ArticleNucleic acids research2008
SCUMBLE: a method for systematic and accurate detection of codon usage bias by maximum likelihood estimation.
Article in Nucleic acids research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Codon-based indices for modeling gene expression and transcript evolution.Computational and structural biotechnology journal · 2021Review
- Genome-wide patterns of codon bias are shaped by natural selection in the purple sea urchin, Strongylocentrotus purpuratus.G3 (Bethesda, Md.) · 2013Article
- Article
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- Characterizing the native codon usages of a genome: an axis projection approach.Molecular biology and evolution · 2011Article
- Genes optimized by evolution for accurate and fast translation encode in Archaea and Bacteria a broad and characteristic spectrum of protein functions.BMC genomics · 2010Article
- Functional biogeography as evidence of gene transfer in hypersaline microbial communities.PloS one · 2010Article
- Relative codon adaptation index, a sensitive measure of codon usage bias.Evolutionary bioinformatics online · 2010Article
- Forces that influence the evolution of codon bias.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2010Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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