Accounting for Calibration Uncertainty in Phylogenetic Estimation of Evolutionary Divergence Times
Accounting for Calibration Uncertainty in Phylogenetic Estimation of Evolutionary Divergence Times
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DOI：
10.1093/sysbio/syp035
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发表时间：
2009-06-01
影响因子：
6.5
通讯作者：
Phillips, Matthew J.
中科院分区：
文献类型：
--
作者：
Ho, Simon Y. W.;Phillips, Matthew J.
根据序列数据估计系统发育分歧时间是许多分子进化研究的重要组成部分。现在人们普遍认识到，发散定年的过程比20世纪60年代祖克坎德尔和鲍林（1962，1965）最初描述的要复杂得多。特别是，已经有很多关键的关注对全球分子钟的假设，导致在发展越来越复杂的技术推断分歧时间从序列数据。为了响应广泛偏离类时钟行为的文献，已经提出并实现了各种本地和松弛时钟方法。本地时钟方法允许在系统发育树的不同部分使用不同的分子时钟，从而保留了经典分子时钟的优势，同时摆脱了单一的全球替代率的限制性假设（Rambaut和Bromham 1998; Yoder和Yang 2000）。大约在同一时间，Sanderson（1997）发表了他的非参数速率平滑算法，该算法通过最小化树中相邻分支之间的速率变化幅度来操作。一个相关的方法，惩罚的可能性，随后实施的最大似然框架（桑德森2002年）。在这种方法中，相邻分支之间的大速率变化是不利的。惩罚的程度由平滑参数确定，该平滑参数的值通过交叉验证过程客观地获得。
The estimation of phylogenetic divergence times from sequence data is an important component of many molecular evolutionary studies. There is now a general appreciation that the procedure of divergence dating is considerably more complex than that initially described in the 1960s by Zuckerkandl and Pauling (1962, 1965). In particular, there has been much critical attention toward the assumption of a global molecular clock, resulting in the development of increasingly sophisticated techniques for inferring divergence times from sequence data. In response to the documentation of widespread departures from clocklike behavior, a variety of local-and relaxed-clock methods have been proposed and implemented. Local-clock methods permit different molecular clocks in different parts of the phylogenetic tree, thereby retaining the advantages of the classical molecular clock while casting off the restrictive assumption of a single, global rate of substitution (Rambaut and Bromham 1998; Yoder and Yang 2000). At around the same time, Sanderson (1997) published his nonparametric rate-smoothing algorithm, which operates by minimizing the magnitude of rate changes between adjacent branches in the tree. A related method, penalized likelihood, was subsequently implemented in a maximum-likelihood framework (Sanderson 2002). In this approach, large rate changes between neighboring branches are penalized. The degree of penalization is determined by a smoothing parameter, the value of which is obtained objectively through a cross-validation procedure.