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测序的进步导致进化史研究的重大变化。现在，研究人员通常会定期组装和分析庞大的遗传数据集，以努力解决构成全球生命树的进化相关性的模式。尽管这种转变导致了人类对进化史的理解的显着改善，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地区和整个生命之树中以异质的方式起作用。这种异质性可能足以挫败分析，因此，越来越多的研究得出了有关进化史的根本性结论，使命对基因组的大量分数进行了采样，并使用该领域的最佳可用方法进行了分析。 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.这些工具包括用于测量进化模型和数据之间拟合的贝叶斯技术，以及统计支持的无限量度。通过整合这些工具并将它们部署在大量的经验数据集中，研究团队将对整个生命树中最重要的进化异质性和冲突结果的最重要驱动因素产生综合理解。这种综合将导致更好的技术来识别和减轻大规模数据集中进化异质性的影响，从而使该领域能够对地球上生命的历史有更准确的了解。该研究项目还将提供新的工具，以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性心理经验，这些经验将培训一群统计学的系统中的系统发育研究者，这些统计系统中的系统发育研究者均表现了NSF的法定委员会的良好依据，这是通过评估良好的依据。