Principled phylogenomic analysis without gene tree estimation

无需基因树估计的有原则的系统发育分析

• 批准号: 2308495
• 负责人: Sebastien Roch
• 金额: $ 29.53万
• 依托单位: University of Wisconsin-Madison
• 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: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308495&HistoricalAwards=false
• 关键词: Principled; phylogenomic; analysis; without; gene

This project aims to improve the estimation of species trees from genomic datasets. This estimation is challenging because different genomic regions evolve under processes that make their evolutionary histories (i.e., gene trees) discordant. This issue is exacerbated by widespread gene tree estimation errors in modern phylogenomic analyses. To address this challenge, this project's primary objective is to devise innovative mathematical, statistical, and computational techniques to analyze phylogenomic datasets without relying on gene tree estimation. This approach will produce more reliable species tree estimates in the presence of confounding processes. Species trees provide an evolutionary and comparative context in which many biological questions can be addressed. They play a vital role in understanding gene evolution, estimating divergence dates, detecting adaptation, studying trait evolution, etc. The developed methods will enhance the precision of biological discoveries based on species trees, advancing research that utilizes phylogenies. The project includes interdisciplinary research training for graduate students as well as the involvement of undergraduate students recruited through local initiatives. New course materials based on the proposed research will be developed for existing graduate courses and be made available through the PI’s website. The project will leverage connections to NSF-funded interdisciplinary institutes.It is well established that different regions of a genome can evolve under different gene trees, due to processes such as incomplete lineage sorting, gene duplication and loss, and lateral gene transfer, complicating the estimation of species trees. Many methods that first estimate gene trees and then combine this information to estimate a species tree are known to have good theoretical guarantees, under the assumption that the true gene trees are known. That assumption is not satisfied in practice. Accounting theoretically for gene tree estimation error has proved challenging and few results are available. Building on prior work by the PI on the rigorous study of stochastic processes arising in this phylogenomic context, the proposed research will establish much-needed theoretical foundations for the analysis of multi-locus, multi-site datasets and the estimation of species trees without gene trees, including the development of novel estimators, the derivation of impossibility results and matching finite sample bounds, and the investigation of the effect of intra-locus recombination. This project will also enable the development of statistically rigorous, scalable algorithms. This interdisciplinary research will involve a close integration of applied probability, statistical theory, graph algorithms, and evolutionary biology.This proposal is jointly funded by the Mathematical Biology and Statistics Programs at the Division of Mathematical Sciences.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.

该项目旨在改进基因组数据集对物种树的估计，因为不同的基因组区域在使其进化历史（即基因树）不一致的过程中进化，这一问题因现代普遍存在的基因树估计错误而加剧。为了应对这一挑战，该项目的主要目标是设计创新的数学、统计和计算技术来分析系统发育数据集，而不依赖于基因树估计。这种方法将在存在基因树的情况下产生更可靠的物种树估计。物种树提供了一个进化和比较的背景，可以解决许多生物学问题，在理解基因进化、估计分化日期、检测适应、研究性状进化等方面发挥着至关重要的作用。所开发的方法将提高精度。该项目包括对研究生的跨学科研究培训以及通过当地举措招募的本科生的参与，并将为现有研究生开发基于该研究的新课程材料。课程并制作该项目将利用与 NSF 资助的跨学科研究所的联系。众所周知，由于谱系排序不完整、基因复制和丢失等过程，基因组的不同区域可以在不同的基因树下进化。横向基因转移，使物种树的估计变得复杂。在假设真实的基因树已知的情况下，已知许多首先估计基因树然后结合这些信息来估计物种树的方法具有良好的理论保证。不满意于事实证明，从理论上解释基因树估计误差具有挑战性，并且基于 PI 之前对这种系统发育背景下出现的随机过程的严格研究，所提出的研究将为该系统奠定急需的理论基础。多位点、多地点数据集的分析和没有基因树的物种树的估计，包括开发新的估计器、不可能结果的推导和匹配有限样本范围，以及研究该项目还将实现严格的、可扩展的算法的开发，这项跨学科研究将涉及应用概率、统计理论、图算法和进化生物学的紧密结合。该提案由数学生物学和进化生物学联合资助。数学科学部的统计项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。