This is known for segments of sequences which are biased in composition over a wide range of length scales. A common practice is to mask these regions before applying an alignment algorithm. Finally, there is a variety of methods for database searching when an alignment against known sequences is performed. Most widely used programs for database similarity searching use local alignment algorithms that are variations on the Smith-Waterman algorithm. Specifically for local multiple alignments, different approaches can yield different results.
Template methods require a template pattern to compare with each of the input sequences.
Progressive alignment methods are analogous to the corresponding methods in global sequence alignment discussed above. The aim is to maximize the ratio probability that the segments found arise from the implied model to the probability that they arise from background residue probabilities. Any of the above methods can be used and has different advantages and disadvantages. In conclusion, the search for DNA sequence similarity is primarily a problem of alignment between a sequence is question and a set of known sequences. Global and local alignment algorithms are available with the most common being the Smith-Waterman algorithm and its variations.