Sequence alignment algorithms are a fundamental component of computational biology and bioinformatics. They are used to compare and find similarities between DNA, RNA, or protein sequences. The alignment process involves assigning scores to all possible alignments of two sequences and then finding the alignment(s) with the highest score. There are two main types of sequence alignment: global and local alignment.
Global alignment is used to compare two entire sequences and identify the best overall alignment between them. It aims to align as many characters as possible, even if some mismatches or gaps are necessary. The Needleman-Wunsch algorithm is a classic example of a global alignment algorithm. It uses dynamic programming to find the optimal alignment between two sequences by constructing a matrix of scores and then backtracking through the matrix to find the optimal path. The resulting alignment shows the similarities and differences between the two sequences across their entire length.
Local alignment, on the other hand, is used to identify regions of similarity between two sequences. It aims to find the best alignment between two subsequences within the two sequences, even if the rest of the sequences do not align well. The Smith-Waterman algorithm is a classic example of a local alignment algorithm. It also uses dynamic programming to find the optimal alignment, but instead of aligning the entire sequences, it only aligns a subset of the sequences that produce the highest score.
The difference between global and local alignment is best illustrated with an example. Consider the following two DNA sequences:
Sequence 1: AGCTGTTACGAGTCGT
Sequence 2: GCTGATTACAGTCGTA
A global alignment of these two sequences might result in:
Sequence 1: AGCTGTTACGAGTCGT-
Sequence 2: -GCTGATTACAGTCGTA
This alignment shows the best overall alignment between the two sequences. The "-" represents a gap inserted to align the two sequences. However, this alignment ignores the fact that there is a highly similar region between the two sequences:
Sequence 1: AGCTGTTACGAGTCGT
Sequence 2: GCTGATTACAGTCGTA
A local alignment of these two sequences would identify this highly similar region:
Sequence 1: TGTTACGA
Sequence 2: TTA-CAGT
This alignment shows the best alignment between the two subsequences that produces the highest score. The "-" represents a gap inserted to align the two sequences.
In summary, global alignment aims to align the entire sequences and identify the best overall alignment, while local alignment focuses on identifying regions of similarity between the two sequences, even if the rest of the sequences do not align well.