SENCA: A Multilayered Codon Model to Study the Origins and Dynamics of Codon Usage

Area of Science:

• Evolutionary biology
• Genomics
• Computational biology

Background:

• Gene sequences evolve at nucleotide, codon, and amino acid levels.
• Disentangling these evolutionary forces requires sophisticated probabilistic models.

Purpose of the Study:

• To introduce SENCA (site evolution of nucleotides, codons, and amino acids), a novel three-layer codon substitution model.
• To provide more accurate estimates of selection compared to classical models by separating evolutionary processes.

Main Methods:

• Developed a three-layer probabilistic model (SENCA) to analyze gene sequence evolution.
• Applied SENCA to study genomic content evolution in prokaryotes and Enterobacteria.
• Proposed new summary statistics to quantify evolutionary processes.

Main Results:

• Identified a universal AT mutational bias across studied genomes.
• Demonstrated that accounting for synonymous codon usage selection impacts nonsynonymous substitution measurements.
• Confirmed that codon usage bias is primarily driven by selection for preferred codons.

Conclusions:

• SENCA offers improved accuracy in estimating evolutionary selection pressures.
• Synonymous substitutions are largely non-neutral, influenced by codon usage bias.
• The model provides new tools to measure the relative importance of different evolutionary forces on gene sequences.