A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

Journal of computational biology: A journal of computational molecular cell biology

MauroCastelli^2,1¹  LeonardoVanneschi^1²  ÁlvaroRubio-Largo^1³ 

[1] Address correspondence to:Prof. Mauro CastelliNOVA IMSUniversidade Nova de LisboaLisbon 1070-312Portugal^2;Department of Computer and Communications Technologies, University of Extremadura, Caceres, Spain^3;NOVA IMS, Universidade Nova de Lisboa, Lisbon, Portugal^1

关键词: memetic computing;    metaheuristic;    multiobjective optimization;    multiple sequence alignment.;   

DOI  :  10.1089/cmb.2018.0031

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

The alignment among three or more nucleotides/amino acids sequences at the same time is known as multiple sequence alignment (MSA), a nondeterministic polynomial time (NP)-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem wherein the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the hybrid multiobjective memetic metaheuristics for MSA is proposed. To evaluate the parallel performance of our proposal, we have selected a pull of data sets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, tree-based consistency objective function for alignment evaluation (T-Coffee), Clustal , and multiple alignment using fast Fourier transform (MAFFT). The comparative study reveals that our parallel aligner obtains better results than MSAProbs, T-Coffee, Clustal , and MAFFT. In addition, the parallel version is around 25 times faster than the sequential version with 32 cores, obtaining an efficiency around 80%.

【 授权许可】

Unknown