A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation.
A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation.
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DOI：
10.1186/s12859-016-1277-1
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发表时间：
2016-10-13
影响因子：
3
通讯作者：
Liu KJ
中科院分区：
文献类型：
--
作者：
Hejase HA;Liu KJ
系统发育树中的分支事件反映了分叉和/或多分叉的物种形成和分裂事件。在存在基因流动的情况下，系统发育不能用树来描述，而是用一种称为系统发育网络的有向无环图来描述。系统发育树和网络通常都是使用多位点序列数据的计算分析来重建的。高通量测序技术的出现带来了两个主要的可扩展性挑战：(1)分类群数量方面的数据集规模；(2)研究中分类群的进化分化。最近的研究已经很好地描述了两个尺度对系统发育树推断的影响；相比之下，系统发育网络推理方法的可扩展性限制在很大程度上是未知的。在本研究中，我们使用从自然小鼠种群中采样的经验数据和使用单一网络模型系统发育的一系列模拟，量化了最先进的系统发育网络推断方法在大规模数据集上的性能。我们发现，在系统发育树推理的情况下，主要的网络推理方法的性能受到数据集规模的两个维度的负面影响。总的来说，我们发现拓扑精度随着分类群数量的增加而降低；随着序列突变率的增加，也观察到类似的效果。最准确的方法是概率推理方法，它可以最大化基于聚结模型的似然或模型似然的伪似然近似。通过概率推理方法获得的精度提高是以运行时和主内存使用的计算成本为代价的，当数据集大小超过25个分类群时，这将变得令人望而却步。在CPU运行数周后，没有一种概率方法能够完成具有30个或更多分类的数据集的分析。我们得出结论，系统发育网络推断的艺术状态远远落后于当前系统发育学研究的范围。迫切需要新的算法开发来解决这一方法上的差距。本文的在线版本（doi:10.1186/s12859-016-1277-1）包含补充材料，可供授权用户使用。
Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study. The impact of both dimensions of scale on phylogenetic tree inference has been well characterized by recent studies; in contrast, the scalability limits of phylogenetic network inference methods are largely unknown. In this study, we quantify the performance of state-of-the-art phylogenetic network inference methods on large-scale datasets using empirical data sampled from natural mouse populations and a range of simulations using model phylogenies with a single reticulation. We find that, as in the case of phylogenetic tree inference, the performance of leading network inference methods is negatively impacted by both dimensions of dataset scale. In general, we found that topological accuracy degrades as the number of taxa increases; a similar effect was observed with increased sequence mutation rate. The most accurate methods were probabilistic inference methods which maximize either likelihood under coalescent-based models or pseudo-likelihood approximations to the model likelihood. The improved accuracy obtained with probabilistic inference methods comes at a computational cost in terms of runtime and main memory usage, which become prohibitive as dataset size grows past twenty-five taxa. None of the probabilistic methods completed analyses of datasets with 30 taxa or more after many weeks of CPU runtime. We conclude that the state of the art of phylogenetic network inference lags well behind the scope of current phylogenomic studies. New algorithmic development is critically needed to address this methodological gap. The online version of this article (doi:10.1186/s12859-016-1277-1) contains supplementary material, which is available to authorized users.
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影响因子：
64.8
作者：
Dasmahapatra, Kanchon K.;Walters, James R.;Briscoe, Adriana D.;Davey, John W.;Whibley, Annabel;Nadeau, Nicola J.;Zimin, Aleksey V.;Hughes, Daniel S. T.;Ferguson, Laura C.;Martin, Simon H.;Salazar, Camilo;Lewis, James J.;Adler, Sebastian;Ahn, Seung-Joon;Baker, Dean A.;Baxter, Simon W.;Chamberlain, Nicola L.;Chauhan, Ritika;Counterman, Brian A.;Dalmay, Tamas;Gilbert, Lawrence E.;Gordon, Karl;Heckel, David G.;Hines, Heather M.;Hoff, Katharina J.;Holland, Peter W. H.;Jacquin-Joly, Emmanuelle;Jiggins, Francis M.;Jones, Robert T.;Kapan, Durrell D.;Kersey, Paul;Lamas, Gerardo;Lawson, Daniel;Mapleson, Daniel;Maroja, Luana S.;Martin, Arnaud;Moxon, Simon;Palmer, William J.;Papa, Riccardo;Papanicolaou, Alexie;Pauchet, Yannick;Ray, David A.;Rosser, Neil;Salzberg, Steven L.;Supple, Megan A.;Surridge, Alison;Tenger-Trolander, Ayse;Vogel, Heiko;Wilkinson, Paul A.;Wilson, Derek;Yorke, James A.;Yuan, Furong;Balmuth, Alexi L.;Eland, Cathlene;Gharbi, Karim;Thomson, Marian;Gibbs, Richard A.;Han, Yi;Jayaseelan, Joy C.;Kovar, Christie;Mathew, Tittu;Muzny, Donna M.;Ongeri, Fiona;Pu, Ling-Ling;Qu, Jiaxin;Thornton, Rebecca L.;Worley, Kim C.;Wu, Yuan-Qing;Linares, Mauricio;Blaxter, Mark L.;Ffrench-Constant, Richard H.;Joron, Mathieu;Kronforst, Marcus R.;Mullen, Sean P.;Reed, Robert D.;Scherer, Steven E.;Richards, Stephen;Mallet, James;McMillan, W. Owen;Jiggins, Chris D.
通讯作者：
Jiggins, Chris D.
影响因子：
4.5
作者：
Mailund T;Halager AE;Westergaard M;Dutheil JY;Munch K;Andersen LN;Lunter G;Prüfer K;Scally A;Hobolth A;Schierup MH
通讯作者：
Schierup MH
影响因子：
10.7
作者：
Bryant, D;Moulton, V
通讯作者：
Moulton, V
DOI：
10.1126/science.1170540
发表时间：
2009-04-17
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Liu K;Victora GD;Schwickert TA;Guermonprez P;Meredith MM;Yao K;Chu FF;Randolph GJ;Rudensky AY;Nussenzweig M
通讯作者：
Nussenzweig M
影响因子：
6.5
作者：
Maddison, WP
通讯作者：
Maddison, WP