LowMACA

LowMACA analyzes low-frequency mutations across protein families using consensus alignment to identify clustered conserved residues and potential driver mutations in cancer genomics.

Key Features:

• Consensus Alignment Approach: Leverages a consensus alignment strategy to combine mutations across proteins sharing functional domains and identify conserved residues with clustered mutations.
• Visualization and Statistical Assessment: Implements visualization and statistical frameworks to assess mutational hotspots and evaluate potential gain-of-function mutations and driver genes.
• Application to Oncogenic Families: Applied to putative oncogenic families such as the Ras superfamily, enabling identification of new potential driver mutations and genes in subfamilies including Rho, Rab, and Rheb.
• Comparison Across Confidence Levels: Compares mutation patterns between known and candidate driver genes to reveal similar clustering behaviors across different confidence levels within protein families.

Scientific Applications:

• Detection of low-frequency driver mutations: Identifies clustered, low-frequency mutations that may indicate oncogenic drivers missed by single-gene analyses.
• Analysis of oncogenic protein families: Aggregates mutations across family members to uncover shared mutational hotspots in families such as the Ras superfamily and its subfamilies Rho, Rab, and Rheb.
• Prioritization of candidate driver genes: Enables comparison of mutation patterns across confidence levels to support prioritization of candidate driver genes.

Methodology:

Consensus alignment combines mutations across proteins sharing functional domains, followed by statistical assessment and visualization to identify conserved residues with clustered mutations and mutational hotspots.