Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

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

tRNA适应指数（tAI）是一种翻译效率指标，它考虑了密码子-tRNA摇摆位相互作用效率的加权值（Sij值）。tAI的初始实现存在显著缺陷。例如，生成的Sij权重是基于酿酒酵母（Saccharomyces cerevisiae）的基因表达进行优化的，而这一指标在不同物种中可能存在差异。因此，为了克服这些限制，开发了一种针对特定物种的方法（stAI）。然而，stAI方法采用了爬山算法来优化Sij权重，这并非获得最佳Sij权重集的理想方法，因为即使在尝试了不同的起始位置后，它也可能难以在复杂的搜索空间中找到全局最大值。此外，与原始实现相比，在计算真菌基因组tAI时，其表现不佳。我们开发了一种新的方法，称为遗传tAI（gtAI），该算法以Python包的形式实现（https://github.com/AliYoussef96/gtAI），它采用遗传算法来获得最佳的Sij权重集，并遵循一种基于密码子使用的新工作流程，能够更精确地计算来自生命三个域的基因组tAI。与stAI相比，gtAI显著提高了与密码子适应指数（CAI）的相关性以及蛋白质丰度的预测（基于经验数据）。