Coev-Web

Coev-Web analyzes molecular coevolution in aligned DNA sequences using the Coev probabilistic model and phylogenetic trees to identify coevolving nucleotide positions and estimate their coevolutionary profiles.

Key Features:

• Identification of Coevolving Positions: Uses the Coev probabilistic model to identify coevolving nucleotide positions and estimate specific nucleotide profiles rather than relying on predefined constraints such as Watson-Crick base pairing.
• Incorporation of Phylogenetic Relationships: Integrates phylogenetic relationships to account for lineage-specific substitution patterns in coevolution analyses.
• Probabilistic Modeling: Models coevolution with a 16 × 16 instantaneous rate matrix that accounts for transition rates and coevolutionary profiles to distinguish coevolving from non-coevolving positions.
• Performance Evaluation: Evaluates model fit using the Akaike Information Criterion (AIC) on simulated, empirical, and illustrative datasets, reporting superior specificity relative to methods that assume independent evolution.
• Simulation Capabilities: Simulates coevolving pairs of positions along a specified phylogenetic tree to explore potential evolutionary trajectories and dependencies between sites.

Scientific Applications:

• Detection of Coevolving Sites: Identify coevolving nucleotide positions and their estimated profiles in aligned DNA sequence datasets.
• Functional and Structural Inference: Infer potential functional or structural implications of nucleotide dependencies from estimated coevolutionary profiles.
• Study of Dependent Substitution During Lineage Evolution: Elucidate mechanisms of dependent substitution by integrating coevolutionary inference with phylogenetic context.
• Simulation-Based Hypothesis Testing: Use simulated coevolving site pairs along phylogenies to investigate evolutionary scenarios and site dependencies.

Methodology:

Applies the Coev probabilistic model to aligned DNA sequences and phylogenetic trees using a 16 × 16 instantaneous rate matrix to model transitions and estimate nucleotide profiles, incorporates phylogenetic relationships, compares models via Akaike Information Criterion (AIC) on simulated and empirical datasets, and supports simulation of coevolving site pairs along specified phylogenetic trees.