ParallelEvolCCM

ParallelEvolCCM quantifies coevolutionary patterns among genomic features by modeling gain and loss probabilities as an R wrapper with parallelization and pre-filtering options for increased efficiency.

Key Features:

• Parallelization: Adds parallelization options to increase the efficiency of comparisons.
• Pre-filtering: Includes several options to pre-filter features before analysis.
• Coevolutionary modeling: Represents the gain/loss probability of each feature as a combination of its own intrinsic rate combined with the joint probabilities of gain and loss with all other features.
• Phylogenetic signal capture: Captures phylogenetic signals to outperform comparative methods based on shared presence and absence of features.

Scientific Applications:

• Genomic feature coevolution analysis: Identifies concerted gains and losses of genes and mobile genetic elements to infer related functional roles and shared evolutionary trajectories.
• Bacterial comparative genomics: Applied to datasets of bacterial genomes such as 1000 genomes of the genus Bifidobacterium.

Methodology:

ParallelEvolCCM builds upon the Community Coevolution Model (CCM), which models gain and loss probabilities of genomic features.