WITHDRAWN: Robust method for calculating the tRNA adaptation index utilizing the genetic algorithm

Abstract

• Genetic tRNA adaptation index (gtAI) method can solve the query of Sij -values optimization and effectively estimate the adaptation index values. • The Genetic tAI method performed well compared to the original tAI and species-specific approach tAI and correlates better with codon adaptation index (CAI) using Spearman’s rank correlation analysis. • The Genetic tAI improved the prediction of protein abundance compared to the species-specific approach tAI method. • Estimates reliable Wi values which reveal the difference between the organisms from different domains of life. The tRNA Adaptation Index (tAI) is a well-known translation efficiency metric that considers weights (Sij-values) for codon-tRNA wobble interaction efficiencies. The initial implementation of tAI had significant flaws. For instance, generated S ij 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 S ij weights, which is not ideal for obtaining the best S ij weights because it can only achieve a local maximum even after using different starting positions. As well as, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach implemented as a python package named genetic tAI (gtAI), which employs a genetic algorithm to obtain the best S ij weights and follows a new workflow that better computes the tAI of many organisms from the three domains of life. We conducted a comparative analysis on 12 model organisms to evaluate the performance of gtAI by examining whether it correlates better with the codon adaptation index (CAI) and protein abundance. The gtAI has significantly improved correlation with CAI than the existing implementations for 9 out of 12 model organisms. It also improved the prediction of protein abundance compared to the stAI method .