sequences to an S4 object for using the msa function. The code

below:

mySeq=DNAStringSet(seq)

all of the msa function generated a multiple sequence comparison

ing the following code,

model=msa(mySeq)

msa function also derives a consensus sequence for sequences

gnment. To show the consensus sequence for these five sequences,

wing code was used,

show(model)

utcome of the above code is shown below, where the last line

e consensus sequence for these five sequences.

names

ATCAGATGTATGGACCCG 1

ATTTGATGTATGGACCCG 2

ATCAGATGTATCCACCCG 3

ATCACATGTATGGACCCG 4

TAACCAATATCGCTTCT 5

ATCAGATGTATGGACCCG Consensus

ollowing R code was used to show a consensus sequence based

gnment,

msaConsensusSequence(model)

function consensusMatrix can be used to show how

s contributed to each residue. The msa package provides

for using multiple sequence alignment algorithms such as

l series and Muscle. The functions include msaClustalW,

talOmega and msaMuscle.

lignment result can be further visualised using the R package

ggmsa. Figure 7.5 shows the alignment pattern for the above

ed sequences. In this plot, the conserved residues were displayed