A Novel Approach to Multiple Sequence Alignment Using Multiobjective Evolutionary Algorithm Based on Decomposition

Multiple sequence alignment (MSA) is a fundamental and key step for implementing other tasks in bioinformatics, such as phylogenetic analyses, identification of conserved motifs and domains, structure prediction, etc. Despite the fact that there are many methods to implement MSA, biologically perfec...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 20; H. 2; S. 717 - 727
Hauptverfasser: Zhu, Huazheng, He, Zhongshi, Jia, Yuanyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithms
Alignment
Bioinformatics
Computational Biology - methods
Databases, Protein
Decomposition
Evolutionary algorithms
Linear programming
MOEA/D
Multi-objective optimization
Multiple sequence alignment
Muscles
Optimization
Sequence Alignment - methods
Silicon
Sociology
Statistics
Tasks
Vectors
multiobjective optimization
multiple sequence alignment (MSA)
Multiobjective evolutionary algorithm framework based on decomposition (MOEA/D)
ISSN:2168-2194, 2168-2208, 2168-2208
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Zusammenfassung:Multiple sequence alignment (MSA) is a fundamental and key step for implementing other tasks in bioinformatics, such as phylogenetic analyses, identification of conserved motifs and domains, structure prediction, etc. Despite the fact that there are many methods to implement MSA, biologically perfect alignment approaches are not found hitherto. This paper proposes a novel idea to perform MSA, where MSA is treated as a multiobjective optimization problem. A famous multiobjective evolutionary algorithm framework based on decomposition is applied for solving MSA, named MOMSA. In the MOMSA algorithm, we develop a new population initialization method and a novel mutation operator. We compare the performance of MOMSA with several alignment methods based on evolutionary algorithms, including VDGA, GAPAM, and IMSA, and also with state-of-the-art progressive alignment approaches, such as MSAprobs, Probalign, MAFFT, Procons, Clustal omega, T-Coffee, Kalign2, MUSCLE, FSA, Dialign, PRANK, and CLUSTALW. These alignment algorithms are tested on benchmark datasets BAliBASE 2.0 and BAliBASE 3.0. Experimental results show that MOMSA can obtain the significantly better alignments than VDGA, GAPAM on the most of test cases by statistical analyses, produce better alignments than IMSA in terms of TC scores, and also indicate that MOMSA is comparable with the leading progressive alignment approaches in terms of quality of alignments.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2015.2403397