An experimental phylogeny to benchmark ancestral sequence reconstruction.
An experimental phylogeny to benchmark ancestral sequence reconstruction.
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DOI：
10.1038/ncomms12847
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发表时间：
2016-09-15
影响因子：
16.6
通讯作者：
Gaucher, Eric A.
中科院分区：
文献类型：
--
作者：
Randall, Ryan N.;Radford, Caelan E.;Roof, Kelsey A.;Natarajan, Divya K.;Gaucher, Eric A.
祖先序列重建（ASR）是一种新兴的方法，它揭示了分子进化的许多关键机制。对这种方法的一个批评是，它无法在生物学背景下验证其算法，而不是计算机模拟。在这里，我们建立了一个实验性的基因使用一个单一的红色荧光蛋白来解决这个批评。进化的植物发生由19个操作分类单位（叶）和17个祖先分叉（节点），显示各种各样的荧光表型。然后，19片叶子作为“现代”序列，我们使用各种算法进行ASR分析，并以已知的祖先基因型和祖先表型为基准。我们证实了计算机模拟，显示所有的算法推断古代序列具有很高的准确性，但我们也揭示了广泛的变化，在表型编码的不正确推断的序列。具体而言，贝叶斯方法纳入率变化显着优于最大简约标准的表型准确性。现存序列的二次抽样对祖先序列的推断影响不大。 祖先序列重建的方法目前正在用计算机模拟进行测试，因为真正的生物学同源性是未知的。在这里，Randall等人建立了一个实验性的基因组学，以基准测试替代祖先序列重建算法在推断祖先基因型和表型方面的性能。
Ancestral sequence reconstruction (ASR) is a still-burgeoning method that has revealed many key mechanisms of molecular evolution. One criticism of the approach is an inability to validate its algorithms within a biological context as opposed to a computer simulation. Here we build an experimental phylogeny using the gene of a single red fluorescent protein to address this criticism. The evolved phylogeny consists of 19 operational taxonomic units (leaves) and 17 ancestral bifurcations (nodes) that display a wide variety of fluorescent phenotypes. The 19 leaves then serve as ‘modern' sequences that we subject to ASR analyses using various algorithms and to benchmark against the known ancestral genotypes and ancestral phenotypes. We confirm computer simulations that show all algorithms infer ancient sequences with high accuracy, yet we also reveal wide variation in the phenotypes encoded by incorrectly inferred sequences. Specifically, Bayesian methods incorporating rate variation significantly outperform the maximum parsimony criterion in phenotypic accuracy. Subsampling of extant sequences had minor effect on the inference of ancestral sequences. Methods for ancestral sequence reconstruction are currently tested with computer simulations, since true biological phylogenies are unknown. Here, Randall et al. build an experimental phylogeny to benchmark the performance of alternate ancestral sequence reconstruction algorithms in inferring ancestral genotypes and phenotypes.
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