Codon-based indices for modeling gene expression and transcript evolution.

Codon-based indices for modeling gene expression and transcript evolution.
复制标题

DOI:
10.1016/j.csbj.2021.04.042
复制
发表时间:
2021
影响因子:
6
通讯作者:
Tuller T
Tuller T
中科院分区:
生物学2区
文献类型:
--
作者:
Bahiri-Elitzur S;Tuller T

文献摘要

参考文献

被引文献

相似文献

密码子使用偏差(Codon usage bias, CUB)是指在大多数基因和生物体中,同义密码子使用频率不同的现象。一般的假设是密码子偏差反映了突变偏差和自然选择之间的平衡。今天,我们了解到密码子的含量是相关的,可以影响所有的基因表达步骤。从20世纪80年代开始,基于密码子的索引已被用于回答所有生物医学领域的不同问题,包括系统生物学、农业、医学和生物技术。一般来说,密码子使用偏倚指数对每个密码子或一小组密码子进行加权,以估计某种编码序列对某种现象的拟合程度(如密码子偏倚、对tRNA池的适应性、某些密码子的频率、转录延伸速度等),通常容易实现。今天有几十个这样的指数;因此,本文旨在综述和比较不同的密码子使用偏差指数、它们的应用和优势。此外,我们进行的分析表明,即使大多数指数旨在捕捉不同的方面,它们也倾向于相互关联。由于密码子使用偏差在不同基因表达步骤中的中心地位,因此不断开发新的索引来捕获当前索引无法建模的其他方面是很重要的。
Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices.
DOI: 10.1111/j.1469-185x.2012.00242.x
发表时间: 2013-02-01
期刊: BIOLOGICAL REVIEWS
影响因子: 10
作者:
Behura, Susanta K.;Severson, David W.
通讯作者: Severson, David W.
DOI: 10.1093/dnares/dsq012
发表时间: 2010-06-01
期刊: DNA RESEARCH
影响因子: 4.1
作者:
Fox, Jesse M.;Erill, Ivan
通讯作者: Erill, Ivan
DOI: 10.1093/bioinformatics/btz080
发表时间: 2019-09-15
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Diament, Alon;Weiner, Iddo;Tuller, Tamir
通讯作者: Tuller, Tamir
DOI: 10.1007/s00239-019-09921-4
发表时间: 2020-03-01
影响因子: 3.9
作者:
Forcelloni, Sergio;Giansanti, Andrea
通讯作者: Giansanti, Andrea
DOI: 10.1080/15476286.2017.1384118
发表时间: 2018-01-01
期刊: RNA BIOLOGY
影响因子: 4.1
作者:
Cohen, Eyal;Zafrir, Zohar;Tuller, Tamir
通讯作者: Tuller, Tamir