As a rule of thumb, if one hundred random alignments have score inferior to the alignment of interest, the p-value in question is likely less that 0.01. Isolated regions of similarity due to non-uniform evolutionary patterns including insertions, deletions, shuffling and other mutational events, may result in artifacts. Unfortunately, under even the simplest random models and scoring systems, very little is known about the random distribution of optimal global alignment scores. Monte Carlo experiments may provide rough distributional results for some specific scoring systems and sequence compositions, but these can not be generalized easily. Except from global sequence alignments, local sequence alignment algorithms are important for similarity searching since large insertions and deletions, exon shuffling and the evolutionary conservation of DNA segments crucial to structure or function of the corresponding protein can often be confined to small regions within large sequences.
Global alignment can easily overlook such similarities.
The main difficulty in this approach is that the size of the window must be specified a priori . If the similarity of interest is substantially longer than a chosen width, the comparison might yield artifactual weak similarity. In contrast, if the chosen width is too long, the scores for extra unrelated base pairs may degrade the signal. Thus, the length of the window should be specified based on the data. Dot plots based on the sliding window methodology are frequently used because they are simpler to program and provide a more comprehensive visualization of the alignment.