Pairwise comparison and alignment of protein or nucleic acid sequences is the foundation upon which most other bioinformatics tools are built.

There are three types of alignments we can consider when aligning sequnces, optimal, global and local alignments:

• Optimal alignments
  
  The alignment that is the best, given a defined set of rules and parameter values for comparing different alignments. There is no such thing as the single best alignment, since optimality always depends on the assumptions one bases the alignment on. For example, what penalty should gaps carry? All sequence alignment procedures make some such assumptions.
   

• Global alignment
  
  An alignment that assumes that the two proteins are basically similar over the entire length of one another. The alignment attempts to match them to each other from end to end, even though parts of the alignment are not very convincing. A tiny example:

          NLGPSTKDFGKISESREFDNQ
           |      ||||    | 
          QLNQLERSFGKINMRLEDALV

• Local alignment
  
  An alignment that searches for segments of the two sequences that match well. There is no attempt to force entire sequences into an alignment, just those parts that appear to have good similarity, according to some criterion. Using the same sequences as above, one could get:

          NLGPSTKDDFGKILGPSTKDDQ
                   ||||
          QNQLERSSNFGKINQLERSSNN

  It may seem that one should always use local alignments. However, it may be difficult to spot an overall similarity, as opposed to just a domain-to-domain similarity, if one uses only local alignment, so global alignment is useful in some cases.

In this section, we will briefly introduce the concept of dynamic programming, which is the algorithm that allows for efficient and complete comparison of two (or more) biological sequences. We will also investigate the effects of various parameters on the results of these comparisons and begin to look at database searching.