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

Codon-optimization in gene therapy: promises, prospects and challenges

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

CodonU: A Python Package for Codon Usage Analysis

Codon Usage Analysis (CUA) has been accompanied by several web servers and independent programs written in several programming languages. Also this diversity speaks for the need of a reusable software that can be helpful in reading, manipulating and acting as a pipeline for such data and file formats. This kind of analyses use multiple tools to address the multifaceted aspects of CUA. So, we propose CodonU, a package written in Python language to integrate all aspects. It is compatible with existing file formats and can be used solely or with a group of other such packages. The proposed package incorporates various statistical measures necessary for codon usage analysis. The measures vary with nature of the sequences, viz. for nucleotide, codon adaptation index (CAI), codon bias index (CBI), tRNA adaptation index (tAI) etc. and for protein sequences Gravy score etc. Users can also perform the correspondence analysis (COA). This package also provides the liberty to generate graphics to users, and also develop phylogenetic tree. Capabilities of the proposed package were checked thoroughly on a genomic set of Staphylococcus aureus.