Inference of Episodic Changes in Natural Selection Acting on Protein Coding Sequences via CODEML

Area of Science:

• Evolutionary biology
• Molecular evolution
• Bioinformatics

Background:

• Understanding natural selection is key to evolutionary biology.
• Episodic selection, characterized by varying selection pressures over time, plays a significant role in molecular evolution.
• Protein-coding sequences provide a rich source of data for studying evolutionary processes.

Purpose of the Study:

• To provide detailed protocols for using the CODEML program to infer episodic natural selection in protein-coding sequences.
• To demonstrate methods for estimating selection intensity and identifying sites under episodic evolution.
• To outline procedures for assessing the robustness and statistical reliability of these inferences.

Main Methods:

• Utilizing the CODEML program within the PAML package for phylogenetic analyses.
• Applying maximum likelihood estimation to quantify selection intensity.
• Implementing hypothesis testing to detect episodic positive selection.
• Employing bootstrapping techniques to validate statistical inference requirements.
• Using an example dataset of protein-coding sequences for practical illustration.

Main Results:

• Established protocols for robust inference of episodic natural selection.
• Demonstrated the application of CODEML for identifying sites with episodic evolutionary histories.
• Validated the utility of bootstrapping for ensuring reliable statistical outcomes.
• Showcased a workflow amenable to automation for large-scale evolutionary surveys.

Conclusions:

• The provided protocols offer a standardized approach for studying episodic selection in protein-coding genes.
• CODEML is a powerful tool for detailed evolutionary analyses, including the detection of varying selection pressures.
• The outlined methods enhance the reliability and extendibility of evolutionary surveys.