Evaluation of iterative alignment algorithms for multiple alignment

Motivation: Iteration has been used a number of times as an optimization method to produce multiple alignments, either alone or in combination with other methods. Iteration has a great advantage in that it is often very simple both in terms of coding the algorithms and the complexity of the time and memory requirements. In this paper, we systematically test several different iteration strategies by comparing the results on sets of alignment test cases. Results: We tested three schemes where iteration is used to improve an existing alignment. This was found to be remarkably effective and could induce a significant improvement in the accuracy of alignments from most packages. For example the average accuracy of ClustalW was improved by over 6% on the hardest test cases. Iteration was found to be even more powerful when it was directly incorporated into a progressive alignment scheme. Here, iteration was used to improve subalignments at each step of progressive alignment. The beneficial effects of iteration come, in part, from the ability to get round the usual local minimum problem with progressive alignment. This ability can also be used to help reduce the complexity of T-Coffee, without losing accuracy. Alignments can be generated, using T-Coffee, to align subgroups of sequences, which can then be iteratively improved and merged.