使用引导树构建多序列进化隐马尔可夫模型。
Using guide trees to construct multiple-sequence evolutionary HMMs.
作者信息
Holmes I
机构信息
Department of Statistics, University of Oxford. 1 South Parks Road, Oxford OX1 3TG, UK.
出版信息
Bioinformatics. 2003;19 Suppl 1:i147-57. doi: 10.1093/bioinformatics/btg1019.
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.
动机
基于分数的渐进比对算法在引导树的连续分支上进行动态规划。类似的概率结构是进化隐马尔可夫模型。这是一种通过在系统发育树的分支上组合传感器（条件归一化的配对隐马尔可夫模型）构建的多序列隐马尔可夫模型（HMM）。
方法
我们提出了从任何配对隐马尔可夫模型构建进化隐马尔可夫模型以及对任何多序列隐马尔可夫模型进行动态规划的通用算法。
结果
我们的原型实现Handel基于索恩 - 岸野 - 费尔斯滕森进化模型，并使用结构参考比对进行基准测试。
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