G+C Content | R Documentation |

Calculates the fraction of G+C bases of the input nucleic acid
sequence(s). It reads in nucleic acid sequences, sums the number of
'g' and 'c' bases and writes out the result as the fraction (in the
interval 0.0 to 1.0) to the total number of 'a', 'c', 'g' and 't' bases.
Global G+C content `GC`

, G+C in the first position of the codon bases
`GC1`

, G+C in the second position of the codon bases
`GC2`

, and G+C in the third position of the codon bases
`GC3`

can be computed. All functions can take ambiguous bases
into account when requested.

GC(seq, forceToLower = TRUE, exact = FALSE, NA.GC = NA, oldGC = FALSE, alphabet = s2c("acgtswmkryvhdb")) GC1(seq, frame = 0, ...) GC2(seq, frame = 0, ...) GC3(seq, frame = 0, ...) GCpos(seq, pos, frame = 0, ...)

`seq` |
a nucleic acid sequence as a vector of single characters |

`frame` |
for coding sequences, an integer (0, 1, 2) giving the frame |

`forceToLower` |
logical. if |

`exact` |
logical: if |

`NA.GC` |
what should be returned when the GC is impossible to
compute from data, for instance with NNNNNNN. This behaviour could
be different when argument |

`...` |
arguments passed to the function |

`pos` |
for coding sequences, the codon position (1, 2, 3) that should be taken into account to compute the G+C content |

`oldGC` |
logical defaulting to |

`alphabet` |
alphabet used. This allows you to choose ambiguous bases used during GC calculation. |

When `exact`

is set to `TRUE`

the G+C content is estimated
with ambiguous bases taken into account. Note that this is time expensive.
A first pass is made on non-ambiguous bases to estimate the probabilities
of the four bases in the sequence. They are then used to weight the
contributions of ambiguous bases to the G+C content. Let note nx
the total number of base 'x' in the sequence. For instance
suppose that there are nb bases 'b'. 'b' stands for "not a", that
is for 'c', 'g' or 't'. The contribution of 'b' bases to the GC base
count will be:

nb*(nc + ng)/(nc + ng + nt)

The contribution of 'b' bases to the AT base count will be:

nb*nt/(nc + ng + nt)

All ambiguous bases contributions to the AT and GC counts are weighted is similar way and then the G+C content is computed as ngc/(nat + ngc).

`GC`

returns the fraction of G+C (in [0,1]) as a numeric vector of length one.
`GCpos`

returns GC at position `pos`

.
`GC1`

, `GC2`

, `GC3`

are wrappers for `GCpos`

with the
argument `pos`

set to 1, 2, and 3, respectively.
`NA`

is returned when `seq`

is `NA`

.
`NA.GC`

defaulting to `NA`

is returned when the G+C content
can not be computed from data.

D. Charif, L. Palmeira, J.R. Lobry

`citation("seqinr")`

.

The program codonW used here for comparison is available at http://codonw.sourceforge.net/.

You can use `s2c`

to convert a string into a vetor of single
character and `tolower`

to convert upper-case characters into
lower-case characters. Do not confuse with `gc`

for garbage collection.

