Multiple alignment using hidden Markov models
- Washington Univ. School of Medicine, St. Louis, MO (United States)
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 Maxkov model approaches.
- Research Organization:
- Stanford Univ., CA (United States)
- OSTI ID:
- 401836
- Report Number(s):
- CONF-9507246--
- Country of Publication:
- United States
- Language:
- English
Similar Records
Protein modeling with hybrid Hidden Markov Model/Neurel network architectures
Supervised learning of hidden Markov models for sequence discrimination