A divide-and-conquer method for scalable phylogenetic network inference from multilocus data
A divide-and-conquer method for scalable phylogenetic network inference from multilocus data
复制标题
从多位点数据进行可扩展系统发育网络推理的分而治之方法
DOI：
10.1093/bioinformatics/btz359
复制
发表时间：
2019
期刊：
影响因子：
5.8
通讯作者：
Nakhleh, Luay K.
中科院分区：
文献类型：
--
作者：
Zhu, Jiafan;Liu, Xinhao;Ogilvie, Huw A.;Nakhleh, Luay K.
MotivationReticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes.ResultsIn this article, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological datasets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference.Availability and implementationWe implemented the algorithms in the publicly available software package PhyloNet (https://bioinfocs.rice.edu/PhyloNet).Supplementary informationSupplementary data are available atBioinformaticsonline.
DOI：
10.1093/bioinformatics/bty295
发表时间：
2018-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Zhu J;Nakhleh L
通讯作者：
Nakhleh L
影响因子：
3
作者：
Zhu J;Yu Y;Nakhleh L
通讯作者：
Nakhleh L