Robustness of ancestral sequence reconstruction to phylogenetic uncertainty.
Robustness of ancestral sequence reconstruction to phylogenetic uncertainty.
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DOI：
10.1093/molbev/msq081
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发表时间：
2010-09
影响因子：
10.7
通讯作者：
Thornton JW
中科院分区：
文献类型：
--
作者：
Hanson-Smith V;Kolaczkowski B;Thornton JW
祖先序列重建（ASR）被广泛用于制定和测试有关古代基因的序列，功能和结构的假设。祖先序列通常推断从现存序列的比对使用最大似然（ML）系统发育算法，计算最有可能的祖先序列假设序列进化的概率模型和特定的同源性-通常是树与ML。然而，真正的遗传学很少有确切的了解。ML方法忽略了这种不确定性，而贝叶斯方法通过在可能的树的分布上整合每个祖先状态的可能性来合并它。目前尚不清楚贝叶斯方法是否能提高系统发育不确定性推断祖先序列的准确性。在这里，我们使用基于模拟的实验，在简化和经验推导的条件下，比较使用ML和贝叶斯方法进行的ASR的准确性。我们表明，将系统发育的不确定性，通过整合拓扑结构很少改变推断的祖先状态，并没有提高重建的祖先序列的准确性。祖先状态重建对底层树的不确定性是鲁棒的，因为产生系统发育不确定性的条件也使祖先状态在合理的树中相同;相反，不同的系统发育产生不同的推断祖先状态的条件对真正的系统发育产生很少或没有歧义。我们的研究结果表明，ML可以产生准确的ASR，即使面对系统发育的不确定性。使用贝叶斯积分来合并这种不确定性既没有必要也没有好处。
Ancestral sequence reconstruction (ASR) is widely used to formulate and test hypotheses about the sequences, functions, and structures of ancient genes. Ancestral sequences are usually inferred from an alignment of extant sequences using a maximum likelihood (ML) phylogenetic algorithm, which calculates the most likely ancestral sequence assuming a probabilistic model of sequence evolution and a specific phylogeny—typically the tree with the ML. The true phylogeny is seldom known with certainty, however. ML methods ignore this uncertainty, whereas Bayesian methods incorporate it by integrating the likelihood of each ancestral state over a distribution of possible trees. It is not known whether Bayesian approaches to phylogenetic uncertainty improve the accuracy of inferred ancestral sequences. Here, we use simulation-based experiments under both simplified and empirically derived conditions to compare the accuracy of ASR carried out using ML and Bayesian approaches. We show that incorporating phylogenetic uncertainty by integrating over topologies very rarely changes the inferred ancestral state and does not improve the accuracy of the reconstructed ancestral sequence. Ancestral state reconstructions are robust to uncertainty about the underlying tree because the conditions that produce phylogenetic uncertainty also make the ancestral state identical across plausible trees; conversely, the conditions under which different phylogenies yield different inferred ancestral states produce little or no ambiguity about the true phylogeny. Our results suggest that ML can produce accurate ASRs, even in the face of phylogenetic uncertainty. Using Bayesian integration to incorporate this uncertainty is neither necessary nor beneficial.
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DOI：
10.1093/bioinformatics/8.3.275
发表时间：
1992-06-01
期刊：
COMPUTER APPLICATIONS IN THE BIOSCIENCES
影响因子：
--
作者：
JONES, DT;TAYLOR, WR;THORNTON, JM
通讯作者：
THORNTON, JM
影响因子：
10.7
作者：
Kolaczkowski, Bryan;Thornton, Joseph W.
通讯作者：
Thornton, Joseph W.
影响因子：
3.9
作者：
Koshi, JM;Goldstein, RA
通讯作者：
Goldstein, RA
影响因子：
64.8
作者：
JERMANN, TM;OPITZ, JG;BENNER, SA
通讯作者：
BENNER, SA
影响因子：
64.8
作者：
Kolaczkowski, B;Thornton, JW
通讯作者：
Thornton, JW