Combining phylogenetic and hidden Markov models in biosequence analysis

摘要: A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also process changes from one to next. These combine phylogenetic molecular evolution, which apply individual sites, and hidden Markov models, allow for site. Besides improving realism ordinary they are potentially very powerful tools inference prediction---for gene finding, example, or prediction secondary structure. In this paper, we review progress on combined present some extensions previous work. Our main result is simple efficient method accommodating higher-order states HMM, allows context-sensitive substitution---that is, effects neighboring bases pattern substitution. We experimental results indicating states, autocorrelated rates, multiple functional categories all lead significant improvements fit model, with effect being particularly pronounced.