Advancing admixture graph estimation via maximum likelihood network orientation.
Advancing admixture graph estimation via maximum likelihood network orientation.
复制标题
DOI：
10.1093/bioinformatics/btab267
复制
发表时间：
2021-07-12
期刊：
影响因子：
--
通讯作者：
Sankararaman S
中科院分区：
文献类型：
--
作者：
Molloy EK;Durvasula A;Sankararaman S
混合，即先前不同种群之间的杂交，是进化中普遍存在的力量。混合存在时的种群进化史可以通过增加系统发育树的附加节点来建模，这些节点代表混合事件。虽然混合图能够更忠实地表示进化历史，但它提出了巨大的推理挑战，并且越来越需要精确、全自动和计算效率高的方法。一个关键的挑战来自于混合图空间的大小。考虑到详尽地评估所有混合图可能会非常昂贵，因此开发了启发式方法来实现对该空间的有效搜索。在流行的TreeMix方法中实现的一种启发式方法包括在优化合适的目标函数的同时向起始树添加边。在这里，我们提出了一个人口统计模型（其中一个混合种群事件发生在叶子上），其中TreeMix和任何其他使用其似然函数的基于起始树的最大似然启发式算法保证会陷入局部最优并返回不正确的网络拓扑。为了解决这个问题，我们提出了一种新的搜索策略，我们称之为最大似然网络导向（MLNO）。我们通过对MLNO的详尽搜索来增强TreeMix，将这种方法称为OrientAGraph。在包括先前发表的混合图在内的评估中，OrientAGraph在4/8模型上的表现优于TreeMix（在其他情况下没有差异）。总体而言，OrientAGraph发现图形具有更高的似然得分和拓扑准确性，同时保持计算效率。最后，提出了改进极大似然混合图估计的几个方向。OrientAGraph可以在Github （https://github.com/sriramlab/OrientAGraph）上获得，使用GNU通用公共许可证v3.0。补充数据可在生物信息学网站获得。
Admixture, the interbreeding between previously distinct populations, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. While enabling a more faithful representation of evolutionary history, admixture graphs present formidable inferential challenges, and there is an increasing need for methods that are accurate, fully automated and computationally efficient. One key challenge arises from the size of the space of admixture graphs. Given that exhaustively evaluating all admixture graphs can be prohibitively expensive, heuristics have been developed to enable efficient search over this space. One heuristic, implemented in the popular method TreeMix, consists of adding edges to a starting tree while optimizing a suitable objective function. Here, we present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return an incorrect network topology. To address this issue, we propose a new search strategy that we term maximum likelihood network orientation (MLNO). We augment TreeMix with an exhaustive search for an MLNO, referring to this approach as OrientAGraph. In evaluations including previously published admixture graphs, OrientAGraph outperformed TreeMix on 4/8 models (there are no differences in the other cases). Overall, OrientAGraph found graphs with higher likelihood scores and topological accuracy while remaining computationally efficient. Lastly, our study reveals several directions for improving maximum likelihood admixture graph estimation. OrientAGraph is available on Github (https://github.com/sriramlab/OrientAGraph) under the GNU General Public License v3.0. Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
64.8
作者：
Mallick, Swapan;Li, Heng;Lipson, Mark;Mathieson, Iain;Gymrek, Melissa;Racimo, Fernando;Zhao, Mengyao;Chennagiri, Niru;Nordenfelt, Susanne;Tandon, Arti;Skoglund, Pontus;Lazaridis, Iosif;Sankararaman, Sriram;Fu, Qiaomei;Rohland, Nadin;Renaud, Gabriel;Erlich, Yaniv;Willems, Thomas;Gallo, Carla;Spence, Jeffrey P.;Song, Yun S.;Poletti, Giovanni;Balloux, Francois;van Driem, George;de Knijff, Peter;Romero, Irene Gallego;Jha, Aashish R.;Behar, Doron M.;Bravi, Claudio M.;Capelli, Cristian;Hervig, Tor;Moreno-Estrada, Andres;Posukh, Olga L.;Balanovska, Elena;Balanovsky, Oleg;Karachanak-Yankova, Sena;Sahakyan, Hovhannes;Toncheva, Draga;Yepiskoposyan, Levon;Tyler-Smith, Chris;Xue, Yali;Abdullah, M. Syafiq;Ruiz-Linares, Andres;Beall, Cynthia M.;Di Rienzo, Anna;Jeong, Choongwon;Starikovskaya, Elena B.;Metspalu, Ene;Parik, Juri;Villems, Richard;Henn, Brenna M.;Hodoglugil, Ugur;Mahley, Robert;Sajantila, Antti;Stamatoyannopoulos, George;Wee, Joseph T. S.;Khusainova, Rita;Khusnutdinova, Elza;Litvinov, Sergey;Ayodo, George;Comas, David;Hammer, Michael F.;Kivisild, Toomas;Klitz, William;Winkler, Cheryl A.;Labuda, Damian;Bamshad, Michael;Jorde, Lynn B.;Tishkoff, Sarah A.;Watkins, W. Scott;Metspalu, Mait;Dryomov, Stanislav;Sukernik, Rem;Singh, Lalji;Thangaraj, Kumarasamy;Paeaebo, Svante;Kelso, Janet;Patterson, Nick;Reich, David
通讯作者：
Reich, David
影响因子：
10.7
作者：
Lipson M;Loh PR;Levin A;Reich D;Patterson N;Berger B
通讯作者：
Berger B
DOI：
10.1142/s0219720012500047
发表时间：
2012-08-01
影响因子：
1
作者：
Gambette, Philippe;Berry, Vincent;Paul, Christophe
通讯作者：
Paul, Christophe
影响因子：
16.6
作者：
Lipson, Mark;Loh, Po-Ru;Patterson, Nick;Moorjani, Priya;Ko, Ying-Chin;Stoneking, Mark;Berger, Bonnie;Reich, David
通讯作者：
Reich, David
影响因子：
10.7
作者：
SAITOU, N;NEI, M
通讯作者：
NEI, M