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。研究人员现在例行公事地收集和分析巨大的基因数据集，以努力有力地解决共同构成全球生命树的进化关联模式。虽然这种转变极大地改善了人类对进化史的理解，但也带来了重大挑战。其中最主要的是，进化以不同的方式跨越基因组的不同区域和生命之树。这种异质性可能强大到足以阻碍分析，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，尽管对很大一部分基因组进行了采样，并使用该领域最好的可用方法对其进行了分析。这个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，并在这个过程中发展对进化史的更准确的理解。研究小组将通过将他们之前开发的一系列统计工具整合到一个连贯的分析策略中来实现这些目标，该分析策略可以识别、解释和解决相互冲突的结果。这些工具包括用于测量进化模型和数据之间的匹配的贝叶斯技术，以及统计支持的无限测量。通过整合这些工具，并将它们部署在大量经验数据集上，研究团队将对生命之树上进化异质性和相互冲突的结果的最重要驱动因素形成综合理解。这种综合将导致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，使该领域能够更准确地理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中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，并开发一种新颖的结构化指导经验，将培训一批不同的初级系统发育研究人员使用统计系统基因组学方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.