Literature DB >> 16679346

Accounting for background nucleotide composition when measuring codon usage bias: brilliant idea, difficult in practice.

Anders Fuglsang.   

Abstract

The effective number of codons used in a gene is a commonly used measure of codon usage. It varies between 20 and 61 (standard genetic code) and indicates to which degree the entire genetic code is used. It is a drawback of this method that it does not take background composition into account. This led Novembre to introduce a variant called Nc' (Novembre JA. 2002. Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol 19:1390-4). In this letter, its properties are under the loupe, with special emphasis on phenomena relating to codon homozygosity. A theoretical misunderstanding regarding this estimator is explained in detail, notably Nc varies between 0 and 61 instead of 20 and 61 (with the standard genetic code). Practical examples from the genome of Pseudomonas aeruginosa are given which demonstrate that the problem is not just theoretical.

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Year:  2006        PMID: 16679346     DOI: 10.1093/molbev/msl009

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  13 in total

1.  Nearly neutrality and the evolution of codon usage bias in eukaryotic genomes.

Authors:  Sankar Subramanian
Journal:  Genetics       Date:  2008-04       Impact factor: 4.562

2.  Strong heterogeneity in nucleotidic composition and codon bias in the pea aphid (Acyrthosiphon pisum) shown by EST-based coding genome reconstruction.

Authors:  Claude Rispe; Fabrice Legeai; Jean-Pierre Gauthier; Denis Tagu
Journal:  J Mol Evol       Date:  2007-10-10       Impact factor: 2.395

3.  Evolutionary Forces and Codon Bias in Different Flavors of Intrinsic Disorder in the Human Proteome.

Authors:  Sergio Forcelloni; Andrea Giansanti
Journal:  J Mol Evol       Date:  2019-12-10       Impact factor: 2.395

Review 4.  Homology-independent metrics for comparative genomics.

Authors:  Tarcisio José Domingos Coutinho; Glória Regina Franco; Francisco Pereira Lobo
Journal:  Comput Struct Biotechnol J       Date:  2015-05-04       Impact factor: 7.271

5.  The impact of RNA structure on coding sequence evolution in both bacteria and eukaryotes.

Authors:  Wanjun Gu; Musheng Li; Yuming Xu; Ting Wang; Jae-Hong Ko; Tong Zhou
Journal:  BMC Evol Biol       Date:  2014-04-23       Impact factor: 3.260

6.  Systematic CpT (ApG) depletion and CpG excess are unique genomic signatures of large DNA viruses infecting invertebrates.

Authors:  Mohita Upadhyay; Neha Sharma; Perumal Vivekanandan
Journal:  PLoS One       Date:  2014-11-04       Impact factor: 3.240

7.  Analysis of Synonymous Codon Usage Bias in Potato Virus M and Its Adaption to Hosts.

Authors:  Zhen He; Haifeng Gan; Xinyan Liang
Journal:  Viruses       Date:  2019-08-14       Impact factor: 5.048

8.  Causes and implications of codon usage bias in RNA viruses.

Authors:  Ilya S Belalov; Alexander N Lukashev
Journal:  PLoS One       Date:  2013-02-25       Impact factor: 3.240

9.  Comparative Genomics of Trypanosomatid Pathogens using Codon Usage Bias.

Authors:  Mayank Rashmi; D Swati
Journal:  Bioinformation       Date:  2013-11-11

10.  Cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape codon usage bias in coronaviruses.

Authors:  Patrick C Y Woo; Beatrice H L Wong; Yi Huang; Susanna K P Lau; Kwok-Yung Yuen
Journal:  Virology       Date:  2007-09-19       Impact factor: 3.616

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