Local alignments like global alignments, but they generate "islands" of areas that have the greatest similarity. This is helpful when the query and sequence are dissimilar, but are suspected to contain domains or small regions of similarity. The BLAST algorithm uses local alignment.
Local alignments differ from global alignments in a few ways:
The Smith-Waterman algorithm is very much like the Needleman-Wunsch algorithm used in global alignments - the hallmark difference is in the scoring methodology.
Unlike global alignment, local alignments have no end gap penalties, allowing small interior alignments to rank higher when scored.
Let's take a quick look at the effects of end gap penalties. The following sequence is aligned globally, with high end gap penalties.
M - N A L S D R T M G S D R T T E T 6 -12 1 0 -3 1 0 -1 3 = -5
Now in this next sequence, we have a local alignment. Notice how the small region in the middle aligns quite nicely.
M N A L S D R T - - - - - M G S D R T T E T 0 0 -1 -4 2 4 6 3 0 0 0 = 10
Without the end gap penalty, the Smith-Waterman alignment algorithm is able to find the best locally matching sequence.
Let's compare two sequences - CGTTCTA and AACGTTGG.
Set up a 2d matrix, as we did earlier in the Needleman-Wunsch example.
We need separate scores for matches, mismatches and gaps.
Any cell that would have a negative value are given 0 instead.
We want to start with the first row and column and gives those a value of 0. Then we want to mark the cells that indicates a match.
Now we fill the rest of our table out. Make sure to keep track of where each cell value came from, as we need this to trace back our optimal alignment.
Note that a mismatch or a match can only come from the cell diagonally up to the left of the current cell. Additionally, gaps may only come from the top or left of the current cell.
Now all we need to do is retrace our steps. First, find the cell with the highest score.
Now we trace back until we get to a cell with 0. Thus, our optimal local alignment becomes:
Thus, we may say that for global alignments, where the sequences are connected along the entire length of their sequences, there is a higher % identity with many small interior gaps. For local alignments, which focus on the best matching regions, there is a lower % identity, but fewer interior gaps and longer end gaps.
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