Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a threeobjective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones
Summary1. Phylogenetic inference is the process of searching and reconstructing the best phylogenetic tree that explains the evolution of species from a given data set. It is considered as an NP-hard problem due to the computational complexity required to find the optimal phylogenetic trees in the space of all the possible topologies. 2. We have developed MO-Phylogenetics, a software tool to infer phylogenetic trees optimizing two reconstruction criteria simultaneously, integrating a framework for multi-objective optimization with two phylogenetic software packages. 3. As a result, researchers in life sciences have at their disposal a high-performance tool including a number of multi-objective metaheuristics that can be applied to phylogenetic inference using the maximum parsimony and maximum likelihood as objectives to be optimized at the same time.
Multiple sequence alignment (MSA) plays a core role in most bioinformatics studies and provides a framework for the analysis of evolution in biological systems. The MSA problem consists in finding an optimal alignment of three or more sequences of nucleotides or amino acids. Different scores have been defined to assess the quality of MSA solutions, so the problem can be formulated as a multiobjective optimization problem. The number of proposals focused on this approach in the literature is scarce, and most of the works take as base algorithm the NSGA‐II metaheuristic. So, there is a lack of a study involving a set of representative multiobjective metaheuristics to deal with this complex problem. Our main goal in this paper is to carry out such study. We propose a biobjective formulation for the MSA and perform an exhaustive comparative study of six multiobjective algorithms. We have considered a number of problems taken from the benchmark BAliBASE (v3.0). Our experiments reveal that the classic NSGA‐II algorithm and MOCell, a cellular metaheuristic, provide the best overall performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.