Literature DB >> 16299393

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

Anders Fuglsang1.   

Abstract

In 1990, Frank Wright introduced a method for measuring synonymous codon usage bias in a gene by estimation of the "effective number of codons," N(c). Several attempts have been made recently to improve Wright's estimate of N(c), but the methods that work in cases where a gene encodes a protein not containing all amino acids with degenerate codons have not been tested against each other. In this article I derive five new estimators of N(c) and test them together with the two published estimators, using resampling under rigorous testing conditions. Estimation of codon homozygosity, F, turns out to be a key to the estimation of N(c). F can be estimated in two closely related ways, corresponding to sampling with or without replacement, the latter being what Wright used. The N(c) methods that are based on sampling without replacement showed much better accuracy at short gene lengths than those based on sampling with replacement, indicating that Wright's homozygosity method is superior. Surprisingly, the methods based on sampling with replacement displayed a superior correlation with mRNA levels in Escherichia coli.

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Year:  2005        PMID: 16299393      PMCID: PMC1456227          DOI: 10.1534/genetics.105.049643

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  13 in total

1.  The 'effective number of codons' revisited.

Authors:  Anders Fuglsang
Journal:  Biochem Biophys Res Commun       Date:  2004-05-07       Impact factor: 3.575

2.  How reliable re-adjustment is: correspondence regarding A. Fuglsang, "The 'effective number of codons' revisited".

Authors:  Sayed-Amir Marashi; Hamed Shateri Najafabadi
Journal:  Biochem Biophys Res Commun       Date:  2004-11-05       Impact factor: 3.575

3.  The 'effective number of codons' used in a gene.

Authors:  F Wright
Journal:  Gene       Date:  1990-03-01       Impact factor: 3.688

4.  On the methodological weakness of 'the effective number of codons': a reply to Marashi and Najafabadi.

Authors:  Anders Fuglsang
Journal:  Biochem Biophys Res Commun       Date:  2005-02-04       Impact factor: 3.575

5.  Correlation of codon bias measures with mRNA levels: analysis of transcriptome data from Escherichia coli.

Authors:  Regina M Goetz; Anders Fuglsang
Journal:  Biochem Biophys Res Commun       Date:  2005-02-04       Impact factor: 3.575

6.  An evaluation of measures of synonymous codon usage bias.

Authors:  J M Comeron; M Aguadé
Journal:  J Mol Evol       Date:  1998-09       Impact factor: 2.395

7.  The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications.

Authors:  P M Sharp; W H Li
Journal:  Nucleic Acids Res       Date:  1987-02-11       Impact factor: 16.971

8.  Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes.

Authors:  T Ikemura
Journal:  J Mol Biol       Date:  1981-02-15       Impact factor: 5.469

9.  Codon usage in bacteria: correlation with gene expressivity.

Authors:  M Gouy; C Gautier
Journal:  Nucleic Acids Res       Date:  1982-11-25       Impact factor: 16.971

Review 10.  Codon usage and tRNA content in unicellular and multicellular organisms.

Authors:  T Ikemura
Journal:  Mol Biol Evol       Date:  1985-01       Impact factor: 16.240

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  19 in total

1.  An entropy-based technique for classifying bacterial chromosomes according to synonymous codon usage.

Authors:  Andrew Hart; Servet Martínez
Journal:  J Math Biol       Date:  2016-10-12       Impact factor: 2.259

2.  Selective pressure dominates the synonymous codon usage in parvoviridae.

Authors:  Sheng-Lin Shi; Yi-Ren Jiang; Yan-Qun Liu; Run-Xi Xia; Li Qin
Journal:  Virus Genes       Date:  2012-09-21       Impact factor: 2.332

3.  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

4.  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

5.  Comparative analysis and supragenome modeling of twelve Moraxella catarrhalis clinical isolates.

Authors:  Jeremiah J Davie; Josh Earl; Stefan P W de Vries; Azad Ahmed; Fen Z Hu; Hester J Bootsma; Kim Stol; Peter W M Hermans; Robert M Wadowsky; Garth D Ehrlich; John P Hays; Anthony A Campagnari
Journal:  BMC Genomics       Date:  2011-01-26       Impact factor: 3.969

6.  Locating protein-coding sequences under selection for additional, overlapping functions in 29 mammalian genomes.

Authors:  Michael F Lin; Pouya Kheradpour; Stefan Washietl; Brian J Parker; Jakob S Pedersen; Manolis Kellis
Journal:  Genome Res       Date:  2011-10-12       Impact factor: 9.043

7.  Translation Comes First: Ancient and Convergent Selection of Codon Usage Bias Across Prokaryotic Genomes.

Authors:  Francisco González-Serrano; Cei Abreu-Goodger; Luis Delaye
Journal:  J Mol Evol       Date:  2022-09-26       Impact factor: 3.973

8.  Codon Usage of Hepatitis E Viruses: A Comprehensive Analysis.

Authors:  Bingzhe Li; Han Wu; Ziping Miao; Linjie Hu; Lu Zhou; Yihan Lu
Journal:  Front Microbiol       Date:  2022-06-21       Impact factor: 6.064

9.  Codon usage is associated with the evolutionary age of genes in metazoan genomes.

Authors:  Yosef Prat; Menachem Fromer; Nathan Linial; Michal Linial
Journal:  BMC Evol Biol       Date:  2009-12-08       Impact factor: 3.260

10.  Base composition and translational selection are insufficient to explain codon usage bias in plant viruses.

Authors:  Daniel J Cardinale; Kate DeRosa; Siobain Duffy
Journal:  Viruses       Date:  2013-01-15       Impact factor: 5.048

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