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

廉价的基因组级DNA测序技术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基因数据集，以努力有力地解决共同构成全球生命之树的进化关系模式。虽然这一转变大大提高了人类对进化史的理解，但也带来了重要的挑战。其中最主要的是，在基因组的不同区域和生命之树上，进化以不同的方式发生作用。这种异质性足以使分析受挫，结果导致越来越多的研究得出了关于进化史的根本矛盾的结论，尽管对基因组的大部分进行了采样，并用该领域现有的最佳方法进行了分析。该项目将在大量基因组规模的经验数据集中检测和量化进化异质性的模式，减轻这种异质性的影响，并在这一过程中对进化史有更准确的理解。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，这些工具可以识别、解释和解决相互矛盾的结果。这些工具包括贝叶斯技术，用于测量进化模型和数据之间的契合度，以及统计支持的无界测量。通过整合这些工具并将它们部署到大量的经验数据集中，研究团队将对生命之树中进化异质性和冲突结果的最重要驱动因素进行综合理解。这种综合将带来更好的技术，用于识别和减轻大规模数据集中进化异质性的影响，使该领域能够更准确地了解地球上的生命历史。该研究项目还将为可视化系统发育树的进化异质性提供新的工具，培训夏威夷和路易斯安那州的研究生研究人员，并开发一种新的结构化指导经验，以培训统计系统发育方法的不同群体的初级系统发育研究人员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.