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

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，以努力有力地解决共同构成全球生命树的进化相关性模式。虽然这一转变极大地改善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，进化在基因组的不同区域和生命之树中以不同的方式起作用。这种异质性可能足够强大，足以挫败分析，因此，导致越来越多的研究得出关于进化历史的根本矛盾的结论，尽管对基因组的大部分进行采样并使用该领域最好的方法进行分析。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，并在此过程中更准确地理解进化历史。研究团队将通过整合一系列统计工具来实现这些目标，他们以前开发了一个有凝聚力的分析策略，可以识别，解释，并解决冲突的结果。这些工具包括用于测量进化模型和数据之间的拟合的贝叶斯技术，以及统计支持的无界测量。通过整合这些工具并将其部署在大量的经验数据集上，研究团队将对进化异质性的最重要驱动因素和整个生命树中相互冲突的结果进行综合理解。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，使该领域能够更准确地了解地球上的生命历史。该研究项目还将提供新的工具，用于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，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识产权进行评估来支持的。优点和更广泛的影响审查标准。

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.