The first major challenge in the multiple alignment procedure is how to rank different alignments i.e. which scoring function to use. Since the sequences have a shared history they are correlated through their phylogeny and the scoring function should ideally take this into account. Doing so is, however, not straightforward as it increases the number of model parameters considerably. It is therefore commonplace to either ignore this complication and assume sequences to be unrelated, or to use heuristic corrections for shared ancestry.
The second challenge is to find the optimal alignment given a scoring function. For pairs of sequences this can be done by dynamic programming algorithms, but for more than three sequences this approach demands too much computer time and memory to be feasible.
A commonly used approach is therefore to do progressive alignment [Feng and Doolittle, 1987] where multiple alignments are built through the successive construction of pairwise alignments. These algorithms provide a good compromise between time spent and the quality of the resulting alignment
Presently, the most exciting development in multiple alignment methodology is the construction of statistical alignmentalgorithms [Hein, 2001], [Hein et al., 2000]. These algorithms employ a scoring function which incorporates the underlying phylogeny and use an explicit stochastic model of molecular evolution which makes it possible to compare different solutions in a statistically rigorous way. The optimization step, however, still relies on dynamic programming and practical use of these algorithms thus awaits further developments.