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Nucleic Acids Research Advance Access originally published online on May 21, 2008
Nucleic Acids Research 2008 36(11):3819-3827; doi:10.1093/nar/gkn288
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Nucleic Acids Research, 2008, Vol. 36, No. 11 3819-3827
© 2008 The Author(s)
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.

Computational Biology

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

Morten Kloster1 and Chao Tang1,2,*

1Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California 94158, USA and 2Center for Theoretical Biology, Peking University, Beijing 100871, China

*To whom correspondence should be addressed. Tel: +1 415 514 4414; Fax: +1 415 514 4797; Email: chao.tang{at}ucsf.edu

Received November 27, 2007. Revised April 22, 2008. Accepted April 25, 2008.

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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