Novel iterative approach to joint sequence alignment and tree inference under maximum likelihood: A critical assessment

Abstract

Multiple sequence alignment (MBA) and phylogeny tree reconstruction are two imporant problems in bioinformatics. In some respect, they represent "two sides of the same coin", since solving either of the two problems would be easier if the solution to the other problem was given. However, most of the currently available algorithms present a solution to only one of these two problems, either completely ignoring the other problem or assuming that its solution is known in advance. Attempts have been made to solve these two problems simultaneously, but they are either too computationally intensive or inappropriate to analyze divergent sequences. Here we derive a new method that addresses these shortcomings by iteratively improving the starting alignment and its corresponding evolutionary tree based on maximum likelihood scores. We show that the method produces trees with significantly better likelihood scores for fairly to highly divergent sequences. Yet, this improvement does not translate directly into an improvement of the tree and alignment quality.