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

IF 2.9 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-09-27 DOI:10.1186/s12859-024-05934-z

Kunjie Fan, Yuanyuan Li, Zhiwei Chen, Long Fan

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引用次数: 0

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

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GenRCA：一种用户友好型稀有密码子分析工具，用于根据基因组中的编码序列全面评估密码子使用偏好。

背景：研究密码子使用偏差对于了解基因表达、进化和基因设计非常重要，它为了解支配基因功能和调控的分子过程提供了重要依据。密码子使用偏倚（CUB）指数是了解不同生物体密码子使用模式的重要指标，无需进行大量实验。考虑到没有适用于所有物种的万能指数，因此亟需一个支持计算和分析多个 CUB 指数以优化密码子的综合平台：在此，我们发布了 GenRCA，它是我们之前的稀有密码子分析工具的升级版本，是一个免费且用户友好的网站，用于对编码序列的密码子使用偏好进行全面评估。在这项研究中，我们人工审核并实现了多达 31 个密码子偏好指数，涵盖了 65 种表达宿主生物，并支持多基因序列的批量处理，旨在改善用户体验，提供更全面、更高效的分析：我们的网站填补了物种特异性 CUB 计算综合工具的空白，使研究人员能够根据 31 种指数的综合列表全面评估蛋白质表达水平，并进一步指导密码子优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.