SWIMmeR

SWIMmeR identifies key (switch) genes by analyzing patterns of molecular co-abundance to investigate phenotypic transitions.

Key Features:

• Network-Based Analysis: Leverages network-based methodologies to analyze gene interactions and detect switch genes exhibiting significant molecular co-abundance patterns.
• Switch Gene Detection: Identifies key (switch) genes associated with phenotypic transitions across biological contexts.
• Implementation: Implemented in R as the successor to SWIM (SWitchMiner), which was originally developed in MATLAB®.
• Computational Optimization: Optimized to be less computationally intensive for gene network analyses.
• Application Breadth: Demonstrated utility across phenotype-specific studies, including complex diseases and plant developmental processes.

Scientific Applications:

• Phenotypic Transition Analysis: Investigation of genes driving phenotypic transitions via molecular co-abundance patterns.
• Cancer Research: Applied in breast cancer studies to identify switch genes linked to disease phenotypes.
• Plant Biology and Agriculture: Applied to grapevine berry maturation to identify genes involved in developmental transitions.

Methodology:

Performs network-based analysis of gene co-abundance to identify switch genes; implemented in R as the successor to SWIM (SWitchMiner) originally developed in MATLAB®, and optimized to reduce computational intensity.