eMB: Collaborative Research: Advancing Inference of Phylogenetic Trees and Networks under Multispecies Coalescent with Hybridization and Gene Flow

每个生物体的DNA都保留着其进化谱系的痕迹，随着DNA的复制，新的遗传变异也随之产生。研究人员分析DNA序列的模式来拼凑生物体的进化史，对这些关系的估计有多种应用。例子包括描述转移性结直肠癌的进化模式或确定不同病毒株之间的关系。进化的历史通常用系统发育树来表示，它显示了生物体之间的祖先和后代关系；然而，遗传信息并不总是垂直传递的，在这种情况下，这种关系应该用网络来表示。例如，两个病毒基因组可以共同感染同一个宿主细胞，导致基因片段的交换，称为重组，从而有助于它们的多样性。此外，生物进化发生在两个不同的水平上：在个体基因水平上和在物种水平上，这限制了个体基因的历史。进化树和网络的准确估计是数学和统计推断中的一个主要问题，因为它们的估计精度直接影响到流行病学、法医学、生物安全和癌症生物学等领域。该项目的主要目标是提高现有方法的可扩展性和准确性，并开发推断系统发育物种网络的新方法。该项目是美国大学和佛罗里达大学的合作项目，提供了宝贵的教育和推广机会。具体地说，研究生和本科生将接受系统发育方法的培训，作为该过程的一部分，从而实现知识的垂直转移。从多基因比对中推断物种水平关系的工作是特定于同时考虑个体基因历史的变异性的模型，这些变异性是由于谱系分类不完整、基因流动、杂交和重组等过程造成的。重点是基于站点的物种方法，其目标是：(1)提高四种物种推理和可扩展性；(2)从根三元组中实现根种树的推理；(3)开发更有效的基于站点的方法来识别杂交物种或重组事件，而不需要外群；(4)从四类群和四类群网络中实现基于站点的物种网络推断。为了实现这些目标，pi将把多个数学思想从单基因树的马尔可夫模型扩展到聚结和基因流的模型，如叶片变换和基于平行距离的测量，这些到目前为止还没有在物种树模型下被正式描述、测试或实现。混合物种识别方法是基于推导比值接近混合参数比值的函数，这与目前在网络上寻找消失多项式的代数方法不同。因此，这项工作有助于数学、统计和生物科学，并有可能在理论和计算界引发新的讨论。一个完整的软件包实现我们的基于站点的物种水平推断方法将免费提供给经验系统发育界。本项目由数学科学部数学生物学项目和环境生物学部系统与生物多样性科学集群共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The DNA of every organism retains a trace of its evolutionary lineage, and as the DNA is copied, new genetic variations are introduced. Researchers analyze patterns in DNA sequences to piece together organisms' evolutionary histories, and the estimation of these relationships has diverse applications. Examples include characterizing the evolutionary patterns of metastatic colorectal cancer or determining the relationship between different viral strains. The evolutionary history is often represented by a phylogenetic tree that displays the ancestry and descent relationships among organisms; however, genetic information is not always transferred vertically, and in these cases, the relationships should be represented by a network. For example, two viral genomes can co-infect the same host cell, leading to the exchange of genetic segments, called recombination, and thus contributing to their diversity. Additionally, organismal evolution occurs at two distinct levels: at the level of individual genes and at the level of species, which constrains the histories of the individual genes. Accurate estimation of evolutionary trees and networks is a major problem in mathematical and statistical inference since the precision of their estimation has a direct impact on fields such as epidemiology, forensic medicine, biosecurity, and cancer biology. The main objective of this project is to improve the scalability and accuracy of current methods and develop new methods for inferring phylogenetic species networks. The project is a collaboration between the American University and University of Florida and offers valuable educational and outreach opportunities. Specifically, graduate and undergraduate students will be trained in phylogenetic methodologies as part of the process, resulting in a vertical transfer of knowledge. The work for inferring species-level relationships from multigene alignments are specific to models that simultaneously account for variability in individual gene histories due to processes such as incomplete lineage sorting, gene flow, hybridization, and recombination. The focus is on site-based species methodologies, with the goals of (1) improving quartet species inference and scalability; (2) implementing inference of rooted species trees from rooted triples; (3) developing more efficient site-based methods for identifying hybrid species or recombination events without the need for an outgroup; and (4) implementing site-based species network inference from quartets and 4-taxon networks. To achieve these goals, the PIs will extend multiple mathematical ideas from Markov models in the single gene tree setting to models under coalescent and gene flow, such as leaf transformations and a measure based on paralinear distance, which so far have not been formally described, tested, or implemented under the species tree model. The method for hybrid species identification is based on deriving functions whose ratio approaches the ratio of the mixing parameter, which is distinct from current algebraic methods that look for vanishing polynomials over networks. As a result, this work contributes to the mathematical, statistical, and biological sciences, and has the potential to spark new discussions in the theoretical and computational communities. A complete software package implementing our site-based species-level inference methods will be freely available to the empirical phylogenetics community.This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences and Systematic and Biodiversity Science Cluster in the Division of Environmental Biology.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.