Collaborative Research: OPUS: CRS: A Synthetic View of Evolutionary Heterogeneity and the Tree of Life

合作研究：OPUS：CRS：进化异质性和生命之树的综合观点

基本信息

批准号：
1950759
负责人：
Jeremy Brown
金额：
$ 11.3万
依托单位：
Louisiana State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950759&HistoricalAwards=false
关键词：
Collaborative Research OPUS CRS Synthetic

项目摘要

The advent of inexpensive genome-scale DNA sequencing has led to major changes in the study of evolutionary history. Researchers now routinely assemble and analyze enormous genetic datasets in an effort to robustly resolve the patterns of evolutionary relatedness that together make up the global tree of life. While this shift has resulted in dramatic improvements in humanity’s understanding of evolutionary history, it brings important challenges. Chief among these is that evolution acts in heterogeneous ways across different regions of the genome and across the tree of life. This heterogeneity can be strong enough to frustrate analyses and, as a result, has led to a growing number of studies that arrive at fundamentally conflicting conclusions about evolutionary history, despite sampling large fractions of the genome and analyzing these with the field’s best available methods. This project will detect and quantify patterns of evolutionary heterogeneity in a large collection of genome-scale empirical datasets, mitigate the impacts of this heterogeneity, and develop a more accurate understanding of evolutionary history in the process.The research team will accomplish these goals by integrating a series of statistical tools that they have previously developed into a cohesive analytical strategy that can identify, explain, and resolve conflicting results. These tools include Bayesian techniques for measuring the fit between evolutionary models and data, as well as unbounded measures of statistical support. By integrating these tools and deploying them across a large collection of empirical datasets, the research team will develop a synthetic understanding of the most important drivers of evolutionary heterogeneity and conflicting results across the tree of life. This synthesis will lead to better techniques for identifying and mitigating the effects of evolutionary heterogeneity in massive datasets, enabling the field to develop a more accurate understanding of the history of life on earth. The research project will also provide new tools for visualizing evolutionary heterogeneity in phylogenetic trees, train graduate student researchers in both Hawaii and Louisiana, and develop a novel structured mentoring experience that will train a diverse group of beginning phylogenetic investigators in statistical phylogenomic methods.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.

廉价的基因组规模 DNA 测序的出现导致了进化史研究的重大变化，研究人员现在经常收集和分析大量的遗传数据集，以有力地解析共同构成全球生命树的进化相关性模式。虽然这种转变极大地提高了人类对进化历史的理解，但它带来了重要的挑战，其中最主要的是，进化在基因组的不同区域和生命树上以异质的方式发挥作用。挫败分析，因此，越来越多的研究得出了关于进化历史的根本上相互矛盾的结论，尽管对基因组的大部分进行了采样并使用该领域最好的可用方法对其进行了分析，该项目将检测和量化异质性的进化模式。大量基因组规模的经验数据集，减轻这种异质性的影响，并在此过程中对进化历史有更准确的理解。研究团队将通过整合他们之前开发的一系列统计工具来实现这些目标一个有凝聚力的分析策略，可以这些工具包括用于测量进化模型和数据之间的拟合度的贝叶斯技术，以及统计支持的无限测量方法。团队将对生命树中进化异质性的最重要驱动因素和相互矛盾的结果进行综合理解，这种综合将带来更好的技术来识别和减轻海量数据集中进化异质性的影响，使该领域能够开发出更多的技术。准确的理解该研究项目还将提供新的工具来可视化系统发育树中的进化异质性，培训夏威夷和路易斯安那州的研究生研究人员，并开发一种新颖的结构化指导经验，以培训不同的初级系统发育研究人员。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。