seqinr (version 1.0-6)

uco: Codon usage indices

Description

uco calculates some codon usage indices: the codon counts eff, the relative frequencies freq or the Relative Synonymous Codon Usage rscu.

Usage

uco(seq, frame = 0, index = c("eff", "freq", "rscu"), as.data.frame = FALSE,
NA.rscu = NA)

Arguments

seq

a coding sequence as a vector of chars

frame

an integer (0, 1, 2) giving the frame of the coding sequence

index

codon usage index choice, partial matching is allowed. eff for codon counts, freq for codon relative frequencies, and rscu the RSCU index

as.data.frame

logical. If TRUE: all indices are returned into a data frame.

NA.rscu

when an amino-acid is missing, RSCU are no more defined and repported as missing values (NA). You can force them to another value (typically 0 or 1) with this argument.

Value

• If as.data.frame is FALSE, the default, a table for eff and freq and a numeric vector for rscu. If as.data.frame is TRUE, a data frame with all indices is returned.

Details

Codons with ambiguous bases are ignored. RSCU is a simple measure of non-uniform usage of synonymous codons in a coding sequence (Sharp et al. 1986). RSCU values are the number of times a particular codon is observed, relative to the number of times that the codon would be observed for a uniform synonymous codon usage (i.e. all the codons for a given amino-acid have the same probability). In the absence of any codon usage bias, the RSCU values would be 1.00 (this is the case for sequence cds in the exemple thereafter). A codon that is used less frequently than expected will have an RSCU value of less than 1.00 and vice versa for a codon that is used more frequently than expected. Do not use correspondence analysis on RSCU tables as this is a source of artifacts (Perriere and Thioulouse 2002). Within-aminoacid correspondence analysis is a simple way to study synonymous codon usage (Charif et al. 2005). If as.data.frame is FALSE, uco returns one of these: [object Object],[object Object],[object Object] If as.data.frame is TRUE, uco returns a data frame with five columns: [object Object],[object Object],[object Object],[object Object],[object Object]

References

citation("seqinr") Sharp, P.M., Tuohy, T.M.F., Mosurski, K.R. (1986) Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes. Nucl. Acids. Res., 14:5125-5143. Perriere, G., Thioulouse, J. (2002) Use and misuse of correspondence analysis in codon usage studies. Nucl. Acids. Res., 30:4548-4555. Charif, D., Thioulouse, J., Lobry, J.R., Perriere, G. (2005) Online Synonymous Codon Usage Analyses with the ade4 and seqinR packages. Bioinformatics, 21:545-547. http://pbil.univ-lyon1.fr/members/lobry/repro/bioinfo04/.

Examples

## Show all possible codons:
words()

## Make a coding sequence from this:
(cds <- s2c(paste(words(), collapse = "")))

## Get codon counts:
uco(cds, index = "eff")

## Get codon relative frequencies:
uco(cds, index = "freq")

## Get RSCU values:
uco(cds, index = "rscu")

## Show what's happen with ambiguous bases:
uco(s2c("aaannnttt"))

## Use a real coding sequence:
rcds <- read.fasta(File = system.file("sequences/malM.fasta", package = "seqinr"))[[1]]
uco( rcds, index = "freq")
uco( rcds, index = "eff")
uco( rcds, index = "rscu")
uco( rcds, as.data.frame = TRUE)

## Show what's happen with RSCU when an amino-acid is missing:
ecolicgpe5 <- read.fasta(file = system.file("sequences/ecolicgpe5.fasta",package="seqinr"))[[1]]
uco(ecolicgpe5, index = "rscu")

## Force NA to zero:
uco(ecolicgpe5, index = "rscu", NA.rscu = 0)