Methods of Analyzing Codon Usage Bias
In the field of bioinformatics and computational biology, many statistical methods have been proposed and used to analyze codon usage bias. Methods such as the 'frequency of optimal codons' (Fop), the Relative Codon Adaptation (RCA) or the 'Codon Adaptation Index' (CAI) are used to predict gene expression levels, while methods such as the 'effective number of codons' (Nc) and Shannon entropy from information theory are used to measure codon usage evenness. Multivariate statistical methods, such as correspondence analysis and principal component analysis, are widely used to analyze variations in codon usage among genes. There are many computer programs to implement the statistical analyses enumerated above, including CodonW, GCUA, INCA, etc. Codon optimization has applications in designing synthetic genes and DNA vaccines. Several software packages are available online for this purpose (refer to external links). Optimizing the occurrence of desired/undesired motifs and sequence composition in all possible reverse translated gene sequences increases the search space exponentially with respect to gene length. For those reasons, the problem could be addressed using optimization algorithms like genetic algorithms (Sandhu et al., In Silico Biol. 2008;8(2):187-92).
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