Using guide trees to construct multiple-sequence evolutionary HMMs.

I Holmes

doi:10.1093/bioinformatics/btg1019

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Using guide trees to construct multiple-sequence evolutionary HMMs.

I Holmes

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Abstract

MOTIVATION: Score-based progressive alignment algorithms do dynamic programming on successive branches of a guide tree. The analogous probabilistic construct is an Evolutionary HMM. This is a multiple-sequence hidden Markov model (HMM) made by combining transducers (conditionally normalised Pair HMMs) on the branches of a phylogenetic tree. METHODS: We present general algorithms for constructing an Evolutionary HMM from any Pair HMM and for doing dynamic programming to any Multiple-sequence HMM. RESULTS: Our prototype implementation, Handel, is based on the Thorne-Kishino-Felsenstein evolutionary model and is benchmarked using structural reference alignments.

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Algorithms Cluster Analysis Evolution, Molecular Gene Expression Profiling Gene Expression Regulation Markov Chains Models, Genetic Models, Statistical Sequence Alignment Sequence Analysis, DNA Sequence Analysis, Protein Sequence Homology Software Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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I Holmes. 2003. Using guide trees to construct multiple-sequence evolutionary HMMs.. https://doi.org/10.1093/bioinformatics%2Fbtg1019

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