entropy {bio3d} | R Documentation

## Shannon Entropy Score

### Description

Calculate the sequence entropy score for every position in an alignment.

### Usage

    entropy(alignment)

### Arguments

`alignment` | sequence alignment returned from `read.fasta` or an alignment character matrix.

### Details

Shannon's information theoretic entropy (Shannon, 1948) is an often-used measure of residue diversity and hence residue conservation.

### Value

Returns a list with five components:

`H` | standard entropy score for a 22-letter alphabet.
`H.10` | entropy score for a 10-letter alphabet (see below).
`H.norm` | normalized entropy score (for 22-letter alphabet), so that conserved (low entropy) columns (or positions) score 1, and diverse (high entropy) columns score 0.
`H.10.norm` | normalized entropy score (for 10-letter alphabet), so that conserved (low entropy) columns score 1 and diverse (high entropy) columns score 0.
`freq` | residue frequency matrix containing percent occurrence values for each residue type.

### Note

In addition to the standard entropy score (based on a 22-letter alphabet of the 20 standard amino-acids, plus a gap character ‘-’ and a mask character ‘X’), an entropy score, `H.10`, based on a 10-letter alphabet is also returned.

For `H.10`, residues from the 22-letter alphabet are classified into one of 10 types, loosely following the convention of Mirny and Shakhnovich (1999): Hydrophobic/Aliphatic [V,I,L,M], Aromatic [F,W,Y], Ser/Thr [S,T], Polar [N,Q], Positive [H,K,R], Negative [D,E], Tiny [A,G], Proline [P], Cysteine [C], and Gaps [-,X].

The residue code ‘X’ is useful for handling non-standard aminoacids.

### Author(s)

Barry Grant

### References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.

Shannon (1948) The System Technical J. 27, 379–422.

Mirny and Shakhnovich (1999) J. Mol. Biol. 291, 177–196.

### See Also

`consensus`, `read.fasta`

### Examples

    # Read HIV protease alignment
    aln <- read.fasta(system.file("examples/hivp_xray.fa",package="bio3d"))

    # Entropy and consensus
    h   <- entropy(aln)
    con <- consensus(aln)

    names(h$H)=con$seq
    print(h$H)

    # Entropy for sub-alignment (positions 1 to 20)
    h.sub <- entropy(aln$ali[,1:20])

    # Plot entropy and residue frequencies (excluding positions >=60 percent gaps)
    H <- h$H.norm
    H[ apply(h$freq[21:22,],2,sum)>=0.6 ] = 0

    col <- mono.colors(32)
    aa  <- rev(rownames(h$freq))
    oldpar <- par(no.readonly=TRUE)
    layout(matrix(c(1,2),2,1,byrow = TRUE), widths = 7,
           heights = c(2, 8), respect = FALSE)

    # Plot 1: entropy
    par(mar = c(0, 4, 2, 2))
    barplot(H, border="white", ylab = "Entropy",
            space=0, xlim=c(3.7, 97.3),yaxt="n" )
    axis(side=2, at=c(0.2,0.4, 0.6, 0.8))
    axis(side=3, at=(seq(0,length(con$seq),by=5)-0.5),
         labels=seq(0,length(con$seq),by=5))
    box()

    # Plot2: residue frequencies
    par(mar = c(5, 4, 0, 2))
    image(x=1:ncol(con$freq),
          y=1:nrow(con$freq),
          z=as.matrix(rev(as.data.frame(t(con$freq)))),
          col=col, yaxt="n", xaxt="n",
          xlab="Alignment Position", ylab="Residue Type")
    axis(side=1, at=seq(0,length(con$seq),by=5))
    axis(side=2, at=c(1:22), labels=aa)
    axis(side=3, at=c(1:length(con$seq)), labels =con$seq)
    axis(side=4, at=c(1:22), labels=aa)
    grid(length(con$seq), length(aa))
    box()

    for(i in 1:length(con$seq)) {
      text(i, which(aa==con$seq[i]),con$seq[i],col="white")
    }
    abline(h=c(3.5, 4.5, 5.5, 3.5, 7.5, 9.5,
             12.5, 14.5, 16.5, 19.5), col="gray")

    par(oldpar)

[Package bio3d version 2.3-4 Index]