Pairwise Sequence Alignment PDF Print E-mail
Written by Nelzo Ereful   
Friday, 23 November 2007

Pairwise sequence alignment (PSA) is used to compare two biological sequences (nucleic acid or protein). It reveals the similarity or homology of the two sequences. The purpose of pairwise sequence alignment is to show where two sequences are similar, and where they differ. As an initial warning, there is no unique, precise, or universally applicable notion of similarity. Consider the words "weak", "week", "wick" and "wake". They sound very similar (if not the same), somewhat similar in their spelling, but they are totally unrelated in their meaning. This is similar to molecular biology. Biological sequences can be similar with respect to their function, their structure, or their primary sequence of amino or nucleic acids. However, there are situations where two sequences have little or no similarity.

Because of the capability of sequence alignment to show similarity among sequences, it allows the elucidation of common ancestral origins among organisms. In this context, comparison is understood to be based on the criteria of evolution. It is important to note that analysis of evolutionary relationships between protein or gene sequences depends critically on sequence alignments. Along with the elucidation of evolutionary relationships among sequences, one can infer whether two proteins have similar function or contain similar structural motifs.



One of the simplest ways of comparing two sequences with one another is by visual method called a dotplot.
Sample dotplot output
Making dotplot is similar to playing two words placed horizontally and vertically (see figure below) and crossing the cell where the two letters of the sentences intersect each other. It is a simple method of comparing sequences and does not need any biological hypothesis. The only device you need in interpreting dot plots is your eyes.
After you cross out the appropriate cells, you'll come up with something like the one below. You may draw a line showing significant alignments, with unaligned residues in the space. The gaps represent that no characters are common on both sequences. A sample output of an on-line submission for dot plot processing is shown at the box at the right side. Of course, as the number of letters or sequences increase, this manual method of comparison becomes impractical. You need the computer to do it for you.

There are a number of Dotplot programs on the internet. These include Dotlet, Dnadot, Dotter and Dottup. Discussions of Dotplot programs is beyond the scope of this module. The student is referred to the various sites where these programs are available.

     T    R   O   P   I    C    A   L   H    U    T

T X                                                         X

O              X

P                   X

I                        X

C                             X

A                                   X

L                                       X

H                                            X

A                            X

T   X                                                     X


Global vs local sequence alignments

There are fundamentally two kinds of alignment: global and local. In global alignment, both sequences are aligned along their entire lengths which includes all characters from each sequence and the best alignment is found. In local alignment, the best subsequence alignment which includes only the most similar local region or regions is found. For example, if you want to find the two most similar sentences between two books, you use local alignment. If you want to compare the sentences end to end, use global alignment.

In global alignment, all amino acids or nucleotides are aligned either with other amino acids or nucleotides and no amino acids or nucleotides is discarded. Local alignment uses the most similar segments between sequences and ignores the rest. It is used to compare two distantly related sequences that share only a few non-contiguous domain.s


Global vs local alignments from[1]



Image:Pdf2.png BLAST AND FASTA from RAI Open Courseware.

Image:Nuvola_apps_kpdf.png Claverie, JM and C. Notredame. 2003. Bioinformatics for Dummies. Wiley Publishing Inc.

Image:Html2.png NCBI BLAST Tutorials.

Image:Nuvola_apps_kpdf.png Fox, J. Understanding web-based BLAST

Last Updated ( Sunday, 30 December 2007 )
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