Markov-modulated Markov chains and the covarion process of molecular evolution
Markov-modulated Markov chains and the covarion process of molecular evolution
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DOI：
10.1089/1066527041887339
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发表时间：
2004-01-01
影响因子：
1.7
通讯作者：
Jean-Marie, A
中科院分区：
文献类型：
--
作者：
Galtier, N;Jean-Marie, A
生物序列进化的共变异（covariion，或site specific rate variation， SSRV）过程是指核苷酸/氨基酸/密码子位置的进化速率随时间变化的过程。本文引入时间连续、空间离散、马尔可夫调制的马尔可夫链作为SSRV过程的表示模型，将已有的理论推广到任何速率变化模型。提出了一种快速对角化相关马尔可夫调制马尔可夫过程生成矩阵的算法。该算法使得系统发育似然计算即使在大量的速率类别和大量的状态下也易于处理，从而使SSRV模型适用于氨基酸或密码子序列数据集。利用该算法，我们研究了在分子系统发育中广泛使用的进化速率伽玛分布的离散逼近的准确性。我们发现，当分析序列的数量超过20个时，无论是在SSRV模型下还是在位点率变异（ASRV）模型下，都需要相对大量的分类才能达到精确的近似。
The covarion (or site specific rate variation, SSRV) process of biological sequence evolution is a process by which the evolutionary rate of a nucleotide/amino acid/codon position can change in time. In this paper, we introduce time-continuous, space-discrete, Markov-modulated Markov chains as a model for representing SSRV processes, generalizing existing theory to any model of rate change. We propose a fast algorithm for diagonalizing the generator matrix of relevant Markov-modulated Markov processes. This algorithm makes phylogeny likelihood calculation tractable even for a large number of rate classes and a large number of states, so that SSRV models become applicable to amino acid or codon sequence datasets. Using this algorithm, we investigate the accuracy of the discrete approximation to the Gamma distribution of evolutionary rates, widely used in molecular phylogeny. We show that a relatively large number of classes is required to achieve accurate approximation of the exact likelihood when the number of analyzed sequences exceeds 20, both under the SSRV and among site rate variation (ASRV) models.