entropy {bio3d}

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)