Multiple Sequence Alignment by Improved Hidden Markov Model Training and Quantum-Behaved Particle Swarm Optimization

• Published in: Life System Modeling and Intelligent Computing pp. 358 - 366
• Main Authors: Li, Chengyuan, Long, Haixia, Ding, Yanrui, Sun, Jun, Xu, Wenbo
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: multiple sequence alignment; Profile hidden Markov model; Quantum-behaved particle swarm optimization
• ISBN: 3642156142; 9783642156144
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-15615-1_43

Multiple sequence alignment (MSA), known as NP-complete problem, is one of the basic problems in computational biology. Presently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization (QPSO) is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and QPSO for the MSA is proposed. In order to evaluate the approach protein sequences are taken. Finally, compared with other algorithms, the results show that the proposed algorithm not only finds out perfect profile HMM, but also produces the optimal alignment of multiple sequences.