bio3d (version 2.4-4)

filter.identity: Percent Identity Filter

Description

Identify and filter subsets of sequences at a given sequence identity cutoff.

Usage

filter.identity(aln = NULL, ide = NULL, cutoff = 0.6, verbose = TRUE, ...)

Value

Returns a list object with components:

ind

indices of the sequences below the cutoff value.

tree

an object of class "hclust", which describes the tree produced by the clustering process.

ide

a numeric matrix with all pairwise identity values.

Arguments

aln

sequence alignment list, obtained from seqaln or read.fasta, or an alignment character matrix. Not used if ‘ide’ is given.

ide

an optional identity matrix obtained from seqidentity.

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.

Author

Barry Grant

Details

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’.

References

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

See Also

read.fasta, seqaln, seqidentity, entropy, consensus

Examples

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)