gtAI

gtAI employs a genetic algorithm to compute species-specific S_{ij} codon–tRNA wobble interaction weights and thereby improve translation efficiency predictions via the genetic tRNA adaptation index across bacteria, archaea, and eukaryotes.

Key Features:

• Genetic Algorithm Optimization: Uses a genetic algorithm instead of hill climbing to optimize parameter search for S_{ij} weights representing codon–tRNA wobble interaction efficiencies.
• S_{ij} Weight Optimization: Explicitly optimizes S_{ij} codon–tRNA interaction efficiencies that underlie translation efficiency calculations.
• Codon Usage Workflow: Implements a codon-usage-based workflow to enhance computation of the tRNA adaptation index (gtAI).
• Species-Specific Adaptation: Adapts computations to individual genomes across bacteria, archaea, and eukaryotes to produce species-specific gtAI values.
• Improved Correlation with CAI: Demonstrates enhanced correlation with the codon adaptation index (CAI) as a measure of codon usage bias and gene expression level.
• Empirical Validation: Shows superior performance in predicting protein abundance compared to the species-specific tAI (stAI) based on empirical data.
• Python Implementation: Provided as a Python-based package for computational analysis.

Scientific Applications:

• Genomic Research: Provides refined translation efficiency metrics for studies of gene expression and regulation across diverse species.
• Protein Synthesis Studies: Improves prediction of protein abundance to support analyses of protein synthesis dynamics.
• Comparative Genomics: Enables comparative studies of codon–tRNA adaptation and conserved translational features across organisms.

Methodology:

gtAI applies a codon-usage-based workflow that optimizes S_{ij} codon–tRNA wobble interaction weights using a genetic algorithm to compute genome-specific genetic tRNA adaptation index values.