Journal of Health Informatics & Management

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Quasi-Optimal Multiple Sequence Alignments

Finding an optimal multiple sequence alignment (MSA) of three or more nucleic acid or amino acid sequences is a fundamental problem of bioinformatics with a large number of publications and citations over the last 30 years. Given a set of sequences, an optimal MSA identifies homologous characters, which have common ancestry. The resulting MSA is used for many downstream applications in medical and health informatics such as constructing phylogenetic trees, finding protein families, predicting secondary and tertiary structure of new sequences, and demonstrating the homology between new sequences and existing families. Unfortunately, techniques that work well for pairwise alignment often become too computationally expensive when they are applied to multiple sequence alignment due the extremely large size of the search space. In fact, it is common for multiple sequence alignment problems to become computationally intractable. This is because multiple sequence alignment is a combinatorial problem, and as the number or size of the sequences in the problem set increases, the computational time required performing an alignment increases exponentially. That is, for n sequences of length l, computing the optimal alignment exactly carries a computational complexity of O(ln). Thus, dynamic programming techniques such as the Needleman-Wunsch algorithm are guaranteed to produce optimal solutions to multiple sequence alignment problems, but are generally impractical for all but the smallest examples. In fact, multiple sequence alignment algorithms using the sum-of-pair heuristic is NP-complete. As a result, most currently-employed multiple sequence alignment algorithms are based on heuristics and must settle for providing a quasi-optimal alignment. In this editorial article, we will summarize the previous works on MSA especially some recent computational methods for quasioptimal multiple sequence alignments. We will also discuss some possible approaches for future works.

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