Soohyun Lee¹, Seyeon Weon^1,2, Sooncheol Lee and Changwon Kang

Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Korea. ¹These two authors contributed equally. ²Present address: Bioinformatics Research Laboratories, Daejeon, Korea.

Abstract

We propose a simple, sensitive measure of synonymous codon usage bias, the Relative Codon Adaptation Index (rCAI), as a way to discriminate better between highly biased and unbiased regions, compared with the widely used Codon Adaptation Index (CAI). CAI is a geometric mean of the relative usage of codons in a gene, and is calculated using the codon usage table trained with a set of highly expressed genes. In contrast, rCAI is computed by subtracting the background codon usage trained with two noncoding frames of highly expressed genes from the codon usage in the coding frame. rCAI has higher signal-to-noise ratio than CAI, considering that non-coding frames would not show codon bias. Translation efficiency and protein abundance correlates comparably or better with rCAI than CAI or other measures such as ‘effective number of codons’ and ‘SCUMBLE offsets’. Within overlapping coding regions, one of the two coding frames dominates in codon usage bias according to rCAI. Presumably, rCAI could substitute CAI in diverse applications.