Using iterative methods for global multiple sequence alignment.
Finding a global optimal alignment of more than two sequences that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time is especially difficult. The dynamic programming algorithm used for optimal alignment of pairs of sequences can be extended to global alignment of three sequences, but for more than three sequences, only a small number of relatively short sequences may be analyzed. Thus, approximate methods are used for global alignment. One class of these is iterative global alignment, which makes an initial global alignment of groups of sequences and then revises the alignment to achieve a more reasonable result. This article discusses several iterative alignment methods. In particular, steps are provided for using the Sequence Alignment by Genetic Algorithm (SAGA).
Mount
David W
DW
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009154:Mutation; D010802:Phylogeny; D011336:Probability; D011995:Recombination, Genetic; D016415:Sequence Alignment / Q000379:methods*