4.5 Article

The Dynamic Codon Biaser: calculating prokaryotic codon usage biases

期刊

MICROBIAL GENOMICS
卷 7, 期 10, 页码 -

出版社

MICROBIOLOGY SOC
DOI: 10.1099/mgen.0.000663

关键词

codon usage; codon bias; Dynamic Codon Biaser; prokaryotes

资金

  1. US National Science Foundation [1661357]
  2. Direct For Biological Sciences
  3. Div Of Biological Infrastructure [1661357] Funding Source: National Science Foundation

向作者/读者索取更多资源

The Codon Bias Database (CBDB) and the Dynamic Codon Biaser (DCB) are tools used to calculate codon usage bias in bacterial genomes, aiding in research on gene expression control, guiding protein structure, evolutionary studies, and phage-host predictions.
Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome's HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage-host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据