PRIMES: Practical Inference Algorithms to Detect Hybridization

系统发育学是进化生物学的一个分支，其主要目的是了解物种之间的进化关系。推断这种关系对于当前关注的重要领域至关重要，例如保护工作、了解传染病动态和改进农业实践。杂交物种形成，或杂交，发生在两个不同的物种通过基因融合创造一个新的物种时，众所周知，它在许多物种的进化中发挥了重要作用，包括蝴蝶、火蜥蜴、向日葵和报春花等。在这个项目中，首席调查员将开发生物界急需的算法，作为检测物种之间杂交的工具。此外，国际和平协会将设计和开展多项活动，鼓励有抱负的学者，特别是那些属于少数群体的学者，在STEM追求职业生涯。这项提议中将开发的算法的理论基础将是网络多物种合并模型(NMSC)，这是一个标准的随机模型，描述了在存在不完全谱系排序和杂交的情况下物种基因之间的不同进化关系。目前在NMSC下推断物种网络的统计一致性方法仅限于一个被称为Level-1的相当简单的网络家族。PI将开发和实施快速和一致的算法，以从基因组数据推断NMSC下更一般的物种网络。这将通过三个目标来实现：(1)将当前的可辨识性结果扩展到更一般的网络族；(2)开发算法来推断物种网络的统计一致性估计器；以及(3)实现有用、可靠和易于使用的算法。软件实现将在可达到的运行时间下处理大型数据集，公开可用，并包括广泛的新功能，这些功能将广泛用于生物界。这项工作将为系统发育推论提供理论和实践两方面的方法。为了增加未被充分代表的群体对生物数学的参与，国际数学联合会将组织和主办题为“全民生物数学：庆祝多样性”的系列研讨会。在这些讲座中，受邀的演讲者将讨论他们的研究以及他们面临的挑战和机遇。此外，PI将为加州州立大学圣贝纳迪诺分校的不同学生社区设计和开展为期一周的暑期课程《进化论数学建模导论：初学者课程》。圣贝纳迪诺是一所为少数族裔服务的机构。PI将参加ICERM 2024秋季学期的项目：“数量系统基因组学的理论、方法和应用”。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Phylogenetics is the branch of evolutionary biology whose main objective is understanding the evolutionary relationships between species. Inferring such relations is crucial for important areas of current concern, such as conservation efforts, understanding infectious disease dynamics, and improving agricultural practices. Hybrid speciation, or hybridization, occurs when two distinct species merge genetically to create a new one, and it is known to have played an important role in the evolution of many species, including butterflies, salamanders, sunflowers, and primroses, among many others. In this project, the Principal Investigator will develop much-needed algorithms to be used by the biological community as a tool to detect hybridization between species. Additionally, the PI will design and conduct multiple activities to encourage aspiring scholars, especially those belonging to minority groups, to pursue a career in STEM. The theoretical foundation of the algorithms that will be developed in this proposal will be the Network Multispecies Coalescent model (NMSC), a standard stochastic model describing the distinct evolutionary relationships between species’ genes in the presence of incomplete lineage sorting and hybridization. Current statistically consistent methods to infer species networks under the NMSC are restricted to a rather simple family of networks known as level-1. The PI will develop and implement fast and consistent algorithms to infer more general species networks under the NMSC from genomic data. This will be achieved with three aims: (1) Expand current identifiability results to a more general family of networks; (2) Develop algorithms to infer statistically consistent estimators of species networks; and (3) Implement algorithms that are useful, reliable, and easy to use. Software implementations will handle large datasets under attainable running times, be made publicly available, and include extensive novel functionalities that will be widely useful to the biological community. This work will advance both theoretical and practical methods for phylogenetic inference. To increase underrepresented groups’ participation in biomathematics, the PI will organize and host a seminar series entitled “Biomathematics for All: Celebrating Diversity.” In these lectures, invited speakers will discuss their research together with the challenges and opportunities they have faced. Furthermore, the PI will design and conduct the summer week-long program “Introduction to the Mathematical Modeling of Evolution: A Week-long Course for Beginners” for the diverse student community of California State University San Bernardino, a minority-serving institution. The PI will attend the Fall 2024 semester program: “Theory, Methods, and Applications of Quantitative Phylogenomics” at ICERM.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.