Akanni Wasiu A, Creevey Christopher J, Wilkinson Mark, Pisani Davide
Department of Biology, The National University of Ireland, Maynooth, Maynooth, Kildare, Ireland.
BMC Bioinformatics. 2014 Jun 12;15:183. doi: 10.1186/1471-2105-15-183.
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.
bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git.
超树将不同的、部分重叠的树进行合并，以生成一个综合结果，从而提供一个从单个系统发育树检查中无法获得的高层次视角。超树可被视为元分析工具，可用于根据先前科学研究的结果进行推断。自从人们意识到用于研究进化趋势的统计检验的效力严重依赖于分类单元密集的系统发育树的使用以来，它们的元分析应用越来越受欢迎。此外，超树已在系统发育基因组学中得到应用，用于合并基因树并基于基因组规模数据集恢复物种系统发育。
在此，我们展示了L.U.St软件包，这是一个用于近似最大似然超树推断的Python工具，并使用胎盘哺乳动物的基因组数据集说明了其应用。给定一组输入树，L.U.St允许计算超树的近似似然性，执行启发式搜索以寻找最高似然性的超树，并对两个或更多超树进行统计检验。为此，L.U.St实现了一个获胜位点检验，允许对作为输入超树拓扑结构集合给出的一组先验选择的假设进行排序。它还输出一个按输入树计算的似然性得分文件，可将其用作CONSEL的输入，以计算两棵树的标准检验（例如，木村-长谷川检验、岛田原-长谷川检验和近似无偏检验）。
这是超树方法的首个完全参数化实现，具有清晰明确的属性，并且比当前可用的超树方法具有多个优势。它易于实现，并且可在任何安装了Python的平台上运行。
代码库页面 - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git。
