Scalable Model-Based Reconstruction of Network Evolution

基于可扩展模型的网络演化重建

• 批准号: 1902892
• 负责人: Cecile Ane
• 金额: $ 72.42万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1902892&HistoricalAwards=false
• 关键词: Scalable; Model; Based; Reconstruction; Network

The availability of full genomes across individuals from many populations and many species offers rich information about past evolutionary history. By comparing the genes of different individuals, one can detect which individuals are most closely related and reconstruct the history of population splits and speciation, as visualized in a phylogenetic tree. Challenges arise however because of genealogical differences between individuals within each species, current or ancestral. This project focuses on the detection of species convergences: when species hybridize, or when individuals from one species migrate to another, or when strains recombine. The history of a group of species is then best described by a network, where a backbone tree represents speciation and extra branches describe gene flow from one population into another. Current methods to estimate phylogenetic networks cannot analyze data sets with more than a few dozen species. Based on novel theoretical foundations, the PIs will develop statistical methods and software that will scale to hundreds of species and thousands of genetic loci. These new methods will also be particularly valuable to advance knowledge in bacterial and virus evolution, where recombination is prevalent. The project will support graduate and undergraduate students, who will gain training beyond traditional disciplinary boundaries with involvement in the larger community of campus researchers interested in networks in data science.Through the mathematical analysis of coalescent processes on phylogenetic networks, the PIs will determine the maximal substructures of these networks that can be theoretically identified from various data types, such as from gene trees, or genetic distances between pairs of individuals, using one or more individuals per populations. Theory will also be developed to determine the amount of data necessary to reconstruct the phylogenetic network with accuracy. These theoretical findings will guide the development of new statistical methods and software to estimate phylogenetic networks from data, with a focus on the use of genetic distances to devise fast algorithms that can handle hundreds of species. These fast reconstruction methods will allow the deployment of a cross-validation method to learn from data the appropriate complexity of the network, that is, the appropriate number of gene flow events. The proposed research will advance knowledge of the evolutionary history in many groups where gene flow and recombination is suspected, such as the early radiation of mammals or land plants, and the evolutionary history of the herpes virus family.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.

来自许多种群和许多物种的个体的完整基因组的可用性提供了关于过去进化历史的丰富信息。通过比较不同个体的基因，人们可以发现哪些个体最接近，并重建种群分裂和物种形成的历史，如系统发育树所示。然而，由于每个物种（当前或祖先）内个体之间的谱系差异，挑战出现了。 该项目的重点是物种趋同的检测：当物种杂交，或当个体从一个物种迁移到另一个物种，或当菌株重组。一组物种的历史最好用网络来描述，其中主干树代表物种形成，额外的分支描述从一个种群到另一个种群的基因流。目前估计系统发育网络的方法不能分析超过几十个物种的数据集。基于新的理论基础，PI将开发统计方法和软件，这些方法和软件将扩展到数百个物种和数千个遗传位点。这些新方法对于推进细菌和病毒进化方面的知识也特别有价值，因为重组是普遍存在的。该项目将支持研究生和本科生，他们将获得超越传统学科界限的培训，参与对数据科学网络感兴趣的更大的校园研究人员社区。通过对系统发育网络上的合并过程的数学分析，PI将确定这些网络的最大子结构，这些网络可以从理论上从各种数据类型中识别出来，例如从基因树，或成对个体之间的遗传距离，每个种群使用一个或多个个体。理论也将被开发，以确定重建系统发育网络的准确性所需的数据量。这些理论发现将指导新的统计方法和软件的开发，以根据数据估计系统发育网络，重点是使用遗传距离来设计可以处理数百个物种的快速算法。这些快速重建方法将允许部署交叉验证方法，以从数据中学习网络的适当复杂性，即基因流事件的适当数量。该研究计划将推进许多群体的进化历史的知识，其中基因流动和重组是可疑的，如哺乳动物或陆地植物的早期辐射，以及疱疹病毒家族的进化历史。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Inconsistency of Triplet-Based and Quartet-Based Species Tree Estimation under Intralocus Recombination

位点内重组下基于三重态和基于四重态的物种树估计的不一致

• DOI: 10.1089/cmb.2022.0265
• 发表时间: 2022
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Hill, Max;Roch, Sebastien
• 通讯作者: Roch, Sebastien

An impossibility result for phylogeny reconstruction from k-mer counts

• DOI: 10.1214/22-aap1805
• 发表时间: 2020-10
• 期刊: The Annals of Applied Probability
• 作者: W. Fan;Brandon Legried;S. Roch

Statistically Consistent Rooting of Species Trees Under the Multispecies Coalescent Model

多物种合并模型下物种树生根的统计一致性

• 发表时间: 2023
• 期刊: Research in Computational Molecular Biology. RECOMB 2023. Lecture Notes in Computer Science. Springer.
• 作者: Tabatabaee, Y.;Roch, S.;Warnow, T.
• 通讯作者: Warnow, T.

Impossibility of Consistent Distance Estimation from Sequence Lengths Under the TKF91 Model

TKF91模型下不可能根据序列长度进行一致的距离估计

• DOI: 10.1007/s11538-020-00801-3
• 发表时间: 2020
• 期刊: Bulletin of Mathematical Biology
• 影响因子: 3.5
• 作者: Fan, Wai-Tong Louis;Legried, Brandon;Roch, Sebastien
• 通讯作者: Roch, Sebastien

Identifiability of local and global features of phylogenetic networks from average distances

• DOI: 10.1007/s00285-022-01847-8
• 发表时间: 2023-01-01
• 期刊: JOURNAL OF MATHEMATICAL BIOLOGY
• 影响因子: 1.9
• 作者: Xu, Jingcheng;Ane, Cecile
• 通讯作者: Ane, Cecile