AF: Medium: Algorithms for Scalable Phylogenetic Network Inference

AF：Medium：可扩展系统发育网络推理算法

• 批准号: 1800723
• 负责人: Luay Nakhleh
• 金额: $ 96万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1800723&HistoricalAwards=false
• 关键词: AF; Medium; Algorithms; Scalable; Phylogenetic

Species phylogenies model how species split and diverge and provide important insight into fundamental biological phenomena and processes, including biodiversity, and trait evolution, while gene trees provide insight into protein structure and function as well as systems biology. Advances in sequencing technologies and assembly methods and the availability of whole-genome datasets have opened up the possibility of transformative improvements in accuracy for estimating species phylogenies and gene trees. Phylogenetic networks extend phylogenetic trees to provide an appropriate model of reticulate evolutionary histories. Reticulate evolution describes the origination of a lineage through partial merging of two ancestor lineages. Recently developed methods allow for statistical inference of phylogenetic networks in order to account for other processes that could be at play during the evolution of the genomes. However, these methods can handle fewer than a handful of genomes. This award will develop methods for estimating large-scale phylogenetic networks from sequence data as well as gene tree estimates. The award will stimulate research in computer science and statistics and will have a major impact on evolutionary biology. The award will contribute open-source code to the PhyloNet software package. Lectures and tutorials will be given to the community on the developments made in the award and on the use of PhyloNet. The award will provide ample opportunities for training students and post-doctoral fellows in cutting-edge, interdisciplinary algorithmic research.The project will be carried out through five activities that are intertwined throughout the lifetime of the award. (1) Development of novel algorithmic techniques for scalable inference of phylogenetic networks that allow for analyzing data sets with tens and even hundreds of genomes. (2) Implementation and of all methods in the PhyloNet software package. (3) Thorough evaluation of the methods in terms of accuracy and computational requirements. (4) Mentoring and training of students and post-doctoral fellows. (5) Dissemination of the results through an open-source software package, publications in peer-reviewed journals and conference proceedings, lectures and tutorials, and course materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物种遗传学模拟了物种如何分裂和分化，并提供了对基本生物现象和过程的重要见解，包括生物多样性和性状进化，而基因树则提供了对蛋白质结构和功能以及系统生物学的见解。测序技术和组装方法的进步以及全基因组数据集的可用性为估计物种遗传和基因树的准确性提供了变革性改进的可能性。系统发生网络扩展了系统发生树，为网状进化历史提供了一个合适的模型。网状进化描述了通过两个祖先谱系的部分融合而产生的谱系。最近开发的方法允许系统发育网络的统计推断，以考虑其他过程中可能发挥作用的基因组的进化。然而，这些方法只能处理少于少数的基因组。该奖项将开发用于从序列数据以及基因树估计中估计大规模系统发育网络的方法。该奖项将刺激计算机科学和统计学的研究，并将对进化生物学产生重大影响。该奖项将为PhyloNet软件包提供开放源代码。将向社区提供讲座和辅导，介绍奖项的发展情况和PhyloNet的使用情况。该奖项将为培养学生和博士后研究员提供充足的机会，进行前沿的跨学科算法研究。该项目将通过五项活动进行，这些活动在整个奖项的生命周期内相互交织。(1)开发新的算法技术，用于系统发育网络的可扩展推理，允许分析具有数十甚至数百个基因组的数据集。(2)PhyloNet软件包中所有方法的实施和执行。(3)在准确性和计算要求方面对这些方法进行全面评价。(4)指导和培训学生和博士后研究员。(5)通过开源软件包、同行评审期刊和会议记录中的出版物、讲座和教程以及课程材料传播结果。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力评估来支持优点和更广泛的影响审查标准。

项目成果

A divide-and-conquer method for scalable phylogenetic network inference from multilocus data

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
• 发表时间: 2019
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Zhu, Jiafan;Liu, Xinhao;Ogilvie, Huw A.;Nakhleh, Luay K.
• 通讯作者: Nakhleh, Luay K.

Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees

通过限制基因树的空间来实际加速物种系统发育的贝叶斯推理

• DOI: 10.1093/molbev/msaa045
• 发表时间: 2020
• 期刊: Molecular Biology and Evolution
• 影响因子: 10.7
• 作者: Wang, Yaxuan;Ogilvie, Huw A;Nakhleh, Luay;Harris, Kelley
• 通讯作者: Harris, Kelley

Integrated likelihood for phylogenomics under a no-common-mechanism model

非共同机制模型下系统基因组学的综合可能性

• DOI: 10.1186/s12864-020-6608-y
• 发表时间: 2020
• 期刊: BMC Genomics
• 影响因子: 4.4
• 作者: Tidwell, Hunter;Nakhleh, Luay
• 通讯作者: Nakhleh, Luay

Empirical Performance of Tree-Based Inference of Phylogenetic Networks

系统发育网络基于树的推理的实证性能

• DOI: 10.1101/693986
• 发表时间: 2019
• 期刊: Leibniz international proceedings in informatics
• 作者: Cao, Zhen;Zhu, Jiafan;Nakhleh, Luay
• 通讯作者: Nakhleh, Luay