Table4_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）是一种考虑密码子-反密码子摇摆相互作用效率加权值（Sij值）的翻译效率度量指标。tAI的初始实现存在显著缺陷。例如，生成的Sij权重是基于酵母菌（Saccharomyces cerevisiae）的基因表达进行优化的，而这一指标在不同物种之间预期会存在差异。因此，为了克服这些局限性，开发了一种针对特定物种的方法（stAI）。然而，stAI方法采用爬山算法来优化Sij权重，这并非理想的选择，因为其在复杂搜索空间中寻找全局最大值时可能遭遇困难，即使使用了不同的起始位置。此外，与原始实现相比，它在计算真菌基因组tAI时表现不佳。我们开发了一种新的方法，命名为遗传tAI（gtAI），并以Python包的形式实现（https://github.com/AliYoussef96/gtAI），该方法利用遗传算法获取最佳Sij权重，并遵循一种基于密码子使用的新工作流程，能够更精确地计算生命三域基因组中的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）。