genes.
• Broad biomedical research use: Extends to studies involving the human and mouse proteome, including research related to degenerative diseases and cancer.

Methodology:

Rapid generation of molecular networks from a comprehensive collection of known physical and regulatory interactions.