UNROOTED GENEALOGICAL TREE PROBABILITIES IN THE INFINITELY-MANY-SITES MODEL
UNROOTED GENEALOGICAL TREE PROBABILITIES IN THE INFINITELY-MANY-SITES MODEL
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DOI：
10.1016/0025-5564(94)00044-z
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发表时间：
1995-05-01
影响因子：
4.3
通讯作者：
TAVARE, S
中科院分区：
文献类型：
--
作者：
GRIFFITHS, RC;TAVARE, S
无限多位点过程通常用于模拟DNA序列样品中观察到的序列变异性。尽管它的流行，抽样理论的过程是相当少的理解。我们描述的树结构的模型，并显示如何可以用来计算序列的样本的概率。我们展示了如何从一组祖先标签未知的网站中产生无根家谱，并由此产生相应的有根家谱。我们推导出递归的概率配置的序列（相当于树）在有根和无根的情况下。我们给出了一个计算方法的基础上蒙特卡罗递归，提供近似的采样概率为任何大小的样本。在几个应用程序中，该算法可以用来找到最大似然估计的替代率，无论是当祖先标记的网站是已知的，当它是未知的。
The infinitely-many-sites process is often used to model the sequence variability observed in samples of DNA sequences. Despite its popularity, the sampling theory of the process is rather poorly understood. We describe the tree structure underlying the model and show how this may be used to compute the probability of a sample of sequences. We show how to produce the unrooted genealogy from a set of sites in which the ancestral labeling is unknown and from this the corresponding rooted genealogies. We derive recursions for the probability of the configuration of sequences (equivalently, of trees) in both the rooted and unrooted cases. We give a computational method based on Monte Carlo recursion that provides approximants to sampling probabilities for samples of any size. Among several applications, this algorithm may be used to find maximum likelihood estimators of the substitution rate, both when the ancestral labeling of sites is known and when it is unknown.