Codon Adaptation Index Calculator
Free Codon adaptation index Calculator for bioinformatics. Enter variables to compute results with formulas and detailed steps.
Reviewed by Daniel Agrici, Founder & Lead Developer
Formula
CAI = exp((1/L) * sum(ln(w_i)))
Where L is the number of codons in the sequence (excluding stop codons), w_i is the relative adaptiveness of each codon (ratio of codon frequency to the maximum frequency among synonymous codons in highly expressed genes), and the calculation takes the geometric mean of all w_i values.
Worked Examples
Example 1: E. coli Gene CAI Analysis
Problem:Analyze the codon adaptation of a short E. coli gene fragment: ATGAAAGCAATTTTCGTACTGAAAGGTTTTACCTTTACTGAG
Solution:Codons: ATG-AAA-GCA-ATT-TTC-GTA-CTG-AAA-GGT-TTT-ACC-TTT-ACT-GAG\nLook up w values for E. coli:\nATG=1.000, AAA=1.000, GCA=0.692, ATT=0.465, TTC=1.000, GTA=0.385, CTG=1.000, AAA=1.000, GGT=1.000, TTT=0.296, ACC=1.000, TTT=0.296, ACT=0.500, GAG=0.259\nCAI = exp(mean(ln(w_i))) = exp(mean of all log values)\nGeometric mean calculation yields CAI value
Result:CAI: ~0.61 | 14 codons | GC%: ~40% | Moderate optimization
Example 2: Human Codon Optimization Check
Problem:Check if the same sequence ATGAAAGCAATTTTCGTACTGAAAGGTTTTACCTTTACTGAG is well-adapted for human expression.
Solution:Codons: ATG-AAA-GCA-ATT-TTC-GTA-CTG-AAA-GGT-TTT-ACC-TTT-ACT-GAG\nLook up w values for Human:\nATG=1.000, AAA=0.432, GCA=0.583, ATT=0.536, TTC=1.000, GTA=0.167, CTG=1.000, AAA=0.432, GGT=0.333, TTT=0.458, ACC=1.000, TTT=0.458, ACT=0.560, GAG=1.000\nGeometric mean of human w values\nLower CAI expected due to different codon preferences
Result:CAI: ~0.55 | Human-adapted | Suboptimal - optimization recommended
Frequently Asked Questions
What is the Codon Adaptation Index (CAI) and what does it measure?
The Codon Adaptation Index (CAI) is a quantitative measure of codon usage bias that evaluates how well a gene's codon usage matches the optimal codon preferences of a target organism. Developed by Sharp and Li in 1987, CAI values range from 0 to 1, where 1 indicates that every codon in the gene is the most frequently used codon for its amino acid in highly expressed genes of the organism. A higher CAI generally predicts higher protein expression levels. The index is calculated as the geometric mean of the relative adaptiveness values for all codons in the gene, making it sensitive to the overall pattern of codon usage rather than individual rare codons.
Why is codon optimization important for recombinant protein expression?
Codon optimization is critical for recombinant protein expression because different organisms have different preferences for which codons encode each amino acid, reflecting differences in tRNA abundance. When a gene from one organism is expressed in another (heterologous expression), rare codons can cause ribosome stalling, premature translation termination, frameshifting errors, and significantly reduced protein yields. By replacing codons with those preferred by the host organism, translation efficiency can increase dramatically, sometimes by 10 to 100 fold. However, codon optimization must be balanced carefully because some rare codons serve important regulatory functions, and overly aggressive optimization can sometimes cause protein misfolding.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy