Percent Identity Filter
Identify and filter subsets of sequences at a given sequence identity cutoff.
filter.identity(aln = NULL, ide = NULL, cutoff = 0.6, verbose = TRUE, ...)
aln |
sequence alignment list, obtained from
|
ide |
an optional identity matrix obtained from
|
cutoff |
a numeric identity cutoff value ranging between 0 and 1. |
verbose |
logical, if TRUE print details of the clustering process. |
... |
additional arguments passed to and from functions. |
This function performs hierarchical cluster analysis of a given sequence identity matrix ‘ide’, or the identity matrix calculated from a given alignment ‘aln’, to identify sequences that fall below a given identity cutoff value ‘cutoff’.
Returns a list object with components:
ind |
indices of the sequences below the cutoff value. |
tree |
an object of class |
ide |
a numeric matrix with all pairwise identity values. |
Barry Grant
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
attach(kinesin)
ide.mat <- seqidentity(pdbs)
# Histogram of pairwise identity values
op <- par(no.readonly=TRUE)
par(mfrow=c(2,1))
hist(ide.mat[upper.tri(ide.mat)], breaks=30,xlim=c(0,1),
main="Sequence Identity", xlab="Identity")
k <- filter.identity(ide=ide.mat, cutoff=0.6)
ide.cut <- seqidentity(pdbs$ali[k$ind,])
hist(ide.cut[upper.tri(ide.cut)], breaks=10, xlim=c(0,1),
main="Sequence Identity", xlab="Identity")
#plot(k$tree, axes = FALSE, ylab="Sequence Identity")
#print(k$ind) # selected
par(op)
detach(kinesin)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.