GenRCA: a user-friendly rare codon analysis tool for comprehensive evaluation of codon usage preferences based on coding sequences in genomes

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 309 - 9
• Main Authors: Fan, Kunjie, Li, Yuanyuan, Chen, Zhiwei, Fan, Long
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.09.2024; BioMed Central; BMC
• Subjects: Adaptation; Analysis; Batch processing; Bias; Codon; Codon - genetics; Codon Usage; Evolutionary genetics; Frequency distribution; Gene design; Gene expression; Gene regulation; Gene sequencing; Genes; Genome - genetics; Genomes; Genomics; Methods; Optimization; Preferences; Protein expression; Proteins; Rare codon analysis; Software; Transfer RNA; User experience; Web applications; Web sites; Websites; China; Protein expression; Rare codon analysis; Codon usage; Gene design
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05934-z

The study of codon usage bias is important for understanding gene expression, evolution and gene design, providing critical insights into the molecular processes that govern the function and regulation of genes. Codon Usage Bias (CUB) indices are valuable metrics for understanding codon usage patterns across different organisms without extensive experiments. Considering that there is no one-fits-all index for all species, a comprehensive platform supporting the calculation and analysis of multiple CUB indices for codon optimization is greatly needed. Here, we release GenRCA, an updated version of our previous Rare Codon Analysis Tool, as a free and user-friendly website for all-inclusive evaluation of codon usage preferences of coding sequences. In this study, we manually reviewed and implemented up to 31 codon preference indices, with 65 expression host organisms covered and batch processing of multiple gene sequences supported, aiming to improve the user experience and provide more comprehensive and efficient analysis. Our website fills a gap in the availability of comprehensive tools for species-specific CUB calculations, enabling researchers to thoroughly assess the protein expression level based on a comprehensive list of 31 indices and further guide the codon optimization.