Parallel H4MSA for Multiple Sequence Alignment

Abstract

Multiple Sequence Alignment (MSA) is the process of aligning three or more nucleotides/amino-acids sequences at the same time. It is an NP-complete optimization problem where the time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In the multiobjective version of the MSA problem, we simultaneously optimize the alignment accuracy and conservation. In this work, we present a parallel scheme for a multiobjective version of a memetic metaheuristic: Hybrid Multiobjective Memetic Metaheuristics for Multiple Sequence Alignment (H4MSA). In order to evaluate the parallel performance of H4MSA, we use several datasets with different number of sequences (up to 1000 sequences) and compare its parallel performance against other well-known parallel approaches published in the literature, such as MSAProbs, T-Coffee, Clustal O and MAFFT. On the other hand, the results reveals that parallel H4MSA is around 25 times faster than the sequential version with 32 cores.