mysequence <- s2c("agtctggggggccccttttaagtagatagatagctagtcgta") GC(mysequence) # 0.4761905 GC1(mysequence) # 0.6428571 GC2(mysequence) # 0.3571429 GC3(mysequence) # 0.4285714 # # With upper-case characters: # myUCsequence <- s2c("GGGGGGGGGA") GC(myUCsequence) # 0.9 # # With ambiguous bases: # GC(s2c("acgt")) # 0.5 GC(s2c("acgtssss")) # 0.5 GC(s2c("acgtssss"), exact = TRUE) # 0.75 # # Missing data: # stopifnot(is.na(GC(s2c("NNNN")))) stopifnot(is.na(GC(s2c("NNNN"), exact = TRUE))) stopifnot(is.na(GC(s2c("WWSS")))) stopifnot(GC(s2c("WWSS"), exact = TRUE) == 0.5) # # Coding sequences tests: # cdstest <- s2c("ATGATG") stopifnot(GC3(cdstest) == 1) stopifnot(GC2(cdstest) == 0) stopifnot(GC1(cdstest) == 0) # # 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 (there are no ambiguous base in these # sequences). # inputdatfile <- system.file("sequences/input.dat", package = "seqinr") input <- read.fasta(file = inputdatfile) # read the FASTA file inputoutfile <- system.file("sequences/input.out", package = "seqinr") input.res <- read.table(inputoutfile, header = TRUE) # read codonW result file # # remove stop codon before computing G+C content (as in codonW) # GC.codonW <- function(dnaseq, ...){ GC(dnaseq[seq_len(length(dnaseq) - 3)], ...) } input.gc <- sapply(input, GC.codonW, forceToLower = FALSE) max(abs(input.gc - input.res$GC)) # 0.0004946237 plot(x = input.gc, y = input.res$GC, las = 1, xlab = "Results with GC()", ylab = "Results from codonW", main = "Comparison of G+C content results") abline(c(0, 1), col = "red") legend("topleft", inset = 0.01, legend = "y = x", lty = 1, col = "red") ## Not run: # Too long for routine check # This is a benchmark to compare the effect of various parameter # setting on computation time n <- 10 from <-10^4 to <- 10^5 size <- seq(from = from, to = to, length = n) res <- data.frame(matrix(NA, nrow = n, ncol = 5)) colnames(res) <- c("size", "FF", "FT", "TF", "TT") res[, "size"] <- size for(i in seq_len(n)){ myseq <- sample(x = s2c("acgtws"), size = size[i], replace = TRUE) res[i, "FF"] <- system.time(GC(myseq, forceToLower = FALSE, exact = FALSE))[3] res[i, "FT"] <- system.time(GC(myseq, forceToLower = FALSE, exact = TRUE))[3] res[i, "TF"] <- system.time(GC(myseq, forceToLower = TRUE, exact = FALSE))[3] res[i, "TT"] <- system.time(GC(myseq, forceToLower = TRUE, exact = TRUE))[3] } par(oma = c(0,0,2.5,0), mar = c(4,5,0,2) + 0.1, mfrow = c(2, 1)) plot(res$size, res$TT, las = 1, xlab = "Sequence size [bp]", ylim = c(0, max(res$TT)), xlim = c(0, max(res$size)), ylab = "") title(ylab = "Observed time [s]", line = 4) abline(lm(res$TT~res$size)) points(res$size, res$FT, col = "red") abline(lm(res$FT~res$size), col = "red", lty = 3) points(res$size, res$TF, pch = 2) abline(lm(res$TF~res$size)) points(res$size, res$FF, pch = 2, col = "red") abline(lm(res$FF~res$size), lty = 3, col = "red") legend("topleft", inset = 0.01, legend = c("forceToLower = TRUE", "forceToLower = FALSE"), col = c("black", "red"), lty = c(1,3)) legend("bottomright", inset = 0.01, legend = c("exact = TRUE", "exact = FALSE"), pch = c(1,2)) mincpu <- lm(res$FF~res$size)$coef[2] barplot( c(lm(res$FF~res$size)$coef[2]/mincpu, lm(res$TF~res$size)$coef[2]/mincpu, lm(res$FT~res$size)$coef[2]/mincpu, lm(res$TT~res$size)$coef[2]/mincpu), horiz = TRUE, xlab = "Increase of CPU time", col = c("red", "black", "red", "black"), names.arg = c("(F,F)", "(T,F)", "(F,T)", "(T,T)"), las = 1) title(ylab = "forceToLower,exact", line = 4) mtext("CPU time as function of options", outer = TRUE, line = 1, cex = 1.5) ## End(Not run)

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