Multiple Alignment Using Hidden Markov Models
Multiple Alignment Using Hidden Markov Models
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发表时间：
1995
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通讯作者：
S. Eddy
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作者：
S. Eddy
描述了一种模拟退火法，用于训练隐马尔可夫模型并从最初未比对的蛋白质或DNA序列中产生多个序列比对。模拟退火法又使用动态规划算法，根据它们的概率和玻尔兹曼温度因子对次优多重比对进行正确采样。在10个不同蛋白质家族的结构比对上对模拟退火法的比对质量进行了评估，并与其他HMM训练方法和ClustanW程序的性能进行了比较。与其他已测试的隐马尔可夫模型训练方法相比，模拟退火法能够更好地在多对齐概率环境中找到近全局最优解。与彼此相比，ClustaW和模拟退火法都不能产生一致的更好的比对。对ClustaW优于模拟退火法的具体情况以及相反情况的检查，提供了对当前隐马尔可夫模型方法的优点和缺点的洞察。
A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignments is evaluated on structural alignments of ten different protein families, and compared to the performance of other HMM training methods and the ClustalW program. Simulated annealing is better able to find near-global optima in the multiple alignment probability landscape than the other tested HMM training methods. Neither ClustalW nor simulated annealing produce consistently better alignments compared to each other. Examination of the specific cases in which ClustalW outperforms simulated annealing, and vice versa, provides insight into the strengths and weaknesses of current hidden Markov model approaches.