Literature DB >> 15081433

The 'effective number of codons' revisited.

Anders Fuglsang1.   

Abstract

Frank Wright [Gene 87 (1990) 23] derived a formula for calculation of a quantity termed the 'effective number of codons' (Nc) based on codon homozygosities. This quantity is a number between 20 and 61 and tells to what degree the codon usage in a gene is biased, i.e., it approaches 20 codons for the extremely biased genes, and approaches 61 for the genes where all possible codons are used with no preference. Among the different measures of codon bias Nc is considered the most useful and has found widespread use in papers dealing with codon usage phenomena. In this paper, the mathematical behaviours of codon homozygosities and Nc are evaluated, using Escherichia coli as the model organism. The results indicate that the classical formula for calculation of Nc could appropriately be substituted under circumstances, where there is bias discrepancy, i.e., when one amino acid (or more) within a degeneracy group is associated with strong codon bias while at the same time others in the same degeneracy group have little bias. An alternative estimator, termed Nc, is proposed and tested against Nc, and performs better when there is such bias discrepancy.

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Year:  2004        PMID: 15081433     DOI: 10.1016/j.bbrc.2004.03.138

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  32 in total

1.  Estimating the "effective number of codons": the Wright way of determining codon homozygosity leads to superior estimates.

Authors:  Anders Fuglsang
Journal:  Genetics       Date:  2005-11-19       Impact factor: 4.562

2.  Predicting essential genes in fungal genomes.

Authors:  Michael Seringhaus; Alberto Paccanaro; Anthony Borneman; Michael Snyder; Mark Gerstein
Journal:  Genome Res       Date:  2006-08-09       Impact factor: 9.043

3.  Compare the differences of synonymous codon usage between the two species within cardiovirus.

Authors:  Wen-qian Liu; Jie Zhang; Yi-qiang Zhang; Jian-hua Zhou; Hao-tai Chen; Li-na Ma; Yao-zhong Ding; Yongsheng Liu
Journal:  Virol J       Date:  2011-06-27       Impact factor: 4.099

4.  A novel framework for evaluating the performance of codon usage bias metrics.

Authors:  Sophia S Liu; Adam J Hockenberry; Michael C Jewett; Luís A N Amaral
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

5.  Interspecific and intragenic differences in codon usage bias among vertebrate myosin heavy-chain genes.

Authors:  Mikio C Aoi; Bryan C Rourke
Journal:  J Mol Evol       Date:  2011-09-14       Impact factor: 2.395

6.  Analysis of codon usage in Newcastle disease virus.

Authors:  Meng Wang; Yong-Sheng Liu; Jian-Hua Zhou; Hao-Tai Chen; Li-Na Ma; Yao-Zhong Ding; Wen-Qian Liu; Yuan-Xing Gu; Jie Zhang
Journal:  Virus Genes       Date:  2011-01-20       Impact factor: 2.332

7.  Relative codon adaptation index, a sensitive measure of codon usage bias.

Authors:  Soohyun Lee; Seyeon Weon; Sooncheol Lee; Changwon Kang
Journal:  Evol Bioinform Online       Date:  2010-05-05       Impact factor: 1.625

8.  Codon Usage Patterns in Corynebacterium glutamicum: Mutational Bias, Natural Selection and Amino Acid Conservation.

Authors:  Guiming Liu; Jinyu Wu; Huanming Yang; Qiyu Bao
Journal:  Comp Funct Genomics       Date:  2010-04-22

9.  Comparative genome analysis of six malarial parasites using codon usage bias based tools.

Authors:  Manoj Kumar Yadav; D Swati
Journal:  Bioinformation       Date:  2012-12-08

10.  Analysis of synonymous codon usage patterns in seven different citrus species.

Authors:  Chen Xu; Jing Dong; Chunfa Tong; Xindong Gong; Qiang Wen; Qiang Zhuge
Journal:  Evol Bioinform Online       Date:  2013-05-23       Impact factor: 1.625

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