Combining phylogenetic and hidden Markov models in biosequence analysis

Siepel, A., Haussler, D. (2004) Combining phylogenetic and hidden Markov models in biosequence analysis. J Comput Biol, 11 (2-3). pp. 413-28. ISSN 1066-5277 (Print)1066-5277

DOI: 10.1089/1066527041410472


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 the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction--for example, for gene finding or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higher-order states in the HMM, which allows for context-dependent models of substitution--that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higher-order states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higher-order states being particularly pronounced.

Item Type: Paper
Uncontrolled Keywords: Computational Biology/*methods/statistics & numerical data Genomics/methods/statistics & numerical data Likelihood Functions *Markov Chains Phylogeny Sequence Analysis, DNA/*statistics & numerical data Sequence Analysis, Protein/*statistics & numerical data
Subjects: bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Siepel lab
Depositing User: Matt Covey
Date: 2004
Date Deposited: 12 Jan 2015 20:30
Last Modified: 12 Jan 2015 20:30
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