基于剖面隐马氏模型的多序列比对

Multiple Sequence Alignment Based On the Profile Hidden Markov Model

多序列比对被称为NP完全问题,是生物信息中最基本的问题之一。目前,广泛使用剖面隐马尔可夫模型解决多序列比对问题。作者在粒子群优化算法的基础上,提出了将量子粒子群优化算法用于剖面隐马尔可夫模型的训练过程,进而构建了一种基于剖面隐马氏模型和量子粒子群优化算法的多序列比对算法。从核酸序列和BaliBASE比对数据库中选取了一些比对例子进行了模拟实验,并与其他算法进行了比较,结果表明,所提出的算法能在有限的时间内不仅能找到理想的隐隐马尔可夫模型,而且能得到最优的比对结果。

Multiple sequence alignment（MSA）,known as NP-complete problem,is one of the basic problems in computational biology.At present Profile Hidden Markov Model（HMM） was widely used in multiple sequence alignment.This manuscript presented the quantum-behaved particle swarm optimization（QPSO） which was based on particle swarm optimization.The proposed algorithm was used to optimize the profile HMM.Furthermore,an integration algorithm based on the profile HMM and QPSO for the MSA was constructed.Then the approach was evaluated by a set of standard instances which are chosen from nucleotides sequences and the benchmark alignment database,name as BAliBASE.Finally our results are compared with other algorithms.The result shown that the proposed algorithm not only finds out the perfect profile HMM,but also obtains the optimal alignment of multiple sequence.