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

eMB：合作研究：通过杂交和基因流推进多物种合并下的系统发育树和网络的推理

• 批准号: 2325776
• 负责人: Adam Knapp
• 金额: $ 8.33万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325776&HistoricalAwards=false
• 关键词: eMB; Collaborative; Research; Advancing; Inference

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.

每一种生物的DNA都保留了其进化谱系的痕迹，随着DNA的复制，新的遗传变异被引入。研究人员通过分析DNA序列中的模式来拼凑生物体的进化历史，对这些关系的估计有着不同的应用。例子包括表征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。进化历史通常由系统发育树表示，显示生物体之间的祖先和血统关系;然而，遗传信息并不总是垂直传递的，在这些情况下，关系应该由网络表示。例如，两个病毒基因组可以共同感染同一宿主细胞，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，生物体的进化发生在两个不同的层次上：个体基因的层次和物种的层次，这限制了个体基因的历史。进化树和网络的精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对流行病学，法医学，生物安全和癌症生物学等领域有直接影响。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。该项目是美利坚大学和佛罗里达大学之间的合作项目，提供了宝贵的教育和推广机会。具体而言，研究生和本科生将接受系统发育方法学培训，作为这一过程的一部分，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中的变异性的模型，这些变异性是由于诸如不完全谱系排序、基因流、杂交和重组等过程引起的。重点是基于站点的物种方法，其目标是（1）提高四元组物种推断和可扩展性;（2）实现从根三元组推断根物种树;（3）开发更有效的基于站点的方法，用于识别杂交物种或重组事件，而无需外群;（4）实现基于站点的物种网络从四元组和4分类群网络推断。 为了实现这些目标，PI将从单基因树设置中的马尔可夫模型扩展到合并和基因流下的模型，例如叶变换和基于双线性距离的度量，这些到目前为止还没有正式描述，测试或在物种树模型下实现。混合物种识别的方法是基于衍生功能的比率接近混合参数的比率，这是不同于目前的代数方法，寻找消失多项式的网络。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网站为基础的物种-水平推理方法将免费提供给经验遗传学社区。该项目由数学科学部的数学生物学计划和环境生物学部的系统和生物多样性科学集群共同资助。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知识价值和更广泛的影响审查标准。