seq[[j]],type= local ,substitutionMatrix=S,

gapOpening=-1.3,gapExtension=-0.3)@score

using the above code, the score matrix was converted to a

matrix using a simple method shown below,

D=max(score)-score

d on this distance matrix, a hierarchical cluster model was

ed to generate a hierarchical cluster tree using the hclust

which has been introduced in Chapter 2 of this book. Figure 7.4(a)

ch a tree.

wards, a function called cluster in the k-mer package was

enerate a clustal tree. Figure 7.4(b) shows such a clustal

can be seen that two trees are very similar. The discovered

hip between sequences in these two trees were identical. For

both trees show that the first sequence and the fourth sequence

rged at first while the fifth sequence was the last one to join the

(a) (b)

he hierarchical trees generated for the five sequences. (a) The tree generated

clust function based on the pairwise alignment scores calculated using the

erman algorithm. (b) The tree generated by the cluster function in the k-

ge.

msa function is another package for multiple sequence

on. Several R functions of Biostrings are required to define

ce object before using the R function of msa for an alignment