Codon usage indices

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Description

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

Usage

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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.

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 (Perrière and Thioulouse 2002, Suzuki et al. 2008). Within-aminoacid correspondence analysis is a simple way to study synonymous codon usage (Charif et al. 2005). For an introduction to correspondence analysis and within-aminoacid correspondence analysis see the chapter titled Multivariate analyses in the seqinR manual that ships with the seqinR package in the doc folder. You can also use internal correspondence analysis if you want to analyze simultaneously a row-block structure such as the within and between species variability (Lobry and Chessel 2003).

If as.data.frame is FALSE, uco returns one of these:

eff

a table of codon counts

freq

a table of codon relative frequencies

rscu

a numeric vector of relative synonymous codon usage values

If as.data.frame is TRUE, uco returns a data frame with five columns:

aa

a vector containing the name of amino-acid

codon

a vector containing the corresponding codon

eff

a numeric vector of codon counts

freq

a numeric vector of codon relative frequencies

rscu

a numeric vector of RSCU index

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.

Author(s)

D. Charif, J.R. Lobry, G. Perrière

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.

Perrière, G., Thioulouse, J. (2002) Use and misuse of correspondence analysis in codon usage studies. Nucl. Acids. Res., 30:4548-4555.

Lobry, J.R., Chessel, D. (2003) Internal correspondence analysis of codon and amino-acid usage in thermophilic bacteria. Journal of Applied Genetics, 44:235-261. http://jag.igr.poznan.pl/2003-Volume-44/2/pdf/2003_Volume_44_2-235-261.pdf.

Charif, D., Thioulouse, J., Lobry, J.R., Perrière, G. (2005) Online Synonymous Codon Usage Analyses with the ade4 and seqinR packages. Bioinformatics, 21:545-547. https://pbil.univ-lyon1.fr/members/lobry/repro/bioinfo04/.

Suzuki, H., Brown, C.J., Forney, L.J., Top, E. (2008) Comparison of Correspondence Analysis Methods for Synonymous Codon Usage in Bacteria. DNA Research, 15:357-365. http://dnaresearch.oxfordjournals.org/cgi/reprint/15/6/357.

Examples

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## 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 happens 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 happens 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)

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