The Codon Adaptation Index (Sharp and Li 1987) is the most popular
index of gene expressivity with about 1000 citations 20 years after its
publication. Its values range from 0 (low) to 1 (high). The implementation
here is intended to work exactly as in the program
codonW written by
by John Peden during his PhD thesis under the supervision of P.M. Sharp.
cai(seq, w, numcode = 1, zero.threshold = 0.0001, zero.to = 0.01)
a coding sequence as a vector of single characters
a vector for the relative adaptiveness of each codon
the genetic code number as in
a value in
a value considered as zero in
Adapted from the documentation of the CAI function in the
codonW writen by John Peden:
CAI is a measurement
of the relative adaptiveness of the codon usage of a gene towards the
codon usage of highly expressed genes. The relative adaptiveness (w) of
each codon is the ratio of the usage of each codon, to that of the most
abundant codon for the same amino acid. The CAI
index is defined as the geometric mean of these relative adaptiveness
values. Non-synonymous codons and termination codons (genetic code
dependent) are excluded. To aid computation, the CAI is calculated as
using a natural log summation, To prevent a codon having a relative
adaptiveness value of zero, which could result in a CAI of zero;
these codons have fitness of zero (<.0001) are adjusted to 0.01.
A single numerical value for the CAI.
Sharp, P.M., Li, W.-H. (1987) The codon adaptation index - a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Research, 15:1281-1295.
Bulmer, M. (1988). Are codon usage patterns in unicellular organisms determined by selection-mutation balance. Journal of Evolutionary Biology, 1:15-26.
Peden, J.F. (1999) Analysis of codon usage. PhD Thesis, University of Nottingham, UK.
codonW used here for comparison is available at
http://codonw.sourceforge.net/ under a GPL licence.
caitab for some
w values from
uco for codon usage tabulation.
# # How to reproduce the results obtained with the C program codonW # version 1.4.4 writen by John Peden. We use here the "input.dat" # test file from codonW (Saccharomyces cerevisiae). # inputdatfile <- system.file("sequences/input.dat", package = "seqinr") input <- read.fasta(file = inputdatfile) # read the FASTA file # # Import results obtained with codonW # scucofile <- system.file("sequences/scuco.txt", package = "seqinr") scuco.res <- read.table(scucofile, header = TRUE) # read codonW result file # # Use w for Saccharomyces cerevisiae # data(caitab) w <- caitab$sc # # Compute CAI and compare results: # cai.res <- sapply(input, cai, w = w) plot(cai.res, scuco.res$CAI, main = "Comparison of seqinR and codonW results", xlab = "CAI from seqinR", ylab = "CAI from codonW", las = 1) abline(c(0,1))
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