Estimation of phylogeny and invariant sites under the general Markov model of nucleotide sequence evolution
Estimation of phylogeny and invariant sites under the general Markov model of nucleotide sequence evolution
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DOI：
10.1080/10635150701247921
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发表时间：
2007-04-01
影响因子：
6.5
通讯作者：
Jermiin, Lars
中科院分区：
文献类型：
--
作者：
Jayaswal, Vivek;Robinson, John;Jermiin, Lars
大多数基于最大似然的方法所使用的核苷酸取代模型假设进化过程是固定的、可逆的和同质的。我们提出了 Barry 和 Hartigan 模型的扩展，当数据包含不变位点并且违反平稳性、可逆性和同质性假设时，该模型可用于通过最大似然（WL）估计参数。与大多数估计不变位点的机器学习方法不同，我们将不变位点的核苷酸组成与可变位点的核苷酸组成分开估计。我们分析了细菌数据集，其中由于缺乏平稳性和同质性而导致的问题先前已被充分注意到，并使用参数引导程序来表明数据与我们的一般马尔可夫模型一致。我们还表明，当应用于一般马尔可夫模型下模拟的数据时，使用我们的方法获得的不变位点的估计相当准确。 [不变的位点；最大似然性；非均质过程；非平稳过程；核苷酸取代；系统发育学。]
The models of nucleotide substitution used by most maximum likelihood-based methods assume that the evolutionary process is stationary, reversible, and homogeneous. We present an extension of the Barry and Hartigan model, which can be used to estimate parameters by maximum likelihood (WL) when the data contain invariant sites and there are violations of the assumptions of stationarity, reversibility, and homogeneity. Unlike most ML methods for estimating invariant sites, we estimate the nucleotide composition of invariant sites separately from that of variable sites. We analyze a bacterial data set where problems due to lack of stationarity and homogeneity have been previously well noted and use the parametric bootstrap to show that the data are consistent with our general Markov model. We also show that estimates of invariant sites obtained using our method are fairly accurate when applied to data simulated under the general Markov model. [Invariant sites; maximum likelihood; nonhomogeneous process; nonstationary process; nucleotide substitution; phylogenetics.].