MAUSA: Using Simulated Annealing for Guide Tree Construction in Multiple Sequence Alignment

Abstract

Multiple sequence alignment is a crucial technique for many fields of computational biology and remains a difficult task. Combining several different alignment techniques often leads to the best results in practice. Within this paper we present MAUSA (Multiple Alignment Using Simulated Annealing) and show that the conceptually simple approach of simulated annealing, when combined with a recent development in solving the aligning alignments problem, produces results which are competitive and in some cases superior to established methods for sequence alignment. We show that the application of simulated annealing to effective guide tree selection improves the quality of the alignments produced. In addition, we apply a method for the automatic assessment of alignment quality and show that in scenarios where MAUSA is selected as producing the best alignment from a suite of approaches (approximately 10% of test cases), it produces an average 5% (p = 0.005, Wilcoxon sign-rank test) improvement in quality.