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The Codon Adaptation Index (CAI) [1] is the most widespread technique for analyzing codon usage bias.As opposed to other measures of codon usage bias, such as the 'effective number of codons' (Nc), which measure deviation from a uniform bias (null hypothesis), CAI measures the deviation of a given protein coding gene sequence with respect to a reference set of genes.
Effective number of codons (abbreviated as ENC or Nc) is a measure to study the state of codon usage biases in genes and genomes. [1] The way that ENC is computed has obvious similarities to the computation of effective population size in population genetics. [2]
Codon usage bias in Physcomitrella patens. Codon usage bias refers to differences in the frequency of occurrence of synonymous codons in coding DNA.A codon is a series of three nucleotides (a triplet) that encodes a specific amino acid residue in a polypeptide chain or for the termination of translation (stop codons).
The frequency of codons, also known as codon usage bias, can vary from species to species with functional implications for the control of translation. The codon varies by organism; for example, most common proline codon in E. coli is CCG, whereas in humans this is the least used proline codon. [53]
Each codon represents a single amino acid in a protein chain. However, there are more codons (64) than amino acids found in proteins (20), so many codons are effectively synonyms. However, there are more codons (64) than amino acids found in proteins (20), so many codons are effectively synonyms.
Protein translation involves a set of twenty amino acids.Each of these amino acids is coded for by a sequence of three DNA base pairs called a codon.Because there are 64 possible codons, but only 20-22 encoded amino acids (in nature) and a stop signal (i.e. up to three codons that do not code for any amino acid and are known as stop codons, indicating that translation should stop), some amino ...
In contrast, in a study of codon usage in Phytoplasmas, it was found that 30 out of 78 open reading frames analysed translated better with this code (UGA for tryptophan) than with the bacterial, archaeal and plant plastid code while the remainder showed no differences between the two codes (Melamed et al. 2003).
A simple gene prediction algorithm for prokaryotes might look for a start codon followed by an open reading frame that is long enough to encode a typical protein, where the codon usage of that region matches the frequency characteristic for the given organism's coding regions. [5]