gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values ( S ij values) for codon–tRNA wobble interaction efficiencies. initial implementation of the tAI had significant flaws. For instance, generated weights were optimized based on gene expression in Saccharomyces cerevisiae , which expected to vary among different species. Consequently, species-specific approach (stAI) was developed overcome those limitations. However, stAI method employed hill climbing algorithm optimize weights, not ideal obtaining best set because it could struggle find global maximum given complex search space, even after using starting positions. In addition, did perform well computing fungal genomes comparison with original implementation. We novel named genetic (gtAI) implemented as Python package https://github.com/AliYoussef96/gtAI ), employs obtain and follows new codon usage-based workflow better computes from three domains life. gtAI has significantly improved correlation (CAI) prediction protein abundance (empirical data) compared stAI.