# GC: Calculates the fractional G+C content of nucleic acid... In seqinr: Biological Sequences Retrieval and Analysis

## Description

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.

## Usage

 ```1 2 3 4 5 6``` ```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, ...) ```

## Arguments

 `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 `TRUE` force sequence characters in lower-case. Turn this to `FALSE` to save time if your sequence is already in lower-case (cpu time is approximately divided by 3 when turned off) `exact` logical: if `TRUE` ambiguous bases are taken into account when computing the G+C content (see details). Turn this to `FALSE` to save time if your you can neglect ambiguous bases in your sequence (cpu time is approximately divided by 3 when turned off) `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 `exact` is `TRUE`, for instance the G+C content of WWSS is `NA` by default, but is 0.5 when `exact` is set to `TRUE` `...` arguments passed to the function `GC` `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 `FALSE`: should the GC content computed as in seqinR <= 1.0-6, that is as the sum of 'g' and 'c' bases divided by the length of the sequence. As from seqinR >= 1.1-3, this argument is deprecated and a warning is issued. `alphabet` alphabet used. This allows you to choose ambiguous bases used during GC calculation.

## Details

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).

## Value

`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.

## Author(s)

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

## References

`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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109``` ``` 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)) res[i, "FT"] <- system.time(GC(myseq, forceToLower = FALSE, exact = TRUE)) res[i, "TF"] <- system.time(GC(myseq, forceToLower = TRUE, exact = FALSE)) res[i, "TT"] <- system.time(GC(myseq, forceToLower = TRUE, exact = TRUE)) } 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 barplot( c(lm(res\$FF~res\$size)\$coef/mincpu, lm(res\$TF~res\$size)\$coef/mincpu, lm(res\$FT~res\$size)\$coef/mincpu, lm(res\$TT~res\$size)\$coef/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) ```

### Example output  ``` 0.4761905
 0.6428571
 0.3571429
 0.4285714
 0.9
 0.5
 0.5
 0.75
 0.0004946237
```

seqinr documentation built on June 9, 2021, 9:06 a.m.