cubfits (version 0.1-2)

Codon Adaptation Index: Function for Codon Adaptation Index (CAI)

Description

Calculate the Codon Adaptation Index (CAI) for each gene. Used as a substitute for expression in cases of without expression measurements.

Usage

calc_cai_values(y, y.list, w = NULL)

Arguments

y

an object of format y.

y.list

an object of format y.list.

w

a specified relative frequency of synonymous codons.

Value

A list with two named elements CAI and w are returned where CAI are CAI of input sequences (y and y.list) and w are the relative frequencey used to computed those CAI's.

Details

This function computes CAI for each gene. Typically, this method is completely based on entropy and information theory to estimate expression values of sequences according to their codon information.

If the input w is NULL, then empirical values are computed.

References

Sharp P.M. and Li W.-H. ``The codon Adaptation Index -- a measure of directional synonymous codon usage bias, and its potential applications'' Nucleic Acids Res. 15 (3): 1281-1295, 1987.

See Also

calc_scuo_values(), calc_scu_values().

Examples

## Not run: 
# rm(list = ls())
# library(cubfits, quietly = TRUE)
# 
# y <- ex.train$y
# y.list <- convert.y.to.list(y)
# CAI <- calc_cai_values(y, y.list)$CAI
# plot(CAI, log10(ex.train$phi.Obs), main = "Expression vs CAI",
#      xlab = "CAI", ylab = "Expression (log10)")
# 
# ### Verify with the seqinr example.
# library(seqinr, quietly = TRUE)
# inputdatfile <- system.file("sequences/input.dat", package = "seqinr")
# input <- read.fasta(file = inputdatfile, forceDNAtolower = FALSE)
# names(input)[65] <- paste(names(input)[65], ".1", sep = "") # name duplicated.
# input <- input[order(names(input))]
# 
# ### Convert to cubfits format.
# seq.string <- convert.seq.data.to.string(input)
# new.y <- gen.y(seq.string)
# new.y.list <- convert.y.to.list(new.y)
# ret <- calc_cai_values(new.y, new.y.list)
# 
# ### Rebuild w.
# w <- rep(1, 64)
# names(w) <- codon.low2up(rownames(caitab))
# for(i in 1:64){
#   id <- which(names(ret$w) == names(w)[i])
#   if(length(id) == 1){
#     w[i] <- ret$w[id]
#   }
# }
# CAI.res <- sapply(input, seqinr::cai, w = w)
# 
# ### Plot.
# plot(CAI.res, ret$CAI,
#      main = "Comparison of seqinR and cubfits results",
#      xlab = "CAI from seqinR", ylab = "CAI from cubfits", las = 1)
# abline(c(0, 1))
# ## End(Not run)