Mirage-evol

Mirage-evol reconstructs gene-content evolutionary histories using a phylogenetic mixture model integrated with a realistic evolutionary rate (RER) model to accommodate heterogeneous gene gain and loss processes across gene families.

Key Features:

• Phylogenetic Mixture Model: Captures heterogeneous evolutionary events by allowing distinct gene gain and loss rates across different gene families.
• Realistic Evolutionary Rate (RER) Model: Integrates an RER model to represent evolutionary dynamics and supports parameter estimation via the expectation-maximization algorithm while limiting parameter complexity.
• High Accuracy in Ancestral Genome Estimation: Produces precise ancestral genome estimates by explicitly modeling variable gain/loss processes among gene families.
• Adaptability to Diverse Taxonomic Groups: Demonstrates improved fit to genome data across diverse taxonomic groups relative to other models.

Scientific Applications:

• Gene Content Evolutionary Analysis: Reconstructs histories of gene gains and losses to elucidate patterns of gene-content evolution.
• Metabolic Function Gene Families: Applied to empirical datasets to identify frequent gene gain and loss events in metabolic function–related gene families across taxa.

Methodology:

Implements a phylogenetic mixture model combined with a realistic evolutionary rate (RER) model to simulate heterogeneous gene gain/loss processes and estimates model parameters using the expectation-maximization algorithm.