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SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation

The genetic code is degenerate—most amino acids can be encoded by from two to as many as six different codons. The synonymous codons are not used with equal frequency: not only are some codons favored over others, but also their usage can vary significantly from species to species and between differ...

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Detalles Bibliográficos
Autores principales: Kloster, Morten, Tang, Chao
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2441815/
https://www.ncbi.nlm.nih.gov/pubmed/18495752
http://dx.doi.org/10.1093/nar/gkn288
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author Kloster, Morten
Tang, Chao
author_facet Kloster, Morten
Tang, Chao
author_sort Kloster, Morten
collection PubMed
description The genetic code is degenerate—most amino acids can be encoded by from two to as many as six different codons. The synonymous codons are not used with equal frequency: not only are some codons favored over others, but also their usage can vary significantly from species to species and between different genes in the same organism. Known causes of codon bias include differences in mutation rates as well as selection pressure related to the expression level of a gene, but the standard analysis methods can account for only a fraction of the observed codon usage variation. We here introduce an explicit model of codon usage bias, inspired by statistical physics. Combining this model with a maximum likelihood approach, we are able to clearly identify different sources of bias in various genomes. We have applied the algorithm to Saccharomyces cerevisiae as well as 325 prokaryote genomes, and in most cases our model explains essentially all observed variance.
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spelling pubmed-24418152008-07-02 SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation Kloster, Morten Tang, Chao Nucleic Acids Res Computational Biology The genetic code is degenerate—most amino acids can be encoded by from two to as many as six different codons. The synonymous codons are not used with equal frequency: not only are some codons favored over others, but also their usage can vary significantly from species to species and between different genes in the same organism. Known causes of codon bias include differences in mutation rates as well as selection pressure related to the expression level of a gene, but the standard analysis methods can account for only a fraction of the observed codon usage variation. We here introduce an explicit model of codon usage bias, inspired by statistical physics. Combining this model with a maximum likelihood approach, we are able to clearly identify different sources of bias in various genomes. We have applied the algorithm to Saccharomyces cerevisiae as well as 325 prokaryote genomes, and in most cases our model explains essentially all observed variance. Oxford University Press 2008-06 2008-05-21 /pmc/articles/PMC2441815/ /pubmed/18495752 http://dx.doi.org/10.1093/nar/gkn288 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Kloster, Morten
Tang, Chao
SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title_full SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title_fullStr SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title_full_unstemmed SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title_short SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
title_sort scumble: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2441815/
https://www.ncbi.nlm.nih.gov/pubmed/18495752
http://dx.doi.org/10.1093/nar/gkn288
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AT tangchao scumbleamethodforsystematicandaccuratedetectionofcodonusagebiasbymaximumlikelihoodestimation