Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance.
Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance.
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DOI：
10.1186/1748-7188-8-28
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发表时间：
2013-11-01
期刊：
影响因子：
--
通讯作者：
Fernández-Baca D
中科院分区：
文献类型：
--
作者：
Chaudhary R;Burleigh JG;Fernández-Baca D
从多拷贝基因树构建物种树一直是植物遗传学中一个具有挑战性的问题。一个困难是，由于进化过程，如基因复制和丢失，深度合并或横向基因转移，潜在的基因可能是不一致的。基因树估计误差可能进一步加剧物种树估计的困难。我们提出了一种新的方法推断物种树的不一致的多拷贝基因树的基础上推广的罗宾逊-Foulds（RF）的距离测量多标记树（多树）。我们证明了计算两棵多树之间的RF距离是NP难的，但是计算一棵多树和一棵单标记物种树之间的RF距离是很容易的。基于此，我们将Mul-trees的RF问题（MulRF）公式化如下：给定多拷贝基因树的集合，找到一个单标记的物种树，使输入Mul-trees的总RF距离最小化。我们开发并实现了一个快速的SPR启发式算法的NP难MulRF问题。我们比较的MulRF方法（可在http：//genome.cs.iastate.edu/CBL/MulRF/）的性能与几个基因树简约的方法，使用基因树模拟，包括基因树错误，基因复制和损失，和/或横向转移。MulRF方法比基因树简约方法产生更准确的物种树。我们还证明了MulRF方法可以在几分钟内从近2,000棵基因树中推断出一棵可信的植物物种树。我们的新的系统发育推断方法，基于广义RF距离，使它能够快速估计物种树从大型基因组数据集。由于MulRF方法，不像基因树简约，是基于一个通用的树距离的措施，它是有吸引力的基因组数据集的分析，其中许多过程，如深聚结，重组，基因重复和损失，以及系统发育错误可能会导致基因树不和谐。在实验中，MulRF方法准确，快速地估计物种树，表明MulRF作为一种有效的替代方法，从大规模的基因组数据集的系统发育推断。
Constructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation. We present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem. We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees. Our new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as well as phylogenetic error may contribute to gene tree discord. In experiments, the MulRF method estimated species trees accurately and quickly, demonstrating MulRF as an efficient alternative approach for phylogenetic inference from large-scale genomic data sets.
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DOI：
10.1109/tcbb.2010.122
发表时间：
2011-07-01
影响因子：
4.5
作者：
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发表时间：
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期刊：
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