L.U.St: a tool for approximated maximum likelihood supertree reconstruction.
L.U.St: a tool for approximated maximum likelihood supertree reconstruction.
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DOI：
10.1186/1471-2105-15-183
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发表时间：
2014-06-12
影响因子：
3
通讯作者：
Pisani D
中科院分区：
文献类型：
--
作者：
Akanni WA;Creevey CJ;Wilkinson M;Pisani D
超级树联合收割机将不同的、部分重叠的树组合起来，以生成一个综合，该综合提供了一个高级透视图，而这是通过检查单个的分解过程无法获得的。超级树可以被视为元分析工具，可以用来根据以前的科学研究结果进行推断。他们的元分析应用越来越受欢迎，因为人们意识到，统计测试的进化趋势的研究的权力，关键取决于使用的分类密集的同源性。除此之外，超级树还在基因组学中得到了应用，它们被用于联合收割机组合基因树，并基于基因组规模的数据集恢复物种的遗传。在这里，我们提出了L.U.ST包，近似最大似然超树推理的Python工具，并使用胎盘哺乳动物的基因组数据集来说明其应用。L.U.St允许在给定一组输入树的情况下计算超树的近似似然，执行启发式搜索以寻找最高似然的超树，并执行两个或更多个超树的统计测试。为此，L.U.St实现了一个获胜站点测试，允许对先验选择的假设的集合进行排名，作为输入超树拓扑的集合。它还输出一个输入树似然分数文件，可用作CONSEL的输入，用于计算两棵树的标准测试（例如Kishino-Hasegawa，Shimidoara-Hasegawa和近似无偏测试）。这是超树方法的第一个完全参数化的实现，它具有清晰的属性，并提供了几个优于当前可用的超树方法的优点。它很容易实现，并且可以在任何安装了python的平台上工作。可用性：bitBucket页面-https://afro-juju@bitbucket.org/afro-juju/l.u.st.git。联系人：Davide. Pisani@bristol.ac.uk。
Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets. Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests). This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed. Availability: bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git. Contact: Davide.Pisani@bristol.ac.uk.
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影响因子：
1.1
作者：
Semple, C;Steel, M
通讯作者：
Steel, M
影响因子：
4.1
作者：
Ragan, Mark A.
通讯作者：
Ragan, Mark A.
影响因子：
10.7
作者：
Romiguier, Jonathan;Ranwez, Vincent;Douzery, Emmanuel J. P.
通讯作者：
Douzery, Emmanuel J. P.
影响因子：
3.5
作者：
Cao, Y;Fujiwara, M;Hasegawa, M
通讯作者：
Hasegawa, M
影响因子：
10.7
作者：
Pisani, Davide;Cotton, James A.;McInerney, James O.
通讯作者：
McInerney, James O.